Capacity value of solar is low as penetration increases which could suppress investment

Posted on June 5, 2015 by

Notes from 33 page: NREL. 2014. Representation of Solar Capacity Value in the ReEDS Capacity Expansion. National Renewable Energy Laboratory. Technical Report NREL/TP-6A20-61182 March 2014

Comparison of Capacity Value at High Penetration.

Several researchers have conducted modeling efforts to quantify the operational value of solar and other VRRE generation at high (10%+) levels of penetration (Perez, Lew, Mills, Madaeni 2012b, Olson).

These studies find that the capacity credit assigned to solar generation declines significantly at high level of energy penetration. As penetration increases, the marginal economic value of PV drops considerably, primarily because of changes in capacity value, but also in energy value (Mills). Clearly, this decrease in value decreases the overall economics of future solar units (Olson) and could suppress additional investment.

An important emerging issue for electricity system operators is the estimation of renewables’ contributions to reliably meeting system demand, or their capacity value. While the capacity value of thermal generation can be estimated easily, assessment of wind and solar requires a more nuanced approach due to resource variability. Reliability-based methods, particularly assessment of the effective load-carrying capacity (ELCC), are considered to be the most robust and widely accepted techniques for addressing this resource variability. This report validates treatment of solar photovoltaic (PV) capacity value by the Regional Energy Deployment System (ReEDS) capacity expansion model by comparing model results against two sources. The first comparison is against values published by utilities or other entities for known electrical systems at existing solar penetration levels. The second comparison is against a time series ELCC simulation tool for high renewable penetration scenarios in the Western Interconnection. Results from the ReEDS model are found to compare well with both comparisons–despite not being resolved at an hourly scale.

 

The results are relevant for other capacity-based models that do not use hourly calculations to model solar capacity value. First, solar capacity value should not be parameterized as a static value but must decay with increasing penetration. This is because, for an afternoon-peaking system, as solar penetration increases the system’s peak net load shifts to later in the day– when solar output is lower. Second, long-term planning models should determine how system adequacy requirements differ between time periods in order to approximate loss of load probability (LOLP) calculations. Within the ReEDS model we resolve these issues by using a capacity value estimate that varies by time-slice. Within each time-slice the net load and shadow price on ReEDS’s planning reserve constraint signals the relative importance of additional firm capacity.

 

An important emerging issue for electricity system operators is the estimation of renewables’ contribution to system adequacy. As supply of electricity must constantly be balanced with demand, system operators typically procure a 10%– 20% capacity reserve margin to meet unplanned outages of existing capacity and unexpected increases in demand (NERC 2013). A generator’s ability to help reliably serve load is measured by its capacity value or effective load carrying capacity (ELCC)—the firm capacity that a generating unit is able to provide during reliability-critical periods. The possibility of outages, whether planned or otherwise, therefore necessitates an accurate and dependable method of assessing each unit’s firm capacity contribution to planning reserves to avoid loss of load. The provision of variable resource renewable energy (VRRE) sources such as wind and solar presents a challenge in the assessment of their contributions to planning reserves.  to. Previous studies have estimated the capacity value of photovoltaic (PV) solar (Duignan et al. 2012; Madaeni et al. 2013; Perez et al. 2006), concentrating solar power (CSP) (Madaeni et al. 2012a), and wind (NERC 2013; Keane et al. 2011; Ensslin 2008), finding a wide range of potential capacity values that depend on technology, resource quality, and correlation of generation and demand, among many factors. Numerous techniques can be used to

estimate the capacity value of renewable and conventional generators, though reliability-based methods are considered to be the most robust and widely accepted methods (Madaeni et al. 2013). Reliability-based techniques assess how the addition of a generator affects the overall reliability of the system, specifically, the likelihood of adequately serving load within a planning year. Within this framework, the capacity value of a VRRE source is defined as the maximum additional load that the electrical system could serve while maintaining the same level of reliability or loss of load expectation (LOLE). The amount of additional load that can be served with the addition of the variable generator is its ELCC and is equivalent to its capacity value. A drawback of this method, however, is that it requires extensive data, including time series spanning several years of load and conventional and renewable generation, as well as an inventory of units within a planning area and their respective maintenance schedules and forced outage rates. ELCC-based methods have emerged as an industry-preferred means for assessing the capacity value of generating sources (Milligan and Porter 2008a; NERC 2011; Perez et al. 2008), and a common practice is to maintain an LOLE of 1 day in 10 years or less.

 

In contrast to reliability-based methods, approximation methods exist that require more modest amounts of system data or that can be performed on generalized systems. Availability of data can particularly be a concern for capacity expansion or capacity planning exercises, which typically are not resolved at the unit or hourly level, but nevertheless require an estimation of VRRE capacity value. One credible method, employed by the Regional Energy Deployment System (ReEDS) model in this report, is the Z-method (Dragoon and Dvortsov 2006), which approximates LOLE through the distribution of a system’s surplus capacity. We supplement the Z-method with a time-period-based method that weighs the relative risk of loss of load within each time period. Utilities and other load-serving entities have historically used a variety of methods to evaluate firm solar capacity. These range from detailed LOLP-based reliability evaluations, to time period-based estimates of solar capacity factors during top-load periods, and even rules of thumb based on engineering judgment (Mills and Wiser 2012). Many utilities do not publically disclose their valuation methodology. There is also uncertainty in characterizing changes in solar capacity value as a function of energy penetration, as there are very few electricity systems with high levels of solar energy penetration to act as case studies. Whatever their method, the assignment of capacity credits to VRRE sources is a part of recognizing and evaluating their economic value (Borenstein 2008)—and therefore becomes increasingly important for justifying their expanded use.

 

Report Outline. The purpose of this report is two-fold: first, to compare solar capacity values modeled by the ReEDS model to other values published in literature, both at low and high levels of penetration. Second, to understand how such factors as resource quality, energy penetration, and coincidence of generation and load profile determine the modeled capacity value of utility- scale solar. Because contributions to system adequacy increase the value of PV capacity to system operators and power producers, a predictive understanding of how capacity value evolves is an important prerequisite to understanding PV value.

 

Sensitivity of Capacity Value to Resource Quality. While system operators maintain additional firm capacity beyond expected peak load to hedge against unexpected demand or system contingencies, in reality, there are only a few hours of the year when system adequacy is a truly pressing concern. The capacity value of a generator is assessed based on its ability to serve load during these times, when the LOLP is greatest. Most electrical systems in the United States are summer-peaking, due to cooling loads. As a result, these ‘reliability-critical’ periods typically occur during summer afternoons, though there are also electrical systems that experience peak demand in the winter, when electrical demand is driven by heating loads.

 

Physical location of a solar unit affects the capacity value of a PV unit at a very basic level. Namely, there is geographic variation in the annual quantity of solar irradiance as well as the diurnal and annual variability in irradiance. Within the ReEDS model, national solar resource is represented at the 134 areas that also serve as load balancing areas (BA). These balancing areas do not necessarily reflect the actual territories of real-world BAs, or specific reliability rules for individual balancing areas.

 

Nevertheless, this level of geographic detail enables the model to account for geospatial differences in resource quality (Figure 1)—particularly statistical availability during reliability-critical periods. Figure 1: Mean

 

Correlation of Load and Solar Generation. As a subtler point, geography influences the cooling and heating loads within a balancing area (BA), which thereby influences the timing of high LOLP hours. The key issue is to understand the degree of correlation between a solar unit’s availability and periods of high LOLP. In general, the correlation of load and solar generation varies enough between BA to warrant detailed investigation.

 

Solar Energy Penetration. Solar PV capacity value is also known to be highly sensitive to increasing levels of PV deployment within the planning region (Perez et al. 2006; Lew et al 2010; Mills & Wiser 2012; Madaeni et al. 2012b; Olson & Jones 2012). PV capacity value is mainly driven by its generation level during the most critical hours of the year, when load is most likely to be dropped due to outages or available capacity. Typically, these periods of time are found during the early evenings of a few weeks of the year, especially for summer-peaking systems. When deployment of PV is at low levels of energy penetration, the additional PV generation does not significantly affect timing of reliability-critical hours. However, since the profile of solar generation is largely coincident with a summer-peaking utility’s load profile, increasing levels of solar generation shifts the critical hours to later hours, when solar irradiance is lower as the sun is setting, decreasing PV capacity value. At high levels of penetration, when net load has been shifted 2 – 3 hours, the capacity factor reaches near-zero levels—as irradiance during the evening is negligible. The most critical hours are typically those with highest levels of net load, i.e., load minus variable generation.

 

To better illustrate the sensitivity of solar capacity value to energy penetration, the capacity factor is modeled for a representative solar unit in the ReEDS ‘p28’ BA, which overlaps with territory served by the Arizona Public Service utility in central Arizona. Demand in this BA is summer-peaking and the top load hours typically occur during late August afternoons.

 

As levels of annual solar energy penetration increase from 0% to 20%, the peak load in the diurnal load profile is reduced and shifted to later in the day (Figures 2 and 3). The capacity factor at the point of peak net load erodes following an exponential form and, as predicted, becomes negligible at high levels of annual energy penetration.

 

In particular, the model uses a high level of spatial resolution—where wind and CSP resources are defined at 356 resource regions and solar PV at the 134 regions that also serve as load BAs. Each resource is regionally characterized by a set of supply curves—constructed from NREL resource assessments (Lopez et al. 2012)—that distinguish resource quality and the cost of accessing the local transmission network. This level of geographic detail enables the model to account for geospatial differences in resource quality, transmission needs, electrical (grid-related) boundaries, political and jurisdictional boundaries, and demographic distributions.

 

The 134 load regions are connected by an aggregated transmission network that gives ReEDS the ability to discern the relative value of development sites across regions.

 

For new investments, ReEDS can choose from a broad portfolio of conventional generation, renewable generation, storage, and demand-side technologies. Plants provide power to meet load, capacity toward adequacy requirements, and operating (spinning or non-spinning) reserves. Conventional generators contribute their nameplate capacity toward adequacy requirements and supply operating reserves while variable renewables contribute their calculated capacity value and require additional operating reserves.

 

Three solar PV system types are modeled—utility-scale (UPV), distributed utility-scale (DUPV), and distributed rooftop. UPV and DUPV are interconnected to the grid at the transmission level and are assumed to be utility controlled, whereas distributed rooftop is connected at the distribution network level, behind the meter. Rooftop PV projections are developed outside of ReEDS, in NREL’s SolarDS model (Denholm et al. 2009) because decisions on rooftop installations are assumed to be made on a different basis (i.e., by individuals) than centralized utility or power-producer decisions. The differences in ReEDS between UPV and DUPV are primarily about size and siting freedom: DUPV systems are smaller and are assumed to be close to load, while UPV systems are wide-ranging. This report exclusively applies to UPV and does not analyze capacity value for DUPV and rooftop PV systems.

 

UPV represents single-axis tracking PV systems with a unit size of 100 MW.

 

The ReEDS transmission network is a 134-node system connected by roughly 300 transmission corridors representing the collection of real transmission lines that cross BA boundaries and are characterized by the carrying capacity of those lines.

 

Capacity Value Calculations. ReEDS uses a measure of a VRRE generator’s ELCC to determine its contributions to planning reserves in each of the 17 time periods. That is, adequacy/reliability is defined in terms of the likelihood that the system (BA, transmission zone, service territory) will have insufficient available generating capacity to meet load during a given period.

 

The Z-method is used by ReEDS to estimate capacity value because it permits the approximation of capacity value without conducting an hourly time-series analysis, which is infeasible given ReEDS’s temporal resolution. However, the Z-method assumption of a Gaussian form can be violated under high-renewable scenarios if the real time-slice probability distribution of VRRE output does not follow a Gaussian distribution.

 

ReEDS Scenario Parameters. ReEDS calculations of solar capacity value were compared to the studies in Table 1 in order to benchmark performance of the model. To facilitate an equitable comparison, scenarios were constructed to match each utility region’s geographic location, existing generation fleet, and PV deployment levels as closely as possible. By default, ReEDS uses historic capacity expansion from 2010 to 2013 and business-as-usual assumptions for capacity expansion projections thereafter.

 

Figure 6. Comparison of solar capacity values in reliability-critical time periods to published values

 

Unfortunately, there are very few actual electrical systems operating at high levels of solar penetration, and so there is scarce available literature on the capacity value of solar on real electrical systems.

 

Figure 7. State-based solar PV capacity values for reliability-critical time periods for WWSIS-2 scenarios in 2020

 

Notice, also, that there is some erosion of capacity value within a time-slice as penetration increases. This is consistent with the hypothesis that within any set region adding more PV increases its self-correlation. As does a system operator, ReEDS has the capability to diversify its resource base somewhat, but not fully, and the intra-time-slice erosion represents the limit of that ability.

 

We suggest that capacity value erosion within a time period is explained through increased self-correlation of energy production, as well as decreases in available high-quality resource sites within the region.

 

Conclusion.

 

ReEDS was designed to represent characteristics that drive variation in investment and operation costs of renewable energy technologies, including geospatial resource assessment and integration of variable resources into a reliable electricity grid. Because these characteristics give the model accurate information about the economic value of, for instance, an additional unit of solar capacity, ReEDS is able to make well-informed investment decisions. Capacity value, as discussed here, is one of the economic components ReEDS includes in its decision making—one that can change dramatically with system configuration and is important to model dynamically.

 

To accurately reflect solar capacity value in capacity expansion decisions, ReEDS models a number of factors that determine its ELCC. These include representation of the statistical availability of a solar unit, a high level of geographic resolution in resource quality and grid conditions, and correlation of residual load and solar generation. Additionally, ReEDS simultaneously considers adequacy issues in all time-slices. Because the value of capacity services is highest during reliability-critical periods, and increased solar generation shifts those periods away from peak solar output, this accounts for the diminishing capacity value of solar at high levels of penetration. We find that capacity value outcomes from the ReEDS model compare favorably with results from hourly resolution ELCC-based analyses for a range of real and modeled levels of solar energy penetration.

 

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The potential role of concentrating solar power

Posted on June 5, 2015 by

Preface. The word “water” appears nowhere in this document, even though that’s a major limiting factor for CSP with thermal storage. Dry cooling is possible, but it lowers the EROI and raises the already way-too-high capital cost. An electric grid that’s mainly renewable can not exist without energy storage, but the low EROI, seasonality, extremely high capital cost, small class 5 region far from transmission, limited water in the Southwest, and lack of a national grid will limit CSP as a solution.

Alice Friedemann www.energyskeptic.com  author of “When Trucks Stop Running: Energy and the Future of Transportation”, 2015, Springer, Barriers to Making Algal Biofuels, and “Crunch! Whole Grain Artisan Chips and Crackers”. Podcasts: Derrick Jensen, Practical Prepping, KunstlerCast 253, KunstlerCast278, Peak Prosperity , XX2 report

***

Notes from 2 National Renewable Energy Laboratory studies:

The Potential Role of Concentrating Solar Power in Enabling High Renewables Scenarios in the United States 2012

AND

Operation of Concentrating Solar Power Plants in the Western Wind and Solar Integration Phase 2 Study 2014

Concentrating solar power (CSP), when deployed with thermal energy storage, provides a dispatchable source of renewable energy.

Large-scale deployment of CSP faces a number of challenges—areas with the best direct normal solar resources are often in remote locations, and CSP faces increasing competition from solar photovoltaics (PV), which has fewer siting restrictions and is currently projected to have a lower overall levelized cost of electricity.

The results show that large-scale deployment of CSP (=10 GW) is dependent on some combination of substantially reduced costs and the use of its ability to provide grid flexibility.

The need for new transmission and the remote locations of CSP resources are key challenges for large-scale deployment of CSP. One important issue is the limited demand for electricity in states with good DNI resources. While states like California are large, the overall demand in the 6 states with excellent DNI resources (with at least some class 3 and above) is only about 12% of total U.S. demand in 2010 (EIA 2011). This requires increasing the use of CSP in regions with lower-quality resources or building long-distance transmission to send CSP generation to surrounding regions. This will likely require building greater connection between the Western and Eastern Interconnections as the Western Interconnection as a whole represents only about 18% of total U.S. demand. Furthermore, within the Western Interconnection, CSP obviously competes with high-quality PV resources, but it also competes with good-quality wind resources, the nation’s highest-quality geothermal resources, and existing hydro.

Two studies sponsored by the U.S. Department of Energy that were completed in 2012 evaluated the potential mix of renewable energy technologies that could serve a large fraction of the U.S. electricity demand and the associated evolution of the U.S. grid to 2050. The SunShot Vision Study evaluated the impact of low-cost solar technologies, while the Renewable Electricity Futures Study analyzed the grid benefits and impacts of providing up to 90% of the nation’s electricity from renewables.

Overall, the studies found a very large range of CSP deployment scenarios, ranging from essentially no new deployment in cases with no cost improvements, to over 100 GW in scenarios with aggressive cost reductions. While some of the scenarios evaluated CSP without storage, the analysis found very limited opportunities for this technology, especially considering projected decreases in PV costs.

But with thermal energy storage, combined with appropriate valuation of its grid flexibility benefits, there could be significant deployment opportunities. These opportunities are dependent on at least 3 factors: decreased cost, ability to deploy new transmission, and appropriate valuation of CSP flexibility.

This deployment will depend on new transmission to connect CSP into the existing grid to supply energy to the southwestern United States and California.

Very high penetration will require longer-distance transmission to supply a greater fraction of the Western Interconnection and exploit CSP resources that exist in western Texas and on the far western edge of the Eastern Interconnection.

In all scenarios, most of the development in the East is actually on the far western part of the interconnection. Constrained transmission disincentivizes CSP in the Western Interconnection because the cost of new transmission is tripled, eliminating the possibility of exports from the highest quality CSP resource regions. 

Finally, large-scale deployment depends on recognition and valuation of CSP’s flexibility and capacity during the system planning process. This valuation is especially important in scenarios where the system becomes highly dependent on variable renewable sources, such as solar PV and wind, and the system requires generators capable of ramping rapidly over a large range of operation. An additional important consideration is the appropriate timing of investment in flexible generation resources, so they are available when less flexible sources of energy are introduced to the grid. Including the value of grid flexibility can produce an overall least-cost energy mix, as opposed to a mix of the lowest-cost energy sources that does not consider the interaction between multiple generation technologies

Overall the studies found a range of opportunities for CSP deployment, largely dependent on reduced technology costs, the ability to construct new transmission, and appropriate valuation of CSP capacity and flexibility, especially in scenarios where the system becomes highly dependent on variable renewable sources such as solar photovoltaics (PV) and wind.

The SunShot Vision study was designed to examine the impacts and benefits of achieving significant cost reductions in solar technologies. The SunShot Vision study used the National Renewable Energy Laboratory’s (NREL) Regional Energy Deployment System (ReEDS) (Short et al. 2011) and Solar Deployment System (SolarDS) (Denholm et al. 2009) models to develop and evaluate a reference scenario, assuming moderate solar energy price reductions, and the SunShot scenario, where the cost of solar energy is reduced by about 75% from 2010 to 2020,

For the SunShot scenario, solar technology installed system prices were assumed to reach the SunShot Initiative’s targets by 2020: $1.00/W for utility-scale PV systems, $1.25/W for commercial rooftop PV, $1.50/W for residential rooftop PV, and $3.60/W for CSP systems with up to 14 hours of thermal energy storage (TES) capacity.2

As with the SunShot Vision study, RE Futures used the ReEDS and SolarDS models to develop a set of reference and high renewable energy scenarios. The biggest single difference between the studies was the basis on which they deployed renewable energy. The SunShot Vision study developed generation mixes solely on an economic least-cost basis, though with an aggressive cost-reduction target for solar technologies. RE Futures set targets for renewable penetrations for the year 2050 (from 30% to 90% of all demand) and evaluated the resulting least-cost geographical deployment of all conventional and renewable generating technologies. Renewable sources essentially competed against each other for market share within this overall renewable requirement.

RE Futures explored a similar set of topics as the SunShot Vision study, such as resource availability, impact on system costs, environmental impacts and benefits, and basic grid operation. ReEDS represents the contiguous United States using 356 CSP/wind resource regions and 134 power control areas. This level of geographic detail enables the model to account for geospatial differences in resource quality, transmission needs, electrical (grid-related) boundaries, and political boundaries. ReEDS dispatches generation within 17 different time slices, including 4 time slices for each season representing morning, afternoon, evening, and nighttime, with an additional summer-peak time slice. This level of temporal detail—though not as sophisticated as an hourly chronological dispatch model—enables ReEDS to consider seasonal and diurnal changes in demand and resource availability. Because there are still significant demand and resource variations that can occur within each of these time slices, ReEDS utilizes statistical calculations derived from hourly data to estimate the capacity value and curtailment of variable wind and solar resources.

Implementation of Concentrating Solar Power CSP uses mirrors or lenses to concentrate sunlight and produce intense heat, which is used to produce electricity via a thermal energy conversion process similar to those used in conventional power plants. ReEDS models CSP plants both with and without TES.

CSP in USA

Figure 1. CSP resource in the United States (U.S. DOE 2012) Note: A small amount of class 1 resource in Florida is not shown. The resource data is translated into a typical DNI year (TDY) hourly resource dataset for representative sites of each CSP/wind resource region.

Because the goals of the CSP SunShot program are focused on dispatchable CSP, CSP without TES was not modeled in the SunShot scenario.

In addition to performance characteristics and costs, available land area (and corresponding capacity) was also established for each resource class. Figure 2 provides the availability of CSP at each CSP/wind resource region4 in the western United States (with a small amount of class 1 CSP available in southern Florida). This resource availability considers land unavailable for 5development based on a large set of exclusions, such as slope and environmental restrictions. After removing this excluded land, remaining area for each CSP resource class is converted into gigawatts of available capacity assuming a plant density of 31 MW/km2 for a system with a solar multiple of 2.6

Overall, the resource base in the United States for CSP is very large—after exclusions, about 7,500 GW, producing about 17,500 TWh of annual CSP electricity generation (more than four times the current U.S. annual demand) could be sited in seven southwestern states (NREL 2012).

CSP by class for solar multiples of 2

Figure 2. CSP available resource by class (for solar multiples of 2) (U.S. DOE 2012

CSP plants must be interconnected to the grid, often requiring new transmission. Based on the CSP resource data, a supply curve representing the cost of connecting individual CSP sites to the existing grid as well as to local demand centers was developed based on a geographic information system database of the resource, existing grid,7 and loads. Additional transmission may be built by ReEDS to enable CSP to provide energy over long distances, even across interconnections, when it is cost competitive compared to other options to provide energy, capacity, and flexibility. In addition to the transmission costs associated with the supply curves, a $120/kW fee for connection to the grid is applied to new CSP plants in ReEDS.

For conventional and non-solar renewable energy technologies, the SunShot scenario and the RE Futures RE-ITI scenario both assume price reductions as projected by Black & Veatch (2012). In the RE Futures RE-ETI scenario, greater renewable technology price reductions are assumed, while the RE-NTI scenario assumes no renewable technology price reductions.

RE Futures assumed substantial adoption of electric and plug-in hybrid-electric vehicles, with some flexibility of when charging occurs.

The primary driver behind the limited near-term growth is the relatively high delivered cost of energy from CSP, as well as limited value of its flexibility at low penetrations of variable generation.

At low penetrations of wind and PV, both sources are incorporated into the existing grid mix with limited additional need for system flexibility—the existing mix of generators is largely able to accommodate the additional variability and uncertainty. At low penetration, the energy value of wind and PV is relatively high, replacing higher-cost fuels with limited curtailment (Mills and Wiser 2012; Denholm et al. 2008). PV also has relatively high capacity value given its coincidence with demand patterns. Under these conditions, the firm capacity and dispatchability of CSP is less valuable, and it must compete essentially on a pure cost-of-energy basis.

As penetration of wind and solar PV increases, grid flexibility requirements also increase. Wind and PV begin to displace less valuable energy sources, and curtailment of these sources may increase (Denholm and Margolis 2007). Capacity value falls, particularly for PV where the demand peak is shifted to the early evening. Under these conditions, the dispatchability of CSP becomes more important, as the grid needs generation sources that can meet demand during the late afternoon and evening (Denholm and Mehos 2011). In both SunShot Vision and RE Futures, this occurs beyond the 2020 timeframe and coincides with projected decreasing costs of CSP technologies. The combination of flexibility requirements and lower-cost CSP corresponds to the significant increase in growth beginning in about 2025 for SunShot, and somewhat later in various RE Futures scenarios. A previous analysis of CSP deployment using the ReEDS model found similar results (Blair 2007).

In the Sunshot scenario, about 83 GW of CSP is installed by 2050, contributing about 8% of total U.S. electric demand.

The results show that large-scale deployment of CSP (=10 GW) is dependent on some combination of substantially reduced costs and the use of its ability to provide grid flexibility. In cases where CSP shows little performance improvement and the grid uses small amounts of variable generation, CSP faces a challenging economic environment, and ReEDS shows relatively little deployment. Overall, the fraction of generation contributed by CSP with storage varies greatly with the different scenarios and actually has the greatest range of contribution of all the technologies evaluated in the RE Futures study. This reflects the sensitivity of CSP to multiple factors

As a result of limitations of deploying CSP exclusively in the West, the highest CSP deployment cases developed substantial CSP resources outside the Western Interconnection, as illustrated in Table 2.

Table 2. Distribution of CSP Capacity in 2050 2050 Capacity (GW) Scenario West ERCOTa

Development occurs in the small part of New Mexico in the Eastern Interconnection, the Texas panhandle, Florida, and Oklahoma.

Figure 7 provides an example system dispatch in the Western Interconnection demonstrating the mix of generation sources used to meet load, as well as the significant net exports that occur even during periods of high demand. It also shows significant curtailment of renewable resources— largely solar generation in the middle of the day; this means that additional non-dispatchable solar (PV or CSP without storage) will largely be curtailed, particularly in high- solar/low demand periods in the spring.

350 300 Curtailment 250 Wind PV 200 CSP Hydropower 150 Gas CT Gas CC 100 Coal Other Nuclear50 Load

The dispatch results from GridView demonstrate three important aspects of CSP with TES. First, by shifting mid-day solar to later in the day, TES reduces renewable curtailment and increases the use of renewable energy, compared to cases without TES or other forms of electricity storage. Second, the dispatchability of CSP and its ability to rapidly ramp addresses the increase in variability created by PV and wind. Finally, CSP provides firm system capacity during the evening when the net load (load minus wind and PV generation) peaks.

Figure 8 illustrates the first two of these benefits during a four-day period in the spring. During these four days, the supply of renewable energy significantly exceeds demand during the middle of the day. Very large amounts of PV create an extremely low net load in the middle of the day, resulting in significant renewable curtailment. About 18% of potential renewable energy generation is curtailed in these four days, and this would be much higher if CSP generation were not able to shift a significant fraction of its generation to later in the day. The large mid-day generation from PV also creates a large upramp in evening demand, which is largely met by CSP, along with other dispatchable renewable and conventional generation sources.

Figure 10 provides a case in the Western Interconnection where wind provides about 31% of annual demand. This four-day period shows a more irregular net- load pattern due to the combined variability of both wind and PV. CSP provides a significant fraction of the net system flexibility to respond to the net demand.

Conclusions

Solar and wind represent the largest renewable resource base in the United States with the technical potential of either technology greatly exceeding the total demand for electricity. However, the variability and uncertainty of these resources requires an increasingly flexible grid at higher penetrations. Recent studies of high penetration renewable scenarios demonstrate the opportunity for the large-scale deployment of CSP with TES to provide a flexible and dispatchable source of energy. These studies find economic opportunities for CSP to provide a significant share of the nation’s generation mix. This deployment will likely depend on reduction in the cost of CSP compared to current costs. In all scenarios evaluated, limited CSP deployment is likely to occur at current costs on a pure economic basis. Achieving even partially the goals of the SunShot Initiative can potentially result in significant deployment. This will depend on two other factors. The first is significant new transmission development. This includes transmission development to connect CSP into the existing grid to supply energy to the southwestern United States and California. Very high penetration will require longer-distance transmission to supply larger areas of the Western Interconnection.

Transformational change, where CSP provides 10% or more of the nation’s electricity, will likely require expanded capacity between the Western Interconnection and Eastern Interconnection. The second factor is recognition and valuation of CSP’s flexibility and capacity value and consideration of this value during the system planning process. This includes appropriate timing of investment of CSP so its flexibility is available when less-flexible sources of energy are introduced to the grid. Including the value of grid flexibility can produce an overall least-cost energy mix, as opposed to a mix of the lowest-cost energy sources that does not consider the interaction between multiple generation technologies.

References
ABB, Inc. (2008). GridView User’s Manual, Version 6.0.
Black & Veatch. (2012). Cost and Performance Data for Power Generation Technologies. Overland Park, KS: Black & Veatch.
Blair, N. (2007). Concentrating Solar Deployment Systems (CSDS) – A New Model for Estimating U.S. Concentrating Solar Power Market Potential. NREL Report No. CP-640-41415. Golden, CO: NREL, 2 pp.
Brinkman, G.; Denholm, P.; Drury, E.; Ela, E.; Mai, T.; Margolis, R.; Mowers, M. (2012). Grid Modeling for the SunShot Vision Study. NREL Report No. TP-6A20-53310. Golden, CO: NREL, 38 pp.
Denholm, P.; Margolis, R. M. (2007). “Evaluating the Limits of Solar Photovoltaics (PV) in Traditional Electric Power Systems.” Energy Policy (35:5); pp. 2852–2861.
Denholm, P.; Margolis, R. M.; Milford, J. M. (2008). “Quantifying Avoided Fuel Use and Emissions from Solar Photovoltaic Generation in the Western United States.” Environmental Science and Technology (43:1); pp. 226–232.
Denholm, P.; Drury, E.; Margolis, R. (2009). Solar Deployment System Model (SolarDS): Documentation and Base Case Results. NREL/TP-6A2-45832. Golden, CO: NREL.
Denholm, P.; Mehos, M. (2011). Enabling Greater Penetration of Solar Power via the Use of Thermal Energy Storage. NREL Report No. TP-6A20-52978. Golden, CO: NREL.
EIA. (November 2011). Electric Power Annual 2010. Washington, DC: U.S. Energy Information Administration. Accessed September 18, 2012: http://www.eia.gov/electricity/annual/.
EIA. (2010). Annual Energy Outlook 2010: With Projections to 2035. DOE/EIA-0383(2010). Washington, DC: U.S. Energy Information Administration. Accessed September 18, 2012: http://www.eia.gov/oiaf/aeo/pdf/0383%282010%29.pdf.
Madaeni, S.M.; Sioshansi, R.; Denholm, P. (2011). Capacity Value of Concentrating Solar Power Plants. NREL/TP-6A20-51253. Golden, CO: NREL. Accessed September 18, 2012: http://www.nrel.gov/docs/fy11osti/51253.pdf.
Mills, A.; Wiser, R. (June 2012). Changes in the Economic Value of Variable Generation at High Penetration Levels: A Pilot Case Study of California. LBNL-5445E. Berkeley, CA: LBNL.
National Renewable Energy Laboratory. (2012). Renewable Electricity Futures Study. Hand, M.M.; Baldwin, S.; DeMeo, E.; Reilly, J.M.; Mai, T.; Arent, D.; Porro, G.; Meshek, M.; Sandor, D. eds. 4 vols. NREL/TP-6A20-52409. Golden, CO: National Renewable Energy Laboratory.
NREL. (2010). “System Advisor Model (SAM) Version 2010.4.12.” Accessed September 18, 2012: https://www.nrel.gov/analysis/sam/.

Short, W.; Sullivan, P.; Mai, T.; Mowers, M.; Uriarte, C.; Blair, N.; Heimiller, D.; Martinez, A. (2011). Regional Energy Deployment System (ReEDS). NREL/TP-6A20-46534. Golden, CO: NREL. Accessed September 18, 2012: http://www.nrel.gov/docs/fy12osti/46534.pdf.
U.S. Department of Energy (DOE). (2012). SunShot Vision Study. NREL Report No. BK-5200-47927; DOE/GO-102012-3037. Washington DC: U.S. Department of Energy.

Notes from 33 page: NREL. May 2014. Operation of Concentrating Solar Power Plants in the Western Wind and Solar Integration Phase 2 Study. National Energy Renewable Lab. Technical Report NREL/TP-6A20-61782 May 2014

The Western Wind and Solar Integration Study (WWSIS) explores various aspects of the challenges and impacts of integrating large amounts of wind and solar energy into the electric power system of the West.

The phase 2 study (WWSIS-2) is one of the first to include dispatchable concentrating solar power (CSP) with thermal energy storage (TES) in multiple scenarios of renewable penetration and mix. As a result, WWSIS-2 provides unique insights into CSP plant operation, grid benefits, and how CSP operation and configuration might need to change under scenarios of increased renewable penetration. Examination of the WWSIS-2 results indicates that in all scenarios CSP plants with TES provide firm system capacity, reducing the net demand and the need for conventional thermal capacity. The plants also reduced demand during periods of short-duration, high-ramping requirements that often require use of lower efficiency peaking units. Changes in CSP operation are driven largely by the presence of other solar generation, particularly photovoltaics (PV).

Use of storage by the CSP plants increases in the higher solar scenarios, with operation of the plant often shifted to later in the day. CSP operation also becomes more variable, including more frequent starts. Finally, CSP output is often very low during the day in scenarios with significant PV, which helps decrease overall renewable curtailment (overgeneration). However, the CSP plant configuration studied was not designed to minimize curtailment, implying further analysis of configuration is needed to understand the role of CSP in enabling high renewable scenarios in the western United States.

CSP with TES is a dispatchable source of renewable energy and can provide valuable grid flexibility services, including the ability to shift energy in time, rapidly change output, and provide firm capacity. The ability to store energy for later use can be particularly valuable in high renewable scenarios during periods when there is limited correlation between the natural supply of solar or wind energy and electricity demand.

Use of storage by the CSP plants increases in the higher solar scenarios, meaning a greater fraction of solar energy is stored for use later in the day. CSP operation becomes more variable, including more frequent starts. • In all scenarios, CSP plants generate at nearly full output during periods of peak net demand, providing high capacity value. • CSP plants are often ramped during periods of high variability of wind and solar, thereby reducing the ramping requirements of conventional thermal and hydroelectric generators. Combined with the high capacity value, this implies these plants provide a potentially significant source of grid flexibility. • CSP output is often very low during the day in the High Solar Scenario. This helps decrease overall renewable curtailment (overgeneration). However, the configuration studied may not be optimal for the High Solar Scenario, implying further analysis of CSP plant configuration is needed to understand its role in enabling high renewable scenarios in the western United States.1

WWSIS-1, released in May 2010, examined the viability, benefits, and challenges of integrating high penetrations of wind and solar power into the western grid. WWSIS-1 found it to be technically feasible if certain operational changes could be made, but it raised questions regarding the impact of cycling on wear-and-tear costs and emissions.

WWSIS-2 modeled four renewable scenarios in the U.S. portion of the Western Interconnection, including the TEPPC 2020 “base” scenario and three 33% renewable scenarios:

  • TEPPC Scenario (9.4% wind, 3.6% solar)
  • High Wind Scenario (25% wind, 8% solar)
  • High Solar Scenario (8% wind, 25% solar)
  • High Mix Scenario (16.5% wind, 16.5% solar).

Two unit-commitment cycles were simulated: a day-ahead (DA) “market” and 4-hour ahead (4HA) “market.” The DA market is used to commit units with long start times or high start costs (coal, nuclear, and biomass generators), using a 48- hour optimization horizon. The extra 24 hours in the unit commitment horizon (for a full 48-hour window) also helps properly schedule storage (including CSP with thermal storage).

Operation of CSP Plants to Provide Peak Capacity. One of the most significant benefits of CSP with TES is to provide firm system capacity by shifting energy to periods of peak demand.

It shows that the natural inflow of solar energy is not entirely coincident with demand, with an offset of about 4 hours. However, the use of storage enables the CSP plants to shift output to periods of highest net demand.

As greater amounts of wind and solar are added to the system, the timing of peak demand can shift, potentially increasing the importance of energy storage in CSP plants.

CSP Operation to Reduce Ramping Requirements. In addition to providing firm capacity, CSP can also replace the need for conventional generators to vary output during periods of high net load variability. This benefit occurs during all seasons, including periods with some of the highest instantaneous net load ramp rates (MW/minute) that occur near sunset on winter days. These ramp requirements are often associated with short duration peak periods. These winter peaks are much lower in magnitude than summer peaks, so typically do not drive peak capacity requirements. However, they often require the use of lower efficiency combustion turbines because the duration of the demand is not long enough to warrant starting a more efficient combined-cycle unit (exacerbated by the need for high ramp rates).

The increased ramp rates demonstrated in Figure 9 and Figure 10 must be met by dispatchable resources. In both the High Wind and High Solar Scenarios, CSP plants are often dispatched to meet demand during the period of highest net load, avoiding the use of other thermal generators, including lower efficiency combustion turbines. Figure 11 shows the CSP generation during the two-week periods that correspond to Figure 9 . It shows a very different mode of operation in response to system demand compared to the summer operation observed in Section 3.2. The overall availability of solar energy is lower, and the plants tend to operate in a fairly narrow window, primarily generating at nearly full output during the peak period. However, the plants also often carry over energy to the following day to meet the morning load peak. During the overnight hours the CSP plants either operate at minimum generation levels or shut down completely. Overall, unlike operation during the summer, CSP plants in the winter generate in a pattern anti-correlated with solar availability.

CSP Operation to Reduce Renewable Curtailment and Overgeneration, The WWSIS-2 scenarios demonstrate that spring presents the most difficult challenges in terms of potential curtailment. Curtailment is driven by a number of factors, including the coincidence of renewable supply with demand patterns as well as grid flexibility. Grid flexibility is driven by factors such as transmission capacity and generation mix, including the ability of conventional generators to ramp over a large range and at a high rate (NERC 2010). During the spring both wind and solar output can be relatively high, but mild weather produces some of the lowest load periods of the year.

Figure 12 shows the net load profiles for the week with the lowest net load of the year, which occurs at about 2 am on March 18 in the High Wind Scenario and at about noon on March 29 in the High Solar Scenario.

The net load drops rapidly and to low levels in the middle of the day, followed by a significant up-ramp as solar production drops. In these cases, the net load drops below what the grid can reliably meet with the installed generation mix. Wind or solar energy must be curtailed so that the conventional generation fleet can maintain generation at some minimal level. The actual generation from PV and wind allowed by the grid in the simulations is shown in Figure 13, which shows significant curtailment.

During periods of lowest net load, nearly all online thermal generation is generating at minimum stable levels around noon each day when PV output is the greatest but before load has peaked.

Significant solar energy is curtailed during the day as shown by the dotted line. This energy is curtailed partly because the start costs of coal generators do not justify turning them off in the morning and back on for the evening load peak.

In the High Solar Scenario, CSP plants in the spring tend to start up in the morning, using as much solar energy as is possible before the large amount of PV generation exceeds what the grid can accommodate due to system flexibility limits. At this point significant curtailment of solar energy begins to occur. CSP plants reduce output or even shut down during the middle of the afternoon and the CSP plant stores as much as possible. It should be noted that this operation is based on a plant utilizing direct storage, capable of sending all energy from the solar field to storage, even during times of high solar field output.

Figure 16 provides the average dispatch profile during the spring season in Arizona for the High Wind and High Solar Scenarios. It shows the CSP plant shifting as much energy as possible to the evening hours in an attempt to avoid curtailment. However, the ability of CSP to avoid curtailment is limited by the configuration of the CSP plant modeled in the study. In all scenarios, the CSP plant configurations are the same—a solar multiple of 2.0 with 6 hours of TES capacity. In this configuration only 3 hours of incident solar energy (at reference conditions) can be stored by the plant. While reference conditions typically do not occur for several hours, this limited storage capacity has a clear impact on the ability of CSP to shift energy during periods of low net demand. Because the modeled CSP plants cannot store a greater fraction of the incident solar energy, this leads to some production during periods of low demand (further reducing the net load) but also resulting in curtailment of CSP generation. (This explains why the area under the High Solar curve is lower than the High Wind curve.) This also introduces more frequent starts, with the average plant (of all plants in the study) increasing starts from about 1.4 times per day to about twice per day during this period.

Overall, these results indicate that CSP is a potentially important tool to avoid “over generation” events where renewable energy supply exceeds demand, considering grid flexibility limits. However, this will require further examination of different CSP plant configuration, as well as their associated costs and benefits. In the High Solar Scenario, a large fraction of CSP generation is curtailed in this spring period due to the limited thermal storage capacity and high solar multiple.

Increased storage capacity needs to be compared to its cost, particularly when this capacity might only be needed for a few weeks or months when the most significant mismatch between solar energy supply and demand patterns occur.

Variation in CSP plant operation is driven mostly by the increases in solar penetration. In the lower penetration of solar, optimal CSP operation is observed to be similar to previous analysis. This includes a “block” dispatch in the summer and a diurnal peaking dispatch in the winter.

In the higher penetration of solar cases, operation of CSP begins to shift to later in the day with greater use of energy storage, more frequent starts, and lower generation in the middle of the day. • In all scenarios evaluated, CSP plants are able to reduce the net peak demand, demonstrating high capacity credit and the potential ability to replace conventional capacity. • CSP plants with rapid ramping capability reduce the need for operation of peaking units during all seasons, including winter when short-term peaks are often observed. • CSP plants with TES can avoid curtailment of mid-day solar, which becomes more important with increased PV penetration. • The optimal configuration of a CSP plant can vary depending on the mix of renewable generators and grid flexibility requirements. In particular, as solar penetration increases and the net load becomes “peakier,” lower solar multiples might be needed to maximize the flexibility of CSP to effectively respond to system variability. This optimal configuration must be balanced against the increased cost of delivered energy due to lower utilization of the plant. This “net-benefit” will be addressed in future studies.

Overall, this study observed a number of quantifiable benefits of CSP with TES. However, several aspects of CSP’s ability to help integrate renewables (including both PV and wind) need further analysis to understand the potential contribution of CSP to overall system flexibility. In particular, the role of CSP in lowering minimum generation constraints and provision of fast ramping capability and other ancillary services will need further analysis in scenarios comparing CSP to other grid flexibility options.

References

Denholm, P.; Wan, Y.; Hummon, M.; Mehos, M. (2013). Analysis of Concentrating Solar Power with Thermal Energy Storage in a California 33% Renewable Scenario. P-6A20-58186. Golden, CO: National Renewable Energy Laboratory.

Denholm, P.; Hummon, M. (2012). Simulating the Value of Concentrating Solar Power with Thermal Energy Storage in a Production Cost Model. TP-6A20-56731. Golden, CO: National Renewable Energy Laboratory.

Denholm, P.; Mehos, M. (2011) Enabling Greater Penetration of Solar Power via the Use of Thermal Energy Storage. TP-6A20-52978. Golden, CO: National Renewable Energy Laboratory.

Lew, D.; Brinkman, G.; Ibanez, E.; Hodge, B.-M.; Hummon, M.; Florita, A.; Heaney, M.; Stark, G.; King, J.; Kumar, N.; Lefton, S.; Agan, D.; Jordan, G.; Venkataraman, S. (2013). The Western Wind and Solar Integration Study Phase 2. NREL/TP-5500-55588. Golden, CO: National Renewable Energy Laboratory.

GE Energy. (2010). Western Wind and Solar Integration Study. NREL/SR-5500-47434. Work performed by GE Energy, Schenectady, NY. Golden, CO: National Renewable Energy Laboratory. Accessed September 2013: www.nrel.gov/docs/fy10osti/47434.pdf.

Jorgenson, J.; Denholm, P.; Mehos, M.; Turchi, C. (2013). Estimating the Performance and Economic Value of Multiple Concentrating Solar Power Technologies in a Production Cost Model. TP-6A20-58645. Golden, CO: National Renewable Energy Laboratory.

Jorgenson, J.; Denholm, P.; Mehos, M.; (2014). Estimating the Value of Utility-Scale Solar Technologies in California Under a 40% Renewable Portfolio Standard. TP-6A20-61685. Golden, CO: National Renewable Energy Laboratory.

Madaeni, S.; Sioshansi, R.; Denholm, P. (2012). “How Thermal Energy Storage Enhances the Economic Viability of Concentrating Solar Power.” Proceedings of the IEEE (100:2); pp. 335–347.

Madaeni, S. H.; Sioshansi, R.; Denholm, P. (2012). “Estimating the Capacity Value of Concentrating Solar Power Plants: A Case Study of the Southwestern United States.” IEEE Transactions on Power Systems (27:2); pp. 1116–1124.

NERC (North American Electric Reliability Corporation). (2010). “Flexibility Requirements and Metrics for Variable Generation: Implications for System Planning Studies.” Princeton, NJ.

Sioshansi, R.; Denholm, P. (2010). “The Value of Concentrating Solar Power and Thermal Energy Storage.” IEEE Transactions on Sustainable Energy (1:3); pp. 173–183.

Short, W.; Sullivan, P.; Mai, T.; Mowers, M.; Uriarte, C.; Blair, N.; Heimiller, D.; Martinez, A. (2011). Regional Energy Deployment Systems (ReEDS). NREL/TP-6A20-46534. Golden, CO: National Renewable Energy Laboratory.

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Vernon VG&E AB 2514 Energy storage report

Posted on June 5, 2015 by

STAFF REPORT: VERNON GAS & ELECTRIC DEPARTMENT   September 2, 2014

[some of the 25-page report is shown below as I attempted to get up to speed on energy storage. Since California is the first state to mandate this, and energy storage is MANDATORY for being able to integrate solar and wind into the electric grid, it will be interesting to see how this unfolds in the future.  Alice Friedemann]

A 10 MW, 40 MWh Lithium Ion battery storage system participating in CAISO wholesale market from 2017 to 2031 has a Net Negative Present Value of $57 million. Results indicate that the installation of a Lithium Ion battery storage system for arbitrage is not cost-effective. The 15 year annual revenues and costs for the Lithium Ion battery storage system are graphed in Figure 1. The large capital expenditure is derived from the construction and installation of the storage device. Annual loan payments are then made to pay down the remaining principal on the loan at the fixed charge rate of 11% over the 15 year life. Operating and maintenance (O&M) costs and imbalance energy costs represent the other costs incurred by the storage device. Every 15 years, the entire battery stack is replaced because of the annual reduction in energy capacity due to cycle life degradation.

Chart of 15 Year Revenues for Lithium Ion Battery

Figure 2: Chart of 15 Year Revenues for Flow Battery

The purpose of energy storage systems is to absorb energy, store it for a period of time with minimal loss, and then release it when appropriate. When deployed in the electric power system, energy storage provides flexibility that facilitates the real-time balance between electric supply and demand. Maintaining this balance becomes more challenging as the contribution of electricity supplied by intermittent renewable resources expands. Typically the balance between supply and demand is achieved by keeping some generating capacity in reserve to ensure sufficient supply at all times and by adjusting the output of fast-responding resources such as hydropower. Energy storage systems, however, have the potential to perform this role more efficiently. Rechargeable batteries are the most familiar form of energy storage technology.

Large battery energy storage systems can be connected to the transmission grid to absorb excess wind or solar power when demand for electricity is low and, in turn, release the power when demand is high.

Pumped hydroelectric energy storage is a mature, commercial utility-scale technology that is currently in operation at many locations throughout the country. Pumped hydro draws off-peak electricity to pump water from a lower reservoir to a reservoir located at a higher elevation.

When demand for electricity is high, water is released from the upper reservoir, run through a hydroelectric turbine and deposited once again in the lower reservoir in order to generate electricity. This application has the highest capacity of the energy storage technologies that were studied. The output is only limited by the volume of the upper reservoir. Projects can be sized up to 4000 MW and operate at approximately 76%–85% efficiency. Pumped hydro plants can have a service life of 50 years, yielding rapid response times that warrant participation in voltage and frequency regulation, spinning and non-spinning reserve markets, arbitrage and system capacity support. While the siting, permitting, and associated environmental impact processes can take many years, there is growing interest in re-examining opportunities in pumped hydro. CAES uses off-peak electricity to compress air and store it in an underground reservoir or in above ground pipes. When demand for electricity is high, the compressed air is heated, expanded, and directed through a conventional turbine-generator to produce electricity. Underground CAES storage systems are most cost-effective with storage capacities up to 400 MW and discharge times of between 8 and 26 hours. Siting CAES plants requires locating and verifying the air storage integrity of an appropriate geologic formation within a service territory of a given utility. CAES plants employing above ground air storage would typically be smaller capacity plants on the order of 3 to 15 MW with discharge times of between 2 and 4 hours. Aboveground CAES plants are easier to site but more expensive to build.

Lead-acid is the most commercially mature rechargeable battery technology in the world. Valve regulated lead-acid (VRLA) batteries are used in a variety of applications, including automotive, marine, telecommunications, and UPS systems. Transmission and distribution applications are rare for these batteries due to their relatively heavy weight, large bulk, cycle-life limitations and maintenance requirements. Serviceable life can vary greatly depending on the application, discharge rate, and the number of deep discharge cycles. Battery price can be influenced by the cost of lead, which is a commodity. Finally, very limited data is available regarding the operation and maintenance costs of lead-acid based storage systems for grid support.

Flow Battery. Vanadium redox batteries are the most mature type of flow battery systems available. In flow batteries, energy is stored as charged ions in two separate tanks of electrolytes, one of which stores electrolyte for positive electrode reaction while the other stores electrolyte for negative electrode reaction. Vanadium redox systems are unique in that they can be repeatedly discharged and recharged. Like other flow batteries, many variations of power capacity and energy storage are possible depending on the size of the electrolyte tanks. Vanadium redox systems can be designed to provide energy for 2 to 8 hours depending on the application. The lifespan of flow-type batteries is not significantly impacted by cycling. Suppliers of vanadium redox systems estimate the lifespan of cell stacks to be 15 or more years.

Lithium-Ion (Li-ion). Rechargeable Li-ion batteries are commonly found in consumer electronic products, which make up most of the worldwide production volume of 10 to 12 GWh per year. A mature technology for consumer electronic applications, Li-ion is positioned as the leading platform for plug-in hybrid electric vehicle (PHEV) and electric vehicles (EV). Given their attractive cycle life and compact nature, in addition to high efficiency ranging from 85%-90%, Li-ion batteries are being considered for utility grid-support applications such as distributed energy storage, transportable systems for grid-support, commercial end-user energy management, home back-up energy management systems, frequency regulation, and wind and photovoltaic smoothing.

Flywheels are shorter energy duration systems that are not generally attractive for large-scale grid support applications that require many kilowatt-hours or megawatt-hours of energy storage. They operate by storing kinetic energy in a spinning rotor made of advanced highstrength materials, charged and discharged through a generator. Flywheels charge by drawing off-peak electricity from the grid to increase rotational speed, and discharge when demand is high by generating electricity as the wheel rotation slows. Flywheels enjoy a very fast response time of 4 milliseconds or less, can be sized between 100 kW and 1650 kW and may be used for short durations of up to 1 hour. Flywheels possess very high efficiencies of about 93% with a lifetime estimated at 20 years. Because flywheel systems are quick to respond and very efficient, they are being positioned to provide frequency regulation services.

Benefits are realized by analyzing energy storage in the three fundamental categories of load leveling, grid operational support and grid stabilization. Within these categories, each application of energy storage can lead to different economic, reliability, and environmental benefits.

Cost and performance data including installed cost, operation and maintenance costs, round trip efficiency and cycle life

The tool itself has gone through extensive review and usage. Sandia National Labs and the US Department of Energy (DOE) have both conducted formal peer reviews of the framework. The DOE has adopted this framework for use by the 16 recipients of the Smart Grid Demonstration program to quantify the costs and benefits of energy storage demonstration projects.

Load Leveling in general terms refers to the practice of generating power off peak when prices and demand are low and using or dispatching this power on peak when prices and demand are high.

Four basic areas of Load Leveling are as follows: 1) Renewable Energy Shifting – The process of capturing electricity generated from renewable sources during periods of over-generation or low demand then, in turn, dispatching the stored electricity to the grid in times of high demand.

2) Wholesale Arbitrage – This method takes advantage of a price difference between markets by capitalizing and profiting from the imbalance between them. 3) Retail Market Sales – The practice of capturing electricity off peak in order to sell to the retail market at on peak pricing for profit. 4) Asset Management – Energy Storage technologies can be used to store and dispatch certain amounts of electricity so that generating units may be run at the most efficient output level. This practice can save wear and tear on the generating units by allowing them to run in an optimal state.

Grid Operational Support can be defined as ancillary services utilized to effectively match supply to demand. These services are typically performed by an Independent System Operator to maintain the reliability of the electric grid. Five different areas were examined with respect to grid operation support applications: 1) Load Following – an ancillary service concerned with maintaining grid balance by adjusting power as demand for electricity fluctuates throughout the day. 2) Operating Reserves – an ancillary service charged with maintaining extra capacity that can be called upon when some portion of the normal electric supply resources suddenly become unavailable. 3) Frequency Regulation – an ancillary service tasked with managing energy flows to reconcile momentary differences between supply and demand. 4) Renewable Energy Capacity Firming – an application using energy storage to produce more consistent power output when renewable resources temporarily drop. 5) Black Start – an ancillary service responsible for providing power to a conventional generator in order to restart after a partial or full shutdown.

Grid Stabilization involves improving reliability. Grid Stabilization can be divided into four components as follows: 1) Renewable Energy Ramping – Using energy storage to mitigate volatility from low wind conditions and high wind cutout. Cut out speed, typically between 45 and 80MPH, causes a turbine to shut down, ceasing power generation. 2) Renewable Energy

Smoothing – Solar and wind resources are intermittent on a second to second basis. Energy storage can assist in smoothing the output volatility of these resources, thus, improving power quality. 3) Backup Power – Energy Storage may be used to ensure highly reliable electric service. In the event of a system disruption, energy storage can be used to ride through the outage. 4) Power Quality – Energy Storage technologies have the potential to function as capacitors and transformer tap changers by providing voltage support for localized reactive power issues.

Calling upon an energy storage device to keep services up during a distribution outage carries with it a host of issues. The energy storage device could not be brought online seamlessly to mitigate customers being impacted by the outage due to safety and technical reasons. The energy storage device, if brought online in this scenario could contribute to a fault causing more profound damage. VG&E customers that might benefit from this type of system are either on an interruptible contract or have redundant power feeds to their facilities.

Deferral of Distribution System Upgrades. Seeing that VG&E does not own or operate significant generation or transmission resources, the focus of this feasibility study centered on the VG&E distribution system. Energy Storage systems can defer the need for distribution system upgrades. Typically, as systems evolve and grow, upgrades are made to serve loading requirements and meet the needs of customers. Installing Energy Storage systems on impacted feeders that are near full-load capacity can defer or eliminate the need for large capital investments to upgrade the system in that specific region. Assuming that the storage system reduces loading on existing equipment, the energy storage system could improve or increase the life of the existing distribution equipment, including transformers and cables.

In their most recent study, R.W. Beck recommended that system upgrades be implemented when the City peak load reached 400 MW. As the national economy has struggled since the mid 2000’s, the VG&E load has remained flat and peak load is currently 193 MW. The VG&E resource planning group, in performing a ten year forecast does not see any appreciable load growth, and therefore, deferral of distribution system upgrades was not an application staff considered

Since 2007, VG&E experiences on average, 32 electrical system outages per year. Outages in the City of Vernon are typically caused by events that are beyond control such as metallic balloons, vehicles striking utility poles, birds and weather related circumstances.

Electricity storage can reduce electricity peak demand and thereby reduce feeder losses. This process translates into a reduction in emissions if peak generation is produced by fossil-based electricity generators. However, since electricity storage has an inherent inefficiency associated with it, electricity storage could increase overall emissions if fossil fuel generators are used for charging.

Inherent Risk. There are some true challenges when assessing the feasibility of energy storage systems that cannot necessarily be accounted for in using the Energy Storage Assessment tool.

First and foremost, energy storage technologies at the grid level are not mature and do not

have a long track history that can be analyzed. Attempting to calculate the cost of emerging technologies is problematic in that many of the technologies still find themselves in the research, testing and development stage rather than in an actual production or in-service environment.

Limited safety data is available when considering emerging technologies that are still in the development stage. Last, with newer technologies and relatively short life expectancy, accurate replacement costs are simply not available.

When attempting to perform a rigorous cost-benefit analysis, valuating the replacement cost of various energy storage technologies is speculative at best.

 

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Stop wasting food

Posted on June 5, 2015 by

[Clearly at the point when food rationing begins due to limited amounts of transportation oil, not wasting food will be important, and composting can expected to be the main way of disposal since garbage trucks will run less frequently. Below are excerpts from 2 articles about food waste. Alice Friedemann]

Bringezu, S. 2014. Assessing Global Land Use.  United Nations Environment Program.

The UN stated that if we don’t stop wasting food, we’ll lose the equivalent of an area of land the size of Brazil to agriculture to make up the gap by 2050. A third of food is wasted due to not enough control of pests, inadequate warehouses, and wasteful food processing and consumption. Over 19 million square miles of land are used in global agriculture, 33% crops and 67% pasture. Cities are covering both, reducing biodiversity. The result is by 2050 with 2 billion more people to feed another 3.3 million square miles of land will be needed, which is the size of Brazil.  We need to restore eroded land, cut meat consumption, and stop cities from expanding.

Griffin, M., et al. 2009. An analysis of a community food waste stream. Agric Hum Values (2009) 26:67–81

Food waste comprises a significant portion of the waste stream in industrialized countries, contributing to ecological damages and nutritional losses. Guided by a systems approach, this study quantified food waste in one U.S. County in 1998–1999.

Approximately 10,205 tons of food waste was generated annually in this community food system. Of all food waste, production waste comprised 20%, processing 1%, distribution 19%, and 60% of food waste was generated by consumers. Less than one-third (28%) of total food waste was recovered via composting (25%) and food donations (3%), and over 7,000 tons (72%) were landfilled. More than 8.8 billion kilocalories of food were wasted, enough to feed county residents for 1.5 months.

The trend toward more processed, packaged, and convenience foods, particularly in industrialized nations, has further increased concern about wastes associated with eating (Munro 1995), since this waste increases the volume of the organic waste stream.

As concern about food waste intensifies (Smil 2003), studies that quantify or estimate food waste have emerged

In 1997, Kantor et al. published a quantitative estimate of food waste across the entire U.S. food system. The study revealed that one-quarter of food produced in the U.S. (96 billion pounds) is wasted yearly.

The analysis reported here is a case study of food waste of one community food system in the U.S. It examined and quantified food waste of a whole food system at a local level.

Food waste occurs during the food system stages of production, processing, distribution, acquisition, preparation, and consumption (Sobal 1999, 2004). Production wastes occur from natural disasters, insect or predator destruction (Kantor et al. 1997), government programs that encourage farmers to overproduce certain foods (Kling 1943; Poppendieck 1986), failure of harvesters to retrieve all food in a field (United States Department of Agriculture 1997a, b), selective harvesting by farmers (Kling 1943), or failure to harvest at all owing to low market prices or poor yields (United States Department of Agriculture 1997a, b). Food waste at the farm level also occurs during storage from spoilage and pest destruction (Kantor et al. 1997). Inefficient processing methods that remove edible as well as inedible portions of food (Kantor et al. 1997) and spillage contribute to food processing wastes (Kling 1943). In Western nations, much processing waste is comprised of what consumers in these countries consider to be inedible portions of food—peels, bones, blood, skins, and eyes— and ‘‘substandard’’ items (edible but blemished or small products). Distribution food waste incurs from improper food handling, packaging, and transportation (Kantor et al. 1997), spoilage (Kling 1943; Marquis 2001), failure of new food items to sell (Senauer et al. 1991), overstocking, and insufficient stock rotation (Kantor et al. 1997). Significant food service waste comes from plate scraps, which in some countries are not salvaged because of food safety considerations, and increased portion sizes

Consumer food waste occurs during food acquisition, preparation, and consumption.

Improper or prolonged storage are a key cause of consumer food waste. During preparation, consumers may remove inedible or blemished portions of foods as well as edible portions such as skins to obtain desired sensory or nutritional qualities. Leftover foods may be fed to pets, decreasing the amount of discarded food but also decreasing availability of foods for humans (Wenlock et al. 1980). The availability of cheap food, particularly in industrialized nations, encourages overbuying and hoarding behaviors that result in waste.

Significant energy losses occur when food is discarded, including the energy used to produce and distribute the food, to process the wasted food, as well as the energy captured in the food itself. Wasted food threatens environmental and community health through destruction of the biophysical environment, air pollution from decaying food, water pollution from runoff or leaching, and rapidly growing landfills. Contrary to popular belief, Rathje and others (1975, 1991) have shown that organic wastes do not decay or evaporate in landfills, owing to the anaerobic environment in which the waste is buried. From an ecological standpoint, minimizing food waste promotes environmental sustainability by conserving energy resources, reducing environmental costs of burning fossil fuels, protecting microhabitats, and preserving water and air quality. From a nutritional standpoint, reducing food waste increases the availability of nutrients to individuals, improving community health (American Dietetic Association 2001) and community food security.

This food waste analysis was conducted for one U.S. County (population 97,000) in Upstate New York State. This county provided a case study of a whole food system that contained an agricultural base (447 farms that occupied one-third of the county’s land area) plus several light industries, as well as a small centrally located city (population 29,000), a university of approximately 19,000 students, and a smaller college of about 5,900 students.

Only within-county farm waste was included because, as with any community embedded in the global food system, it was impossible to identify all of the farms across the state, nation, and world that supplied at least some food to the county.

The largest food waste in the producer subsystem was from grains and milk. This can be attributed to the high volume of these foods produced in the county. Milk is also perishable and freshness is highly valued, which leads to greater waste than among more durable commodities.

Table 1 Production food waste Commodity Planted Harvested Wasted Yield/acre Generated

Whether unharvested crops are left in the fields or later picked and discarded has different impacts on the environment. Food left in the field has a less negative environmental impact than food transported to a landfill because the energy for transporting the unharvested goods is conserved and the decaying crops add nutrients back into the soil. Producers interviewed for this study stated that they typically left unharvested foods in the field to be recycled into the soil. Thus, although the unharvested foods from the producer subsystem were classified as food waste since they were unavailable for human consumption, almost none of the 2,000 tons of unharvested foods from county farms entered the landfill. The only exception was milk, which was not used as fertilizer or animal feed in this county and thus was discarded, sending 99 tons into the waste stream annually.

Much of the waste from processors was also recycled into the soil, either as fertilizer or compost. All four wineries returned some processing wastes to the soil, diverting 5,000 pounds of solid waste and 7,300 gallons of liquid waste from the waste stream. Two bakeries (Bakery 1, Bakery 4) gave away the majority of their waste for animal feed, diverting over 18 tons (36,270 pounds) from the landfill. Four bakeries (Bakeries 1-4) also distributed dayold products to their employees or to local food pantries and soup kitchens, removing over 89.5 tons (179,058 pounds) from the waste stream.

Table 2 Processing food waste Processor Generated waste/year Recovered waste/yeara

Significant food waste was generated from fast food restaurants, full service restaurants, and hotels with restaurants. These sources were the highest because restaurants must discard not only the wastes from meal preparation but also plate waste not eaten by consumers. Substantial waste was generated by supermarkets, since quality standards and consumer demands for fresh produce, dairy, and bakery items result in many edible but imperfect foods being discarded. However, supermarkets were extensively involved with local food distribution programs. All but one of the supermarkets gave out-of-date food items to local food pantries and soup kitchens. Meat waste from two supermarkets was given to rendering companies located outside of the county.

Table 3 Distribution food waste Distributor Number in county

Consumption An estimated 6,146 tons of food waste was generated at the consumer level (Table 4), more than any other stage in the county food system. In the county, composting awareness was high

Cooperative Extension estimates suggested that about 10% of the 34,500 county households (3,446 households) composted

Table 4 Consumption food waste Unit Waste loss factor

Over 8.8 billion kilocalories were lost through food waste each year in the county. which means that these energy losses are enough to feed all of the county’s 96,659 residents for 45 days,

This investigation was a case study of food waste across the whole spectrum of a community food system. It showed that a considerable amount of food waste occurred at the consumer stage of the system and to a lesser extent at food production, processing, and distribution stages. In this community food system, most food waste was sent to the landfill (72%), although a portion was composted (25%) and some was diverted to emergency food programs (3%).

Policy changes at the corporate level could include incentives for food service companies, stores, or institutions to donate leftover foods to emergency food organizations, such as bonuses for food service managers or reimbursement for the cost of the food. In the United States, vendors who choose to donate unused foods are protected from liability in foodborne illness cases by the Good Samaritan Law mandatory composting within communities.

In both businesses and households, it is possible that significant savings in money and energy could occur, since less solid waste would be hauled to landfills.

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Expanding rail infrastructure to accommodate growth in agriculture and other sectors

Posted on June 5, 2015 by

Excerpts from 103 page: Keith, K. Jan 2013. Maintaining a track record of success. Expanding rail infrastructure to accommodate growth in agriculture and other sectors. TRC Consulting.

[I’m working on a book about the distribution of food when declining oil supplies force rationing, so what follows are bits and pieces from the book, not in any order or organized to give them meaning. It seems to me after reading this that we’ll wish we had a lot more short-line rail to haul food to cities. Much of our class 1 rail is designed to get short haul agricultural products to export via port cities. Alice Friedemann]

  • Soybean acres 75 million acres to possibly 78 million acres, based upon mostly growth in export demand.
  • Wheat acres remain in 55-58 million acre range, depending on global food needs.
  • Corn acres remain in the 90 to 92 mil acre range and ethanol-from-corn production stays at about 13 to 15 billion gallons annually.
  • Total planted crops in the U.S. moves from 250 mil acres to 254 mil acres as CRP declines gradually.

ROADS – annual federal and state investment gap $194 billion/year page 38

page 39 estimated gap in highway investments

The rail sector for many years had excess capacity, although some rail yards were known bottlenecks for switching as necessary traffic exchanges took place. cycle times of cars and dwell times in switching yards began to increase and peaked in 2006.

New shuttle train facilities require investment costs of $20 million+, so there is considerable investment risk.

Barges utilizing waterways tend to be very fuel efficient and the most cost-efficient per ton-mile of movement, but waterways by their nature are not available everywhere, and so accessibility can be limited. Railroads also are more fuel efficient and cost efficient than trucks. On a ton-mile basis, trucks are the most expensive freight mode, but trucks can originate and deliver freight to almost any location.

From a cost standpoint, trucks are not favored for long-distance moves, but rail access can be problematic, sometimes pushing freight onto trucks, even though it is more costly. Short-line railroads provide rail access for about 40% of the grain and oilseed volume moved by rail (either at origin or at destination),

TRUCKS TOO page 25 domestically truck 80% rail 20% (DDG)

trucks page 26, 27 soybeans

Corn transport movements shown in Figure 8 are trending toward heavier use of rail in the export market, but much heavier use of trucks in the domestic market. It is difficult to conceive the enormous impact that the growth in the ethanol use of corn has had on this market. The ethanol industry has expanded by about 7-fold in the last 8 years and now consumes roughly 5 billion bushels of corn, virtually of all of which is delivered directly to ethanol plants by truck (the exceptions are a few ethanol plants in Arizona, California and West Texas). So, more than 1/3 of the U.S. corn crop— about 25% of U.S. grains and oilseeds volume— moves by truck to ethanol manufacturers. With this change in corn utilization patterns, railroads have picked up additional shipment volume in ethanol, as the majority of ethanol movements are railed; and DDG movements, of which railroads ship 20-30%. This new development in industrial agriculture seems to be leveling off, but not likely to decrease much in size, unless dramatic policy shifts were to occur or energy economics change substantially. But this episode in agriculture does demonstrate one important fact—how rapidly transportation infrastructure changes can take place. Hundreds of new facilities were built in just a few years, including new infrastructure, upgraded infrastructure for rail bridges and heavier track, to accommodate the new ethanol-related growth—-to handle a shift of 25% of the total output of grain/oilseeds-base agriculture. From the USDA data, the modal share for trucked corn seems to be the major beneficiary of this structural change, but the associated changes in product and by-product markets have created other transportation challenges of great significance to U.S. agriculture and the rail and barge industries.

SHORTLINE RAIL

Given that over 40% of food/ag products shipped by rail are either originally shipped or ultimately received on a short line, this provision remains very important to maintaining a fluid agricultural rail system in the U.S. Short lines don’t represent a huge part of the ton-miles of rail carriage (about 1%), but for agriculture, they frequently provide the critical link to actually provide access to the ultimate origin or destination.

Average           Miles of

# Carloads       Tonnage          Length             Track, road, or

2010            in tons             of haul            navigable water

Class 1 Rail                 29,200,000      1,851,000,000    914                    95,700

Class II & III Rail       7,800,000         600,000,000     32                    43,000

Truck                                                   8,778,000,000                         4,016,000

Inland water                                           532,000,000                              25,320

 

The U.S. Bureau of Census and U.S. Department of Transportation 2007:

Tons                            Ton Miles

Total Movements        12,543,000,000           3,345,000,000,000

 

Single Mode Movements

Truck                           8,779,000,000             1,342,000,000,000

Rail                              1,861,000,000             1,344,000,000,000

Waterway                       404,000,000                157,000,000,000

 

Multi-mode movements

Truck/Rail                      226,000,000                197,000,000,000

Truck/Water                   145,000,000                  98,000,000,000

Rail/Water                        55,000,000                  47,000,000,000

Unknown                    1,097,000,000                160,000,000,000

 

Agriculture-related Shipments—volumes, All modes of transport:

Cereal Grains (02)                   514,000,000 tons for 203,000,000,000 ton/miles

Ag Products (03)                    212,000,000 tons for   88,000,000,000 ton/miles

Animal Feeds/Proteins (04)    246,000,000 tons for   76,000,000,000 ton/miles

Milled Grain Products (06)     120,000,000 tons for   51,000,000,000 ton/miles

Other Foodstuffs/Oils (07)     468,000,000 tons for   171,000,000,000 ton/miles

 

Non-agricultural products, all modes of transport by volume

Coal 25%, Chemicals/plastics/rubber 10%, Sand/gravel 7%, Metals/machines 6%, Petroleum/products 5%, wood products 3%, Fertilizer 2%

Barges utilizing waterways tend to be very fuel efficient and the most cost-efficient per ton-mile of movement, but waterways by their physical nature are not available everywhere. Railroads also are more fuel efficient and cost effective than trucks where available, but accessibility can be an issue. Trucks are the most universally accessible mode, providing door-to-door service, but trucking is also the most expensive form of bulk transportation, and least fuel-efficient.

an assessment is made of the potential impacts of government programs that could expand the rail sector’s capacity to alleviate highway congestion and create a more efficient transportation platform for the national economy.

Train Speed and Carloads Plus Intermodal Units50

New shuttle train facilities require investment costs of $20 million+, so there is considerable investment risk.

analyzes the financial investment incentives of the following government programs:

Railroad Rehabilitation and Improvement Financing (Federal Railroad Administration)

Investment Tax Credit of 25% and Accelerated Depreciation

Accelerated Depreciation and “Bonus” Depreciation of 50%

General Business Tax Rate Reduction of corporate rates from 35% to 25% The conclusion is that the investment tax credit of 25% and accelerated depreciation yielded the most incentive for investment, generating a 21% decline in present value of the after-tax investment cost. It was assumed that this incentive would be adequate to close the gap in rail infrastructure funding and increase the rate of investment by the soybean marketing

Economically, rail rates for long distance moves in agricultural commodities cost about 3.2 cents per ton-mile. This compares to 1.5 to 3 cents per ton-mile for barge movements, depending on season and river market conditions, but the commercial waterways are not able to compete geographically for all agricultural markets. And shipments by rail can move from the Midwest to Pacific Rim nations in as little as 18 days compared to 50 days by barge and ocean-going vessel. Comparatively, truck moves of grain currently cost about 15 cents per ton-mile for the first 25 miles, then 6 to 7 cents per ton-mile thereafter. From a cost standpoint, trucks are not favored for long-distance moves, but rail access can be problematic, sometimes pushing freight onto trucks, even though it is more costly. Short-line railroads provide rail access for about 40% of the grain and oilseed volume moved by rail (either at origin or at destination), and increasingly efficient rail loading points like unit train and shuttle shippers provide closer access to long-distance markets for producers trucking soybeans from their farming operation. As the preferred hauler of heavy cargo like grain, soybeans, fertilizer and coal, rail moves almost 50% of the ton-mile freight in the U.S. at a much lower cost than truck movements. Across all types of freight, truck costs average over 16 cents per ton-mile compared to less than 4 cents per ton-mile for rail.

The question now is whether private sector investment incentives will be adequate to meet both private company and broad societal goals. If the rail industry cannot justify building and maintaining transport capacity

The fundamental economics of rail—movement of heavy tonnage at ¼ to ½ the cost available through truck transport—is compelling. Census transport flow data of 2007 show that rail transportation was used in 47% of the rail ton-miles of all commodities shipped in the U.S. The market is using rail because of its lower cost and logistics of long-distance and heavy load moves. If there are ways to expand the amount of additional tonnage moved at lower cost by rail, there are benefits achievable throughout the rest of the economy.

With the intense competition between the U.S. and South America as principal oilseed suppliers to global markets, U.S. soybean exports have become increasingly seasonal, with over 75% of total movements shipped in the first six months of the marketing year. Will rail capacity continue to be adequate to service this intense seasonal need for soybeans?

The significance of trucking within local markets has taken on new importance for agriculture as biofuels have become a major source of demand. The growing strength of the movement of soybeans out the Pacific Northwest rail corridor has underscored the market links that rail provides. Yet, food and agriculture is but one of a large array of industries—coal, petroleum, autos, chemicals, consumer and many others—that share the rail capacity to move products and resources, and some of these sectors are in rapid transition today. The energy sector, in particular, is being altered fundamentally by the oil and gas fracking industry which will change overall rail movement volumes and the direction of transportation flows.

capacity utilization for the different freight modes. Both Class I and Class II (regional railroads) plus Class III (short line railroads) are broken out in Table 1.

The average Class I haul distance, at a length of 914 miles, is quite different from a typical short line haul of 32 miles. Regional railroads average about 180 miles per movement; and short lines average 25 miles. The total inventory of highway miles is 4.0 million miles with roughly 1 million miles in urban settings and 3 million miles classified as rural. There are 25,320 miles of navigable rivers in the U.S. available to barge traffic. Comparing the most recent data available, total annual rail tonnage hauled (including the short line industry) is 2.4 billion tons; trucks haul about 8.8 billion tons and waterways haul 0.523 billion tons.

The Texas Transportation Institute estimates that highway congestion added $100.9 billion to the cost of the national economy in 2010 and caused 1.94 billion gallons of fuel to be wasted.2

Truck mileage is only 10% of total vehicle miles traveled on highways, but is probably responsible for 25 to 30% of congestion volume, based upon recent analytical work.

Table 3. This table demonstrates that to the extent that freight transportation movements can be shifted from truck to either rail or barge, there are economic benefits, highway congestion benefits, fuel efficiency savings and environmental benefits from lower greenhouse gas emissions. Freight train and barge movements can assist in reducing highway traffic, reduce national fuel consumption, and contribute fewer fuel-related emissions in freight transport. Barge and rail movements will never be able to match the convenience of door-to-door trucking, but more efficient freight transfers between modes through intermodal and other transfer facilities that will permit a maximum of tonnage to be hauled by rail and barge can have substantial economic and other societal benefits. Table 3. Comparison of Modal Efficiencies and Performance Truck Rail Barge Cost per ton-mile

Table 4. Total Flows of Commodity and Goods, U.S., 2002 and 2007, U.S. Bureau of Census Total Movements Single Mode Movements Truck

Agriculture-Related Shipments – Volumes, All Modes, (mil tons) Cereal Grains (02) 561 514 264 203 Ag Products (03) (incl. soybeans) 259 212 109 88 Animal Feeds/Proteins (04) 228 246 51 76 Milled Grain Products (06) 109 120 49 51 Other Foodstuffs/Oils (07) 449 468 162 171 Total Agriculture Related Rail 1,606 1,560 635 589 Shipment Volumes (14%) (12%) (20%) (18%)

Other Non-Ag Products, All Modes, in Order of Volume, 2007 Coal (25%) Chem/plastics/rubber (10%) Sand/gravel (7%) Metals/machines (6%) Petroleum/products (5%) Wood products (3%) Fertilizer (2%) Source: U.S. Bureau of Census and Dept. of Transportation. In the middle section of Table 4 are shown the total shipment tons and ton-miles for five census categories that comprise agricultural and food-related products. Soybean movements are contained in the “Ag Products” (03) category, listed separately from “Cereal Grains.” Soybeans make-up 40-50% of this category. For major soybean producing states, the percentage of soybeans is much higher. Overall, ag and food related product movements comprise 12-14% of U.S. tonnage moved and 18-20% of transportation ton-miles on a national basis.

Figure 5. Modal Share Data, All Commodities, 2007, Census and DOT FAF Data U.S. Census Data

Figure 6 shows modal share for Cereal Grains and Ag Products. Census data reflect a stronger modal share percentage for both rail and water than does FAF. Census data suggest 51% of total ton miles are moving by rail and 24% by barge. The DOT FAF data are remarkably different from Census numbers, but do reflect the additional counting of farm truck movements to farm bins (if harvested commodities are first stored on the farm), then the additional truck movements to the first point of sale in the commercial marketing channels.

Figure 7 shows U.S. wheat domestic market and export market modal shares. The wheat export market has traditionally been dominated by rail as Midwestern wheat is railed to Texas ports and northern tier states rail much of export wheat out of the Pacific Northwest ports. But even domestic wheat movements by rail are growing in proportion to other modes as wheat is being shipped longer distances to domestic milling locations that tend to be higher volume flour millers. Figure 8 shows modal shares for corn, and domestic movements of trucked corn have expanded from roughly 65% to 80% in 12 years. Virtually all this growth is due to the rapid expansion of ethanol capacity in locations where trucking corn is the least-cost option. The rail portion of corn exports have also grown from about 27% to 40% during the same period. Corn transport movements shown in Figure 8 are trending toward heavier use of rail in the export market, but much heavier use of trucks in the domestic market. It is difficult to conceive the enormous impact that the growth in the ethanol use of corn has had on this market. The ethanol industry has expanded by about 7-fold in the last 8 years and now consumes roughly 5 billion bushels of corn, virtually of all of which is delivered directly to ethanol plants by truck (the exceptions are a few ethanol plants in Arizona, California and West Texas). So, more than 1/3 of the U.S. corn crop— about 25% of U.S. grains and oilseeds volume— moves by truck to ethanol manufacturers. With this change in corn utilization patterns, railroads have picked up additional shipment volume in ethanol, as the majority of ethanol movements are railed; and DDG movements, of which railroads ship 20-30%.

Figure 7. U.S. Wheat Market: Modal Shares of Domestic and Export Movements

Figure 8. U.S. Corn Market: Modal Shares of Domestic and Export Movements

Figure 9. U.S. Soybean Market: Modal Shares of Domestic and Export Movements

Figure 10. U.S. Soybean Meal Produced and Railed to Domestic and Export Locations

Figure 11. Eastern and Western Railroad Grain/Oilseed Shipments, 2001 – 2011

Figure 13. Train Speed and Carloads Plus Intermodal Units

How do you measure U.S. rail capacity? Rail capacity is determined by a number of factors: 1) locomotive availability; 2) car availability; 3) number of trained employees; 4) infrastructure capacity; 5) logistics systems operational efficiencies; and 6) external factors, such as weather, strikes, congestion at ports.

The major railroads all have a target number of “optimal” cars on line for a given amount of infrastructure (track, rail yards, interchanges, etc.) and operational technology/ capacity.

The table below shows the recent pattern in federal and state government revenues and expenditures on roads and highways. More recent data than 2006 are available, but are preliminary. Recent rates of expenditures for roads and highways are about $257 billion. Revenues raised from gas taxes, tolls and other sources are not keeping pace with expenditures, so unless substantial changes can be made in the gas tax rate, or appropriate more general funds, the gap in highway funding will continue to widen, forcing governments to look for other solutions to traffic growth. Table 7. Federal and State Highway Expenditures and Revenues Generated for Roads, DOT Federal and State Highway Expenditures and Revenues Generated for Roads (billions of dollars) Year 2001 2002 2003 2004 2005 2006 Fed Expenditures 69 78 85 82 85 81 State Expenditures 142 146 153 156 158 176 Total Expenditures 211 224 238 238 243 257 Total Road Revenue 125 131 132 136 147 155 Expenditures less Revenue 86 93 106 102 96 102

The number of sand cars being shipped into fracking areas has increased an estimated 250,000 per year, or about 1.7% of normal total rail car volumes.

Figure 26. U.S. Rail Intermodal Traffic

U.S. agricultural markets have gone through some rapid transformations in the last decade. Corn used for ethanol production and DDG production has expanded to about 40% of the corn market. Soybean markets have benefited from expanded biofuels through biodiesel production. Export markets for both soybeans and wheat have strengthened with global income growth. Pacific Rim country exports have grown rapidly, causing increased demand for U.S. West Coast originations for export moves.

Soybean acres move up from 75 million acres to possibly 78 million acres, based upon mostly growth in export demand. Wheat acres remain in 55-58 million acre range, depending on global food needs. Corn acres remain in the 90 to 92 mil acre range and ethanol-from-corn production stays at about 13 to 15 billion gallons annually. Total planted crops in the U.S. moves from 250 mil acres to 254 mil acres as CRP declines gradually.

Exports tend to be more dependent on both rail and barge for shipment to port locations. This will tend to expand the rail modal share, a trend that is already visible (see Table 14). Table 14. Modal Share Summary: 2010 and 5-year average Modal Share Summary: 2010 and 5-year average, percent Corn Wheat Soybeans All Grains

The positive train control technology was mandated by safety legislation passed in 2008, and the Federal Railroad Administration has estimated that the total cost to the Class I carriers will be $5.8 billion.

Rail infrastructure to serve the U.S. soybean sector, other sectors of agriculture, and all other parts of the national economy that depend on rail can be divided into two parts. First is the general infrastructure—the mainline track, the rail yards, the switching terminals, and bridges— that are utilized by every rail-served sector, as well as some passenger trains. Secondly, the rail infrastructure at origins or destinations that serve the soybean and other commodity sectors that come from private investments by elevators, processors, port receivers, livestock and poultry operations, food companies or other business linked to the agriculture/food/biofuel system.

Over 40% of food/ag products shipped by rail are either originally shipped or ultimately received on a short line, which don’t represent a huge part of the ton-miles of rail carriage (about 1%), but for agriculture, they frequently provide the critical link to actually provide access to the ultimate origin or destination.

  • Upgrades to Class I mainline tracks and signal control systems
  • Improvements to significant rail bridges and tunnels
  • Upgrades to secondary mainlines and branch lines to meet 286,000 pound standards Expansion of terminals, intermodal yards, international gateways
  • Port facilities
  • Class I rail service and support such as fueling stations, maintenance facilities

What both the Cambridge-AAR Study and the AASHTO 2010 Report indicate is that to attain a continuing share of total freight with possible increases in ton-miles shifted from highways to rail will require investments both in mainline tracks and major interchange points that go well beyond current investment strategies of carriers. Where do railroads invest money in infrastructure today? Where do railroads spend todays’ CapEx dollars (Capital Expenditures)? Table 17 tracks average CapEx spending by Class I’s over the last 5 years. Over 50% of total CapEx is in steel rails, ties, grading and ballast—basics of maintaining and expanding a railroad. Locomotives and freight train cars add another 20%.

Table 17. Average Capital Expenditures of Class I Railroads, 5-year Average, 2007-2011

It is of some interest to note that roughly 75% of railroads’ CapEx spending—the spending on road infrastructure—is a cost not paid by trucks directly, but rather through fuel taxes, tolls and heavy vehicle use tax (maximum of $550 per year). According to DOT data, trucks represent about 10% of vehicle miles traveled on U.S. roads and highways in 2010. With federal and state spending on roads and highways at $257 billion (2006 data), potentially a sizeable portion of this expense could be attributable to truck traffic.

In a study by COBANK, Change on the Rural Horizon: Managing the Expansion of Grain Storage in the Corn Belt, it is noted that total on-farm and off-farm grain storage capacity increased by 17 percent from 2005 to 2011, and commercial capacity grew 24% during this same period. This market response to structural shifts in agriculture has allowed a rapid modernization of the commercial sector to place storage in more optimal locations, to position receiving/loading operations at points that better locate commodities for market accessibility, and utilize faster/newer technology. It has also contributed to a more rapid upgrade of transportation infrastructure than would have otherwise occurred.

Total soybean and corn planted area has increased 20 million acres (7% of U.S. tillable land base) in less than a decade, and has caused a rapid modernization of the commercial marketing and processing sector at the same time.

The soybean sector’s challenges in becoming more efficient on rail movements include:

  • The natural growing season will always produce relative surpluses near harvest that will cause soybeans to seek a “home.” Markets can resolve dislocations caused by excess surplus (caused by good crops), but at a price.
  • U.S. soybeans have an especially intense seasonality component, as 75% to 80% of export soybeans must be moved in the September through February period to optimize North American export opportunities, prior to South American harvest and shipping season.
  • Seasonality issues, plus the intensity of harvest to put soybeans/grain in storage as quickly as possible to maintain high quality means that elevators and processors need high capacity dumping. Many facilities have truck dumping capacity to handle 30 to 50 trucks per hour. And the entire marketing system has had to build considerable excess capacity to ensure timely harvest service.

For the physical marketing sector, surplus capacity costs money, but with railroads, surplus car/power capacity is particularly expensive. With grain car leasing costs at $500-$600 per car, it is expensive to leave such equipment idle for extended periods. Seasonality of rail car usage is a fundamental problem with reducing cost in the soybean and other bulk agricultural sectors. To meet the challenge of efficient utilization of equipment and to encourage soybean and grain shipments throughout the year, not just during the rush of harvest, shuttle programs have been developed by the Class I carriers to obtain commitments from shippers to utilize dedicated locomotives and cars throughout the year. Railroad shuttle programs vary by carrier, but many have the following features: Railroad Shuttle Programs 1) Dedicated power (locomotives) and equipment (cars, which may be rail-owned or private) 2) Specified shuttle origins and destinations that can handle allowable train sizes (75s, 90s, 100s or 110s) 3) Restricted time to load and unload (generally 15 hours) 4) Destinations for western railroads include export locations, domestic feeders and a number of facilities in Mexico 5) Adequate track at shipper and receiver location to load a train as a single unit (110 car train requires about 7,400 feet of track) 6) Commitment by shipper (or receiver) to load/receive a specified number of trains per month for an identified period (generally for 1-year, but it could be for 6-months or 2-years) 7) In some programs, if shuttle capacity is not needed by a shipper, the shipping capacity can be sold to other shuttle loaders on the railroad’s system through auction systems Advantages/Disadvantages of Shuttle Programs 1) Railroads provide supply source and destination flexibility by continuing to add to origins and destinations capable of handling shuttle-sized capacity 2) Shuttle programs are market responsive; if loading capacity is surplus it can be traded and repositioned to other locations 3) The commitment to utilize equipment throughout the year helps the railroad manage assets and reduce costs 4) The shipper/receiver and farmer benefit from lower rates (a 20 to 35-cent/bushel difference in single car rates compared to shuttles is common in the western U.S); in the eastern U.S., 15 to 25-cent/bushel differences are typical, but will vary depending on distance to market; in some markets, pricing differences are handled through contracts with the receiver

7) To participate in shuttle programs, the shipper or receiver must make sizeable investments in track and equipment (to meet the 15-hour window for loading). The track investment alone for industrial track and grading can be $2-3 million

Investment Cost of Shuttle Loading Facilities

The attractive economics of shuttle loading has driven investments and the number of locations has increased rapidly, more than doubling since 2000. But the investment costs are sizeable. Many of these facilities are located outside existing townships (so-called “greenfield” locations) to permit handling areas for large trains and associated storage/handling operations. Recent shuttle facilities are costing investors in the range of $18 million to $25 million in investment costs. A recent facility in South Dakota was built at an announced cost of $35 million. Where is this money being invested? Some recent typical cost ranges are shown in Table 18.

Table 18. Current Investment Costs for Rail Shuttle Facility Investment Item

Shuttle Loader: Alton Grain Terminal, Alton, North Dakota This shuttle facility, located in eastern North Dakota, equi-distant between Fargo and Grand Forks, was originally built in 2001 with about 2 million bushels storage and 14,000 feet of rail track to be able to load shuttle trains going both north and south. It can load up to a 130-car train. The plant originally cost $9 million to build. In 2004 an additional 2 million bushels capacity of storage was added, as was a fertilizer rail receiving, storage and truck load out facility. The fertilizer portion is owned by Alton Agronomy LLC,

Fertilizer capacity was expanded in 2008 to 40,000 tons of storage. This facility is located on the BNSF Railway, and was one of the first in the area. It ships corn, wheat and soybeans. In past years, corn was the highest volume commodity, but since an ethanol plant was located in Casselton (50 miles to the south), soybean shipments have come to dominate movements. Annual volumes are running about 27 million bushels with 60-70% of that amount comprised of soybeans. Alton Grain Terminal is owned by Halstad (Minn.) Cooperative and 7 other nearby cooperatives that ship part of their grain and oilseeds through the Alton plant. This terminal does business sourcing with approximately 50 elevator locations in eastern North Dakota and Western Minnesota, and the typical elevator shipment to the shuttle facility is 30-35 miles. Direct producer deliveries are about 50% of the elevator’s volume, and farmers deliver direct from as far as 50 miles away or more. Profile of Facility Facility: Alton Grain Terminal, Alton, North Dakota shuttle loader, BNSF Railway connection Location: Eastern North Dakota, 50 miles north of Fargo, at the crossing point of Interstate 29 and highway 200, just west of the Minnesota border. Receives: All truck receipts, 50% from farmers, 50% from elevators; receives soybeans, corn and wheat. Currently 60-70% of volume is soybeans. Every truck receives and official grade from North Dakota Grain Inspection prior to dumping. This is a little unusual as probably only 10% or fewer of U.S. shuttle operations officially grade every load. Official grades are used for consistency of inbound and outbound movements. Dumping capacity is 45 trucks per hour. Draw Area: Generally within a 75-mile radius.

Fertilizer Receiving: Facility is owned by Alton Agronomy LLC and leased to Agrium and Mosaic; receives 30-50 car units from Agrium; and 85-car units from Mosaic. It is associated with a buying group and handles and loads for movement to surrounding area cooperatives and farmers. Alton Terminal runs the logistics of the operation for the owner, Alton Agronomy. New Infrastructure Considerations: While the facility has plenty of track for loading soybean and grain trains, management for the operation is considering putting in a rail spur to handle cars related to the fertilizer operation. Generally there is adequate capacity, but in some cases, an additional rail spur would alleviate a problem of where to locate fertilizer cars during busy soy or grain train loading periods.

Minnesota is the fourth largest soybean crushing state in the U.S. by total volume. Ranked in order of total volumes crushed, the top five states are: Iowa, Illinois, Indiana, Minnesota, and Missouri. The table below offers a comparison of the U.S. and Minnesota on production, crush, exports and export customers (counting net importing states as “importers” of Minnesota-produced meal). Minnesota and U.S. Soybean and Soymeal Production

Building capacity to dump 60 to 80 trucks per hour in some cases. Obviously, this kind of capacity is built solely to serve the harvest-time capacity needs, but increasingly such capacity is needed to serve customers that are attempting to harvest in a short time period to maintain crop quality and quantity. The benefits to the farmer are quick truck turnaround at harvest and the ability to manage harvest flows of equipment and soybeans with greater precision and predictability. Keeping trucks on the road rather than waiting in line to dump is worth money at harvest. Both the soybean farmer and the processor benefit by improving soybean harvest quantity and quality. Shipping Soybean Meal: While 25% to 30% of the typical Minnesota soy processor’s meal output is trucked to local or regional feeding operations, the biggest changes in meal markets are coming in the rail markets. Minnesota is participating in a wide range of rail meal markets—-exports out of the U.S., California and Washington feed markets, Canada, South Central U.S. and even the Northeastern U.S. As rail markets for whole soybeans and other grains have transitioned in the last 10 years toward 100-car unit trains and shuttle trains, so have meal markets, but somewhat more slowly. Some meal shippers, and some domestic receivers, are building capacity to handle up to 100-car trains. Sometimes receivers of such trains break the trains apart or load out some of the meal onto trucks at destination for subsequent delivery to other users.

State-by-State Supply-Demand Deficits. With a $20 million price tag for a new shuttle-loading operation, and a cost for a new soybean processor in the range of several hundred million dollars, positioning new plants is done very carefully by commercial businesses. Every state has a very different and distinctive profile of supply and utilization for soybeans, corn, wheat and other grains.

On the next three pages are estimated net state export (or import) data for major oilseed producing and consuming states. The gray circles indicate a surplus that is available for export. The white circles indicate a deficit that has to be filled by bringing the commodity into the state.

Figure 32. Estimated Net Corn Exports for the 2012 Crop Year, by State and Export Port

Soybean and corn net exports for 2012 will be constrained in both Iowa and Illinois by the drought conditions. With an estimated 332 million bushels of soybeans being shipped out of the Pacific Northwest and 844 million bushels out of the center gulf, soybean exports will be down from 2011, but still a pretty healthy export volume is expected.

To look at net export data without the variability that one-year’s weather (like 2012) can have on exportable supplies from individual states, we calculated 5-year average net exports for 17 states that are being reviewed as part of this study. These data are presented in the table below for both individual commodities, soybeans, corn and wheat, and a combined total, labeled “all commodities.”

Table 19. 17-State Net Exports of Soybeans, Corn and Wheat, 5-yr Average, 2008-2012 17-State Net Exports of Soybeans, Corn and Wheat, 5-year Avg., 2008-2012*,

Figure 34. 17-State Net Exports of Corn, Wheat, and Soybeans, 5-yr

Table 20. Grains (excluding Soybeans) State

Table 21. Soybeans State and Regional Modal Movement Patterns, DOT Freight Analysis Framework (FAF) Data, 2007

Truck Tons Truck Rail Water Production Divided by Production (ratio)

The FAF data provide a much truer sense (in particular for grains shown in Table 20) of the transport movements near the farm level, and how important truck movements are in delivering soybeans and grain to market. Most truck moves are within a 50-mile radius of the destination, although some moves go well beyond this range to reach processors or shuttle-train loading elevators. But even though the moves are generally short, they are none-the-less necessary to reach markets. The USDA modal share data provide the best reflection of the final modal move to the destination, although some of the movements by truck are at destination where shuttle trains are sometimes unloaded into trucks for further distribution.

Table 27. Comparison of Truck, Rail, and Barge Movements for “Typical” Grain Movements Comparison of Truck, Rail, and Barge Movements for “Typical” Grain Movements $4.17/mile for first 25 miles (applies to truck only) $2.33/mile for first 100 miles Marginal per mile cost over 100 mi. (applies to truck only)

Table 27 is intended to provide some general guidelines on current market rates for transportation. Truck transportation generally applies to shipments of 200 miles or less, but movements can sometimes go further. The fixed cost of loading and unloading the truck vehicle makes shorter distance trips more expensive on an average rate per ton-mile basis. Going beyond 25-miles in a truck raises the marginal cost for additional ton-miles by $0.063. This rate is based upon an approximate $3.75 per gallon diesel fuel price. About 40% of the variable cost in truck miles is the price of fuel. (This compares with railroads for which fuel costs represent about 20% of the variable operating cost.) So, a 10% rise in the price of diesel will cause an increase of about 4% in the truck rate. Truck rates can also increase during periods of heavy truck demand. The rail rate in Table 27 is an industry average published by AAR for 2011, but is confirmed as realistic by some current rail rates. USDA reports a Nov 2012 unit train rate plus fuel surcharge of $3,823 per car for soybeans from Minneapolis to New Orleans. This rate is equivalent to $.028 per ton (Current $ per ton-mile) Truck* Rail* Barge* Avg. Cost Avg. Cost Avg. Cost Per Ton-Mile Per Ton-Mile Per Ton-Mile $0.153 $0.087 $0.032 $0.020 (Range: 0.02-0.045) (Range: 0.015-0.03) $0.063 mile. The Nov 2012 rate plus surcharge for a corn shuttle train from Des Moines to Galveston is $.031 per ton-mile. The Nov 2012 rate for a soybean shuttle from Fargo to Seattle is $.037 per ton-mile. Rail rates will vary by season, by size of shipment, by distance to market, by type of commodity, fuel pricing and other market factors.

The fundamental cost-efficiencies offered by rail are through savings in fuel, labor and other variable operating costs spread over a higher quantity of bushels than for trucks. The cost of accessing rail, including cost of loading, unloading, scheduling, and providing product at a rail loading point are the primary barriers in shifting from truck to rail movement. Beyond the economics of fuel efficiency and variable cost savings provided by rail over truck movements, railroads can gain significant further efficiencies by increasing unit shipment sizes and incentivizing shippers/receivers to load and unload quickly to accelerate cycle times. In the western U.S. typical shuttle trains make 3 or more cycles per month between the Midwest and destination markets on the west coast. Single car shipments as part of a merchandise train may require almost a month for the same round trip. In the eastern U.S., unit train and shuttle-type shipments can save 7-9 days from the normal cycle time of trains.

Some short-lines or regional railroads compete directly with truck traffic, which generally requires that the short line have both agricultural and non-agricultural customers to build adequate volumes to be competitive. Other short-lines function as economic linkages to a mainline railroad move, and provide the benefit that the elevator shipper or receiver can have direct access to rail that avoids a physical transfer of product.

Road Damage and Repair Issues: It is widely acknowledged and well-documented that heavier trucks cause a great proportion of the highway and road damage that leads to more frequent repair, maintenance and need for replacement . The Congressional Budget Office, Economic and Budget Issue Brief: Spending and Funding for Highways (January, 2011), found that pavement damage by trucks ranged from about 5 to 55 cents per mile, depending on truck weight, the number of axles and its operating range (urban vs. rural, and interstates vs. paved roads). A recent study that has particular applicability to data available for the national highway system is: Feasibility of Containerized Transport in Rural Areas and its Effect on Roadways and Environment: A Case Study, Agribusiness, Food, and Consumer Economics Research Center, Report number CP-03-11, by F. Fraire, S. Fuller, et al, 2011. From data contained in this report and from Dept of Transportation data from 2012 National Transportation Statistics, Bureau of Transportation Statistics, the analysis in Table 30 was constructed. Table 30. Pavement Costs of Truck Travel Above the Level Compensated by Fuel Taxes* Type of Road Truck Miles, 2010 Interstate Principal Arterial Minor Aterial Collector Road TOTAL ANNUAL COST 68.8 bil miles 137.6 bil miles 40.1 bil miles 40.1 bil miles Uncompensated Marginal Pavement Costs Per Loaded Truck-Mile $0.047 per mile $0.204 per mile $0.283 per mile $0.686 per mile Annual Cost Pavement $3.23 billion $28.1 billion $11.3 billion $27.5 billion $70.1 billion

The Fraire-Fuller study estimated truck road damage impacts being reduced by approximately 20% to account for 38% of trucks on highways being on average substantially smaller than 80,000-pound vehicles.

Based upon the Federal Highway Administration’s Cost Allocation Study, revised in 2000, it is estimated that trucks add an additional 20.06 cents in congestion costs per mile traveled.

Additional reading:

AAR. 2007. National Rail Freight Infrastructure capacity and investment study.

FRA. 2010. National Rail Plan – Moving forward.

NSTP. 2007. National surface transportation NST policy and revenue study commission. 260 pages.

2009 NST Paying our way, a new framework for transportation finance

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Reducing fuel consumption of medium & heavy-duty vehicles 2014 National Research Council

Posted on June 4, 2015 by

Look at the enormous waste of fuel when JIT supply chain trucks ramped up in the late 70s  (page 63).

JIT wasteful tonnnage by trucks

Excerpts from the 117 page: NRC. 2014. Reducing the Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: First Report. National Research Council.

The fuel consumption and greenhouse gas (GHG) emissions of medium- and heavy-duty vehicles (MHDVs) have become a focus of legislative and regulatory action in the past few years. Section 101 of the Energy Independence and Security Act of 2007 (EISA 2007), Pub. L No. 110-140 §101, mandated the U.S. Department of Transportation to promulgate fuel consumption standards for MHDVs for the first time.

the National Research Council (NRC) in 2010 completed Technologies and Approaches to Reducing the Fuel Consumption of Medium- and Heavy-Duty Vehicles, referred to henceforth as the Phase One Report.

Improving in-use efficiency of fuel use in MHDVs-by driving innovation, advancement, adoption, and in-use balance of technology through regulation. At the same time, the committee seeks to advise on pathways to accomplish this, subject to the following constraints: (a) holding life-cycle cost of technology change or technology addition to an acceptable level; (b) holding capital cost of acquiring required new technology to an acceptable level; (c) acknowledging the importance of employing a balance of energy resources that offers national security; (d) avoiding near-term, precipitous regulatory changes that are disruptive to commercial planning; (e) ensuring that the vehicles offered for sale remain suited to their intended purposes and meet user requirements; (f) ensuring that the process used to demonstrate compliance is accurate, efficient, and not excessively burdensome; and (g) not eroding control of criteria pollutants or unregulated species that may have health effects.

As truck efficiency regulation advances, there are trade-offs that must be addressed. Metrics of interest are fuel efficiency, GHGs, cost, criteria pollutants, and energy security. The primary trade-off is GHGs versus fuel efficiency, when several fuels and their associated technologies are considered.

Natural gas accounts for about 25 percent of all U.S. energy use, yet only 0.1 percent is used in transportation, equivalent to about 0.5 billion gallons per year of petroleum fuel. However, in the short time since the release of the Phase One Report (NRC, 2010), natural gas has emerged as an economically attractive option for commercial vehicles. This has been driven by the rapid development of low-cost production of unconventional natural gas.

In order for medium and heavy trucks to use natural gas fuel rather than diesel, the most significant changes needed are the onboard fuel storage method and the means of introducing and igniting the fuel in the engine. Onboard fuel storage is by high pressure, effected by either compressed natural gas (CNG) cylinders (3,600 pounds per square inch is typical) or cryogenic containers filled with liquefied natural gas (LNG). For using natural gas in place of gasoline, the spark ignition engine carries over with modest changes, but fuel storage is still by one of the above two methods. Natural gas engines are well developed, although improvements can be pursued in

Natural gas’s inherent GHG benefit by virtue of its low carbon content (~28%) is partially negated by lower efficiency in currently available engines and the higher GHG impact of methane emissions. In addition, a natural gas leakage correction to GHG impact could negate the inherent tailpipe CO2 advantage.

Due to the economics-driven rapid adoption of natural gas, there is urgency to develop an optimum solution in Phase II Rule standards for both GHG emissions and fuel consumption (as well as criteria emissions) that will accommodate this fuel without artificially disrupting prevailing commercial transportation business models. As a specific example, the GEM certification tools need to include natural gas engine maps to more accurately quantify the emissions and fuel economy of natural gas vehicles.

There are four regions of the tractor-van trailer combination truck that are amenable to aerodynamic design improvements, including the various tractor details, the tractor-trailer gap, the trailer underbody, and the trailer tail.

The Phase I Rule had the effect of encouraging the adoption of technologies for reducing fuel consumption. Such reductions can be achieved by technological improvements to the vehicle as well as by improvements in operations, changes in behavior of drivers, and so forth. The Phase One Report considered other approaches (referred to, perhaps imprecisely, as nontechnical approaches) such as intelligent transportation systems; construction of lanes exclusively for trucks; congestion pricing; driver training; and intermodal operations (NRC, 2010, pp. 159 et seq.). Also considered were market-based instruments such as fuel taxes. Another viable approach would entail adjusting size and weight restrictions on trucks. For example, this might include greater use of vehicles that have favorable LSFC such as longer combination vehicles, which have greater freight capacity than the notional tractor-trailer, which can have a combined gross vehicle weight of 80,000 lb.5

Regarding the potential for technological change in the MY2019-2022 time frame, the committee, in its investigations to date, has not identified any combustion or other engine technologies beyond those identified in the NRC (2010) Phase One Report that would provide significant further fuel consumption reduction during the Phase II Rule time frame.

A further consideration is the gross vehicle weight assumed in the GHG Emissions Model (GEM) simulation, which for Class 8 vehicles is based on a payload weight of 38,000 lb, an intermediate load value. The Agencies adopted payload values for the GEM simulation calculations that are representative of real-world truck use, instead of merely

using the maximum gross combination vehicle weight rating (GCVWR) for the vehicle weight class. This captures the situation that over half of trucks on the road are volume limited,5 meaning the trailer is filled up with containers without reaching the weight limit. In such a case the combined tractor trailer is not at full GCVW of 80,000 lb, the maximum allowed weight for un-permitted interstate transit.6

Finding 8-1. While it may seem expedient to focus initially on those classes of vehicles with the largest fuel consumption (i.e., Class 8, Class 6, and Class 2b, which together account for approximately 90 percent of fuel consumption of MHDVs), the committee believes that selectively regulating only certain vehicle classes would lead to very serious unintended consequences and would compromise the intent of the regulation. Within vehicle classes, there may be certain subclasses of vehicles (e.g., fire trucks) that could be exempt from the regulation without creating market distortions. (NRC, 2010)

The recommendation that NHTSA conduct a pilot program had two broad purposes: first, the agency would gain experience with certification testing, data gathering, compiling, and reporting. The trial period was envisaged as a means for developing and refining the regulatory processes before the official start date of the program. Second, the pilot program would include gathering data on fuel consumption from several representative fleets of commercial trucks (e.g., long-haul, delivery vans, specialty vehicles, and large pickups). These data would provide a real world check on the effectiveness of the regulatory design on the fuel consumption of trucking fleets in various parts of the marketplace and in various regions of the country (NRC, 2010, p. 188).

[Nobody did their homework assignments from the 2010 study!]

The Agencies, however, declined to undertake a pilot program.9 Data gathering and comparing the performance of vehicles specified via the Phase I Rulemaking process versus current methods of specifying trucks for customers (using OEM specification tools) could nonetheless have begun in 2011 and been continued until now. Data gathering should be ongoing. At least some kind of demonstration programs could have been done, perhaps even with simulations. Omissions that were due to the absence of a demonstration program include the following: 1. The lack of baseline data from a few representative national fleets prior to the rulemaking, such as would enable comparison with post-rulemaking (after 2014) fuel efficiency. This would have also started to facilitate the comparison of real-world test data with compliance data.

Unintended Consequences. Interventions into complex systems inevitably produce unintended consequences. Fuel consumption regulations, in purposely trying to change product characteristics and mixes, could produce incentives and behaviors that may result in unintended consequences, either beneficial or detrimental. For example, some analysts have noted that original equipment manufacturers (OEMs) responded to the Corporate Average Fuel Economy (CAFE) standards by producing vehicles that counted as trucks for regulatory purposes

Two heavy-duty gasoline engine manufacturers (Ford13 and GM14) said that the Phase I Regulations are considerably more difficult to achieve for gasoline engines than they are for diesel engines in vehicle classes where both engine types are available (notably Classes 2b and 3). Both manufacturers have indicated that marginalization or elimination of gasoline engines from this segment is a possible future outcome based on present forecasts, and this feedback should be carefully considered when setting Phase II Regulations applicable to this segment. The Agencies may wish to consider whether such consequences are likely and, if so, to what extent they will be detrimental to the long-run health of the industry and the goals of reduced fuel consumption and GHG reduction, and if such second-order impacts can or should be mitigated.15

Other Recommendations in the NRC Phase One Report That Were Not Addressed by the Agencies

Recommendation 4-2. Because the potential for fuel consumption reduction through dieselization of Class 2b to 7 vehicles is high, the U.S. Department of Transportation/ National Highway Traffic Safety Administration (NHTSA) should conduct a study of Class 2b to 7 vehicles regarding gasoline versus diesel engines considering the incremental fuel consumption reduction of diesels, the price of diesel versus gasoline engines in 2010-2011, especially considering the high cost of diesel emission control systems, and the diesel advantage in durability, with a focus on the costs and benefits of the dieselization of this fleet of vehicles.

Diesel engines present an opportunity for incremental fuel efficiency gains and, for some vehicles, may have the advantage of better durability.

there was already a move from diesel to gasoline direct injection technology at the middle of the MHDV range, as noted in the Phase One Report (NRC, 2010, p. 64). Likewise a shift to more fuel efficient, smaller displacement, greater power-density diesel engines was then becoming apparent and was expected to motivate continued downsizing, as with passenger cars. An analysis by Frost & Sullivan, a consultancy, indicates 15 liter (15 L) engines will continue dominating the Class 8 engine market through 2018 but then are expected to lose market share to 11 L to 12 L and 12 L to 14 L engines.26While the consequences of these moves are reduced fuel consumption and reduced CO2 emissions, they may also have implications for the market as a whole and may influence factors such as supply chain and fuel choice.

The state of the economy will have a significant impact on MHDV vehicle-miles traveled (VMT), fuel consumption, and GHG emissions, particularly as macroeconomic trends affect growth and activity in the construction and manufacturing industry sectors. But in addition to the general economic health condition of the nation, the other potentially relevant factors discussed here include (1) the emergence of natural gas as a significant transportation fuel; (2) the role of biofuels; (3) the growing interest in the United States in dimethyl ether (DME) as a fuel; (4) the viability of electrification of the vehicles; (5) the development of automated and/or connected vehicles; (6) the implementation of green logistics; and (7) background regulatory developments.

Natural Gas The natural-gas-fueled engine, using either liquid natural gas (LNG) or compressed natural gas (CNG), is not a new technology. Natural gas engines were produced as early as 1860 and now power about 120,000 vehicles on U.S. roads.27

Coal to liquid

FIGURE 1-1 Illustrative pathway for vehicle fuels production and use.

The application of natural gas for MHDVs has been more recent, however, and earliest uses were for transit buses and municipal vehicles. Over the past two decades, the natural gas engine has served as a niche technology in the MHDV market, present in mostly urban refuse haulers and transit bus applications. Natural gas is often referred to as a “bridge fuel,” since it is a way to bridge the diesel-fuel dominance of the MHDV market to the next non- petroleum-based fuel-yet to be identified to the point of having a broad consensus. Common production pathways and uses for natural gas and other current and future MHDV fuels are illustrated in Figure 1-1. The MHDV natural gas market developed slowly before circa 2010. Purchasers other than municipal fleets, which are subsidized by the government, had difficulty justifying the higher purchase price of the vehicle despite the lower cost of natural gas compared to diesel fuel. Furthermore, the cost of constructing fueling stations across the country 27 http://www.ngvamerica.org/media_ctr/fact_ngv.html. ranges between $600,000 to over $1,000,000 per station for compressed natural gas and nearly twice that per liquefied natural gas station.28 Municipal vehicles, which run routes during the day and are centrally garaged at night, can be readily refueled at the garage, making them good applications for this niche technology. In recent years, the gap between natural gas and diesel fuel prices has dramatically widened.29 Moreover, advancements in technology have enabled manufacturers to develop more natural gas engine options and attendant vehicle technologies to achieve reliability and durability similar to that of the diesel. Together these circumstances make natural gas a viable choice for future commercial over-the-road fleets. A variety of natural gas engines suited to a wide range of MHDV applications will be available by 2015. As more OEMs are introducing natural gas options to their product line, the share of CNG/LNG MHDVs continues to grow.

ACT Research predicts30 that the natural gas market share of MHDV truck and bus (includes municipal and refuse) could be as high as 36 percent by 2020. For these predictions to play out, the CNG/LNG infrastructure must be expanded.

While there has been a significant increase in the number of natural gas fueling stations over the past years, the infrastructure is still nascent and will require large investments to provide enough stations to prevent disruption in routes and travel times for longer-haul trucks. Another consideration in the future use of natural gas in the MHDV market is the rapid growth and output of hydraulic fracturing (“fracking”) in natural gas drilling. Fracking has greatly increased the supply and availability of natural gas while reducing its cost. EPA and some states are now exploring more rigorous regulation of fracking operations. Regulations are one of several factors that could significantly increase the cost or reduce the availability of natural gas. This would reduce the incentive to move toward natural gas fuels and technologies in the MHDV sector. Affordable fuel prices and a growing infrastructure all bode well for the future of natural gas in MHDVs.

However, if the price of fuel continues to be favorable vis-à-vis diesel, the transportation sector will have to compete with other sectors (e.g., electricity and heating) for domestic natural gas. (The exporting of natural gas could affect prices as well.) Predicting how this might affect the MHDV market is difficult. Analysts predict that as the economy improves, the price of natural gas will increase (AEO, 2013) but so will the price of petroleum-based fuels. Another important issue raised by fuels such as natural gas is, on the one hand. the distinction between vehicle fuel consumption and GHG and, on the other, the life- cycle analysis of the fuel consumption and GHG using natural gas as a fuel (well to wheels).

Biofuels. The current state of biofuel research, development, and production suggests that the biofuels produced in abundance over the next decade will likely be blends containing ethanol, gasoline, or biodiesel. In its 2013 Energy Outlook, the DOE’s Energy Information Administration (EIA) forecasts that the consumption of next-generation biofuels (including pyrolysis oils, biomass-derived Fischer-Tropsch liquids, and fuels derived from renewable feedstocks) by the transportation sector will increase to about 0.4 million barrels per day (BPD) from 2011 to 2040. This compares with 1.6 million BPD of diesel during the same period.

Ethanol has Been used as a blend in gasoline engines for over three decades.

In 2001, the production of ethanol as a share of gasoline volume was only 1 percent. By 2011, the share rose to 10 percent (EIA, 2012). This is largely due to the first Renewable Fuel Standard (RFS) program, which was enacted in 2006 as a part of the Energy Policy Act of 2005. As a result of EISA 2007, the Renewable Fuels Standard “RFS2” mandated renewable fuel consumption of 36 billion gallons (35 billion of ethanol equivalent and 1 billion of biodiesel) by 2022. Although higher blends of ethanol are approved as a transportation fuel by EPA (E15 and E85), the majority of vehicles in the United States use E10. Higher blends can produce fewer GHG, but the higher blends usually exhibit less “tank mileage” (miles per gallon), because of the inherent lower energy content (i.e., enthalpy) of ethanol. Every 10 percent of ethanol in the fuel reduces fuel economy by approximately 3.5 percent (Knoll et al., 2009). Further, distribution infrastructure becomes more difficult at higher blends. Ethanol is a solvent, so its chemistry is prone to dissolving the hydrocarbon residue and water that are often found in the pipeline, which can render the transported fuel out of specification, especially if tanks and pipes are not properly cleaned before switching products.

Biodiesel Studies by EPA and others indicate that the fuel consumption of B5, the most commonly used biodiesel, is about 2 percent worse than that for conventional diesel.31

In 2001 biodiesel production was 9 million gallons. By 2011, it was nearly 100 times higher, at 967 million gallons. While this growth is significant, it represents only 1 percent of the total diesel production by volume. Consumption of biodiesel in 2011 was 878 million gallons (EIA, 2012). Similarly, RFS and EISA 2007 (RFS2) require consumption of 1 billion gallons biomass-based diesel. Tax credits and incentives through the RFS2 have had a positive influence on the production and consumption of biodiesel. Soybeans make up 57 percent of the biodiesel feedstock. Thus, droughts such as that the United States experienced in 2012 can cause the price of biodiesel to vacillate markedly, giving users little reason to purchase the fuel. The use of biofuels is well established in the United States. The growth in production and consumption still relies in a large part on incentives and tax credits. Nonfood-derived cellulosic feedstock is another consideration in the growth of these biofuels, but large-scale production and consumption is years away (NREL, 2012). A further fuel not yet in widespread use is so-called renewable diesel fuel, which is bio-oil refined to remove oxygen and which resembles petrol-derived fuels.

Dimethyl ether (DME) may show promise as an alternative fuel. Synthesized from methanol, it can be produced from biomass, natural gas, or coal. Its thermal efficiency and performance are comparable to those of diesel. DME typically sells at a premium to energy value (i.e., costs more for the same enthalpy). DME is liquefied at 50 pounds per square inch (psi) (or 345 kilopascal (kPa)), so its use requires similar tankage to propane. DME is expected to have the same selling price as a diesel gallon equivalent. 32 As with most alternative fuels, developing engine and vehicle modifications and the distribution infrastructure for the fuel are the most obvious obstacles to widespread use of DME in the near term. DME currently has minimal transportation applications in the United States.

Fischer-Tropsch. Other alternative fuels, known as Fischer-Tropsch (FT) or gas-to-liquid (GTL) fuels, are available in the market but are currently produced in very modest volumes: only about 200,000 barrels a day, which is equal to less than 1 percent of global diesel demand a day (NYT, 2012). These fuels are produced via the FT chemical process, using natural gas, coal, or biomass as feedstock. FT fuels are interesting because they reduce dependency on crude oil and, depending on the feedstock used, may reduce the CO2 footprint as compared with petroleum-based fuels.

These benefits notwithstanding, FT fuels are expensive to produce. Capital costs, the reliability of cost-effective feedstock, and the logistics of sourcing and transporting feedstock are all considerable. Analysts believe that FT fuels will be cost-effective only when natural gas and oil prices are out of balance. As long as natural gas and oil price differentials remain relatively aligned, the large investment in FT technology will be unsustainable (NYT, 2012).

Electrification The electrification of the light-duty fleet appears to be finally achieving traction after many years of false starts and slow progress,33 raising the potential for electric or hybrid medium- and heavy-duty vehicles to reduce CO2 emissions and fuel consumption. There are a number of technology alternatives for incorporating electrification into the MHDV fleet, including (1) hybrid-electric vehicles (HEVs); (2) electrified accessories; (3) fully electric power trains; (4) electrified power take-off (PTO); (5) plug-in hybrid-electric vehicles (PHEVs); (6) external power to electric power train for zero emission vehicle (ZEV) corridors; and (7) alternative fuel/hybrid combinations.34

Sleeper berth. Solutions include battery-operated HVAC and auxiliary power units (APUs), start/stop systems, and truck stop electrification. Of course, given the range limitations of current vehicle battery technology, electrification is more feasible for some types and modes of MHD vehicles than others. For example, battery-powered motors are least feasible for long-haul heavy-duty trucks that usually travel hundreds of miles per day but may be very promising for service fleets where vehicles perform a number of local deliveries or other jobs per day and then are parked overnight at a centralized base, where they can be plugged in and recharged. One estimate is that up to 6.4 percent of power train systems in MHDVs (including buses) will be electric or hybrid by 2020.35 This represents slightly over 130,000 units, of which about two- thirds are projected to be hybrids and one-third pure electrics.36

Other analysts predict that electric and hybrid vehicles will represent only niche markets before 2030, when more significant market penetration is expected.37

Another important alternative-fuel technology involves hydrogen fuel cells as the power plant; such fuel cells are projected to significantly penetrate the MHDV sector by the early 2020s. Several light-duty vehicle manufacturers are developing fuel-cell vehicles (FCVs) for commercial introduction, including Hyundai in 2014 and Honda in 2015, with others planning introductions from 2017 to 2020.38 This will result in technology validation, hydrogen infrastructure development, and cost savings that will eventually benefit the commercialization of FCVs in the MHDV sector. California is supporting the introduction of FCVs through a partnership with vehicle manufacturers and other stakeholders that has developed a roadmap for installing the infrastructure needed for the commercialization of FCVs.39

Fuel cells are also being developed to provide auxiliary power for trailer refrigeration, used in some 300,000 refrigerated trucks.

By replacing the small diesel engines with the more efficient fuel cell, users will see fuel savings of approximately 10 gallons a day per unit,

The carbon dioxide and fuel consumption benefits of both electric and fuel-cell vehicles will depend to a significant degree on the emissions characteristics of the source used to generate the electricity or hydrogen fuel that powers the vehicle (Babaee et al., 2014).

Energy consumption and emissions associated with fuel production, distribution and processing, vehicle efficiency, and end-of-life may contribute to a substantial share of overall vehicle emissions and energy consumption.

Recommendation 1.10: NHTSA, in coordination with EPA, should begin to consider the well-to-wheel, life-cycle energy consumption and greenhouse emissions associated with different vehicle and energy technologies to ensure that future rulemakings best accomplish their overall goals.

Caterpillar Inc. is currently building 45 automated, 240-ton mining trucks to operate at an Australian iron-ore mine without an onboard operator (Berman, 2013).

Optimistic estimates are that the first automated long-haul trucks (ALHTs) may be commercially viable by the mid- to late-2020s, and could decrease fuel consumption by 15 to 20 percent compared to today’s traditional fleets through more

 

Green Logistics

Examples of such measures that could impact MHDVs are access control (including lane restrictions), urban traffic control measures, road pricing, smart traffic lights that provide more information to drivers on road conditions and traffic, ramp metering, and other fleet and fuel management approaches..

Electrification of accessories provides a 3 to 5 percent fuel consumption reduction if applied as a package on a hybrid vehicle. This benefit is more effective in urban driving conditions and in short-haul use; line-haul applications will benefit less.

It was confirmed by testing that a further reduction of 1 to 1.5% in fuel consumption may be obtained with thinner oils once durability has been confirmed. Thermostatic control of oil cooler-a solution used selectively in the past-can maximize lubricant performance over a broad temperature range. Some testing has reported a reduction in fuel consumption closer to 2%. The effect is more pronounced for cold starts and low-load operation.

o Hybrid power trains, including regenerative braking, engine downsizing, engine shut- off, enabling electrification accessories, plug-in hybrids, etc. No additional new hybrid systems have been identified in the reviews to date. However, given the high duty-cycle dependency, energy storage methods, costs, and relatively large potential fuel consumption reductions projected across most vehicle classes, NHTSA should form a study focused in this area to identify current realistic penetration rates and appropriate simulation and test methodologies to determine the resulting potential for fuel consumption reduction. Several manufacturers pointed out that with the ever more rapid rates at which new energy sources and new energy storage technologies are being adopted, the points of regulation and the certification methodologies need to be examined and potentially modified to more accurately evaluate and credit this trend. Improvements to be evaluated included propulsion system dynamometer certification instead of engine-only certification; more emphasis on transients in modeling, simulation, and testing; and standards and certification only at the vehicle level.

o Vehicle mass; vehicle lightweighting. The truck weight impacts the power needed to move the vehicle through rolling resistance, climbing grades, and accelerations. Use of lightweight materials and structures, such as cab structures, wheels, fifth wheel, bell-housing, etc., have contributed to reducing weight in tractors; additionally, aluminum composite panels have reduced the weight of trailers. A barrier to further reduction is the higher cost of light materials. Lightweighting is simultaneously balanced by the increase in vehicle mass needed to accommodate additional systems and equipment, such as new emission control equipment, aerodynamic improvement equipment, waste heat recovery, and hybrid components. No additional new technologies have been identified to date.

o Fuel efficiency or greenhouse gases versus cost. Reducing fuel use or GHG emissions may not be the most economically attractive scenario. Technology costs in some cases may exceed fuel savings over the vehicle life, and the least expensive fuel and technology combination may not offer the best efficiency or lowest GHG scenario. At a higher level, fuel choices may have substantial economic impacts beyond the trucking industry. For example, an advanced aerodynamic device that offers drag reduction of less than 1 percent is unlikely to offer payback during the first period of ownership if the weight and cost cross a certain threshold.

o Energy security versus efficiency and emissions. The use of alternative energy resources or a balancing of source uses may not yield highest efficiency, lowest GHG, or lowest criteria pollutants, but it may satisfy compelling national needs. For example, natural gas, as a domestic fuel, displaces imported oil. However, a spark- ignited natural gas engine is generally less energy efficient than a diesel engine.

These metrics all have different currencies, and it is impossible to establish exchange rates between them from purely technical arguments. The balancing of these metrics is an issue of policy.

COMMENTS ON THE CALHEAT REPORT FOR THE CALIFORNIA ENERGY COMMISSION

The California Hybrid, Efficient and Advanced Truck Research Center (CalHEAT) recently completed a study of trucks in California (Silver and Brotherton, 2013). The report provides valuable information related to the baseline of vehicles in California. The methodology used may serve as a model for developing a baseline for commercial vehicles in the entire United States. The abstract from that study reads as follows:

The CalHEAT reports also note that . . . as the first step in the development of this Roadmap, CalHEAT performed a California Truck Inventory Study to better understand the various types of trucks used in California, their relative populations, and how they are used. The analysis included nearly 1.5 million commercial medium and heavy-duty trucks, grouped by weight and application, to establish a baseline inventory and determine fuel use and potential for efficiency and emissions improvements. CalHEAT also conducted Phase I research to characterize the California truck population by size, use, and emissions, and prepared a baseline report of available technology and pathways for improvement.

ANNEX FIGURE 4A-7 North American Class 8 production. SOURCE: ACT Research.

FIGURE 4A-5 ATA truck tonnage index and ATA truck loads index NOTE: S.A., seasonally adjusted. SOURCE: ACT Research, copyright 2013. ANNEX FIGURE 4A-8 North American Class 5 thru Class 7 production. SOURCE: ACT Research.

ANNEX FIGURE 4A-10 Average age of active population of U.S. Class 8 vehicles. SOURCE:

fracturing process.5 At the surface, an integrated management plan is needed to address the supply, handling, reuse, and disposal of the fracking fluid to ensure sustainability throughout the production cycle.

In the electric power sector, the low price of NG has directly caused the closure of coal plants, as it has become more economical to use combined-cycle NG plants (with thermal efficiencies up to 65 percent) for electricity production. However, fuel price is the dominant contributor to the cost of electricity (55 percent). One analysis concluded that the break-even fuel price is between $4 and $6 per million British thermal units (mmBTUs). 6 In the heavy-duty transportation sector, price has a less direct effect on the use of NG as a fuel because delivering and compressing (or liquefying) the fuel account for a large share of the price at the pump. The break-even price of NG relative to diesel fuel is around $6 per million BTU (predelivery, not at the pump). If the costs of NG vehicles themselves come down relative to the costs of their diesel counterparts (discussed in the next section), the break-even value could be as high as $9 to $12 per million BTU. If, as projected by the Energy Information Administration (EIA), the price of NG in 2035 is about $7 per million BTU (EIA, 2013a), its use in the transportation sector will likely depend in part on future technological improvements. Currently, the biggest obstacles to NG use for freight transportation are (1) the lack of widespread and dependable infrastructure, (2) the substantial increase in weight and cost of the fuel tanks compared to diesel tanks, and (3) the availability of NG vehicles, although almost all MHDV manufacturers now offer a NG engine. More detailed discussion of infrastructure and technology follows in a later section of this chapter. Pipeline and infrastructure investment in the United States and Canada is likely to exceed $200 billion over the next 25 years (see footnote 2). EIA expects increased production, lower imports, higher exports, and higher prices, as shown in Table 5-1.

NG largely consists of methane, which is a powerful GHG. Leakage, most of which is estimated to come from gas production activities, could negate the hoped for climate benefits of reducing CO2 emissions by replacing other fossil fuels with NG. Methane has a shorter lifetime in the atmosphere than carbon dioxide, but its higher radiative forcing -that is, its ability to redirect heat that would otherwise escape the atmosphere-means that over 100 years it has 20 times the GHG impact of CO2. One analysis concluded that after taking into account current estimates of leakage, converting heavy-duty diesel trucks would have a net negative effect on climate change for centuries.4 One estimate of gas leakage, based on measurements at 190 onshore gas production sites, is 0.42 percent of the total gas production (Allen et al., 2013). Note that this leakage exceeds the amount of NG currently used in transportation. Other estimates of fugitive emissions have been significantly higher (e.g., Howarth et al., 2011).

NATURAL GAS ENGINES AND VEHICLES Technology Overview NG internal combustion engines are a well-developed and established technology. There are over 11 million NG vehicles worldwide, including passenger vehicles. In the United States, NG-fueled MHDVs, especially transit buses, have been incentivized for roughly 20 years in some states as part of emission- reduction programs. For MHDVs to use natural gas fuel, the most significant differences from current vehicles are the onboard fuel storage method and, for compression ignition (diesel-fueled) vehicles, the means of introducing and igniting the fuel in the engine. On-vehicle storage is either by high-pressure (3,600 psi is typical) CNG cylinders or by cryogenic containers filled with LNG. An illustration comparing on-vehicle storage of NG with diesel is shown in Figure 5-3.

Prices are per million BTU in 2012 dollars. Note that a million BTU is equivalent to about 8 gallons of diesel fuel. Thus, natural gas costs on the order of $2.00 per gallon equivalent, much less than diesel fuel. SOURCE: EIA (2013a).

For the same truck mission, the CNG tank plus fuel weighs about four times as much as a diesel tank plus fuel. LNG tanks and fuel weigh about twice as much as diesel. The cost of either CNG or LNG storage adds $40,000 to $50,000 to the cost of a heavy truck, but with the current low price of NG, the payback period for long-haul trucks is on the order of only 2 years. There are three general technical classifications of NG engines, as shown in Table 5-2. Either CNG or LNG can replace gasoline with only modest changes to the spark ignition (SI) engine. Compression ignition (CI) engines are more complicated; NG can be used in combination with diesel fuel (dual-fuel); or it can supply all the energy to a high-pressure direct-injection (HPDI) CI engine, in which a small amount of diesel fuel is needed to achieve ignition.

As of 2010, the number of medium- and heavy-duty NG vehicles in the United States is estimated to have been between 30,000 and 50,000, out of roughly 10 million total MHDVs (TIAX for American Natural Gas Association [ANGA]).

Fuel Consumption and GHG Comparisons of Natural Gas and Diesel Engines

When a fuel is combusted, its CO2 release per unit heat released is a function of its carbon content. Because it has a relatively low carbon content, NG releases about 28 percent less CO2 per BTU of heat than diesel fuel. However, SI engine efficiency is considerably lower than that of mature diesel engines, especially at light loads, partially offsetting the inherent GHG benefit of NG. In addition, unburned methane may be emitted by the vehicle, and upstream emissions from the production and delivery of the NG must be considered in a well-to-wheels comparison. Methane is a very potent GHG, so if these emissions are significant, NG vehicles could contribute more to GHG emissions than diesel vehicles.

While the results differ somewhat, NG engines and vehicles generally emit about 5 to 20 percent less CO2 (Krupnick, 2010; Kamel et al., 2002; Greszler, 2011). The advantage for NG is very dependent on the drive cycle. One estimate of the impact of methane emissions, shown in Figure 5-5, is that they reduce the CO2-equivalent GHG emissions benefit of NG from 13 percent to only 5 percent. This review of the data comparing diesel and NG engines affirms that the chemical advantage of NG for low GHG emissions is largely (but not completely) offset by the lower efficiency of most NG-fueled heavy-duty engines. This will need to be considered in setting specific GHG and fuel consumption standards for medium- and heavy-duty vehicles using NG, as was done in setting different standards for gasoline and diesel engines.

With the recent increase in the availability of low-cost NG, it is anticipated that its use in long-haul Class 7 and Class 8 trucks will increase, especially using LNG as a means of extending vehicle range.

Heavy trucks are 30 to 40 times heavier than passenger vehicles.

Liquefied motor vehicle. NG fuel could allow for the design of more efficient and less costly engines. However, a standard for in-use motor vehicle NG could require further processing of some pipeline gas before compression or liquefaction. Given the limited demand for motor vehicle NG currently envisioned, compared to nationwide consumption for other purposes such as home heating and power generation, it is not clear if suppliers of NG would invest in the gas cleanup needed for motor vehicle fuel. This could result in NG fueling stations being unavailable in certain areas where pipeline gas does not meet motor vehicle fuel specifications. This could impede the increased use of NG as a motor vehicle fuel. Finally, as noted above, the fuel tank, whether for CNG or LNG, accounts for most of the cost increment for NG vehicles over equivalent gasoline or diesel vehicles. In addition to cost, weight can be an issue. The cheapest solid steel (Type 1) cylinders weigh four to five times as much as gasoline or diesel tanks of the same capacity; advanced (Type 3) cylinders with thin metal liners wrapped with composite weigh about half as much as Type 1 tanks, although they cost more. Tanks with polymer liners weigh even less, but are even more expensive. Higher pressure tanks (up to 10,000 psi) could reduce fuel storage space, but at added cost and increased energy required to compress the gas.

FIGURE 5-6 Conceptual diagram of compressed natural gas filling stations. SOURCE:

http://www.afdc.energy.gov/fuels/natural_gas. html.

but diesel fuel still has about 1.7 times the volumetric energy density of LNG. As shown in Figure 5-3, the approximate range per 100 gallons in a long-haul truck is: 650 miles (diesel); 380 miles (LNG); and 170 miles (CNG). LNG fueling stations are similar in configuration and operation to gasoline and diesel fuel retail outlets. LNG is delivered to the fueling station in tanker trucks, stored there, and dispensed into vehicles with cryogenic LNG storage tanks (see Figure 5-7). Many LNG fueling sites supply CNG as well. The main disadvantage of LNG is that it gradually boils off as ambient heat penetrates the tank no matter how well insulated it is.

CNG Infrastructure. NG is moved throughout the United States in an extensive network of pressurized pipelines. According to the American Gas Association (AGA, undated), there are 1.9 million miles of CNG distribution lines and an additional 300,000 miles of transmission lines. The transmission lines are for long distance interstate transport and operate at high pressure, from 200 to 1,500 psi. The distribution and service lines to homes operate at low pressure, approximately 50 psi to less than 1 psi. CNG refueling stations for vehicles are connected to points in the distribution pipeline network. The gas industry spends over $6 billion per year on the transmission lines and $4 billion per year expanding the distribution system. There are 632 public CNG vehicle refueling locations in the United States (AFDC, undated) and around 1,200 including stations for private fleets (Weeks, 2013).

CNG stations are estimated to cost $600,000 to $1 million, with time-fills in the lower part of the range and fast-fills in the upper (ANGA, undated). These costs will be a considerable hindrance to the growth of CNG as a passenger vehicle fuel. One study estimated that between 16,000 and 32,000 stations would be needed to support a thriving NG vehicle population, with a much smaller number of refueling sites needed for heavy trucks (ANGA, undated). Equipping another 9,000 public stations will cost in the range of $5.4 to $9 billion.

LNG use is growing in transportation, especially for trucks that operate almost continually, but it probably precludes the use of LNG in light-duty vehicles. Only about 40 nonprivate LNG fueling stations are in operation in the United States, many of them in California (AFDC, undated; TIAX, 2009). Clean Energy Fuels Corporation has been establishing a cross-country network of LNG fueling stations (“America’s Natural Gas Highway”), with near-term plans for 100 LNG stations. Shell is working with TravelCenters of America to offer LNG for highway trucks and has a joint cooperation agreement on LNG with Volvo. LNG is produced at only about 50 to 60 sites in the United States, and there are a few LNG import terminals, so transport distance to LNG dispensing stations could be a detriment.

NG can be converted to other fuels using well-known processes and technology. Each has advantages and disadvantages for storage, GHG impact, and cost: o Dimethyl ether (DME) o Methanol o Ethanol o Gas-to-liquids (GTL) o Ammonia o Electricity o Hydrogen (for fuel cell vehicles

GTL plants have been established worldwide where low-cost NG is available. To be profitable, the scale of GTL plants is enormous as is the capital investment, and the production of high-value chemicals in addition to fuel is important. Shell and Sasol are the largest GTL producers. Operating since 2011, Shell’s Pearl GTL facility in Qatar is one of the largest such plants in the world (140,000 BPD products),

NG is the source for 95 percent of hydrogen production in the United States, and fuel cells are candidates for certain heavy vehicles such as buses and drayage tractors. In California, fuel cell heavy vehicles are a key option where zero-emission vehicles are needed. There are 10 hydrogen refueling stations in the United States, most of them in California (AFDC, undated).

NG is a feedstock for anhydrous ammonia, a feasible engine fuel that can be stored as a liquid at pressures similar to propane. Although it has been demonstrated in both SI and CI combustion systems, its toxicity and acute incompatibility with the human body make its widespread use as a transportation fuel impractical. About 30 percent of U.S. electricity comes from burning NG (EIA, 2013b), and this fraction is growing rapidly. Combined-cycle (gas turbine/steam turbine) technology can be highly efficient. Some units are over 60 percent efficient, much higher than coal-fired generation. Electric vehicles (battery electric or plug-in hybrids) can take advantage of NG in this path, thereby displacing petroleum.

Only electricity and hydrogen from NG can produce lower GHG emissions than direct combustion of NG in engines due to the inherently high efficiency of battery electric and fuel cell vehicles.

Key factors influencing the decision to purchase a NG vehicle are the fuel cost savings, initial cost premium for the vehicle, and ready access to refueling facilities.

Currently about half of new refuse trucks are NG-fueled, and that is expected to rise to 90 percent soon, in part because of local, state, and national incentives. However, refuse trucks collectively are not large consumers of fuel, so the greater opportunity for NG substitution is in Class 7 and Class 8 tractor-trailer rigs, which use about 20 times as much.

References (very few of them)

6 Revis James, Electric Power Research Institute, “The Role of Natural Gas in the Electricity Sector,” Presentation to the Board on Energy and Environmental Systems, September 11, 2012.

Mark Boling, Southwestern Energy, “Forum on Unconventional Natural Gas Issues: Water Quality,” Presentation to the Board on Energy and Environmental Systems, September 11, 201

Berman, D.K. 2013. Daddy, what was a truck driver? Wall Street Journal, July 23. Broder, J., and C. Krauss. 2012. “A big, and risky, energy bet.” New York Times. December 17.

4 Steven Hamburg, Environmental Defense Fund, “Methane leakage from natural gas production, transport and use- Implications for the climate,” Presentation to the Board on Energy and Environmental Systems, September 11, 2012.

Lempert, R. 2007. Scenario analysis under deep uncertainty. Modeling the Oil Transition: A Summary of the Proceedings of the DOE/EPA Workshop on the Economic and Environmental Implications of Global Energy Transitions, D.L. Greene, ed., ORNL/TM- 2007-014.

North American Council for Freight Efficiency (NACFE) and Cascade Sierra Solutions (CSS). 2013. Barriers to the Increased Adoption of Fuel Efficiency Technologies in the North American On-Road Freight Sector. July

Silver, F., and T. Brotherton. 2013. Research and Market Transformation Roadmap to 2020 for Medium-and Heavy-Duty Trucks. CEC-XXX2013-XXX. Draft Rev # 7 Dated 6-14-2013. Sacramento, Calif.: California Energy Commission.

38A. Webb, 2013, “Auto makers renew interest in fuel-cell vehicles: Despite cost, political hurdles.” Available at http://wardsauto. com/vehicles-amp-technology/auto- makers-renew-interest-fuelcell-vehicles-despite-cost-political-hurdles. 39

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Commercial scale cellulosic ethanol still not happening in 2016 – why?

Posted on June 4, 2015 by

Here’s Rapier’s latest column on cellulosic ethanol explaining why it still isn’t commercial yet, despite attempts since the 1900’s.  He points out that we have been able to create cellulosic ethanol since 1900, but not economically.

February 13, 2016. Cellulosic Ethanol Falls A Few Billion Gallons Short.

Rapier, R. June 22, 2015 Cellulosic ethanol is going backwards. Energy trends insider.

May’s numbers are now in, and the situation has gotten worse. After reporting 288,685 gallons of cellulosic ethanol in April, May’s numbers only amounted to 114,018 gallons. This is only about 2.4% of the nameplate capacity of the announced commercial cellulosic ethanol plants. If we use year-to-date numbers, the annualized capacity is still less than 3% of nameplate capacity for facilities that cost hundreds of millions of dollars to build. Let that soak in. POET alone spent $275 million, with U.S. taxpayers footing more than $100 million of that bill. Abengoa reportedly received $229 million from taxpayers for its project. For this (plus however much that was spent by INEOS), the combined plants are running at an annualized capacity of 1.7 million gallons of ethanol, which would sell on the spot market today for $2.6 million.

We can conclude from this that the three companies with announced commercial cellulosic ethanol facilities — INEOS, POET, and Abengoa (NASDAQ: ABGB) — are finding the going much tougher than expected. I believe that the costs to produce their cellulosic ethanol are higher than the price they will receive for the ethanol. This is the sort of monthly cash drain that led to the shutdown of everyone else that ever tried to produce cellulosic ethanol commercially.

I suspect that INEOS has given up trying to produce cellulosic ethanol (their press releases have certainly dried up), and I suspect that the others aren’t too far behind. And there will be more tax dollars that have been flushed down the drain in pursuit of cellulosic ethanol, which companies have tried to produce economically — without success — for more than 100 years. It seems that those who do not learn history waste a lot of taxpayer money repeating it.

Rapier, R. May 20, 2015. Where are the Unicorns? 

In the 2007 EISA, Congress mandated that 100 million gallons of cellulosic ethanol had to be blended into the fuel supply in 2010, 250 million gallons in 2011, and then rapidly ramping to 16 billion gallons per year by 2022. Despite the mandates, there was no cellulosic biofuel produced in 2010 or 2011, and only 20,000 gallons were produced in 2012 by a company that subsequently declared bankruptcy. In 2013 about 230,000 gallons of cellulosic biofuel were produced by KiOR, which also subsequently went bankrupt.

I have written a number of articles on the cellulosic ethanol situation. To understand what cellulosic ethanol is, and to see that the history of this fuel in the U.S. dates back about 100 years, see my 2010 article Cellulosic Ethanol Reality Begins to Set In, my 2012 article The First Commercial Cellulosic Plant is NOT About to Open, or my 2013 article Why I Don’t Ride a Unicorn to Work.

The “First” Commercial Cellulosic Ethanol Plant is Announced

Several companies have either claimed they were about to open commercial cellulosic ethanol facilities, or that they have indeed done so. Each time this happens, there are headlines proclaiming that commercial cellulosic ethanol is a reality. My response to that is always essentially “You have to give it a few years before making that assessment.” Today, I provide evidence that despite the headlines, commercial cellulosic ethanol production has yet to be demonstrated.

In 2012, INEOS Bio and its joint venture partner New Planet Energy announced the opening of the Indian River County BioEnergy Center in Florida. Jim Greenwood, who was President and CEO of Biotechnology Industry Organization (BIO), testified before the House Committee on Agriculture “The biorefinery is a major landmark for this country. It’s the first commercial cellulosic refinery.”

Two things. The first, as you will see if you read my previous articles, is that the country’s first commercial cellulosic ethanol refinery was built nearly 100 years ago. Second, if I build a spaceship, and I tell you that commercial travel to Mars is now at hand — you would probably want to see me commercially fly that spaceship to Mars. In other words, I have to be both technically capable and it has to be economically viable before I can claim commercial success. If I spend a billion dollars and customers pay me a total of $5 million to take them to Mars, I am not a commercial success even if I am a technical success. With cash flow like that I would require heavy subsidies to keep my venture in operation.

Back to INEOS. Despite the May 2012 proclamation that the facility was about to open, it wasn’t until July 31st, 2013 that INEOS issued a press release that the company “is now producing cellulosic ethanol at commercial scale. First ethanol shipments will be released in August.” The nameplate capacity of this plant was 8 million gallons of cellulosic ethanol per year. In December 2013, the company issued a press release that said in part:

“Bringing the facility on-line and up to capacity has taken longer than planned due to several unexpected start-up issues at the Center. These efforts have highlighted some needed modifications and upgrades.”

Nine months later, in September 2014, the company issued another press release that read in part:

“INEOS Bio’s Vero Beach facility has recently completed a major turn-around that included upgrades to the technology as well as completion of annual safety inspections. We are now bringing the facility back on-line. In addition we will soon finish installation of equipment that will be used to remove impurities from one of our process streams that have been negatively impacting operations. This equipment will be commissioned and brought online over the remainder of the year.”

There have been no further operational updates. So what does the INEOS plant say about commercial cellulosic ethanol? Keep in mind that nobody disputes that you can build a plant to make cellulosic ethanol. The issue has always been about cost — due to complexity and high energy inputs. That’s why the cellulosic ethanol plants from 100 years ago were shut down.

POET Also Announces the “First” Commercial Cellulosic Ethanol Plant

Then there is POET, one of the largest producers of ethanol in the world. On July 7, 2011 the U.S. Department of Energy announced a $105 million loan guarantee to POET for the development of its 25 million gallon per year corn cob-to-ethanol facility, dubbed Project Liberty, at Emmetsberg, Iowa. Construction of the facility was expected to begin in August 2011, and cellulosic ethanol production was slated to begin in May 2013.

In September 2014, more than a year later than projected, in an announcement that must have been a surprise to INEOS, POET issued a press release: First commercial-scale cellulosic ethanol plant in the U.S. opens for business. The grand opening was attended by Willem-Alexander, King of the Netherlands, U.S. Secretary of Agriculture Tom Vilsack, Deputy Under Secretary Michael Knotek of the DOE, Iowa Governor Terry Branstad and Lieutenant Governor Kim Reynolds and thousands of guests. From the press release:

“Some have called cellulosic ethanol a ‘fantasy fuel,’ but today it becomes a reality,” said Jeff Broin, POET Founder and Executive Chairman. “With access now to new sources for energy, Project LIBERTY can be the first step in transforming our economy, our environment and our national security.”

To be clear, I never called it fantasy fuel, I just compared commercial cellulosic ethanol to a unicorn. The “commercial” modifier is important, because once again we have known for a very long time how to produce cellulosic ethanol.

Abengoa Announces a Commercial Cellulosic Ethanol Plant 

Next up was Abengoa (NASDAQ: ABGB), which had been building a cellulosic ethanol plant in Hugoton, Kansas. In October 2014 they announced the grand opening of the facility, an event attended by U.S. Secretary of Energy Dr. Ernest Moniz, Kansas Governor Sam Brownback and Kansas Senator Pat Roberts. The press release stated in part:

“Abengoa’s new industry-leading biorefinery finished construction in mid-August and began producing cellulosic ethanol at the end of September with the capacity to produce up to 25 million gallons per year.”

Last week, here was what Abengoa CEO Manuel Sánchez Ortega said about the facility when addressing Q1 2015 earnings:

“With regards to Hugoton, we continue working in the startup of the plant where we are making progress everyday resolving the issues that we have encountered, all of them related to the mechanical part of the plant. The bad news is that we still have our work to do to fix all identified challenges and the good news is that none of this are related to the biochemical process, which is the innovative part of the project.”

Is commercial cellulosic ethanol a reality? Certainly not yet according to Hugoton.

Report Card

While both INEOS and Abengoa have announced problems, to my knowledge POET has been silent about their progress. But they do all report production numbers to the EPA. The newest numbers were released today for April 2015 production.

Keep in mind that April 2015 marks nearly 2 years since INEOS announced they were producing commercial cellulosic ethanol. For POET and Abengoa, April marked the 8th month since they had announced the beginning of production. The total announced nameplate capacity of these 3 plants is 58 million gallons. So how close have they come to achieving this capacity?

Through March, EPA had listed year-to-date production of 286,237 gallon of cellulosic ethanol. The newly released data show that for April, the year-to-date cellulosic ethanol production was 574,922 gallons. This means that April’s production was 288,685 gallons. Annualized, this comes out to be 3.5 million gallons from plants with total announced nameplate capacity of 58 million gallons. Total production for the record month of April was then only 6% of nameplate capacity. That’s pretty bad considering these companies are at least 8 months into their learning curves.

The reason this is significant is that the production volumes have to be supported by the capital that is spent. If, in reality, only a fraction of the nameplate capacity can be reached (and clearly from the INEOS and Abengoa updates the capital costs are still rising) the hundreds of millions of dollars of capital buy very few actual gallons of capacity. Production at only a fraction of nameplate capacity will destroy the economics of the process and in turn the notion that commercial cellulosic ethanol is now a reality. A publicly traded company would eventually have to take an impairment against the facility — as KiOR did prior to their bankruptcy.

Still No Unicorns

While companies are rushing to take credit for commercial production of cellulosic ethanol, a look at the numbers released by the EPA today tells a different story. They warn of very high capital costs per actual gallon of production — a recipe for commercial failure. On the positive side, April’s numbers were slightly greater than the year-to-date production of the previous 3 months combined. If I had to guess, based on the lower capacity of INEOS and the ongoing problems at Abengoa, that production is predominantly POET’s.

POET probably does have the greatest chance of success. By co-locating their cellulosic ethanol process adjacent to one of their corn ethanol plants, they can share infrastructure, energy, and personnel, driving down costs. But the capital cost of that facility was announced at $275 million. Even if we assume that all of April’s production was from POET, it’s going to take a lot more than 3.5 million gallons of ethanol per year to support that level of capital spending – at least commercially. On the spot market that much ethanol per year would currently sell for about $5 million. Production rates will have to be much higher to justify hundreds of millions in capital spending.

Of course you can subsidize all sorts of schemes into existence. The real question is whether there is a realistic pathway to the process standing on its own commercially. We will check back in on this developing situation later in the year, but it’s going to require exponential production increases over the next few months to salvage the economics. Alas, despite claims of unicorn sightings, I still can’t find one to ride to work

 

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Wind, solar, and storage impact on the California grid

Posted on June 4, 2015 by

California Energy commission. June 2010. Research evaluation of wind generation, solar generation, and storage impact on the California Grid. 131 pages.

Excerpts:

This report analyzes the effect of increasing renewable energy generation on California’s electricity system and assesses and quantifies the system’s ability to keep generation and energy consumption (load) in balance under different renewable generation scenarios.

In particular, researchers assessed 4 key elements necessary for integrating large amounts of renewable generation on California’s power system. Researchers concluded that accommodating 33% renewables generation by 2020 will require major alterations to system operations.

They also noted that California may need between 3,000 to 5,000 or more megawatts (MW) of conventional (fossil‐fuel‐powered or hydroelectric) generation to meet load and planning reserve margin requirements.

The study examines the relative benefit of deploying electricity storage versus utilizing conventional generation to regulate and balance load requirements.

Researchers also noted the effectiveness of storage technologies, in comparison to conventional generation, to meet energy systems’ need to accommodate large output changes of energy resources in a relatively short period.

Introduction

The integration of renewable energy resources into the electricity grid has been intensively studied for its effects on energy costs, energy markets, and grid stability. These studies all conclude that the variability and high‐ramping characteristics of renewable generation create operational issues.

Project Purpose

This research identifies key issues and assesses the effects of high renewable penetrations on intra‐hour system operations of the California Independent System Operator (California ISO) control area. It also looks at how grid‐connected electricity storage might be used to accommodate the effects of renewables on the system.

The research focuses on required changes to current systems to balance generation and load second‐by‐second and minute‐by‐minute, and to do so in the most cost‐effective manner. The study also assessed potential benefits of deploying grid‐connected electricity storage to provide some of the required components—including regulation, spinning reserves, automatic governor control response3, and balancing energy—necessary for integrating large amounts renewable generation.

The objective was to measure the effects of the variability associated with large amounts of renewable resources (20 percent and 33 percent renewable energy) on system operation and to ascertain how energy storage and changes in energy dispatch strategies could accommodate those effects and improve grid performance.

Automatic generation control operates the generators that supply regulation services (up and down) every 4 seconds to keep system frequency and net interchange error as scheduled. The real‐time dispatch buys and sells energy from generators participating in the real‐time or balancing market every five minutes to adjust generator schedules to track a system’s load changes.

Regulation in MW is the amount of second‐by‐second bandwidth or controllability used in balancing generation and load.

Spinning reserve is the excess amount of on‐line generation capacity over the amount required to supply load and available to respond to sudden load changes or loss of a generator.

Governor response is the near‐instantaneous adjustment of each generators output in response to system frequency changes, caused by the generator speed‐governing device.

System performance degraded, in terms of maximum area control error excursions and North American Electric Reliability Corporation control performance standards, significantly for 20 percent renewables penetration and became extreme at 33%

Droop is the gain on the generator’s local speed‐governing device, that is, how sensitive the generator’s output is to changes in system frequency. Ancillary services are those services that generators sell to the California ISO to enable system reliability and to follow load. Balancing energy is the energy the California ISO buys and sells every five minutes via real‐time dispatch to follow load.

Automatic generation control is the computer system at the California ISO that controls the generators in real time to balance load and generation second‐by‐second renewables penetration, using the same automatic generation control strategies and amounts of regulation services as today. Without adjustment to the automatic generation control and the amount of regulation procured maximum area control error excursions went from a typical band today of the order of ±100 MW to several times that in the 20 percent renewables scenario and to as much as 3,000 MW of error in the 33 percent scenarios. Such an excursion is not tolerable and would possibly cause other system protective devices to operate such as interrupting transmission flows to adjacent power systems.

 

The amount of regulation, without storage and using existing control algorithms, required to maintain system performance within acceptable limits for a 20 percent renewable case in 2012 was ±800 MW in the up and down direction, roughly double today’s amount.7

The amount of regulation and imbalance energy dispatched in real time, without storage and using existing control systems to maintain system performance, within acceptable limits during morning and evening ramp hours for 33 percent renewable cases in 2020 was 4,800 MW. The amount of regulation and imbalance energy dispatched in real time, without storage and using existing control algorithms, to maintain system performance within acceptable limits during non‐ramp hours to address system volatility for the 33 percent renewable cases in 2020 was approximately an additional 600 MW. By comparison, 1,200 MW of storage added to the baseline 400 MW of regulation provided superior results by comparison. (See Table 1).

Generally, the largest deviations in system performance occurred twice per day, once during the morning and once during the evening, corresponding to the interaction of diurnal production of wind and solar resources and fluctuation of demand. Accordingly, degradation of system performance appears to be predominantly caused by renewable ramping in the morning and evening along with traditional morning and evening load ramps.

Increasing regulation amounts, without the use of storage and improved control algorithms, can improve system performance. However, roughly 2‐to‐10 times the amount of today’s regulation and balancing capacity would be required to maintain system performance absent other operating protocols, such as limiting ramp rates and new services that could be developed as alternatives to address renewable ramping as well as scheduling and forecasting errors.

Large‐scale storage can improve system performance by providing regulation and imbalance energy for ramping or load following capability. The 3,000 to 4,000 MW range of fast‐acting storage with a two‐hour duration achieved solid system performance across all renewable penetration scenarios examined.

storage can be up to 2 to 3 times as effective as adding a combustion turbine to the system for regulation purposes. The relative effect of each depends on how much storage or regulation and balancing is already in the system. When the system has sufficient resources for stabilizing system performance, the incremental benefit of either technology approaches zero. This is an incremental ratio of the effect a combustion turbine or a storage device each have on system performance, and not an indicator of how much total capacity of each technology may be needed to manage the large ramping phenomena.

 

Without the use of storage, ramping of combustion turbine generators and hydro‐electric generation is likely to increase. This may likely have detrimental effects on equipment maintenance costs and life of the equipment, and greenhouse gas emissions because the resources will be asked to generate more often at less than optimal production ranges as well as to remain committed—that is, on‐line—in anticipation of ramping needs.

Governors’ executive order S‐14‐08 established a goal of 33% energy from renewable resources to serve California customer load by 2020. This will require significant increases in ancillary services (regulation) and real‐time dispatch energy, with attendant changes in the day ahead schedules of generation production by hour to ensure that such services are available— that is, that enough generators will be on‐line with excess capacity available during each hour. Such a change in scheduling practice will incur additional economic costs in the production of power. The use of storage in conjunction with new control and generation ramping strategies offers innovative solutions that are consistent with the need to continue to comply with current North American Electric Reliability Corporation system performance standards. Electricity storage promises to be a useful tool to provide environmentally benign additional ancillary service and ramping capability to make renewable integration easier. However, while this report concludes that the system flexibility provided by storage is more efficient than equivalent conventional generation capacity, it has not performed a comparative cost‐benefit analysis either in terms of fixed capital or variable costs.

The California ISO control area as simulated would require between 3,000 and 5,000 MW of regulation and energy for balancing and ramping services from fast resources (hydroelectric generators and combustion turbines) for the scenario of 33% renewable penetration scenario in 2020, absent other measures to address renewable ramping characteristics (See Table 1). The range reflects the different seasonal patterns in the days studied, as well as the mix of fast storage (capable of 10MW/second ramping) versus fast new and upgraded conventional units (combustion turbine and hydro expected as of 2020). The large ramping requirement is driven by the combination of solar generation and wind generation variability that is forecasted for the 33% scenario. Included within this variability is the steep, yet highly predictable, production curve associated with solar resources as the sun comes up in the morning and sets in the evening. Some of this ramping requirement can be satisfied by altering the likely system commitment for conventional generation to maintain a large amount of gas‐fired combustion turbines on‐line for ramping. It also may be possible to alter the scheduling of hydroelectric facilities and pump‐storage facilities so as to assure adequate ramping potential at critical periods, although there are environmental and operational difficulties associated with this potential solution.

Finally, altering or controlling the ramp rate of wind and solar resources for known ramping events such as sunrise and sunset can reduce regulation, balancing, and ramping requirements, but at the cost of curtailing renewable output.

 

The moment‐by‐moment volatility of renewable resources may need up to twice the amount of automatic generation control or regulation compared to todayʹs levels in the 20 percent scenario and somewhat more in the 33%. This is consistent with prior studies and manageable based on simulations using existing and anticipated sources of supply.

Generation ramping requirements to meet the morning load increase and the evening load decrease, as well as potentially other large changes in net load during the day, require large changes to generation dispatch in very short periods and may be the major operational challenge to ensuring reliability under a 33% renewable scenario.

Under the 33% renewable scenario, these ramps will be difficult to manage in the current paradigm of regulation and balancing energy/real‐time dispatch, where automatic generation control and real‐time energy dispatch must be used to counteract large renewable ramping behavior and scheduling / forecast errors. There should be an investigation into new protocols for renewable ramping and provide incentives for incentivizing the needed flexibility to reduce its effects would appear to be in order.

Fast storage (capable of at least 5 MW/second if not up to 10 MW/second in aggregate) is more effective than generally slower conventional generation in meeting the need for regulation and ramping capability

Use of storage avoids greenhouse gas emissions increases associated with committing combustion turbines strictly for regulation, balancing, and ramping duty.

A 30‐to‐50 MW storage device is as effective or more effective as a 100 MW combustion turbine used for regulation purposes, given the use of the storage‐specific control algorithms as mentioned in (4) above, the faster response of the storage as compared to a gas turbine,

Table 1 summarizes the quantitative benefits of using storage to address minute‐to‐minute volatility

this analysis recommends at least 400 MW or more additional regulation (but not balancing energy) for the 20 percent Renewables Portfolio Standard scenario while the California ISO report recommends 250 to 500 MW more depending on the season.

Possible imposition of requirements on renewable resources to accommodate their effects on grid operation, such as ramp rate limits on renewable resources, more accurate short‐term forecasting, sub‐hourly scheduling, and other possibilities.

Should electricity storage be directly linked to renewable installations or be procured by the California ISO as an ancillary service on behalf of the system as a whole? Whether renewable developers are required to provide or procure storage capabilities or the California ISO is required to procure it on behalf of the system as a whole will affect the stateʹs generation resource planning. The location of the storage (at the renewable resourceʹs location or elsewhere) will affect the planning of future power transmission lines as well.

 

The prospective benefits to California from the development of fast electricity storage resources for use in system regulation, balancing, and renewable ramping mitigation are significant. Specific benefits of fast electricity storage include:

  • Management of large renewable energy ramping and management of increased minute‐ to‐minute volatility without degrading system performance and risking interconnection reliability.
  • Reduced procurement of very large amounts of regulation, balancing, and reserves from conventional generators, which may be either very expensive or infeasible.

Avoidance of keeping combustion turbines on at minimum or midpoint power levels to support regulation and load following.

o Avoids increased greenhouse gas emissions.

o Avoids higher energy costs due to combustion turbine energy displacing lower cost combined‐cycle gas turbines and/or hydroelectric energy.

Can the California ISO system withstand a disturbance control standard event with 20 percent and 33 percent renewable resources, assuming that they displace existing thermal resources?

  • What is the storage equivalent of a 100 MW combustion turbine (CT)?

These values were provided to the research team by the California ISO, based on projects currently in the interconnection queue which would realize the 20 to 33 percent renewable portfolio standard level. Between 2009 and the high case for 2020, wind generation nameplate capacity increases by over fourfold.19 Concentrated solar generation increases by a factor of 25 over the same time period. Table 3. Generation Capacity by Type (MW)

Under typical circumstances the California ISO’s frequency regulation needs are achieved today by having about a dozen generators on AGC control in order to meet its WECC/NERC frequency performance obligations. However, under high renewable scenarios, the number of units needed on AGC may need to be many times greater. In addition to AGC service, the California ISO also operates a balancing energy market to respond to deviations between the scheduled and actual level of generation output on an hour‐to‐hour basis in real‐time operation. Although balancing energy responds at a slower rate than AGC, the operation of both of these markets overlap significantly, and they both impact the California ISO’s overall frequency and ACE performance. Therefore, both AGC and balancing energy needs are examined in this study.

 

The 2020 High scenario required very large amounts of regulation. Consequently, in order to ensure that units with higher ramp rates were available to provide sufficient regulation, some additional cases were run where all the CTs and hydro units remained on at 20 percent minimum so as to have the required regulation bandwidth available. (Otherwise regulation duty would fall on CCGT and other slower units, degrading performance).

The goal of this task was to define storage facility scenarios above and beyond the existing pumped storage facilities that exist in California (e.g., Helms and Castaic plants). The researchers began by using an infinite storage capacity model in order to see how much would be used by the system for each of the modeled days in 2012 and 2020. For this purpose infinite storage was defined as 10,000 MW with a 12‐hour discharge duration. The amount of power used from this stored energy source used by the model in 2012 and 2020 provides an indication of how much storage power capacity is required in various RPS and AGC scenarios. The energy used (charging or discharging) during major ramping periods is an indication of the energy needed.

An inability to withstand deep discharge cycles means, in effect, that additional capacity needs to be installed in order to provide effective capacity. Thus, if a technology were deployed that were limited to 50 percent discharge, it would be necessary to provide twice the capacity of a technology of one that had no such limit. Thus, a storage system with a 50 percent limit would in effect need 12,000 MWh of storage where the study had determined that a 3,000 MW, 2‐hour unit was required.

The United States Congress is considering legislation to establish tax incentives for large‐scale electricity storage

Table 4. Outcomes summary Year / Renewable Scenario Current 20% RPS 33% RPS Low Estimate 33% RPS High Estimate

Determining Levels of Storage Required to Accommodate Renewables (Infinite Storage Approach)

Cases studied with storage levels of 10,000 MW and 12 hr duration Maximum ACE > 3000 MW in 2020 3200 – 4800 MW Required variously Some improvement via altered scheduling Results varied numerically but were qualitatively consistent 3,000 MW of storage was “sweet spot” except in April

For all study days, researchers observed increasing degradation of ACE as the share of renewables increased in the generation portfolio. ACE performance was severely degraded in all of the 2012 and 2020 cases, with maximum ACE levels more than doubling and tripling the 2009 levels as shown in Figure 20. With an AGC bandwidth of 400 MW and no storage additions, the maximum observed ACE variation within one day was ‐600 MW to +1,100 MW for July 2012, and ‐1,900 MW to over +3,000 MW for July 2020 High. These results were obtained with all conventional units (CT, hydro, and CCGT) on regulation. The CCGT units are actually much slower than the others and are normally not in regulation.

 

As illustrated in Figure 21, frequency deviation is fairly unchanged across scenarios, varying up to around 0.06 Hz. This is because the bias of the WECC system is such that it takes a very large imbalance to generate a 0.1 Hz deviation.

The predominant cause of ACE degradation in future years is the ramping of wind down and solar up in the mornings, and vice versa in the evenings. Variability of renewable production in the high renewables cases of 2020 cause additional ACE movement. Wind production decreases in the morning roughly an hour before solar production increases, depending on the day of the year. As such, there is a large drop in wind production in the morning, followed by a rapid pick up of solar an hour later. This occurs just as load is ramping up. The reverse occurs at the end of the day. Commitment of the combustion turbines and combined‐cycle turbines as needed to accommodate the renewable generation greatly restricts the ramping ability of the remaining conventional generation.

Droop adjustments have little impact on system performance because the ramp rates required to make up for sudden changes in renewable production are beyond what conventional generation can provide. Note that this does not mean that droop should be revisited for conditions where the amount of conventional generation on line is greatly reduced and insufficient system droop is available for a large unit trip. However, the conventional unit droop is sufficient today for evening conditions and light load in the event of a nuclear plant trip and can be reasonably expected to be so in the future.

The amount of regulation required for AGC to maintain ACE within todayʹs limits was 800 MW in 2012, roughly double today’s amount, and 3,200 to 4,800 MW in the 2020 High renewables scenarios, roughly 8 to 12 times today’s amount. Infinite storage at first failed to adequately control ACE as expected, using the output of the conventional AGC system. When large‐scale storage was configured as a resource similar to conventional generation, providing regulation services results were suboptimal. Using a fast and very large storage system resulted in excellent ACE performance in all scenarios once the storage control algorithms were developed, as described in the following section.

The ability of AGC to control renewables volatility and ramping using todayʹs controls and protocols was evaluated. Researchers found that the amount of regulation required for AGC to maintain ACE within todayʹs limits was 3,200 to 4,800 MW in the2020 High renewables scenario. This was not because of momentary volatility; lesser increases are needed for that. Rather, such amounts were required to address diurnal ramping, especially that of the centralizing thermal solar production.

Analysis of the 2020 High scenario for the July day show that 3,200 MW of regulation is needed to accommodate the renewable evening ramping. Still more is required to maintain ACE at nominal levels. Researchers found that April 2020 would require in excess of 4, 000 MW of regulation. Even then, the performance is marginal.

The researchers and the California ISO observed that procuring this much regulation from conventional units when renewable production was quite high posed problems in and of itself. Renewable production in these scenarios peaks at 10,000 MW or more, well in excess of 20 percent of generation required. If the conventional units are scheduled strictly on an economic basis, the CTs will be the first units to be displaced by the renewables. Hydroelectric and nuclear generation will generally be the last to be displaced. CTs normally provide a significant amount of the regulation capacity in the system. CCT units generally have much lower maximum ramp rates and cannot provide the same regulation service as combustion turbines. As noted above, the generation schedules were constrained to maintain combustion turbines on during the day and available for regulation service so that these very high levels of regulation could be realistically provided. Aside from the ramping phenomena, the renewables cause increased volatility during normal operation. This was observed to result in increased ACE and degraded performance, but nearly to the same degree as the ramping phenomena. Accordingly, it was investigated how much additional regulation would be required to maintain system performance during the hours 10 AM to 6 PM – i.e., between ramps. The results of this are shown in Table 5. It can be seen that if ACE maximum should be maintained below 500 MW and CPS1 above 180, for example, increased regulation will be needed in 2012 and 2020. As a general observation, it seems that in 2012 800 MW or more is required and in 2020 as much as 1,600 MW. Hey, it looks to me like 3200 MW:

 

When large‐scale storage was configured as a resource similar to conventional generation providing regulation services results were suboptimal. The conventional AGC had primarily proportional control with limited integral gains in the control algorithm. This is because in the California ISO area, the AGC is not the primary mechanism for following ramping; the real time dispatch is. As a result, the AGC typically has to deal with relatively small fluctuations (at 400 MW of regulation procured, the California ISO AGC regulation bandwidth is 1 to 2 percent of system load or less). A ramp of 20 to 25 percent greatly exceeds AGC ability to respond.

The conventional generators overall are slower than the storage and would not be stable with as aggressive an integral gain as the storage system will be. Also, the amounts of storage employed versus conventional generation will be different.

3.6. Requirements for Storage Characteristics The key parameters for system storage are the power level, the duration or energy capacity, and the rate limit on changes to power output.

It was determined that the California ISO control area has maximum benefit from (a) 3,000 MW of storage power capacity with at least (b) a two‐hour duration and that the (c) ramping capabilities have to be 10 MW/second or greater. The 10 MW/second requirement translates to achieving 3,000 MW of output from zero in five minutes. Thus, if there is 3,000 MW of storage with a 5 MW/minute ramp capability (and a 2 hour duration) it would seem that there is a need for faster storage capable of making up the 1,500 MW deficiency that accrues at the end of five minutes – so that 1,500 MW of 10 MW/second storage is required, but with less duration. (Much less; it would need to produce a ramp down over the next five minutes; so that the total energy would be 125 MW hours; e.g. the duration is 125 MWh/1,500 MW or 5 minutes. A similar set of mathematics can be performed for any combinations of technologies with differing rate limits. This implies that a lower capacity cost technology such as CAES can be combined with high performance and higher cost technology such as Li‐Ion batteries or super‐capacitors.

The rate limit performance of the storage system overall is a critical parameter. As noted above, researchers assessed system performance for differing rate limits on the storage. The storage system must have an aggregate rate limit of at least 5 MW/second for a 3,000 MW aggregate system, and 10 MW/second is preferable. (10 MW/second out of 3,000 MW equates to 0.33 percent/second or 20 percent/minute in general).

A key policy question in developing a portfolio of renewable integration solutions is, how does equivalent storage compare to an investment in a new gas turbine for the same service? Storage is more expensive per MW provided, and it has a limited amount of energy it can supply to the system.

A gas turbine, on the other hand, can continuously inject energy to system as long as it has a fuel supply.

 

To help assess the question of whether a gas turbine provides more benefits for less money, researchers determined the rough equivalency of storage by examining the incremental impact of a single additional 100 MW CT. In particular, researchers evaluated the system performance impact of 100 MW of incremental CT dedicated to regulation and load following and compared that with the incremental impact of storage systems of different sizes.

Then one CT with a capacity of 110 MW with 50 percent of capacity allocated to regulation was added to the mix. This CT had a very high rate limit – 120 percent of capacity in 5 minutes. (The large CT units (over 500 MW) are significantly slower. The very small units are this fast or faster).

Then, instead of the CT, storage units of 50 and 100 MW were added to the model, and the test cases were repeated. Again, this was run twice. As expected, the 50 MW storage unit produced benefits similar to the CT in some cases and varied in others. The 100 MW unit exceeded the metrics improvement of the CT by far.

3.8. Issues With Incorporating Large Scale Storage in California

The results of this report indicate that renewable ramping creates volatility in the system and that storage has the technical potential to help address this volatility. However, key policy questions are how to best promote various ramping solutions and how to account for tradeoffs among them. Imposing ramping limits on renewable resources as an interconnection requirement would address volatility and leave open the question of which solution to use (storage, combustion turbine, or other means). Resource ramping limits are feasible for the ramp up phenomena (at some lost energy production), but not for the ramp down, which is technically difficult (requires storage in some form either at the resource or at the system level).

However, compared to other solutions, storage appears to have benefits and may be preferred in some instances. Without storage, CT ramping would need to increase. This has three basic impacts: • Increased maintenance costs and reduced lifetime from additional wear and tear • Postponed de‐commitment of CT units • Increased GHG emissions

Storage could absorb the volatility and limit CT ramping, diminishing these adverse impacts. Though storage units are more expensive than CTs, the avoided emissions and wear and tear may make the incremental cost worthwhile. Additional research needed to assess additional CT maintenance costs and to value emissions reductions. Figure 42 and Figure 43 show the benefits storage has for both CT and hydro generators in terms of reduced ramping in response to renewables. As the amount of storage increases, the amount of unit ramping decreases.

Excessive ramping up and down of hydro units has environmental implications for downstream water levels and may even by impractical in extreme cases.

 

The acquisition of regulation and ramping services from storage in the amounts identified will be a significant cost to the system. How these costs will be allocated – either to the entire market as an ancillary service or to renewable resources in effect by imposition of ramping rate limits has profound economic implications for renewable developers and the future economic viability of renewable resources.

Conclusions and Recommendations

There are five major conclusions from this research work:

  1. The California ISO control area will require between 3,000 and 4,000 MW of regulation / ramping services from ʺfastʺ resources in the scenario of 33 percent renewable penetration in 2020 that was studied. The large ramping requirement is driven by the combination of solar generation and wind generation variability that is forecasted for the 33% scenario. Some of this ramping requirement can be satisfied by altering the likely system commitment for conventional generation to maintain a large amount of gas fired combustion turbines on‐line available for ramping. It also may be possible to alter the scheduling of hydroelectric facilities and pump‐storage facilities so as to assure adequate ramping potential at critical periods, although there are environmental and operational difficulties associated with this.
  2. The moment by moment volatility of renewable resources will require additional AGC regulation services in amounts (up to doubling todayʹs levels) that can be reasonably procured.
  3. The ramping requirements twice a day or more require much more response and will be the major operational challenge.
  4. Fast storage (capable of 5 MW/second in aggregate) is more effective than conventional generation in meeting this need and carries no emissions penalties and limited energy cost penalties.
  5. Use of storage also avoids greenhouse gas emissions increases associated with scheduling combustion turbines ʺonʺ strictly for regulation and ramping duty.

An alternative to providing large‐scale fast system ramping is to constrain the ramp rates of wind farms and central thermal solar plants so as to reduce the need for system ramping resources. This is an interconnection requirement in some island systems today. Meeting ramp rate limits on up ramping is easy enough to do at some lost energy production; meeting down ramp requirements is more technically difficult.

Storage at the site of the renewable resources or as a market service that renewable producers can acquire is an alternative to a system ancillary service with identical benefits and results.

There are a number of policy issues at the state and federal level around this concept today which are elaborated in the report. The most important is to determine if ramping restrictions and support are the financial responsibility of the renewables operator or the market; and related to that what storage investments will qualify for what investment tax credits and how these are linked to renewables facilitating increased renewable generation.

The accommodation of 33% renewable generation resources is the goal established by the Governor for the state. To achieve this goal will require major alterations in system scheduling and operations under current paradigms, which will be costly in terms of energy costs and GHG emissions. The use of storage in conjunction with new control and ramping strategies offers a way to avoid these costs and provide current levels of system reliability and performance at lower risk. While it is yet to be investigated, storage also promises to be a useful tool in making use of DR as an additional ancillary service provider to facilitate renewable integration.

The 3,000 to 4,000 MW of storage which could be used to address renewables management requires a ramp rate capacity of 5 to 10 MW/second, or 0 to full power charging / discharging in 5 minutes. This equals or exceeds the ramping capabilities of most conventional generating units, and particularly the larger combustion turbines. Smaller combustion turbines in the California ISO database can meet this ramp rate requirement, but there are insufficient quantities of such units to provide the required 3,000 to 4,000 MW of fast ramping. Hydroelectric units are capable of changing output levels at these rates. However, it is unclear if the hydroelectric units have sufficient range available for regulation at these levels without having to operate in hydraulic forbidden zones. The hydro units also have very limited amount of water available in the fall and winter months, so they are not available as a regulation resource during a number of months.

A duration of two hours for the storage systems was found to be sufficient for the regulation, ramping and load following applications. The measurement of the relative effectiveness of storage to a combustion turbine demonstrates that, depending upon system conditions and other factors, a 30 to 50 MW storage device is as effective as a 100 MW CT used for regulation and ramping purposes. This is an incremental figure measured across a range of system scenarios; that relative performance figure of merit would not obtain across the entire range of regulation resources0 – 5,000 MW of course.

The acquisition of regulation and ramping services from storage in the amounts identified will be a significant cost to the system. How these costs will be allocated – either to the entire market as an ancillary service, or to renewable resources in effect by imposition of ramping rate limits, has profound economic implications for renewable developers and the future economic viability of the renewable resources.

The development of the ancillary service protocols for storage will definitely affect the R&D and engineering directions taken by the grid storage industry and need to be validated and made known as soon as practical. For instance, the two‐hour duration requirement is a significant parameter that will affect which storage technologies are in play or not. Similarly, the ramp rate requirements for grid storage in this application will have implications for the technologies developed and deployed. A careful study of the implications of acquiring very large amounts of regulation / reserves / load following via the market is in order.

The California ISO is considering changes to the market and the energy management system to integrate several hundred MWs of limited energy storage resources such as flywheels and batteries in the regulation market. These devices typically have very fast response rates and can switch between charge and discharge modes within 1 second. They also have very limited amount of energy storage capability, typically 15 minutes of energy, and therefore require constant monitoring to ensure they can continue to provide their full regulation range and are energy‐neutral over a 10 to 15 minute period.

The study was optimistic in one critical way – the impact of large forecast errors for renewable production, especially forecast errors associated with wind production, was not studied. The wind forecast errors assumed in the scheduling and dispatch were as actually observed on the studied days in 2008‐2009 and were not significant. Addressing larger wind power forecast error problems will further emphasize the benefits of storage as compared to conventional generation used for regulation as these units would have to be kept on for longer periods in order to provide against forecast error.

Note that the system has to be able to withstand the expected worst case scenario for coincident ramping seasonally –it cannot be designed and operated for averages if there are significant probabilities of reliability‐threatening coincident ramping. Literally hundreds of second‐by‐second simulation of the California power system were performed for each of the four days and four renewable scenarios developed. These simulations produced the conclusions and results described above. The conclusions and recommended control algorithms and dispatch protocols need to be validated across a much larger sample of days than the four seasonal typical weekdays chosen.

Finally, the study scope did not include examination of the costs of either greatly increasing procurement of ancillary services or of deploying large amounts of grid connected storage.

As indicated by this study, procurement of very large amounts of regulation and reserves from conventional units may cause market distortions. If so, new market and regulatory protocols may be required.

  • What incentives at the federal or state level are indicated to support storage resource development? And how should these be linked to renewable facilitation? It seems that storage should meet the technical performance characteristics identified in this report as validated and amended by the California ISO in order to qualify. The state may wish to communicate this concept to the U.S. Congress which is contemplating investment tax credits for storage.

Third, the Energy Commission should fund additional research on new energy storage technologies that can be integrated with large concentrated solar and PV installations. The goal is to reduce the variability of the solar energy production and to reduce the rapid and large ramp ups in the morning and ramp downs at sunset. Existing molten salt thermal storage is both expensive and operationally challenging. New technologies are needed now before the large solar plants are all designed and built.

Specific benefits of fast storage include: • Management of large renewable ramping as well as increased minute to minute volatility without degrading system performance and risking interconnection reliability. • Management of renewable volatility and ramping without having to procure very large amounts of regulation and reserves, which may be either very expensive or infeasible. • Reduced breakage and maintenance of the thermal and hydro generation fleet as they will be subject to less volatility and stress as the energy storage resources will absorb a lot of the rapid changes in energy production. • Avoidance of keeping combustion turbines on at minimum or midpoint power levels to support regulation and load following. o Avoids increased GHG emissions. o Avoids higher energy costs due to combustion turbine energy displacing lower cost CCGT and/or hydroelectric energy.

7.0 Glossary ACE Area Control Error AGC Automatic Generation Control CAES Compressed Air Energy Storage California ISO California Independent System Operator CCGT Combined‐cycle gas turbine CPS Control Performance Standard CPUC California Public Utilities Commission CS Concentrated solar CT Combustion turbine EAP I Energy Action Plan I EAP II Energy Action Plan II Energy Commission California Energy Commission GW gigawatt GWh gigawatt‐hour IOU investor‐owned utility kW kilowatt kWh kilowatt‐hour MRTU Market Redesign and Technology Upgrade MW megawatt MWh megawatt‐hour PIER Public Interest Energy Research NERC North American Electric Reliability Corporation T&D transmission and distribution VAR volt‐ampere reactive WECC Western Electricity Coordinating Council

Posted in Energy Storage, Renewable Integration | Comments Off on Wind, solar, and storage impact on the California grid

Sacramento SMUD AB 2514 Energy storage report

Posted on June 4, 2015 by

[The state of California has realized that it’s unlikely a larger or national transmission grid will be built to share and balance variable renewable power, and is going to Plan B, energy storage. The problem is, all of the utilities reported back that they weren’t going to be able to add any for reasons reported below. The larger utilities, PG&E, etc must comply, whether they like it or not]

Excerpts from the of 47 page: SMUD AB 2514 report to the state of California on adding Energy storage

CONCLUSION: Defer establishing energy storage procurement targets until more viable and cost-effective energy storage systems become available.

Since 2008, SMUD has invested over $30 million dollars in internally and externally funded research to understand and prepare SMUD and its customers for eventual deployment and utilization of energy storage. Staff has been conducting various field demonstrations, studies, and assessments of different storage technologies, used for different applications ranging from transmission scale to distribution scale to customer scale systems. On technical issues, this body of work has assessed technology performance including such factors as efficiency, reliability, and durability. On economic issues, this body of work has assessed capital costs, installation costs, operation costs , value , and cost effectiveness. Additionally through this body of work, staff has assessed grid integration issues and strategies for interconnecting, aggregating , visualizing and controlling storage systems from grid planning and operations perspectives.

In February 2010, the California Assembly formally recognized the benefits of energy storage through passage of Assembly Bill AB 2514 titled “Energy Storage Systems.” The bill was authored by Chair of the Assembly Rules Committee Nancy Skinner in partnership with then California Attorney General Jerry Brown. The bill passed both houses on September 9, 2010 and was signed by Governor Schwarzenegger on September 10, 2010. In passing the bill, the legislature found that increased deployment of energy storage systems can 1) help integrate increased amounts of variable, intermittent, and off-peak wind and solar energy that will be entering the California power mix on an accelerated basis; 2) avoid or defer the need for new fossil fuel peaking plants and avoid or defer distribution and transmission system upgrades, 3) reduce the use of high carbon-emitting power plants during high electricity demand periods and 4) provide the ancillary services otherwise provided by high carbon-emitting fossil-fueled plants.

October 21, 2013, the CPUC issued Decision D.13-10-040 requiring California’s three investor- owned utilities (IOUs), PG&E, SCE, and SDG&E, to procure 1,325 MW in aggregate of electricity storage projects by 2020 across each of the transmission, distribution and customer grid domains. The specific targets by domain, IOU and year are shown in the following table.

Table 1. Specific Energy Storage Procurement Targets under D.13-10-040

The Decision allows for procurement of all stationary energy storage technologies, except pumped hydro greater than 50 MW. This resource type was excluded because the CPUC was concerned the “sheer size of pumped storage projects would dwarf other smaller, emerging technologies; and as such would inhibit the fulfillment of market transformation goals.”

For the 2014 solicitation cycle, PG&E’s need is larger than SCE’s. PG&E intends to procure approximately 78 MW of storage primarily at the transmission grid level. Transmission & Distribution procurement will focus on three basic configurations; Standalone Energy Storage, Hybrid/Co-Located Energy Storage and Energy Storage Providing a T&D reliability function (Transmission or Distribution Asset). PG&E expects its total need will be filled through a new Energy Storage RFO, competitive solicitations authorized in other proceedings (i.e. the Long Term Procurement Plan, Resource Adequacy, or RPS proceedings), an application for a storage project to meet a utilityidentified storage opportunity. PG&E will rely on existing CPUC- approved customer programs to meet the targets for the customer segment.

In June 2012, the Redding Electric Utility (REU) received City Council authorization for long- term extension of the Utility’s Energy Storage Program, including permanent load shifting through the procurement and installation of several ice storage facilities (“Ice Bear”) throughout the service area. This technology permanently shifts air conditionerdriven peak demand to off-peak hours thereby increasing electric system efficiency and reducing operating costs. The program has proven to be a successful and cost-effective means of improving electric system efficiency for REU, given their climate and load patterns. This type of thermal energy storage meets the requirements of AB 2514 since it is cost-effective, reduces demand for peak electrical generation and also stores thermal energy for direct use for heating or cooling at a later time in a manner that avoids the need to use electricity at that later time. In August of 2014, REU established procurement targets around this program of 3.6 MW by 2016 and 4.4 MW by 20206

SMUD considered10 energy storage technologies in this report including six battery chemistries, pumped storage, compressed air energy storage, flywheels and thermal energy storage. The battery chemistries considered are: lithium ion, lead acid batteries, advanced lead acid, flow batteries, sodium sulfur batteries, and sodium metal halide (sodium nickel chloride is within the genre of battery technology). Each technology offers different operating, performance, capital expense, operating expense, footprint, safety, and technology readiness levels. Pumped Storage

One key advantage of this system is that the gravitational energy stored in the upper reservoir can be stored for long periods of time with virtually no energy loss. Pumped storage is an efficient way to augment baseload generation from conventional power plants. However, efficiency is limited by the efficiency of the pump and turbine unit used in the facilities. It also requires two proximal large reservoirs with a sufficient amount of water surface and pressure elevation between them. Suitable geologic formations are rare and tend to be found in remote off-grid locations, such as mountains, where construction is difficult or restricted.

Compressed Air Compressed Air Energy Storage (CAES) technology is particularly well-suited for energy-intensive applications such as peak-shifting or spinning reserves. CAES converts inexpensive, excess off-peak electricity into compressed air through the use of a motor and compressor. The compressed air is typically stored in sealed underground air pockets or caverns. When electricity is required, the system returns the compressed air to the surface. The air is then heated with natural gas and put through expanders to power a generator, which in turn produces electricity. While CAES utilizes natural gas, the technology uses less fuel than conventional gas turbines – in some cases two-thirds less.

Flywheels are approximately 85% efficient, the response time is extremely rapid, and while duration is low (typically between a few seconds and a few minutes); flywheels can provide a significant power surge. For example, the world’s largest flywheel has an effective capacity of 160 MW and a discharge time of around 30 seconds Thermal Energy Storage Thermal energy storage refers to storage systems that store heat or cooling (in the form of chilled or frozen water) to displace electrical air conditioning load during peak periods. In the case of California, ice thermal storage is particularly relevant. Most firms in this space offer large-scale systems for commercial businesses such as airports, convention centers, or large hotels.

Overall, Li-ion batteries offer high performance, high efficiency, small footprints, and high power density. Li-ion offers the most diversity in terms of subchemistries and borrows heavily from consumer electronics and electric vehicles industries.

Zinc bromide redox batteries use a reversible zinc electroplating process to charge and discharge the electrolyte in the batteries. This relatively complex electrochemical reaction has caused problems in the past with battery life and membrane clogging. Most of the entrants in this space, claim that they have solved these durability problems and can now produce long- lasting batteries. Zinc bromide batteries still require pumps and fluid flow (as do all flow batteries), which can lead to operations and maintenance issues during the long life of a stationary energy storage asset. Although it is a relatively rare and expensive element, vanadium is an excellent energy storage medium with very smooth voltage profiles and low internal resistance. Thus, a vanadium redox battery is capable of extremely long life and high efficiencies compared to other flow battery technologies. No manufacturer, however, has yet successfully figured out how to reduce the costs of these flow batteries to the point where they can compete with other chemistries such as Li-ion or advanced lead-acid.

Sodium metal halides high-temperature chemistry was originally invented in the 1970’s. Sodium metal halide batteries have the advantage of relatively low-cost materials, primarily sodium, zinc, and some nickel. This battery chemistry is still more expensive than Li-ion chemistries such as NCA and LFP. Additionally, sodium metal hydride batteries operate at very high operating temperatures (between 250°C and 350°C), which creates safety and efficiency risks that add to the cost of engineering the systems.

Table 2. Energy Storage Technologies and Best-Suited Utility Applications (ex customer-sited applications) Flywheel Li-ion Advance Thermal Sodium d LeadEnergy Metal

Summary of Energy Storage Deployments According to Navigant Research, 126,073.6 MW (599 systems) of energy storage are currently deployed globally. Another 34,860 MW (comprising 165 systems) are in the pipeline, which refers to projects that have been announced, projects that are funded, or projects currently under construction. Of the nearly 35,000 MW of energy storage in the pipeline, 89% is pumped hydro (traditional or small-scale variants), leaving 3,801 MW of advanced energy storage in the pipeline. Since 2000, 30,465 MW of energy storage have been deployed globally. Asia-Pacific leads the market with 20,317 MW installed, followed by Europe with 8,448 MW deployed, the Middle East with 1034 MW deployed and North America with 622 MW deployed.

The majority of these installations are pumped storage, which accounts for the high volume of storage installed over the past 15 years. Since 2000 in North America, 622 MW of energy storage have been installed, 619 MW in the United States. Of these 619 MW, 572 MW have been advanced energy storage technologies such as advanced batteries, flywheels, or compressed air, for example. 2013 and 2011 were the standout years for energy storage in the United States. In 2011 103 MW were installed in the U.S. and in 2013 that number more than tripled with 341 MW installed. Globally, as of the third quarter of 2014 (Figure 1), there are 23 energy storage technologies installed. Excluding pumped storage, these technologies account for 2730 MW of projects. North America leads the market with 19 technologies installed on the grid system.

Figure 1. Megawatts Deployed Energy Storage Projects by Region and Technology, Excluding Pumped Storage 3Q14

Table 3. Summary of SMUD Energy Storage Demonstrations

SMUD is considering a 400 MW, $800M pumped hydro facility at Iowa Hill.9 SMUD has performed extensive feasibility studies to understand the value that such a project would provide, with estimates ranging from $80-294/kW-yr depending on various factors including the extent and type of renewable generation on the grid, as well as the use of single speed versus variable speed drives within the pumped hydro storage plant. SMUD has also considered compressed air energy storage (CAES) by evaluating over 25 potential sites in and around the SMUD service territory. However, each site was found to have some significant risk associated with it, whether geological, technological, legal, or logistical. The most promising site has complex land rights issues, and the time-frame there would be 10 years or more to develop such a site for a compressed air energy storage plant.

One of the most significant challenges facing energy storage is the integration of storage equipment with other infrastructure, including distributed generation, grid assets, communications equipment, and data acquisition and control systems. Utilities currently must coordinate with multiple vendors, many of which are unfamiliar with the other components of the system, particularly energy storage.

Findings, and Lessons Learned. EPRI, Palo Alto, CA: 2013. 3002001256.; U.S. Energy Storage Project Case Studies: Results, Findings, and Lessons Learned in 2012. EPRI, Palo Alto, CA:

  1. 1024281.; Distributed Energy Storage Systems: Field Deployments and Lessons Learned. EPRI, Palo Alto, CA: 2013. 1024283.

Reliability, a primary concern for utilities, needs to be proven for widespread adoption of energy storage systems. Several vendors are at the pilot stage and have deployed few systems. In the Anatolia project, the RES vendor had a manufacturing defect that caused SMUD to shut down all the RES units. SMUD encountered multiple failures with various components including cooling fans, capacitors, SD cards, and modems. In Alameda County’s SmartGrid demonstration, the battery DC breakers repeatedly tripped from overcharging. Multiple others have also reported issues with charging and discharging behavior, as well as failed breakers and inverters.

distributed energy storage systems have not been fully optimized for certain applications. At Anatolia, the smoothing application did not work as effectively on RES units as on CES units. Furthermore, SMUD’s storage scheduling software was set up for individual unit programming, whereas fleet-level programming is more useful for utility-owned distributed energy resources. Additionally, SMUD received complaints that the RES units were too noisy when operating in smoothing mode caused by the high rate of switching occurring in the inverter. Multiple utilities, including SMUD, have found that some battery systems lack desirable safety mechanisms, such as remotely operated bypasses in case of a fault.

Communications were also a significant challenge in the Anatolia project. The customer broadband used for RES communication had unstable internet connectivity, and the connection with the cellular modem used for CES units was lost regularly until the cellular provider expanded coverage in the area. Also, in one instance, there was interference between a RES unit and customer broadband equipment.

Reliance on third party provided telecommunications has initially proven to be problematic in SMUD’s Mitsubishi Energy Storage Demonstration as well, resulting in problems with control and monitoring systems including fire protection monitoring.

Another significant lesson is that storage projects take longer than anticipated. With a lack of in-house expertise on new technology, SMUD has routinely found technical efforts to be more complex and time-consuming than expected. At Anatolia, SMUD had never worked with high resolution monitoring equipment on underground feeders and had issues with monitor phasing and SCADA integration. This need for troubleshooting can be further complicated when working with residential systems, as it may require schedule coordination, and some customers complained about the frequency and duration of visits. Other delays included UL and IEEE certification of RES units and the component failures described above.

As is sometimes the case with research and development, technologies occasionally are found to be inadequate or not ready to be scaled from bench scale to field demonstration scale. This proved to be the case with SMUD’s zinc bromine flow battery demonstration project with Premium Power. During the course of this project, difficulties in meeting the design and operational requirements arose and the use cases to be demonstrated were thus forced to be modified or removed by Premium Power. The original power rating of 500 kW and energy rating of 3,000 kWh expected from the system was downgraded to 160 kW and 640 kWh respectively. Additionally, the roundtrip efficiency goal of 66% was not attainable, with the system only reaching 40% roundtrip efficiency. As a result of these shortcomings, SMUD cancelled this research project, deeming this vendor’s technology not technically viable for field trial. Another lesson learned from SMUD’s energy storage technology demonstration work is that not all vendors and suppliers are financially stable. SMUD was awarded DOE funding to conduct demonstration of substation sited energy storage with Satcon and A123. The project would have demonstrated a 500 kW / 500 kWh system located at SMUD headquarters. However, before equipment could be installed, Satcon and A123 went bankrupt (for unrelated reasons). As a result, SMUD cancelled the project. This suggests energy storage vendors and the market as a whole is still developing. Finally, a key challenge with energy storage is projecting and deriving value from energy storage assets due to lack of familiarity with the system.

A broad challenge facing all utilities considering storage is that storage must be used for multiple different applications simultaneously to derive significant value. However, the degree to which one storage asset may be used simultaneously for multiple applications is currently unclear.

In its solar EV charge port project, SMUD found that simply measuring the efficiency of the system is challenging. Actual efficiencies, as well as lifetimes and other battery characteristics, can vary depending on how the battery is used for different applications. More reliable information can inform better decisions on storage investments, including technology selection, sizing, placement, and operating strategy.

Table 4. Summary of Value Analysis Results

  1. EPRI and E3 looked at the value of energy storage in a variety of locations and applications. The study assessed a wide range of benefits:

price arbitrage for SMUD, regulation revenues, system capacity benefits, deferred distribution investments, reduced customer demand charges, reduced customer TOU rate charges, increased power reliability and improved power quality. Figure 5 shows the results and they range from a present value of $150 to $950/kWh of energy storage capacity. 11Benefits Analysis of Energy Storage: Case Study with the Sacramento Utility Management District. EPRI, Palo Alto, CA: 2011.

Figure 5. EPRI/E3 Value Analysis Results As part of SMUD’s demonstration of customer and transformer sited energy storage (discussed above), Navigant Consulting conducted a value analysis of the configurations tested12: SMUD owned, transformer sited; SMUD owned, customer sited; and customer owned, customer sited. The value analysis was based upon Navigant’s benefit calculation methodology shown in Figure 6. It focused on the applications tested during the demonstration: electric energy time shift, voltage support, distribution upgrade deferral, time of use energy cost management, and electric power reliability. The range of values for each configuration is shown in Figure 7 and ranges from a net present value of $60 to $210/kW of energy storage capacity.

To complement the development of SMUD’s Iowa Hill PHS project, SMUD partnered with Energy Exemplar and EPRI and won a US DOE FOA grant to model the value of the Iowa Hill project. The analysis13found values ranging from $80 to $294/kW-yr, 13Modeling and Evaluation of Iowa Hill Pumped-Hydro Storage Plant: Value in SMUD and in Larger Region depending on the penetration of renewables and other market assumptions. Using this as a benchmark, SMUD’s resource planning group14assessed the value of a 135 MW CAES plant and found similar values in 2030. 5

Current energy storage installed costs vary significantly, not only between technologies but also from project to project within a specific technology, or even vendor. Factors such as grid connection fees, system installation, land acquisition, and other site specific costs will affect the cost of energy storage from project to project all things being equal. Cost ranges in terms of both power and energy are plotted in Figure 8 for comparison. Flywheel energy ranges ($/kWh) are plotted on the secondary y-axis. Practically speaking, flywheels are only used in power- intensive applications such as frequency regulation and most commercial flywheel systems are 15-minute systems. This puts flywheels at a disadvantage when comparing flywheel technology on an energy basis. The most mature technologies, pumped hydro, lead acid batteries, and NaS batteries have the smallest ranges in terms of both energy and power cost. Overall, these technologies have not experienced significant innovation in the past ten years.

Lithium ion is unique in the sense that the figures presented here represent a blending of the most expensive and least expensive subchemistries within lithium ion, both in terms of energy and power. Therefore, the large range of costs is a function of the diversity of subchemistries, some of which are developed for high-power applications, and others developed for high-energy applications.

Flow batteries have the widest range of costs, and this is primarily a function of the varied sub-chemistries and manufacturing models that are being tested within this battery type. Vendors building facilities with large electrolyte storage tanks may have higher $/kW figures than vendors opting to build identical modules. However, this strategy will result in a much lower $/kWh. Sub-chemistries that rely on expensive, albeit efficient and high-performance inputs such as vanadium, will have higher upfront costs than zinc or iron-based subchemistries. Not shown in the figure above are thermal energy storage costs. These are highly site specific depending on building’s layout, existing HVAC equipment and the amount of thermal storage required. In 2012, SMUD commissioned15a technical potential study for large thermal energy storage systems in its service territory, focusing on adding chilled water to existing HVAC systems. The thermal energy storage would be used to shift cooling loads to off peak hours. Detailed onsite surveys were done to estimate: the potential cooling capacity that could be shifted, installation costs, and customer willingness to adopt. The study found costs ranging from ~$50 to $70/ton-hour for chilled water systems and ~$210 to $230/ton-hour for ice storage systems. Installed costs however are not the only metric by which to compare different storage technologies because their application and life-cycle characteristics can be quite different, even within the same technology type. As noted above for example, comparing installed costs of flywheels used for frequency regulation (i.e., a power application) to batteries used for energy arbitrage (i.e., an energy application) can be misleading. In this instance, to have comparable life-cycles, batteries would require replacement and this would need to be considered as a variable O&M expense in any life-cycle analysis. Unfortunately, for many emerging storage technologies there is not yet sufficient data on useful life and annual O&M cost by application to understand lifecycle costs adequately. Unfortunately, no recent and comprehensive analysis can be found in literature that compares storage technologies on life-cycle bases for different applications. A 2013 Sandia National Laboratory report16has information on life-cycle costs, but it is primarily based upon vendor provided and has not been verified with independent real world performance data.

Unfortunately, the study only assessed two combined applications for use of the storage systems – renewable integration/time shifting, and transmission and distribution grid support. Figure 9 below from the EPRI report shows the results of their analysis in $/kWh using low and high costs and efficiencies specific to each technology.

Figure 9. Levelized Cost of Delivered Energy for Energy Storage Technologies Compared to CCGT Though dated, the results show that of the technologies analyzed PHS and CAES are the most cost competitive with using a combined cycle gas turbine to integrate renewables and align the renewable energy production with a utility’s peak load when the energy is most valuable. Projected Energy Storage Costs Market research firm Navigant Research has published that the majority of the cost reductions in each technology will come from developments in the systems integration piece of the supply chain and not in reduced costs from the technologies themselves. Systems integration is woefully underdeveloped in the storage industry. Currently, many technology developers devote significant resources to integrate technologies into energy storage projects.

Some technologies, such as sodium metal halide or flywheels, have few vendors.

When evaluating technical viability, it’s important to consider not only the energy storage technology but also the balance of system, communication and control software, and integration with existing software platforms.

Pumped hydro storage (PHS) and compressed air energy storage (CAES) have a long history of full-scale implementation. In the 1890s, the initial PHS system prototypes were built in Italy and Switzerland. By the 1920s and early 1930s, the first pumped hydro system was built in America, and reversible pump-turbines with motor-generators became available. Since then, PHS has matured and become a widespread energy storage technology with a worldwide installed capacity of about 123GW.22 There are currently two existing CAES facilities in the world: a 290 MW facility in Huntorf, Germany built in 1978; and a 110 MW facility in McIntosh, Alabama built in 1991. Both PHS and CAES can have very large system sizes with high power and energy, making them ideal for utility applications such as load management and operating reserves. The disadvantage is that both PHS and CAES have geographical limitations. PHS requires a reservoir, and underground CAES requires certain geological formations for storing compressed air. If those conditions are available, then PHS and CAES are viable options for bulk grid applications. Sodium sulfur batteries, flywheels, lithium ion batteries, advanced lead acid batteries, vanadium redox flow batteries, and zinc bromide flow batteries have been deployed in commercial applications over the last five years, if not longer.

One of the most significant challenges facing energy storage is the integration of storage equipment with other infrastructure, including distributed generation, grid assets, communications equipment, and data acquisition systems. Furthermore, there are multiple layers of communication that can be difficult to coordinate, especially when some are proprietary.

Cost Effectiveness. Per the guidance provided by Section 2836.2 of AB 2514, staff assessed the cost effectiveness of energy storage for a variety of standalone uses – summarized in Table 5 – and bundled uses.

Table 5. Summary of Applications and Cost Effectiveness

Renewable Energy Shifting – SMUD could use energy storage to store excess renewable energy and discharge during times of high need. However, SMUD currently does not have an issue with excess renewable energy and would get little value from this application. Wholesale Market Arbitrage and Cost Optimization – This application uses energy storage to charge during times of low energy cost and discharge during times of high energy cost. SMUD has analyzed this application in detail, but does not project a large enough, persistent (e.g. occurring over many hours a year) difference between on-peak and off-peak prices to make this cost effective.

Asset Management is the use of energy storage to defer investments in generation, distribution or transmission upgrades. This is applicable to SMUD; however SMUD is currently long on capacity.

In addition, as part of its value analysis, SMUD conducted a comprehensive review of current distribution assets to see if energy storage could defer any investments. SMUD found that its distribution system is robust and could use energy storage for deferral in a very small number of locations and the dollar value of deferral was small relative to the cost of energy storage.

Load Following – SMUD could use energy storage for load following, however SMUD currently uses its hydro resources for load following and they are very cost effective. Operating Reserves – Energy storage could be used to provide operating reserves but SMUD currently has enough reserves for the foreseeable future from its thermal and hydro assets.

Frequency Regulation – Similar to Load Following, SMUD could use energy storage for frequency regulation, but SMUD uses its hydro resources for this and they are cost effective.

Renewable Energy Capacity Firming – SMUD could use energy storage to increase the effective capacity of its renewable resources. However, SMUD currently purchases firming services from the CAISO (using thermal resources) at a competitive price.

Black Start – Energy storage could provide Black Start capabilities for SMUD, but SMUD currently has that capability in existing power plants and does not need more capability.

Renewable Energy Ramping – SMUD does not have wind in its Balancing Authority (BA) that would require ramping support. SMUD does have PV in its BA, but at current penetrations and through post-2020, staff’s current analysis indicates that SMUD can handle PV ramping with current assets.

Renewable Energy Smoothing – For SMUD’s large solar Feed In Tariff projects, energy storage could provide smoothing to mitigate the impacts (e.g. voltage violations, excessive equipment cycling, etc.) of large fluctuations in PV output. SMUD is currently demonstrating the technical viability of this but it has not proven cost effective as a standalone application.

Backup Power – Energy storage owned by SMUD or its customers could provide backup power during outages. However, SMUD has top tier SAIDI, SAIFI and CAIDI scores, so system uptime is very high and the need for backup power is low in SMUD’s service territory. In addition, when outages do occur, staff research indicates that the value of having backup power is low for most customer segments. One exception is the industrial segment, but most industrial customers likely already have backup power systems in place.

Power Quality – Using energy storage to manage power quality on a feeder is applicable, but staff has not found it to be cost effective relative to traditional power quality control equipment (e.g. load tap changers, voltage regulators, etc.). Industrial customers and data centers have high power quality requirements that energy storage could help meet, but they likely already have equipment in place to manage power quality and would not need to add energy storage for this purpose.

Results

Based upon this body of research, staff finds storage at this time is not cost effective with the exception of large pumped hydro storage. Consequently, staff recommends the SMUD Board of Directors should decline to establish an energy storage procurement target for December 31, 2016 and December 31, 2020 at this time.

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Wind and Solar diurnal and seasonal variations require energy storage

Posted on June 4, 2015 by

Preface. Currently energy storage is accomplished with the 67% of fossil fuels (natural gas and coal) used to generate electricity, especially natural gas which can kick in within microseconds to make up for failing wind, or run all the time when there’s no wind or sun.

But the vast majority of states will never have to worry about energy storage because they have minimal wind, solar, or both. Currently just 9 states contribute 88% of solar generation, and 9 states generate 74% of wind power.  The dreams of most states going 100% renewable will always be a dream (Gattie 2019).

Solar power can’t possibly provide power year-round, it varies far too much (Smil, V. 2017. Power Density. MIT):

“Differences in monthly insolation averages depend on latitude and cloudiness: in Oslo the difference between January and June is 16-fold; in Riyadh it is only 2-fold.

As reflected by actual PV electricity generation, in 2012 the German output was 4 TWh in May and just 0.35 TWh in January, 11 times less, an order of magnitude disparity (BSW Solar 2013).

Average capacity factors correlate with total irradiance. in places where it is less than 150 W/m2 they will be below 12%, for the insolation between 150 and 200 W/m2 they will range up to 20%, and in the sunniest locations with irradiance in excess of 200 W/m2 they will be up to 25%. Actual performance data show that even in sunny Spain, most plants have capacity factors of less than 20%, and in cloudy temperate climates that indicator will dip below 10%. In addition, only about 85% of a PV panel’s DC rating will be transmitted to the grid as AC power”

Alice Friedemann   www.energyskeptic.com  author of “When Trucks Stop Running: Energy and the Future of Transportation”, 2015, Springer and “Crunch! Whole Grain Artisan Chips and Crackers”. Podcasts: Practical Prepping, KunstlerCast 253, KunstlerCast278, Peak Prosperity , XX2 report

***

Mulder, F. M. 2014. Implications of diurnal and seasonal variations in renewable energy generation for large scale energy storage. Journal of Renewable and Sustainable Energy. 14 pages.

Excerpts:

Large scale implementation of solar and wind powered renewable electricity generation will use up to continent sized connected electricity grids built to distribute the locally fluctuating power. Systematic power output variation will then become manifest since solar power has an evident diurnal period, but also surface winds—which are driven by surface temperatures—follow a diurnal periodic behavior lagging about 4 hours in time. On an ordinary day a strong diurnal varying renewable electricity generation results when combining wind and solar power on such continent sized grid. Comparison with possible demand patterns indicates that coping with such systematically varying generation will require large scale renewable energy storage and conversion for timescales and storage capacities of at least up to half a day. Seasonal timescales for versatile, high quality, generally applicable, energy conversion and storage are equally essential since the continent wide insolation varies by a factor ~3, e.g., in Europe and Northern Africa together. A first order model for estimating required energy storage and conversion magnitudes is presented, taking into account potential diurnal and seasonal energy demand and generation patterns.

The insolation (solar radiation power per square meter at the earth’s surface) is daily modulated between zero and a maximum that depends on the latitude on earth and the season (Figure 1). For instance in Edmonton in Canada, Delft in the Netherlands, and Astana in Kazachstan (~52 North), there is a factor of 6 between the insolation in mid-summer and mid-winter due to the reduced instantaneous light intensity and time of daylight as shown below:

FIG. 1. Top: daily insolation at noon during the months of the year on the indicated northern latitudes. See also Fig. S1 in the supplementary material4 for the total daily insolation. Bottom: estimated average cubed windspeed v3 in the US for on shore (blue) and off shore (purple) locations (based on data from Ref. 5), and a simple sinusoidal approximation as in Eq. (2) (green).

FIG. 1. Top: daily insolation at noon during the months of the year on the indicated northern latitudes. See also Fig. S1 in the supplementary material4 for the total daily insolation. Bottom: estimated average cubed windspeed v3 in the US for on shore (blue) and off shore (purple) locations (based on data from Ref. 5), and a simple sinusoidal approximation as in Eq. (2) (green).

In Mexico City, the Western Sahara and Nagpur in central India (~19.5 North), the factor between summer and winter reduces but still reaches a sizeable factor of about ~1.5. Thus in principle a factor of 6 to 1.5 difference per solar power collecting footprint between seasons occurs, next to the diurnal day and night fluctuations, and varying cloud covers. These seasonal and diurnal influences multiply with each other to obtain the total solar power. For a multi-grid connected surface area spanning Europe and Northern Africa this will mean on average a sizeable factor ~3–4 between summer and winter insolation, modulating the day and night diurnal variation on a seasonal scale.

Wind resources within a continent sized electricity grid depend on the instantaneous wind speeds averaged over the grid surface area. It is well known that the wind power is about two times stronger in winter than in summer on northern latitudes (Figure 1).

Within this seasonal timescale there is however also a diurnal periodicity of relevance. In meteorological literature a number of data studies are available of the near surface layer average wind speeds over extended surface area’s in Africa and the North Atlantic, and the US in which thousands of local weather stations have been taken into account. The general insight gained is that there is a diurnal variation in wind speeds with significant amplitude, where the peak in wind amplitudes occurs in the afternoon and the minimum 12 h earlier in the early morning.

For instance in Ref. 7, the instantaneous wind speed averaged over a ~800 X 1000 km2 surface area on an ordinary day could be 4–5 m/s while the minimum could be 2 m/s (Figure 2). The wind speed amplitude has such diurnal pattern because it is driven by the surface temperature, i.e., the solar radiation heating the surface and atmosphere above it drives the observed wind speeds.

Since the kinetic energy contained in flowing air scales with the third power of the wind speed a factor 2 in wind speed amplitude means a factor 8 in recoverable energy in wind turbines.

Offshore: The wind patterns and diurnal variation in those is determined by the significant differential heating of the water and land area. During the day the land warms relatively fast due to solar light absorption and the cooler and denser air from the adjacent ocean flows over the land.13 At night the land cooling takes place by the continued emission of infrared radiation and the air flows reverse. Since the infrared is absorbed in the atmosphere more readily than the visible light (which is the basic origin of the Greenhouse effect) the nightly cooling process is on average relatively slower and the near coastal winds during night are therefore also driven less powerful than during daytime.

The sea breeze can extend several 100 km into the sea which means that the (future) wind turbines in those regions are under the influence of such diurnal wind patterns.14 It is also noted in Ref. 13 that in winter time the temperature difference between land and ocean is reduced and the sea breeze largely disappears.

FIG. 2. Daily averaged v3 for a large 800 X 1000 km2 area in the US and the average surface temperature for three consecutive days (constructed from data in Ref. 7).

A second factor that is important to determine a future energy storage scale is the connectivity of the generating facilities on large extended power grids. This determines how much electricity from distant sources, transmitted at the speed of light through the electricity grid, contributes to the instantaneous integrated output of the grid. The plans for long distance power transport include connected grids on the size of, e.g., Europe plus northern Africa; i.e., latitudes from ~24 to 62 and distances ~2000 X 3000 km2. Much larger grids are not considered in view of the large cost and increasing transport losses; the cost will increase faster than linear with distance traveled since also the amount of peak power to be transported will grow with the surface area that is connected.

This distance constraint makes that here a calculation for a large but limited range of latitudes and longitudes is considered. The result can, however, be extrapolated to the worldwide generating capabilities and storage demands because other independent grids will mostly be located on similar latitudes.

RESULTS Renewable power variation on continent sized grids.

The average latitude is then 43, which thus assumes that the solar power installations are considerably more south than currently. In addition equal contributions of a longitude ~5, 5, and 15 were taken corresponding to about 1600 km in the east-west direction. Such range of latitude and longitude corresponds roughly to grids spanning Northern Africa to Europe, and it is also within the latitude range where, e.g., the high density population and energy use is of the USA, North/East India, and China. The majority of installed capacity may be anticipated in such latitude range, e.g., to minimize transport costs and crossing of state borders.

FIG. 3. Estimated output per day of wind and solar power in the months of the years indicated. Due to the geographical locations of the facilities above the equator (see text) a significant variation in output power throughout the year is expected. GEA and IPCC (lower curves) indicate two different levels of renewable energy implementation.

Energy demand patterns. The energy use is modeled according to available current demand patterns throughout the year for electricity and primary energy. The assumption is that much of the current and future demand is, and will be, organized in time for functional reasons that cannot easily be altered to a large extent, but the use of electricity relative to primary energies can be altered.

Coping with the summer daytime peak and lower output during winter and at night will mean partly storing the peak electricity supply from renewables for use at night and in winter. On seasonal timescales, this involves renewable electricity conversion into a suitable form of stored primary energy or fuel.

Energy storage and conversion scales. To cope with the described systematic variability of renewable electricity generation, the current approach is to power up and down fossil fuel powered stations. In this way renewables reduce the operational filling factor of these stations, have an impact on the business model of these facilities, but do not really replace fossil fuel generating capabilities. The additional storage capacity required then “only” covers the time it takes to power up or down the fossil fuel powered stations to maintain the grid stability, if possible. From the modeled daily output by 2050 in Figure 4 it is clear that coping with the renewables peak power by switching off fossil power alone is not enough, since significantly more renewable power is produced than can be switched off. Thus assuming that renewable power generating capabilities essentially should replace fossil fuel based power generating capabilities and electricity will be converted to primary energy forms like fuels this will necessarily represent renewable energy storage and conversion on an unprecedented scale.

First, the daytime renewable electricity generation which is larger than the demand is stored for later use in the night, leading to a short time storage demand. Note that such short term storage also includes load or demand time shifting of, e.g., electrical vehicles. The remaining surplus of renewable energy is assumed to be converted to primary energy (e.g., high energy density fuels, see below) and stored for the longer seasonal timescale.

FIG. 5. Schematic of installed rated power. (a) Fossil generating capabilities and renewable solar and wind power without renewable energy storage option. To guarantee security of supply practically the full conventional fossil capacity will be required. (b) and (c) With long and short term storage of renewable energy part of the fossil capabilities can be replaced progressively by renewable powered facilities, or be fueled with renewable fuel.

More extended grid scale, extending towards the southern hemisphere would address the summer winter variability, while even larger east-west grids also spanning the entire globe would also address the day and night variability. However, the feasibility of such power harvesting and grid is not  clear in view of geographical factors such as available land area, depth of oceans, and geopolitics. Also the losses for each 1000 km may be 3% for high voltage DC lines,19,20 the AC-DC conversion, and back taking an additional 1.5% each. For distances up to 20,000 or 30,000 km the transmission then amounts to 0.9852 X 0.9720 or 30 = 0.53 or 0.39. In addition, such a very long distance grid should transport not on GW scale, as local power grids are currently built for, but rather on the level of power use of a continent, i.e., TW scale, which will also make it highly costly, if feasible. Thus also with such investment in a world grid, losses are non-negligible (and cannot be reused).

For less long distances, e.g., the distance from Norway to the Sahara (~4100 km), smaller losses occur (transmission = 0.86), but as stated above the daily and seasonal storage are not addressed. Counteracting seasonal effects could be possible with a grid extending from Norway to below the equator (e.g., Angola) which is a distance of 9300 km (transmission 0.73), but then the day and night variability is not addressed.

For smaller grid scales in principle, the weather conditions become less averaged and more fluctuating, and also more dependent on the specific location. The “short term” storage facilities then likely needs extension of the capacity towards storage times of days in order to deal with several unfavorable renewables generation days. The seasonal scale will depend on the more local average climate.

Based on the above both short term daily and long term seasonal storage is required on scales that will only be feasible for few storage options.21–23 Important scalable options for short term storage are heat storage24 (high temperature storage for CSP, low temperature heat) and batteries25 (sun-PV, wind). Currently applied pumped hydropower relies on the presence of suitable geographic factors and is thus limited in scale. The use of batteries as electricity store will require low cost and far improved lifetime during prolonged cycling of the batteries.

For seasonal scale energy storage artificial fuels are required. Hydrogen can be produced from renewable electricity and water26,27 using, e.g., alkaline electrolysis with relatively inexpensive Ni based catalysts. Ammonia stands out in energy density for static stores as it is liquid at 10 bars and room temperature (RT) with an energy density of 22.5 MJ/kg higher heating value (HHV), and it contains only abundant H and N.28,29 More conventional fuels with highest energy density up to 49 MJ/kg (propane) would require carbon, but can in principle be generated from renewable power, water and CO2 using existing technologies.30

However, ultimately in a fossil fuel poor energy economy CO2 has to be captured from air since central point sources would produce only a fraction of the needs.31

BIOFUELS FOR LARGE SCALE STORAGE?

Refs. 26 and 32, the use of biomass for biofuel generation is essentially excluded as viable large scale option. In the IPCC report,2 however, it is indicated with large uncertainty that biomass could contribute between 10% and 100% of future energy use. To gain some insight in this matter we use recent estimation of the energy production from biofuels per year to come to a surface area that would be required for producing chosen amounts of biofuel. As a reference the current energy use is expressed in required production of ml oil/m2 of earth surface and biofuel production in terms of oil equivalent per m2 earth surface (Fig. 7).

With a higher heating value of 34 MJ/l (gasoline) and an earth total surface area of 5.1 X 1014 m2 the current energy use of ~500 EJ/yr equals 28.8 ml oil/m2; an oil film of 28.8 um thick on the entire globe (Fig. 7). One of the often mentioned high yield biofuel sources that would not compete with food production directly is switchgrass. Its net energy yield in the form of bioethanol is reported33 as 6 MJ mÀ2 yrÀ1 which is equivalent to 176 ml oil mÀ2yrÀ1 (heating value of ethanol is 23.43 MJ/l). In order to power the world with switchgrass bioethanol one thus requires at least 28.8/176=16.3% of the entire surface of the globe, or ~half of the land area (assuming appropriate climate conditions).

For poplar trees the result is similar.34

For biodiesel from palmoil an estimated 0.6 l mÀ2 yrÀ1 is reported. In general for biodiesel 2.2 units of oil are the net energy gain for a harvest of 3.2 units,35 i.e., 600 X 2.2/3.2=412 ml mÀ2 yrÀ1 is the gain. So for palmoil 28.8/412=7.0% of the earth surface would be required to produce 500 EJ/yr. These numbers are relative to the entire surface of the globe, including oceans, poles, deserts, permafrost and mountains, regions with wildly different and incompatible climate conditions. The current agricultural area is quoted as 49X106 km2=4.9X1012 m2 in 2010 by the Food and Agriculture Organization of the United Nations, which is almost 1% of the earth’s surface area. For switchgrass and poplar, and palmoil thus 16, respectively, 7 times more than the current agricultural area would be required to produce sufficient biofuel to reach the higher limit of 500 EJ/yr. In such perspective 10% of that appears as an enormous amount of additional area which needs to be made accessible for agricultural activities in a sustainable manner.

In addition the biomass will need to act as a valuable carbon source for materials fabrication and as such may become too precious as fuel.

Biofuels could also be considered to cover “only” the seasonal storage needs as described above, next to solar and wind power. In that case the 27 EJ by 2050 could be realized with a lower demand on space, which however still equals for palmoil ~7% X 27/500=0.38% of the surface of the earth. This corresponds to 42% of the current agricultural area (which will generally not be suitable for growing palmoil).

FIG. 7. Illustration comparing the current yearly energy demand with the amounts of experimentally verified yearly optimal biofuel yields. The unit is expressed in ml oil equivalent per square meter of total earth surface for the demand and in units of ml of oil equivalent per used square meter for the yields.

REFERENCES

Gattie, D. 2019. 100 percent renewable energy isn’t a response to climate change — it’s a retreat. The Hill.

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