Charles A. S. Hall Conventional oil peak was 2005

The global production of conventional oil began to decline in 2005, and has followed a path over the last 11 years very close to our scenarios assuming low estimates of extractable ultimate resource (1.9 Gbbl)

John L. Hallock Jr., Wei Wub, Charles A.S. Hall, et al. Forecasting the limits to the availability and diversity of global conventional oil supply: Validation? Energy. Volume 64, 1 January 2014, Pages 130–153

Abstract:  Oil and related products continue to be prime enablers of the maintenance and growth of nearly all of the world’s economies. The dramatic increase in the price of oil through mid-2008, along with the coincident (and possibly resultant) global recession, highlight our continued vulnerability to future limitations in the supply of cheap oil. The very large differences between the various estimates of the original volume of extractable conventional oil present on earth (EUR) have, at best, fostered uncertainty of the risk of future supply limitations among planners and policy makers, and at worse lulled the world into a false sense of security. In 2002 we modeled future oil production in 46 nation-units and the world by using a three-phase, Hubbert-based approach that produced trajectories dependent on settings for EUR (extractable ultimate resource), demand growth, percent of oil resource extracted at decline, and maximum allowable rates of production growth. We analyzed the sensitivity of the date of onset of decline for oil production to changes in each of these input parameters. In this current effort, we compare the last eleven years of empirical oil production data to our earlier forecast scenarios to evaluate which settings of EUR and other input parameters had created the most accurate projections. When combined with proper input settings, our model consistently generated trajectories for oil production that closely approximated the empirical data at both the national and the global level. In general, the lowest EUR scenarios were the most consistent with the empirical data at the global level and for most countries, while scenarios based on the mid and high EUR estimates overestimated production rates by wide margins globally. The global production of conventional oil began to decline in 2005, and has followed a path over the last 11 years very close to our scenarios assuming low estimates of EUR (1.9 Gbbl). Production in most nations is declining, with historical profiles generally consistent with Hubbert’s premises. While new conventional oil discoveries and production starts are expected in the near term, the magnitudes necessary to increase our simulated production trajectories by even 1.0% per year over the next 10 years would represent a large departure from current trends. Our now well-validated simulations are at significant variance from many recent “predictions” of extensive future availability of conventional oil.

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Charles A. S. Hall “EROI of different fuels and the implications for society”

Charles A.S. Hall, Jessica G. Lambert, Stephen B. Balogh. 2014. EROI of different fuels and the implications for society. Energy Policy 64: 141-152.

Highlights:

  • For nations examined, the EROI for oil and gas has declined during recent decades.
  • Lower EROI for oil may be masked by natural gas extracted/used in oil production.
  • The EROI trend for US coal is ambiguous; the EROI for Chinese coal is declining.
  • Renewable energies lack desirable fossil fuel traits, including often higher EROI, but create fewer pollutants.
  • Declines in EROI of main fuels have a large impact on economies.

Abstract: All forms of economic production and exchange involve the use of energy directly and in the transformation of materials. Until recently, cheap and seemingly limitless fossil energy has allowed most of society to ignore the importance of contributions to the economic process from the biophysical world as well as the potential limits to growth. This paper centers on assessing the energy costs of modern day society and its relation to GDP. Our most important focus is the characteristics of our major energy sources including each fuel’s energy return on investment (EROI). The EROI of our most important fuels is declining and most renewable and non-conventional energy alternatives have substantially lower EROI values than traditional conventional fossil fuels. At the societal level, declining EROI means that an increasing proportion of energy output and economic activity must be diverted to attaining the energy needed to run an economy, leaving less discretionary funds available for “non-essential” purchases which often drive growth. The declining EROI of traditional fossil fuel energy sources and the effect of that on the world economy are likely to result in a myriad of consequences, most of which will not be perceived as good.

1. Introduction

Energy has played a critical role throughout human society’s demographic, economic and social development. The availability and quality of various energy and material resources to a society is linked to the general trend of the settlement, growth, and eventual decline experienced by each civilization (White, 1959 and Tainter, 1988). A society must have an energy surplus for there to be division of labor, creation of specialists and the growth of cities, and substantially greater surplus for there to be wide-spread wealth, art, culture and other social amenities. Economic fluctuations tend to result, directly or indirectly, from variations in a society’s access to cheap and abundant energy (Tainter, 1988 and Cleveland et al., 1984). Today, fossil fuel resources are among the most important global commodities and are essential for the production and distribution of the rest. Fossil fuels supply greater than 75% of the total energy consumed by societies (EIA data for various years as discussed in Hall et al., 2009). The prosperity and stability of modern society is inextricably linked to the production and consumption of energy, especially oil (Odum, 1973, Hall et al., 1986 and Hall and Klitgaard, 2012; Tverberg, 2012).

Economic production, exchange and growth requires work and consequently a steady and consistent flow of energy to do that work. Longer intervals of sustained economic growth in countries and the world have been punctuated by numerous oscillations; i.e. there are periods of economic expansion but also recession. In general, the growth of real GDP is highly correlated with rates of oil consumption (Murphy et al., 2011). Four out of the five recessions experienced since 1970 can be explained by examining oil price shocks (Hamilton, 2009, Hall and Groat, 2010 and Jones et al., 2004). During periods of recession, oil prices tend to decline, eventually encouraging increased consumption. Alternatively, during periods of expansion, oil prices usually increase and higher energy consumption and economic expansion are eventually constrained by these higher prices (Jones et al., 2004). Economic growth and stability is dependent on not only the total quantity of energy accessible to society but also the cost of this energy to different sectors of that society. Jones et al.’s (2004) article, Oil Price Shocks and the Macroeconomy, demonstrates a clear relation between oil price and GDP. The main conclusions drawn from this and similar assessments are:

(1) Decreases in GDP during the post WWII period are chiefly attributable to oil price shocks, not government policy.
(2) Oil price shocks are the only novel or surprising price movement observed in a 2-year window of time prior to a recession.
(3) Oil price shocks lead to costly reallocations of people and industries as well as fluctuations and pauses in investment. This influences industrial output and subsequently GDP (Jones et al., 2004).

1.1. Economic cost of energy

The ratio of the monetary cost of energy compared to the GDP generated for the same year gives a quantitative index of how much money is invested in energy on average to generate a unit of wealth. This can be calculated by dividing the money required to buy energy by the total gross domestic product. When this ratio is low, typically around five percent, economies grow strongly (Hall and Klitgaard, 2012). When this ratio is high, about 10% (and, historically, up to 14%), recessions tend to occur. A sudden climb (followed by a subsequent decline) in the proportion of the GDP spent for energy occurred during the two 1970s and the mid-2008 “oil price shocks” (Hall and Cleveland, 1981, Hamilton, 2009 and Hall and Klitgaard, 2012). Rapid increases in the economic cost of energy (e.g. from five to ten percent) result in the diversion of funds from what is typically devoted to discretionary spending to energy acquisition (Hall and Klitgaard, 2012). Consequently, large changes in energy prices influence economies strongly.

The energy and economic communities currently host strongly polarized views about whether the quantity of fossil fuel resources ultimately available to society is declining and, if so, the potential repercussions of this for societal well-being and economic growth. Much of the argument used by the energy community revolves around the concepts of “net energy” and “energy return on investment” (EROI). For example, while most energy scientists accept the economists’ argument that there is a lot of oil left in the ground and that higher prices will encourage its extraction and production, they also point out that when more money is required more energy is required too, and that there is a limit to how much we can pay for oil that occurs as one approaches using a barrel of oil to extract another barrel of oil. Such net energy analysis is sometimes called the assessment of energy surplus, energy balance, or, as we prefer, EROI.

1.2. Types of EROI analyses and associated boundaries

Energy return on investment (EROI) is a means of measuring the quality of various fuels by calculating the ratio between the energy delivered by a particular fuel to society and the energy invested in the capture and delivery of this energy. Much of the current EROI analysis literature tends to focus on the net or surplus for a given project, industry, nation, fuel, or resource, for example recent discussions on the “energy break even” point of EROI for corn based ethanol, i.e. whether the EROI is greater than 1:1. The apparently different results from this seemingly straightforward analysis generated some controversy about the utility of EROI. But, the variation in these findings is mostly the result of the choice of direct and indirect costs associated with energy production/extraction included within the EROI calculations: i.e. the boundaries of the denominator (Hall et al., 2011). The possible boundaries of the various net energy assessments evaluated in this study are illustrated in Fig. 1.

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Fig 1. Boundaries of various types of EROI analyses and energy loss associated with the processing of oil as it is transformed from “oil at the well-head” to consumer ready fuels (figure from Lambert and Lambert (in preparation) based on calculations by Hall et al. (2009)).

These and other boundary issues are addressed in Murphy et al.’s recent paper, Order from Chaos: A Preliminary Protocol for Determining the EROI of Fuels ( Murphy et al., 2011). We clarify further the boundaries used in the EROI calculations given here into the following categories derived from Hall et al. (2009):

(1) Standard EROI (EROIST): A standard EROI approach divides the energy output for a project, region or country by the sum of the direct (i.e. on site) and indirect (i.e. offsite energy needed to make the products used on site) energy used to generate that output. It does not include e.g. the energy associated with supporting labor, financial services and the like. This EROI calculation is applied to fuel at the point where it leaves the extraction or production facility (well-head, mine mouth, farm gate, etc.). This approach allows for the comparison of different fuels even when the analysts do not agree on the rest of the methodology that should be used ( Murphy et al., 2011).

(2) Point of Use EROI (EROIPOU): Point of use EROI is a more comprehensive EROI that includes additionally the costs associated with refining and transporting the fuel. As the boundaries of the analysis are expanded, the energy cost of getting it to that point increases, resulting in a reduced EROI ( Hall et al., 2009).

(3) Extended EROI (EROIEXT): This expanded analysis considers the energy required not only to get but also to use a unit of energy. In other words, it is the EROI of the energy at the mine mouth required for that energy to be minimally useful to society, for example to drive a truck ( Hall et al., 2009).

(4) Societal EROI (EROISOC): Societal EROI is the overall EROI that might be derived for all of a nation’s or society’s fuels by summing all gains from fuels and all costs of obtaining them. To our knowledge this calculation has yet to be undertaken because it is difficult, if not impossible, to include all the variables necessary to generate an all-encompassing societal EROI value ( Hall et al., 2009). We develop a preliminary method for deriving EROISOC at the national level in another paper in this series ( Lambert et al., 2013).

We next present the historical and ongoing trends in EROI findings for various energy sources and discuss the potential impact of low EROI fuels on the continuance of a high EROI society.

2. Meta-analysis of EROI values for various fuel sources

Our research and that of Dale (2010) summarizes EROI estimates for the thermal energy delivered from various fossil fuels and also the electric power generated using fossil fuel and various other energy technologies. These initial estimates of general values for contemporary EROI provide us with a beginning on which we and others can build as additional and better data become available. We have fairly good confidence in the numbers represented here, in part because various studies tend to give broadly similar results. Values from different regions and different times for the same fuels, however, can give quite different results. Given this, we present these values with considerable humility because there are no government-sponsored programs or much financial support to derive such numbers.

EROI values for our most important fuels, liquid and gaseous petroleum, tend to be relatively high. World oil and gas has a mean EROI of about 20:1 (n of 36 from 4 publications) (Fig. 2) (see Lambert et al., 2012 and Dale, 2010 for references). The EROI for the production of oil and gas globally by publicly traded companies has declined from 30:1 in 1995 to about 18:1 in 2006 (Gagnon et al., 2009). The EROI for discovering oil and gas in the US has decreased from more than 1000:1 in 1919 to 5:1 in the 2010s, and for production from about 25:1 in the 1970s to approximately 10:1 in 2007 (Guilford et al., 2011). Alternatives to traditional fossil fuels such as tar sands and oil shale (Lambert et al., 2012) deliver a lower EROI, having a mean EROI of 4:1 (n of 4 from 4 publications) and 7:1 (n of 15 from 15 publication) (Fig. 2). It is difficult to establish EROI values for natural gas alone as data on natural gas are usually aggregated in oil and gas statistics (Gupta and Hall, 2011 and Murphy and Hall, 2010).

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Fig 2. Mean EROI (and standard error bars) values for thermal fuels based on known published values. Values are derived using known modern and historical published EROI and energy analysis assessments and values published by Dale (2010). See Lambert et al. (2012) for a detailed list of references. Note: please see text for discussion as all these values should not necessarily be taken at face value.

The other important fossil fuel, coal, has a relatively high EROI value in some countries (U.S. and presumably Australia) and shows no clear trend over time. Coal internationally has a mean EROI of about 46:1 (n of 72 from 17 publications) (see Lambert et al., 2012 for references) (Fig. 2). Cleveland et al. (2000) examined the EROI values for coal production in the United States. They found a general decline from an approximately 80:1 EROI value during the mid 1950s to 30:1 by the middle of the 1980s. Coal, however, regained its former high EROI value of roughly 80:1 by 1990. This pattern may reflect an increase in less costly surface mining. The energy content of coal has been decreasing even though the total tonnage has continued to increase (Hall and Klitgaard, 2012). This is true for the US where the energy content (quality) of coal has decreased while the quantity of coal mined has continued to increase. The maximum energy from US coal seems to have occurred in 1998 (Hall et al., 2009 and Murphy and Hall, 2010).

Meta-analysis of EROI values for nuclear energy suggests a mean EROI of about 14:1 (n of 33 from 15 publications) (see Lambert et al., 2013 for references) (Fig. 3). Newer analyses need to be made as these values may not adequately reflect current technology or ore grades. Whether to correct the output for its relatively high quality is an unresolved issue and a quality correction for electricity appears to contribute to the relatively high value given here.

Mean EROI of published papers

Fig 3. Mean EROI (and standard error) values for known published assessments of power generation systems. Values derived using known modern and historical published EROI and energy analysis assessments and values published by Dale (2010). See Lambert et al. (2012) for detailed list of references. Note: please see text for discussion as all these values should not necessarily be taken at face value.

Hydroelectric power generation systems have the highest mean EROI value, 84:1 (n of 17 from 12 publications), of electric power generation systems (see Lambert et al., 2012 for references) (Fig. 3). The EROI of hydropower is extremely variable although the best sites in the developed world were developed long ago (Hall et al., 1986).

We calculate the mean EROI value for ethanol from various biomass sources using data from 31 separate publications covering a full range of plant-based ethanol production (e.g. EROI of 0.64:1 Pimentel and Patzek, 2005 for ethanol produced from cellulose from wood to EROI of 48:1 for ethanol from molasses in India (Von Blottnitz and Curran, 2007)). These values result in a mean EROI value of roughly 5:1 with an n of 74 from 31 publications (Fig. 2). It must be noted, however, that many of the EROI figures (33 of the 74 values) are below a 5:1 ratio (see Lambert et al., 2012 for references) and diesel from biomass is also quite low (2:1 with an n of 28 from 16 publications) (see Lambert et al., 2012 for references). The average is skewed in a positive direction by a handful of outliers (four EROI figures are above 30:1) (Von Blottnitz and Curran, 2007 and Yuan et al., 2008 in Dale, 2010). We believe that outside certain conditions in the tropics most ethanol EROI values are at or below the 3:1 minimum extended EROI value required for a fuel to be minimally useful to society.

Wind power has a high EROI value, with the mean perhaps as high as 18:1 (as derived in an existing meta-analysis by Kubiszewski et al., 2010) or even 20:1 (n of 26 from 18 publications) (see Lambert et al., 2012 for references) (Fig. 3). The value in practice may be less due to the need for backup facilities.

An examination of the EROI literature on solar photovoltaic or PV energy generation shows differences in the assumptions and methodologies employed and the EROI values calculated. The values, assumptions, and parameters included are often ambiguous and differ from study to study, making comparisons between PV and other energy EROI values difficult and fraught with potential pitfalls. Nevertheless, we calculated the mean EROI value using data from 45 separate publications spanning several decades. These values resulted in a mean EROI value of roughly 10:1 (n of 79 from 45 publications) (see Lambert et al., 2012 for references) (Fig. 3). It should be noted that several recent studies that have broader boundaries give EROI values of 2 to 3:1 (Prieto and Hall, 2012, Palmer, 2013 and Weissbach et al., 2013), although these are not weighted for the higher quality of the electricity when compared with thermal energy input. Geothermal electricity production has a mean EROI of approximately 9:1 (n of 30 from 11 publications) (see Lambert et al., 2012 for references) (Fig. 3).

A positive aspect of most renewable energies is that the output of these fuels is high quality electricity. A potential draw back is that the output is far less reliable and predictable. EROI values for PV and other renewable alternatives are generally computed without converting the electricity generated into its “primary energy-equivalent” (Kubiszewski et al., 2009) but also without including any of the considerable cost associated with the required energy back-ups or storage. EROI calculations of renewable energy technology appear to reflect some disagreement on the role of technological improvement. Raugei et al. (2012) attribute some low published EROI values for PVs to the use of outdated data and direct energy output data that represents obsolete technology that is not indicative of more recent changes and improvements in PV technology (Raugei et al., 2012). EROI values that do reflect technological improvements are calculated by combining “top-of-the-line” technological specifications from contemporary commercially available modules with the energy output values obtained from experimental field data. Other researchers contend that values derived using this methodology do not represent adequately the “actual” energy cost to society and the myriad energy costs associated with this delivery process. For example Prieto and Hall, 2012 calculated EROI values that incorporate most energy costs, with the assumption that where ever money was spent energy too was spent. They use data from existing installations in Spain, and derived EROI values of roughly 2.4:1, considerably lower than many less comprehensive estimates. Similarly low EROI values for roof top PVs with battery back up were found by Palmer (2013), although it should be noted that the outputs of both systems were higher quality electricity. Nearly all renewable energy systems appear to have relatively low EROI values when compared with conventional fossil fuels. A question remains as to the degree to which total energy costs can be reduced in the future, but as it stands most “renewable” energy systems appear to be still heavily supported by fossil fuels. Nevertheless they are considerably more efficient at turning fossil fuels into electricity than are thermal power plants, although it takes many years to get all the energy back.

3. Methodology

We summarize existing studies of EROI while attempting to understand the differences among them. Specifically, published values of EROI for similar fuels sometimes are substantially different leading to large differences within the published data for EROI assessments. To reduce these differences Murphy et al. (2011) derived a “standard protocol” for calculating EROI. While recognizing the uncertainties involved in, and inherent to, all EROI calculations, Murphy et al. (2011) proposed that these differences largely can be reduced when similar boundaries are used for the assessment. The generation of EROI values is best developed using industry- or government-derived data on energy outputs and energy costs in physical units, or by using a “process energy method” based on measured energy costs of components. But, more commonly part or all of the data is only in financial units. Energy cost values can be derived from financial costs that can be translated into energy costs using energy intensities (i.e. energy used per monetary unit for that type of activity). Unfortunately, most companies consider their costs proprietary knowledge.

Different boundaries and variables differ between nations and may result in conflicting or inconsistent data (Lambert et al., 2013). Only a few countries, including the US, Canada, the UK, Norway, and China keep the necessary industry-specific estimates of energy costs required to perform an EROI analysis. Fortunately, this data, taken as a whole and within a given country, seems to be relatively consistent with information available from various non-governmental sources e.g. Gagnon et al. (2009), or the differences make logical sense. A short description of our methodology for each respective fuel follows.

3.1. Oil and gas

Oil and gas EROI values are typically aggregated together. The reason is that since both often are extracted from the same wells, their production costs (capital and operations) are typically combined, and therefore the energy inputs for EROI calculations are very difficult to separate. Obtaining reliable data on global petroleum production and its associated investment costs can be very difficult since most production is from national oil companies, whose records tend not to be public. Gagnon et al. (2009) estimated global oil and gas EROI from 1992 to 2006 using data from most publicly-traded oil companies summarized by John Herold Company. Cleveland et al., 1984; Hall et al., 1986 and Guilford et al., 2011 used time series data for oil and gas production in the US from several sources (mostly the U.S. Energy Information Agency and Census of Mineral Industries) going back to 1919. Relatively good time series data is available for Norway (Grandell et al., 2011), Mexico (Ramirez, in preparation), Canada (Freise, 2011 and Poisson and Hall, 2013) and China (Hu et al., 2011 and Hu et al., 2013).

3.2. Coal

We evaluate the EROIST for US and Chinese coal, two of the world’s largest producers. For both nations direct energy gains and costs were derived in physical and energy units available from government sources (Balogh et al., 2012 and Hu et al., 2013). In the U.S. analysis, direct energy consumption and indirect costs were derived from physical and financial data published in the U.S. Census of Mineral Industries and the U.S. Economic Census reports (various years, 1919 through 2007). Direct energy consumption was converted from physical units to joules, and the energy equivalent of the indirect monetary costs was derived using the average energy intensity for the entire economy for a given year multiplied by the nominal dollar cost. A sensitivity analysis (for China) was derived using the energy intensity for all “engineering” sectors (see Hu et al., 2013 for details).

4. Results

EROI for various fuels varies from 1:1 to 100:1 with, in general, the highest values being for coal in the US and oil and gas from 1970 to 1990. There is a tendency for EROI for oil and gas production to increase during earlier years of development and then decline over time and with rate of exploitation. We organized existing published and unpublished EROI values by fuel type, year and individual study. This information, presented in Table 1, summarizes our existing knowledge of EROIs for various energy sources by EROI value, geographic region, and time.

Table 1. Published EROI values for various fuel sources and regions (adapted from Murphy et al. (2011). (1) EROI values in excess of 5:1 are rounded to the nearest whole number. (2) EROI values are assumed to vary based on geography and climate and are not attributed to a specific region/country

4.1. Global oil and gas (Petroleum)

The EROI for petroleum  production appears to be declining over time for every place we have data. Gagnon et al. (2009) were able to generate an approximate “global” EROI for private oil and gas companies using the “upstream” financial database maintained and provided by John H. Herold Company. These results indicate that the EROI for publicly-traded global oil and gas was approximately 23:1 in 1992, 33:1 in 1999 and 18:1 in 2005 (Fig. 4). This “dome shaped” pattern seems to occur wherever there is a long enough data set, perhaps as a result of initial technical improvements being trumped in time by depletion.

fig 4 global oil and gas EROI values and trends 1990-2010

Fig. 4. Gagnon et al. (2009) estimated the EROI for global publicly traded oil and gas. Their analysis found that EROI had declined by nearly 50% in the last decade and a half. New technology and production methods (deep water and horizontal drilling) are maintaining production but appear insufficient to counter the decline in EROI of conventional oil and gas.

4.1.1. United States oil and gas

Three independent estimates of EROI time series for oil and gas production for the United States are given in Fig. 5 along with some important oil-related historical events (Cleveland et al., 1984, Hall et al., 1986 and Guilford et al., 2011).

fig 5 published EROI values for oil and production in the US

 

 

 

 

 

 

 

Fig 5. Time series analyses of oil and gas production within the US including several relevant “oil related” historical events. Each analysis demonstrates a pattern of general increase then decline in EROI with an additional impact of increased exploration/drilling.

The data show a general pattern of increase and then decline in EROI over time except as impacted by changes in exploration (drilling) intensity (in, for example, the late 1970s and early 1980s). During the mid 1970s–1980s and late 2000s, the price of oil increased as did exploration intensity, as measured by increased feet drilled and energy used. EROI values tend to decline when there is an increase in the energy required for exploration and drilling. But, usually increased drilling was linked to little or no additional oil discoveries; hence EROI values declined (Fig. 6). The greatly increased amount of money being spent for oil and gas development in the US in recent years suggests that despite the recent increases in production the EROI may continue to decline.

fig 6 US Oil and Gas EROI Values and trends 1990-2010

 

 

 

 

 

 

Fig 6. US oil and gas values published by Guilford et al. (2011) from 1992 to 2007.

4.1.2. Canadian oil and gas

Two independent EROI estimates for Canadian production of oil and gas (blue line, from Freise, 2011) (Fig. 7) and oil, gas and tar sands combined (red line, from Poisson, in press) demonstrate that the EROI of conventional oil and gas in Canada has declined considerably in recent decades. Freise (2011) estimates the EROI of western Canadian conventional oil and gas over time from 1947 to 2010. Freise agrees with Poisson that the later values are probably more accurate.

fig 7 EROI values for oil and gas production in Canada

Fig 7. Two independent estimates of EROI for Canadian petroleum production: oil and gas (blue line, from Freise, 2011) and oil, gas and tar sands combined (red line, from Poisson and Hall, in press). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Poisson and Hall (in press) found that the EROI of conventional oil and gas has decreased since the mid-1990s from roughly 20:1 to 12:1, a 40% decline. The EROI of conventional combined oil–gas–tar sands has also decreased during this same period from 14:1 to 7.5:1, a decline of 46% (Fig. 8) (Poisson and Hall (in press)). Poisson and Hall’s estimated EROI values for Canadian oil and gas are about half those calculated by Freise and their rate of decline is somewhat less rapid (Freise, 2011 and Poisson and Hall, 2013) (Fig. 8).

fig 8 canada oil and gas EROI values and trends 1990-2010

 

 

 

 

 

 

Fig 8. Canada oil and gas and oil, gas and tar sand values by Freise (2011) and Poisson and Hall (in press).

Poisson and Hall’s estimate of the EROI of tar sands is relatively low, around 4.5 (a conservative (i.e. high) estimate, using only the front end of the life-cycle); incorporating tar sands into total oil and gas estimates decreases the EROI of the oil and gas extraction industry as a whole (Poisson and Hall, in press). These estimates would be lower if more elements of the full life-cycle (e.g. environmental impact) were included in the calculation.

4.1.3. Norwegian oil and gas

Norwegian conventional oil and gas fields are relatively new and remain profitable both financially and with regard to energy production. Grandell et al. (2011) estimate that the EROI of oil and gas ranged from 44:1 (during the early 1990s) to 59:1 (1996), to approximately 40:1 (during the latter half of the last decade) (Fig. 9) (Grandell et al., 2011).

fig 9 new assessments of EROI for oil and gas from various countries

 

 

 

 

 

 

 

Fig 9. Time series data on EROI for oil and gas for Norway, Mexico and the Daqing oil field in China based on several papers published in the 2011 special issue of the journal Sustainability and works in progress.

4.1.4. Mexican oil and gas

Ramirez’s trends for the EROI of Mexican oil and gas suggest that this country may have peaked twice in the past decade. The EROI for conventional oil and gas production in Mexico declined from roughly 60:1 in 2000 to 47:1 the following year, but returned to 59:1 by about 2003 (Fig. 9) (Ramirez, in preparation). This was followed by a steady decline over the following six years reaching 45:1 by 2009. The collapse of production from the Cantarell field in the Gulf of Mexico, once the world’s second largest, appears largely responsible for this decline.

4.1.5. Chinese oil and gas

The EROI for the Daqing field, China’s largest conventional oil field, has declined continuously from 10:1 in 2001 to 6:1 in 2009 (Fig. 9) (Hu et al., 2011).

4.2. Dry natural gas

The data represented in Fig. 10 includes analyses for a portion of the US and for all of Canada. Since most published numbers combine data on natural gas with that of oil, it is usually difficult or impossible to assess the production costs of these fossil fuel resources independently. Sell et al.’s (2011) trends for EROI of natural gas in Pennsylvania (US) has an undulating decline; from roughly 120:1 in 1986 to 67:1 in 2003. This value is probably a high value as some important indirect costs were not included. Freise (2011) estimated the EROI of western Canadian natural gas from 1993 to 2009 and found that the EROI of natural gas has been decreasing since 1993 through 2006, from roughly 38:1 to 14:1. This trend shifted in 2006 as exploitation intensity declined resulting in a steady increase and an EROI of roughly 20:1 by 2009 (Fig. 10) (Freise, 2011). Aucott and Melillo (2013) found values similar to Sell et al. for “fracked” shale gas from Pennsylvania from, presumably, “sweet spots”.

fig 10 published EROI values for natural gas

Fig 10. Two published studies on the EROI of dry (not associated with oil) natural gas: Sell et al. (2011) examined tight natural gas deposits in western Pennsylvania in the US, and Freise (2011) analyzed all convention natural gas wells in western Canada.

4.3. Coal

The only EROI analyses for coal production are from the US and China because information on the energy expended to extract coal in other areas of the world appears unavailable. Time series of EROI for coal production for the United States and China are given in Fig. 11. A great deal of variability in EROI is evident in these figures. This data, however, have significant holes (e.g. no data is reported for approximately 30 years, from the mid 1950s to the mid 1980s). Cleveland’s work provides additional information for three noncontiguous years that is only partly consistent with Balogh et al.’s findings. Hu et al. (2013) establishes annual data for Chinese coal production for the years 1994 through 2009. These show very little variation in EROI values.

fig 11 EROI values for coal

Fig 11. EROI for US and Chinese coal production derived from Cleveland (1992), Balogh et al. (unpublished data) and Hu et al. (2013).

5. Research challenges

There are four major challenges for calculating the EROI of various fuels at the national, regional and global scale.

First is the lack of data on fuel used during the extraction process. Data on on-site non-traded fuels generally is not readily available, although ideally they are reported to government agencies undertaking census work. Often indirect energy cost calculations must be derived from financial data. This requires converting currency into an energy equivalent (e.g. mega joules used per dollar of GDP). Methods for accomplishing this conversion usually assume that expenditure for inputs to the energy industry are the same as for society more generally or for an engineering component. They may be less accurate but sensitivity analyses can be undertaken to address uncertainties. Ideally Input–output analysis is undertaken which can give much more accurate results. This analysis used to be done by a team at the University of Illinois but these results are seriously outdated. A more recent analysis may have been done at Carnegie-Mellon’s Green Building Program, from which useful general values can be obtained (See Prieto and Hall, 2012, chapter 4).

Second is the issue of variation in scale. Are studies at the regional level comparable to those at the national level and how do these “size up” when presented next to “international” studies that include a small subset of representative countries? Various variables and boundaries often vary with the scale of investigation making it difficult to compare data among diverse analyses.

Third, energy analysts are not in agreement on what indirect costs should and should not be included in an EROI assessment. When complete systems are analyzed for solar PV installations, their financing, their operations and maintenance costs and their backups are included the energy costs are about three times larger than for just the modules and inverters. One very contentious indirect cost is the inclusion or exclusion of the energy cost of supporting human labor (Murphy et al., 2011). This can result in varying and potentially controversial assessments especially when assessing fuels where small differences may determine whether that fuel is perceived as a viable energy option (e.g. corn-based ethanol).

Fourth, is that the quality or utility of these various fuels is represented differentially within different data sets. Total primary energy consumption values at the global level published by the U.S. Energy Information Agency, International Energy Agency (IEA), and BP (Hayward, 2010); Smil, 2008, tend to be similar. They occasionally vary, however, in their method of addressing “primary energy” conversion. For example, EIA data includes the heat generated by nuclear power in its energy output assessments. Various researchers, government agencies and industry organizations present data from a variety of sources using various assessments, e.g. national (EIA), global (IEA) and industrial (BP). Laherrère addressed this issue at the 2011 ASPO conference (Laherrère, 2011). He also noted that IEA data is presented as the direct electricity generated for nuclear and hydropower, while EIA data includes waste heat produced by nuclear fission.

There is a broadly consistent pattern to our results, as indicated by the similar temporal patterns of different studies, all of which (except coal) have declined over time (and with increased effort) and by the fact that regions developed for oil and gas for a longer period (e.g. US, China or anywhere over time) have lower EROIs, while newer developments (e.g. Norway) tend to have higher values. If and as the Murphy et al. (2011) protocol is more universally followed we expect even greater consistency in results.

6. Discussion

Our research summarizes EROI estimates for all industrial fuels, and for the three major fossil fuels, coal, oil and natural gas over time. These initial estimates of general trends in EROI provide us with a beginning on which we and others can build as additional and better data become available.

6.1. Historical perspective (1900–1939)

The industrial revolution was in full swing by the early 1900s (Fig. 2). Abundant high quality coal from relatively thick seams (with high EROI), capable of generating an enormous amount of energy, was harnessed by humans to do all kinds of economic work including: heating, manufacturing, the generation of electricity and transportation. Biomass energy, in the form of wood burning for domestic use (heating and cooking), remained an important contributor to the world’s energy portfolio (Perlin, 1989). During this period the oil industry was in its infancy and was primarily used for transportation and lighting (in the form of kerosene in non-urban/non-industrial regions). High quality oil remained a small contributor to the energy mix until the end of the 1930s although it was increasing rapidly on a global scale (Hall and Klitgaard, 2012).

6.2. Historical perspective (1940–1979)

The massive WWII war effort during the 1940s saw increased use of coal and oil for the manufacture and use of war machinery. During the post-war era, the great oil discoveries of the early twentieth century found a use in global reconstruction and industrialization. Throughout the 1950s and 1960s the repair of war-torn Europe and the proliferation of western culture resulted in massive increases in the manufacturing and transport of goods and the oil necessary for their production and use. By the late 1960s the EROI of coal (mostly from deep mines) began to decline while the EROI of oil remained high. The EROI for coal production in the US declined from 80:1 in the 1950s to 30:1 in the 1970s (Cleveland et al., 1984). During this time period, coal was mined almost exclusively in the Appalachian mountain region areas of the US using a combination of room and pillar mines with conventional and continuous mining methods. The coal initially extracted from these locations was a combination of anthracite and high quality bituminous coal, coal with high BTUs/ton. As the best coal was used first, the EROI for coal decreased over time.

The quality of coal being produced was decreasing while world oil production was increasing. The peak of US oil production in 1970 and subsequent peak of US conventional natural gas in 1973 meant an increased reliance on OPEC oil. Increases in oil prices reflected in part increased energy required to purchase this fuel. The price of other economic activities increased at similar rates (Hall and Klitgaard, 2012). After the oil shocks of the 1970s, oil prices surged in the US and around the world, stimulating both increased drilling activity and greater interest in the exploitation of more marginal resources (those with higher production costs) (Guilford et al., 2011). Increased drilling activity in the U.S. did not result in increased production but caused a sharp decline in the EROI for conventional oil and gas for the US between the early 1970s and mid 1980s. The oil shocks of the 1970s temporarily halted a long period of increased oil use. It also generated a global oil market and price, destroying an advantage once held by the US. Recovery of production did not occur in the continental US where oil production had declined since its peak in 1970; until the rather recent uptick in 2008 following the introduction of the “new” technologies of horizontal drilling and hydraulic fracturing. New work on the EROI for oil and gas produced by horizontal drilling and rock fracturing indicate that the EROI can be very high, in part because it is not necessary to pressurize the fields (e.g. Aucott and Mellilo 2013; Moeller and Murphy personal communication; Waggoner personal communication) but that these high values are likely to decline substantially as production is moved off the “sweet spots”.

6.3. Historical perspective (1980–2009)

In the 1980s post “energy price shock” era, oil that had been found but not developed suddenly became worthy of developing. Many world oil resources, incentivized by higher prices, were developed; some were over developed. Important gains in oil and gas production occurred in some non-OPEC countries including Norway, Mexico, and China. Heating and transportation, historically fueled by coal, was transformed to oil and gas. Energy from coal production shifted to, and remained essential to, manufacturing and increasingly the production of electricity. The EROI of US coal returned to 80:1 by about 1990. This pattern reflects a shift in the quality of coal extracted, the technology employed in the extraction process and especially the shift from underground to surface mining. A shift in mining location, from Appalachia to the central and northern interior states of Montana and Wyoming and extraction method, from underground to surface mining (area, contour, auger, and mountain top mining techniques) have resulted in less energy required to mine and beneficiate coal. The energy content of the coal extracted, however, has decreased. The coal currently mined is lower-quality bituminous and sub-bituminous coal with much lower BTUs/ton (Hall et al., 1986 and Hall and Klitgaard, 2012) The increased efficiency of surface mining seems to just about compensate for the decline in the quality of the coal mined. See Section 7 for a consideration of environmental externalities.

Between 1985 and the early 1990s international oil and gas prices fell, then remained stable until 2000 while drilling effort declined until the mid 2000s. Thus the 1990s was a period of abundant oil and plummeting oil prices bringing the real cost of oil back to that of the early 1970s (Hall and Klitgaard, 2012). Discretionary spending in the US and other western nations, often on housing, increased. The late 1990s was a time of reduced oil exploration efforts apparently resulting in an increase in EROI. The mid 2000s marked an increase in global oil and gas exploration efforts (Smil, 2008). Discretionary spending decreased with the energy price increases from 2007 to the summer of 2008. Oil prices hit an all time high of $147 per barrel in the summer of 2008 (Read, 2008). This extra 5–10% “tax” from increased energy prices was added to the US (and other) economy as it had been in the 1970s, and much discretionary spending disappeared (Hall et al., 2008). Speculation in real estate (in the US) was no longer desirable or possible as consumers tightened their belts because of higher energy costs (Hall and Klitgaard, 2012). The stock market crashed in September 2008 reducing market value by $1.2 trillion and forcing the Dow to suffer its “biggest single-day point loss ever” (Twin, 2008), and most Western economies have essentially stopped growing since. In general there has been a decade-by-decade decline in growth of the US economy since 1935, in step with the decline in the annual rate of change for all oil production liquids globally (Hall et al., 2012). Even though the global EROI for producing oil and gas continues to be reasonably high, it is probable that the EROI of oil and gas will continue to decline over the coming decades (Gagnon et al., 2009). The continued pattern of declining EROI diminishes the importance of arguments and reports that the world has substantially more oil remaining to be explored, drilled and pumped. “High” EROI values for oil and gas production are increasingly attributable to the inclusion of high EROI natural gas (e.g. the EROI of Norwegian oil is about half that for oil and gas combined) (Grandell et al., 2011). The recent declining trend is described by Grandell et al. as probably due to “aging of the fields.” It is likely that varying drilling intensity has had minimal impact on the net energy gain of these fields. Grandell et al. (2011) expects the EROI of Norwegian oil and gas production “to deteriorate further as the fields become older”. Meanwhile, China’s use of oil has expanded enormously so that China has been importing a larger and larger proportion of its oil from the rest of the world. Recently, China has increased its oil exploitation efforts tremendously, both inside and outside of China. Even so, Hu et al. (2011) suggest that China appears to be approaching its own peak in oil production. Since 2008 producers have shifted increasingly to non-conventional oil and gas resources (tar sands, shale oil and gas) which have increased production but also costs. New technologies such as horizontal drilling and hydro-fracturing are currently keeping the total levels of non-conventional and conventional natural gas production in the US at rates similar to those of 1973 from conventional natural gas alone. Given the numerous shifting environmental variables and social issues surrounding horizontal drilling and “fracking”, it is difficult to predict the future of non-conventional oil and gas (Hall and Klitgaard, 2012).

Already some areas of production from the Barnett and Haynesville formations appear to have reached a production plateau (Hughes, 2013). Recent analyses by Hughes (2013) argue against assuming production will continue to increase. Much of the discussion about “peak coal” (e.g. Patzek and Croft, 2010) involves changing mining technology and capacity, rather than the quantity and quality of coal that remains available for extraction. Peak coal will likely have the greatest impact on the world’s largest coal user, China. Nations with abundant untapped coal resources (i.e. the US, Australia and Russia) are likely to be less affected. The total recoverable coal estimated for the US alone is approximately 500 billion tons. US coal production in 2009 was about one billion tons. Although it is difficult to predict future production technology, environmental issues, consumption patterns and changes in EROI, it appears that coal may be abundantly available through the next century.

6.4. Renewable energy sources

Alternative renewable energies lack many of the undesirable characteristics of fossil fuels, including direct productions of carbon dioxide and other “pollutants”, but also lack many of the highly desirable traits of non-renewable fossil fuels. Specifically, renewable energy sources:

• are not sufficiently “energy dense”,
• tend to be intermittent,
• lack transportability,
• most have relatively low EROI values (especially when corrections are made for intermittency), and
• currently, lack the infrastructure that is required to meet current societal demands.

If we were to replace traditional nonrenewable energy with renewables, which seems desirable to us in the long run, it would require the use of energy-intensive technology for their construction and maintenance. Thus it would appear that a shift from non-renewable to renewable energy sources would result in declines in both the quantity and EROI values of the principle energies used for economic activity.

Although wind, apparently relatively favorable from an EROI perspective and photovoltaic (PV) energy, are currently the world’s fastest growing renewable energy sources, they continue to account for less than one percent of the global energy portfolio (REN21, 2012). Nevertheless there are many informal reports of PV reaching “price parity” with fossil fuels and to many the future of PV is very bright.

Proponents of EROI assessments using actual operational installations (rather than laboratory estimates) believe that, in order to portray renewable energy technology accurately, it is necessary to make note of the fact that these technologies are dependent upon (i.e. constructed and maintained using and therefore subsidized by) high EROI fossil fuels.

Higher EROI values found in conceptual studies often result from assumptions of more favorable conditions (within simulations) than those actually experienced in real life.

For example, English wind turbines were found to operate considerably fewer hours per month than anticipated (Jefferson, 2012).  Kubiszewski et al. (2010) infer that variations in EROI values, in the case of reported EROI values for wind energy, (between process and input output analyses) stems from a greater degree of subjective system boundary decision-making by the process analyst, resulting in the exclusion of certain indirect costs. Other researchers believe that the focus of EROI assessments must be on net energy produced from existing installations and variables associated with wind and PV modules once they have entered the infrastructure rather than extrapolating into the future. Examination of concrete input and output data from operational facilities, e.g. wind turbines (Kubiszewski et al., 2010), appears to offer the best opportunity to calculate wind and PV EROI values accurately.

Also of concern is that wind and PV technology are not “base load technologies”, meaning that future large scale deployment, beyond 20 percent of the grid capacity, will likely require the construction of large, energy intensive storage infrastructures which, if included within EROI assessments, would likely reduce EROI values considerably. In the case of wind, the cost for inclusion within a wind EROI analysis requires not only the initial capital costs per unit output but also the backup systems required for the 70 or so percent of the time when insufficient wind is blowing. Thus, the input for an EROI analysis of wind and PV technology is by and large “upfront” capital costs. This is in sharp contrast to the less well known “return” over the lifespan of the system. Therefore, a variable referred to as “energy payback time” is often employed when calculating the EROI values of wind and other renewable energy sources. This is the time required for the renewable energy system to generate the same amount of energy that went into the creation, maintenance, and disposal of the system. The boundaries utilized to define the energy payback time are incorporated into most renewable EROI calculations. Other factors influencing wind and PV EROI values include energy storage, grid connection dynamics and variations in construction and maintenance costs associated with the installation location. For example, off-shore turbines, while located in wet salty areas with more reliable energy-generating winds, require replacement more often. Turbines located in remote mountainous areas require long distance grid connections that result in energy loss and reduced usable energy values (Kubiszewski et al., 2010).

7. Policy implications

In conclusion, the EROI for the world’s most important fuels, oil and gas, has declined over the past one to two decades for all nations examined. It remains possible that the relatively high EROI values for the natural gas extracted during, and often used for, the production of oil may mask a much steeper decline in the EROI of oil alone. Declining EROI is probably already having a large impact on the world economy (Murphy and Hall, 2010 and Tverberg, 2012). As oil and gas provide roughly 60–65% of the world’s energy, this will likely have enormous economic consequences for many national economies.

Coal, although abundant, is very unevenly distributed, has large environmental impacts and has an EROI that depends greatly on the region mined. A general decline in the energy content of US coal resource over time may be compensated by a shift from energy-intensive underground mining of relatively high quality (but declining) Eastern US coal resources to lower-cost surface mining of lower energy-content Western US coal resulting in no clear trend in EROI for coal.

The decline in EROI among major fossil fuels suggests that in the race between technological advances and depletion, depletion is winning. Past attempts to rectify falling oil production i.e. the rapid increase of drilling after the 1970 peak in oil production and subsequent oil crises in the US only exacerbated the problem by lowering the net energy delivered from US oil production (Hall and Cleveland, 1981). Increasing prices, thought by most economists to negate depletion through increasing incentives for exploitation, cannot work as EROI approaches 1:1, and even now has made oil too expensive to support the high economic growth it once did.

It would be tempting, from a net energy perspective, to recommend that we replace fossil fuels with renewable energy technologies as the EROI for fossil fuel falls to a level where these technologies become competitive. While EROI analyses generate numerical assessments using quantitative data that include many production factors, they do not include other important data such as climate change, air quality, health benefits, and other environmental qualities that are considered “externalities” to these analyses. The energy intensive carbon capture and sequestration (CCS) required to reduce fossil fuel emissions to levels equivalent with that of wind or PV electricity production would reduce the final coal EROI value considerably ((e.g. Akai et al. 1997 in Dale, 2010 and Lund and Biswas, 2008).

EROI figures do not take into account the high life-cycle greenhouse gas emissions from thermal electricity production, and coal-fired systems in particular (Raugei et al., 2012). This could, with difficulty, be worked into future, more comprehensive EROI calculations. Most alternative renewable energy sources appear, at this time, to have considerably lower EROI values than any of the non-renewable fossil fuels. Wind and photovoltaic energy are touted as having substantial environmental benefits. These benefits, however, may have lower returns and larger initial carbon footprints than originally suggested (e.g. the externalities associated with the mining of neodymium and its subsequent use in wind turbine construction). The energy costs pertaining to intermittency and factors such as the oil, natural gas and coal employed in the creation, transport and implementation of wind turbines and PV panels may not be adequately represented in some cost-benefit analyses. On the positive side, the fact that wind and PV produce high quality electricity needs to be considered as well.

Thus society seems to be caught in a dilemma unlike anything experienced in the last few centuries. During that time most problems (such as needs for more agricultural output, worker pay, transport, pensions, schools and social services) were solved by throwing more technology investments and energy at the problem. In many senses this approach worked, for many of these problems were resolved or at least ameliorated, although at each step populations grew so that more potential issues had to be served. In a general sense all of this was possible only because there was an abundance of cheap (i.e. high EROI) high quality energy, mostly oil, gas or electricity. We believe that the future is likely to be very different, for while there remains considerable energy in the ground it is unlikely to be exploitable cheaply, or eventually at all, because of its decreasing EROI. Alternatives such as photovoltaics and wind turbines are unlikely to be nearly as cheap energetically or economically as past oil and gas when backup costs are considered. In addition there are increasing costs everywhere pertaining to potential climate changes and other pollutants.

Any transition to solar energies would require massive investments of fossil fuels.

Despite many claims to the contrary—from oil and gas advocates on the one hand and solar advocates on the other—we see no easy solution to these issues when EROI is considered.

If any resolution to these problems is possible it is probable that it would have to come at least as much from an adjustment of society’s aspirations for increased material affluence and an increase in willingness to share as from technology.

Unfortunately recent political events do not leave us with great optimism that such changes in societal values will be forthcoming.

References

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Posted in Charles A. S. Hall | Leave a comment

Wind turbines hit limits to growth before 50% wind power penetration

Material requirements of 50% wind power in the USA hit limits to growth

Wind turbines can’t be made forever because natural gas, coal, oil, uranium (thorium), neodymium, and other energy resources and minerals needed for wind turbines are finite, and the energy to recycle is limited.

Oil, the master resource, coal, and natural gas are required to make the millions of tons of steel, copper, fiberglass, plastic, epoxy, and concrete as well as deliver and maintain hundreds of thousands of wind turbines providing 50% or more of electricity as fossil fuels decline.

  • 2,029,104,500 MWh = Wind power to equal 50% of annual electricity generation in 2013 (4,058,209,000 MWh / 2)
  • 5,606.4 MWh power per year per 2 MW turbine (2 MW * .32 national average capacity * 24 hours * 365 days) summer
  • 361,926 Number of 2 MW turbines required (2,029,104,500 MWh / 5,606.4 MWh) You’d need 531,318 wind turbines to allow for the lowest capacity of .218 in august 2013 (EIA).
  • Area required 104,586 square miles — the entire state of Colorado (361,926 2 MW turbines * 2 * 92.47 acres per MW) (AWEO)
  • Materials per 2 MW turbine in short tons: 265.5 steel, 1025.5 concrete, 39 iron, 3 copper,  24.3 fiberglass, 10 epoxy, 2.4 plastic (average of Elsam, Guezuraga), and rare earth metals neodymium 800 pounds, dysprosium 130 pounds (ED).
  • Total amount of materials needed for 361,926 wind turbines in short tons: 96,091,353  steel, 371,155,113 concrete containing 74,213,022 cement (20% of concrete), 144,770 tons neodymium, 23,525 dysprosium, 14,115,114 iron, 1,085,778 copper, 8,794,802 fiberglass, 3,619,260 epoxy, 868,622 plastic
  • Annual steel production world-wide 1,833,395 tons in 2014 (worldsteel) = 52 years of world steel production (96,092,353 / 1,833,395)
  • Annual cement production USA 142,464,000 tons (USGS) = 52% of annual cement production (roads, buildings, sewers, and other infrastructure will suffer)
  • Neodymium world production is 7,840 tons/year. Windmill turbines would require 18.5 years of production. Dysprosium production is 112 tons/year requiring 210 years of dysprosium production (ED).
  • Fossil energy required to build windmills: The vehicles that mine iron ore run on diesel. Vehicles and equipment that process iron ore are mostly made of steel. Iron and steel are made by blast furnace or direct reduction using coal or natural gas. Imported steel arrives on ships burning diesel. Cement (20% of concrete) is made in a kiln using coal or natural gas. Fiberglass, epoxy, and plastic are made out of petroleum.

If the plan is to build 150% wind power to increase the capacity credit for reliable power, or immigration and birth rates increase the US population to 1 billion as expected in census projections by 2100, triple all of the above figures. Since the rest of the world also wants wind power and have increasing populations, perhaps multiplying by 10 would be more realistic, or by 12, since many material requirements were left out (i.e. transmission / distribution lines and towers, substations, roads, etc).

ED. 2015. Neodymium. Dysprosium. ElementsDatabase.org

EIA. 2015. Table 6.7.B. Capacity Factors for Utility Scale Generators Not Primarily Using Fossil Fuels, January 2008-November 2014. U.S. Energy Information Administration.

Prieto, P. A.  21 Oct 2008. Solar + Wind in Spain/ World. Closing the growing gap? ASPO International conference.

USGS. 2011. Cement production. United States Geological Society. 127,200,000 long tons converted to 142,464,000 short tons (2,000 lbs)

Worldsteel. 2014. Monthly Crude Steel Production 2014Pig iron 2013 + DR 2013. worldsteel.org (converted from long to short tons).

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Wind’s dirty secret: it goes on vacation in the summer

USA summer map 2-14

 

Figure 1. Summer wind across the USA is nearly all sluggish Class 3 (light blue), Class 2 (orange) or class 1 (blank) (NREL).

Someday wind and solar will need to contribute over 50% of power due to declining fossil fuels and uranium

Is this possible?

Wind (and solar) have to contribute the majority of renewable power, because there isn’t enough potential power from biomass, geothermal, hydropower, or energy storage for reasons discussed further below.

Wind is unpredictable, uncertain, unreliable — off at the pub like a Dickensian father drinking his paycheck anywhere from 57.0% to 78.2% of the time in the United States (EIA), barely blowing in the summer, and not ever, year-round, at commercially viable speeds (class 3 or above) in the South Eastern United States.

Across the entire continental United States, wind blows the most and the hardest in winter, mainly in remote states far from dense coastal population centers where the energy is most needed. Will windy states share their wind? There’s not enough prime and non-prime wind, solar, geothermal, or hydro potential power in the 11 western states of the WECC region of the electric grid to replace existing WECC fossil fuel and nuclear plants.

California’s peak demand is in the summer, when wind blows the least, so the state of California looked at importing Wyoming wind power.  But this would require adding 957 MW of natural gas plants since Wyoming wind isn’t blowing much in the summer either (NREL 2014).

Are there any banks willing to lend utilities, governments, or private businesses trillions of dollars to build tens of thousands of miles of transmission lines costing $5.5 million dollars per mile (BG) to build a national grid first, and then enough wind farms to move winter wind power from windy states to the rest of the nation?  Will states be willing to send power across state lines that they won’t benefit from (which they are already reluctant to do) at a time when they need all the power they can get as fossil fuels decline?

Nearly all wind maps show the yearly average, which is meaningless and misleading. What matters is how much wind there is every hour, every day, every week, every month, every season. Notice below how much wind varies over seasons (EIA 2015b). A tremendous amount of energy storage would be needed to compensate for lack of wind energy over the summer.

wind capacity 2001-13 usa

Grid operators care about wind at even smaller time intervals — 10 minutes or less — so that they can quickly curtail wind power when supply exceeds demand or ramp up natural gas plants when wind dies down. The electric grid needs to balance supply and demand within a few percent of 60 Hz, or there’s a black out.

It’s not okay for wind to disappear 68% of the time, because the electric grid needs to be up 24 x 7 for computers, finance, credit cards, banks, cash registers, refrigeration; to pump sewage, drinking and irrigation water; for agriculture, food processing and packaging, radios, cellphones, refineries, heat and air conditioning, hospitals, pharmacies, police, fire, etc. Vehicles depend on electricity to pump fuel and make gas or diesel at refineries.

The more wind energy contributes to overall power, the less it can be counted on.  This is counter intuitive, but consider that if wind provides only 1% of all power, it’s pretty easy for nuclear and fossil plants to compensate for a lack of wind.

But if wind had to provide 50% of all power because oil, natural gas, coal, and uranium were declining, with half as many fossil and nuclear power plants as exist now, these plants would have a hard time (due to lack of fuel) to compensate for a lack of wind power.

The kind of power matters a great deal — the grid depends on resources that can ramp up and down quickly to keep the grid from blacking out if a large coal or nuclear power plant unexpectedly shuts down (mainly natural gas plants).  In a renewable electric system, when wind (or solar) surges or dies, the only renewable energy resources that can ramp up or down are:

  • Solar CSP with thermal storage. Only 7 southwestern states have enough sun/lack of humidity for CSP plants to be built, but solar CSP with thermal storage plants need water, which is limited in these 7 states.
  • Biomass is very limited because photosynthesis is inefficient and produces little renewable biomass per year.  Most biomass will be needed for transportation fuel since heavy freight transportation relies nearly completely on diesel, and to make the 500,000 products that use fossil fuels as a feedstock (asphalt in roads, plastics, fertilizer, etc).
  • Pumped Hydro Storage and Compressed Air Energy Storage are very limited geographically and also very expensive
  • Battery energy storage is far from being commercial, most types reach material limits before being able to store enough power for 4-12 hours (Barnhart), and too expensive

Geothermal is base load power which can’t be used to ramp power up or down.

Wind can only add to the blaze of power from coal, natural gas, and nuclear power plants. It can not replace them.

Great Britain’s office of science and technology estimated that if the proportion of generation from wind ever reached 50%, reliability would fall to 7-9% (GBHP).

So if 25 gigawatts (GW) of wind capacity were built to replace 25 GW of retiring fossil and nuclear plants, and the amount of wind power that could be depended on at peak demand was 5 GW (the capacity credit), you’d need to build an 20 GW of nuclear and fossil plants to back up the wind farms or 45 GW to replace 25 GW of power, nearly double as much power (GBHL).

Plan B would be to over-build wind power to compensate. 625 GW of wind farms might have a 25 GW capacity credit, and if transmission wires spanned the United States, even more than that in the winter, but perhaps less than 25 GW in the summer.

If wind is mostly missing over an entire season across a continent,  how can wind be a renewable “solution”?

In the USA maps below, areas that are blank (class 1) or orange (class 2) are not commercially viable.  Most class 3 aren’t either, unless near a city and existing transmission lines with enough extra capacity.

Wind Energy Resource Atlas of the United States  Winter, Spring, Summer, Autumn (NREL)

USA winter map 2-12

 

 

 

 

 

 

 

 

USA spring map 2-13

 

 

USA summer map 2-14

 

 

 

 

 

 

 

USA autumn map 2-15

 

wind capacity by region 2001-13

 

Iowa Wind maps by month 50 meters high  

Only Red, Orange, Yellow, and Green are economically viable. In July & August, wind is class 1 and 2

IA-0-wind-speed-chart

 

 

 

 

 

 

IA-1-wind-speed-January-50m

 

 

 

 

 

 

January

IA-2-wind-speed-Feb

 

 

 

 

 

February

IA-3-wind-speed-Mar

 

 

 

 

 

March

IA-4-wind-speed-April

 

 

 

 

 

April

IA-5-wind-speed-may

 

 

 

 

 

May

IA-6-wind-speed-June

 

 

 

 

 

June

IA-7-wind-speed-july

 

 

 

 

 

July

IA-8-wind-speed-august

 

 

 

 

 

August       

IA-9-wind-speed-september

 

 

 

 

 

September

IA-10-wind-speed-october

 

 

 

 

 

 

October

IA-11-wind-speed-november

 

 

 

 

 

 

November

IA-12-wind-speed-december

 

 

 

 

 

December

 

When you drill down to hourly wind capacity by month wind looks totally crazy:

hourly wind capacity factors muskegon Michigan

Projected Monthly Capacity Factor Distribution for an Average Annual Capacity Factor of 25% Muskegon, MI Airport Figures from 1997, 2000, and 2003

There are many other reasons why wind power will not save us as well.

References

AWEO. 2009. Areas of industrial wind facilities. aweo.org/windarea.html.  I averaged the 107 acres/MW (and averaged the few with ranges). This site also says the number of acres may over twice as large: “That may still not be enough for maximum efficiency. More recent research at Johns Hopkins University by Charles Meneveau suggests that large turbines in an array need to be spaced 15 rotor diameters apart, increasing the above examples to 185-250 acres required per installed megawatt. Remember that capacity is different from actual output. Typical average output is only 25% of capacity, so the area required for a megawatt of actual output is 4 times the area listed here for a megawatt of capacity. And because 60% of the time wind turbines produce power at a rate far below average, even more (2.5×, perhaps, for a total of 10×) — dispersed across a wide geographic area — would be needed for any hope of a steady supply”.

Barnhart, C. 30 Jan 2013. On the importance of reducing the energetic and material demands of electrical energy storage Energy Environ. Sci., 2013,6, 1083-1092

BG. 2008. Transforming America’s power: The Investment Challenge 2010-2030. Brattle Group. Table 4-1 costs per mile: $5,452,900 (avg of 765 kV $6,577,600 & 500 kV $4,328,200. High kV transmission lines are required to prevent losses over long distances).

DOE. June 1980. Standby Gasoline Rationing Plan. U.S. Department of Energy

EIA. 2015. Table 6.7.B. Capacity Factors for Utility Scale Generators Not Primarily Using Fossil Fuels, January 2008-November 2014. U.S. Energy Information Administration.

EIA. 2015b. Wind generation seasonal patterns vary across the United States. U.S. Energy Information Administration.

2012 2013 2014 Max Percent difference
January 39.0 33.2 40.1 20.8
February 33.5 34.9 34.3 4.2
March 39.0 35.5 39.4 11.0
April 36.5 40.4 43.0 17.8
May 34.5 36.9 34.3 7.6
June 33.6 32.3 35.8 10.8
July 23.6 25.3 26.5 12.3
August 22.4 21.8 22.4 2.8
Sept 23.8 27.5 26.0 15.5
October 32.6 31.2 31.5 4.5
November 30.0 37.1 42.3 41.0
December 34.1 31.6 0.0 8.3
382.6 387.7 375.6
Average 31.9 32.3 34.1
Overall average 32.8

Elsam Engineering. 2004. “Life Cycle Assessment of Offshore and Onshore Sited Wind Farms. Vestas Wind System. (235 tons steel, 887 tons concrete, 39 tons iron, 3.6 tons copper (Elsam) and 296 tons steel, 1164 tons reinforced concrete, 39.35 tons iron, 2.4 tons copper,  (Guezuraga)

GBHL. 2007-2008. The Economics of Renewable Energy. House of Lords Select Committee on Economic Affairs 4th Report, Great Britain.

GBHP. May 2014. Intermittent Electricity Generation. Great Britain Houses of Parliament, office of science & technology PostNote number 464.

Guezuraga, B. 2012. Life Cycle Assessment of two different 2 MW class wind turbines. Renewable Energy 37: 37-44.

NREL. Wind Energy Resource Atlas of the United States. Maps: 2-12 Winter, 2-13 Spring, 2-14 Summer, 2-15 Autumn. National Renewable Energy Laboratory.

NREL. March 2014. California-Wyoming Grid Integration Study Phase 1-Economic Analysis. National Renewable Energy Laboratory.

Stover, D. Nov 22, 2011. The myth of renewable energy. Bulletin of Atomic Scientists.

 

 

 

 

Posted in Electric Grid, Electrification, Seasonal, Wind | Tagged , , , | Leave a comment

Wind power capacity value — effective load carrying capability (ELCC)

NREL. 2008. Determining the Capacity Value of Wind: An Updated Survey of Methods and Implementation. National Renewable Energy Laboratory.

Electric systems must have sufficient reserves so that resources are adequate to meet customer demand. Because electricity demand cannot be known in advance with certainty, and because generation can experience mechanical or electrical failures which take it out of service (i.e., experience forced outages), a planning reserve that consists of installed capacity in excess of load requirements is necessary to maintain reliability. This reserve is applied in the planning time frame (i.e., one year or more), and is a determination of system adequacy: is there sufficient installed generation to meet load obligations?

The level of wind capacity value is a matter of debate in some regions, due to the variability of wind power and its relationship with load. Utilities and other entities typically allocate some capacity value to wind power, although at a lower level than other energy technologies.

With over 18 GW of installed wind capacity in the United States as of first quarter 2008, and another 4 GW under construction, the question of wind’s capacity value (sometimes called capacity credit) is gaining more attention. System planners will need to grapple with how to determine the capacity value of wind energy. It is clear that wind’s primary value is as an energy resource, but the extent to which it contributes towards system adequacy is an important question. Effective load carrying capability (ELCC) is an often-used metric to assess capacity credit, not only for wind plants, but for any power plant. A typical power plant has a relatively low forced outage rate, which implies a high availability rate. This translates into an ELCC value that is typically a large percentage of the conventional plant rated capacity.

Because wind generators only generate electricity when the wind is blowing, wind’s availability rate (the rate that power and energy can actually be provided) is a function of the wind speed throughout the year. Therefore, the effective forced outage rate for wind generators may be much higher, from 50% to 80%, when recognizing the variable availability of wind. In addition, wind’s value to the electric system may also vary. The output from some wind generators may have a high correlation with load and thereby can be seen as supplying capacity when it is most needed. In this situation, a wind generating plant should have a relatively high capacity credit. The output from other wind generating plants may not be as highly correlate d with system load, and therefore would have a lower value to the electric system a nd should receive a lower capacity credit. The correlation of wind generation with system load, along with th e wind generator’s outage rate, is the primary determinants of wind capacity credit.

With modern interconnected power systems, the LOLP does not necessarily measure the probability that load will be shed because of insufficient generation. The LOLP metric measures the risk that generation cannot meet the peak demand unless capacity is imported. When performing a reliability analysis it is necessary to sel ect a risk target. This is often chosen to be a LOLE of 1 day per 10 years. This roughly corresponds to a 0.9997 probability that generation will be sufficient to cover load without unexpected imports.

ELCC is driven by the timing of high LOLP hours. Figure 3 shows a typical LOLP duration curve. A generator that contributes a signifi cant level of capacity during the top 200 hours will have a higher capacity value (ELCC) than a unit that delivers the same capacity during hours 600 – 800 instead. Generators that reduce LOLP the most will make the highest contribution to system adequacy and reliability, and will therefore have a higher ELCC. If a plant were unable to generate any power during the approximately 80 0 hours shown in the graph, its capacity value would be very close to zero.

To calculate ELCC, a database is required that contains hourly load requirements and generator characteristics. For conventional generators, ra ted capacity, forced outage rates, and specific maintenance schedules are the primary requirement s. For a variable resource such as wind, at least one year of hourly power output is required, but more data is always better.

My comment: As you can see blow, the higher the penetration level, the less capacity credit, because the more you depend on wind, the less likely it is to be there when you need it the most at peak periods:

wind capacity credit vs penetration level

House of Lords. 2007-2008. The Economics of Renewable Energy. House of Lords Select Committee on Economic Affairs 4th Report, Great Britain. 

Peak demand and capacity credit

A cost due to intermittency comes from the need to have enough capacity available to meet peak demand. No power station is guaranteed to be available at peak demand. So industry holds extra capacity over and above the expected peak demand to cope with stations that turn out not be available when most needed, or higher than expected demand. As a rule of thumb, a 20% margin of extra capacity has been sufficient to keep the risk of a power cut due to insufficient generation at a very low level.

A fossil-fueled station has around a 5% chance of not being available to generate at the time of the system peak because of breakdowns or essential maintenance. One plant’s breakdown is rarely correlated with another. Nuclear plants have a similar risk.

But for renewables it is very different. At peak demand not only are the chances of a wind farm not being fully available much higher but it is very likely that, if so, nearby wind farms will also be at least partially unavailable because it is not windy in the area. This correlation will fall for distant wind farms—for example, the wind could well blow in Scotland when conditions in Cornwall are calm. But within the UK, the correlation does not fall to zero.

As a result, the proportion of renewable generation which can be relied on at peak demand is much lower than for fossil fuel plants and more complicated to calculate. We received several estimates of how far wind capacity could be counted on to contribute to meet peak demand—its “capacity credit”. BERR uses a range of between 10% and 20% of wind stations’ capacity, so that 25 GW of wind plant could displace between 2.5 and 5 GW of conventional plant. E.ON suggest that the capacity credit of wind power in the UK should be only 8%.

As wind generation increases, its capacity credit will tend to fall because low winds over part of the country can affect many wind turbines simultaneously. Extra, offsetting conventional plant is needed. The Renewable Energy Foundation’s rule of thumb is to treat the square root of the wind capacity in GW as if it were conventional capacity. On that basis, for example, 25 GW of installed wind generation capacity could be counted on for the same contribution to peak demand as 5 GW of conventional capacity; and it would take 36 GW of wind plant to match 6 GW of conventional plant.

It is clear that much conventional capacity will be required to support renewable generators coming on stream in the period up to 2020, during which many of Britain’s coal and nuclear power plants are scheduled to close. To replace them, the Government has calculated that 20–25 GW of new power stations will be needed by 2020—about a quarter of today’s 76 GW of electricity capacity. But that calculation assumes replacement on a like-for-like basis and does not take account of the target for renewables. If 30 GW of additional renewable capacity were required to meet the EU’s 2020 target for the UK (and its capacity credit was less than 6 GW), a further 14–19 GW of new fossil fuel and nuclear capacity will still be needed to replace closing plants and meet new demand. The total new installed electricity generating capacity required by 2020 would thus be roughly double the level needed if renewable generation were not expanded.  [20-25 GW new power = 30 GW of wind to get 6 GW of reliable capacity plus 14-19 GW fossil / nuclear to back up wind when it’s not blowing = 44-49 GW total power]

The intermittent nature of wind turbines and some other renewable generators means they can replace only a little of the capacity of fossil fuel and nuclear power plants, if security of supply is to be maintained. Investment in renewable generation capacity will therefore largely be in addition to, rather than a replacement for, the massive investment in fossil-fuel and nuclear plant required to replace the many power stations scheduled for closure by 2020. The scale and urgency of the investment required is formidable, as is the cost.

We have a particular concern over the prospective role of wind generated and other intermittent sources of electricity in the UK, in the absence of a break-through in electricity storage technology or the integration of the UK grid with that of continental Europe. Wind generation offers the most readily available short-term enhancement in renewable electricity and its base cost is relatively cheap. The evidence presented to us implies that the full costs of wind generation (allowing for intermittency, back-up conventional plant and grid connection), although declining over time, remain significantly higher than those of conventional or nuclear generation (even before allowing for support costs and the environmental impacts of wind farms). Furthermore, the evidence suggests that the capacity credit of wind power its probable power output at the time of need) is very low; so it cannot be relied upon to meet peak demand. Thus wind generation needs to be viewed largely as additional capacity to that which will need to be provided, in any event, by more reliable means

Other documents at www.parliament.uk:

Houses of Parliament. May 2014. Intermittent Electricity Generation. Parliamentary office of science & technology PostNote number 464.

In 2013 UK wind power could produce 11 GW at maximum capacity, but only 7-25% of that figure was reliable capacity.  If the proportion of generation from wind rises towards 50%, the proportional reliability of wind will fall from 17-25% (in 2013) towards 7-9%. The contribution of solar is zero because reliable capacity is measured at annual peak demand, which occurs in winter, after dark. Wind’s contribution is not zero as the wind is generally blowing somewhere in Great Britain.  Fossil-fueled and nuclear had a 77-95% reliable capacity.

Motion 781: “… it is unlikely that wind farms could provide a significant part of the United Kingdom’s energy needs, given the cost of their construction, their short life span, the expense of the electricity they produce and their low generating efficiency…”

 

December 2013. MISO 2014 Wind Capacity Credit Report, Planning Year 2014-2015 Wind Capacity Credit.

ELCC wind capacity credit MISO midwest

 

The probabilistic measure of load not being served is known as Loss of Load Probability (LOLP) and when this probability is summed over a time frame, e.g. one year; it is known as Loss of Load Expectation (LOLE). The accepted industry standard for what has been considered a reliable system has been the “Less than 1 Day in 10 Years” criteria for LOLE. Th is measure is often expressed as 0.1 days/year, as that is often the time period (1 year) over which the LO LE index is calculated.

Effective Load Carrying Capability (ELCC) is define d as the amount of incremental load a resource, such as wind, can dependably and reliably serve, while considering the probabilistic nature of generation shortfalls and random forced outages as driving factors to load not being served. Using ELCC in the determination of capacity value for generation resources has been around for nearly half a century. In 1966, Garver demonstrated the use of loss-of-load probability mathematics in the calculation of ELCC 1 To measure the ELCC of a particular resource, the reliability effects need to be isolated for the resource in question from those of all the other sources.

This is accomplished by calculating the LOLE of two different cases: one “With” and one “Without” the resource. Inherently, the case “with” the resource should be m ore reliable and consequently have fewer days per year of expected loss of load (smaller LOLE).

Do averages have any meaning when it comes to wind?

Every year a different ELCC results, but what does this figure really mean in terms of running the grid?  They came up with a figure of 14%. But if you look at the MISO chart on page 12 with 72 critical peak load hours over 8 years and how much wind was blowing (the capacity), there were 39 times when it was less than 14% and 33 times when it was more. So you can’t ever rely on wind for 14% power. there were times when it was only contributing 1.6% of it’s potential, far from average capacity. You’d better have a lot of other kinds of power plants to back wind up the 54% of the time it isn’t there for you at peak load hours.

Aha! I kept looking around, and Nova Scotia Power agrees with me:

The graph below shows the 30 highest load hours over the past 4 years and the coincident available wind capacity during those hours. In this plot it can be seen that while wind generation may be present at near nameplate capacity during some high load hours, it may not be there at all in other high load hours. While the hourly capacities shown here may average to a large figure, that ELCC would not adequately represent the true system operating requirements. In fact, in 1/3 of the peak load hours shown here wind generation is at 10% or less.
This bi-modal wind generation behavior makes the inherent averaging LOLE method of calculating capacity value of wind, with matched load–wind shapes, rather optimistic. The risk of overstating capacity value of wind is designing a system with inadequate firm capacity to serve load in all peak hours.

Nova Scotia load vs wind power and capacity value

LOLE is inherently an averaging quantity:
LOLE = Average (LOLP)/100*8784/24 Where LOLP = Loss of Load Probability As such LOLE quantity does not adequately preserve the high LOLP values impact on the system. The plot below (see slide 12) is an example of the LOLP distribution throughout a year and it illustrates why averaging of LOLP quantities into an annual LOLE quantity may not be the best method for calculating NSPI’s capacity value of wind

The cost of under-or over-estimating the capacity value of wind is asymmetrical. Over-estimating the capacity value of wind and then operating the system accordingly could result in inadequate resources to meet peak system demand. That situation has much more severe consequences than under-estimating wind resources capacity and having more than adequate resources to meet peak demands, particularly as NSPI is a winter peaking utility. There are uncertainties associated with load growth. For example, we have seen the highest system peak firm load in history in January 2014, which adds to the importance of carrying adequate planning reserve margin . The planning reserve margin can be compromised by assigning a high capacity value to wind generation in the planning process. For example, a 27% capacity value for committed wind generation of 550-600 MW means that ~150-160 MW, or 40% of the required planning reserve, may or may not be available depending on the wind generation output.

Nova Scotia used a cumulative frequency analysis to come up with a wind capacity value of 12% — GE used a different method and came up with 27% wind capacity.

Nova Scotia Power. April 2014. Capacity Value of Wind Assumptions and Planning Reserve Margin

IEA. 2013. Wind Task 25 Design and operation of power systems with large amounts of wind power. Phase two 2009–2011. International Energy Agency.

Capacity value of wind power

wind power capacity credit

Figure 18. Capacity credit of wind power, results from ten studies, showing reduction of capacity value as penetration level increases. New York on-off-shore (blue vertical line in chart) shows the range of capacity value for wind, when calculated only for onshore sites (10%) or only offshore sites (40%).

The question of whether there is sufficient capacity at some future date is known as the power or resource adequacy question. This is related to the long-term reserve or planning reserve that power systems carry.

To answer questions such as: “Can wind substitute for other generation in the system and to what extent?” and “Is the system capable of meeting a higher (peak) demand if wind power is added to the system?” the capacity value of wind can be calculated. Capacity value is the portion of installed capacity that will provide additional load carrying capability to meet projected increases in system demand.

This contribution is typically measured in either MW or as a percentage of installed wind capacity. The term “capacity credit” may also be used. Two international task force papers (partly including Task 25 collaboration) have recommended the use of loss of load expectation (LOLE) and related methods, such as calculating the Effective Load Carrying Capability (ELCC) to calculate the capacity value of wind (Keane et al., 2011; NERC, 2011). Wind generation will have a capacity value that is often close to the average power produced by wind power (capacity factor) at low penetrations and will decline as wind penetration increases. In the results summarized in Figure 18, the range is from 40% of installed wind power capacity (in situations with low wind penetration and a high capacity factor at times of peak load), to 5% in higher wind penetrations, or if regional wind power output profiles correlate negatively with the system load profile (a low capacity factor at times of peak load). The aggregation benefits apply to capacity credit calculations – for larger geographical areas, the capacity credit will be higher. The relative wind capacity credit, as percent of installed wind capacity, is reduced at higher wind penetration, but the extent of this decrease depends on the geographical dispersion of the additional wind plants and the smoothing that results from this dispersion. This is demonstrated by comparing the cases of Mid Norway with one and three wind power plants. In essence, it means that the wind capacity credit for all installed wind in Europe or the United States is likely to be higher than that of the individual countries or regions, with the same penetration level. Indeed, this is true only when assuming that the grid is not limiting the use of the wind capacity (i.e., just as available grid capacity is a precondition for allocating capacity credit to other generation). The results presented in Figure 18 for capacity value of wind power are from the following studies: · Germany (Dena, 2005) · Ireland (AIGS, 2008) · Norway (Tande & Korpås, 2006) · Quebec (Bernier & Sennoun, 2010) · UK (Ilex Energy & Strbac, 2002) · US Minnesota (EnerNex/WindLogics, 2004 and 2006) · US New York (GE Energy, 2005) ·US California (Shiu et al., 2006) · US EWITS study (EWITS, 2010).

The US Eastern Wind Integration Study EWITS did several calculations for capacity value of wind. The study used three scenarios of 20% wind penetration and one scenario of 30% penetration. Three load and wind profile years were used for each scenario. Using the 2004 profile year, the capacity value of wind (Effective Load Carrying Capability ELCC) was 14-18% of wind rated capacity. The 2005 profile results are 14-20%, and the 2006 profile results are 16-23% (EWITS, 2010). The 30% results are 16-19% for profile years 2004-2006, respectively. When a new transmission overlay was added, the results changed significantly, ranging from 24% to 33% of wind rated capacity, depending on penetration.

The inability to integrate all wind power in the system can be seen as occasional curtailments of wind power that may be needed due to either transmission congestion or insufficient balancing or stability issues in the system. Increasing power system flexibility through increasing transmission to neighboring areas, generation flexibility, demand-side management, and optimal use of storage (e.g., pumping hydro or thermal), in combination with market aggregation and operation closer to real time, will impact the amount of wind that can be integrated cost effectively.

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Is there enough renewable energy to replace fossil fuel electricity generation?

NREL. July 2012. U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis. National Renewable Energy Laboratory.

This document is a thought experiment that uses GIS mapping to estimate how much renewable power could possibly be generated in each state regardless of cost, distance from transmission infrastructure, reliability or time-of-dispatch, current or future electricity loads, etc.

Solar and wind are the largest renewable resource base in the United States and not surprisingly would need to contribute about 90% of renewable power in a world without fossil fuels, since geothermal, hydropower, and biomass are extremely limited by both location and potential power generation.

But wind and solar both have very low capacities and are very seasonal. The EIA only has 4 months of data on Solar PV, but so far it’s ranged from a capacity of 23.3% in  November 2014 to 33.1% in September 2014, and solar CSP has ranged from 13.1% in November 2014 to 25.9% in September 2014 (EIA).

Wind capacity has ranged from 21.8% in August 2013 to 43% in April 2014, when power is least needed in states with hydropower, because this is when hydroelectric power is at its best from rain and snow-melt due to it’s need to be released from full reservoirs so they don’t overflow dams.  Wind blows mainly in the winter so for the rest of the year wind will not be able to provide much power, especially in the summer.

In contrast, nuclear power was up 99.2% of the time in January 2014, and other fossil plants also had high capacities – it’s hard to imagine how the electric grid can run 24 x 7 in the future on 90% wind and solar without storage.

Clearly most of the potential power identified could never be built because of cost, being too far from  cities and towns, a lack of transmission lines, inability of enough flexible generation to balance uncertain, unreliable, and unpredictable wind and solar power, and at over 50 percent renewables, additional generation would be difficult without enough energy storage and the geothermal, hydropower, or biomass power to balance wind and solar (when supply and demand don’t match within 1% or less, the electric grid comes down).

Locations were restricted somewhat by areas such as national parks and marine sanctuaries, onshore wind net capacity factor must be 25.5% or more, solar PV and CSP on slopes 3% or less, and other factors were used to restrict the areas.

In the real world, most of these resources would not be built because their capacity isn’t high enough (class 3 or higher wind), or they’re too far from the transmission grid.  It costs an average of $3 million dollars per mile to build transmission lines, which lose 3 to 10% of power the longer the distance traveled or the lower the generation power of the wind or solar farm.

In a resource-constrained world it is likely that states will not want to share their power with other states. This is manifested already by how hard it is to get transmission lines to cross state borders. States prefer to keep their power within-state.

States east of the Rockies don’t have enough renewable power for 100% fossil-free electricity generation

What’s of interest in the maps below is the paucity of wind and solar resources the states east of the Rockies have.  All of the states east of the Rockies (36 states) except for Texas are in the eastern interconnection part of the electric grid, where 70% of the population lives.  The Midwest has wind power, but expanding the grid to send it to the east coast would cost hundreds of billions of dollars. States tend to keep power generation within their borders, and it can take ten years to overcome opposition to transmission lines and towers, especially if they’re going to cross state lines to deliver power elsewhere.

The 11 western states (WECC) don’t have enough prime and non-prime resources to replace existing fossil fuel and nuclear plants despite having more energy resources than the eastern connection, so it’s very unlikely that even if a super-grid were built they’d send electricity to the Eastern connection.  Yet 7 of these western states are the only place that solar CSP can be built, and without solar CSP WITH THERMAL STORAGE, there is no way tobalance wind and solar PV with renewable power, since there’s not enough pumped hydro or compressed air energy storage locations in the United States.

Only Texas (ERCOT), its own island of power, has much wind and solar power, but whether there’s enough and if it can be balanced with hydropower or solar CSP and energy storage is unclear.

Below are maps of the USA with 89% of their potential from the three main sources of renewable power: Rural utility-scale PV (58%), Concentrating Solar Power (24%), and onshore wind (7%). Not shown are energy resources with less than 1% contributions (urban utility-scale PV .5%, Rooftop PV .2%, Biopower .1%, hydrothermal .1%, or hydropower .1%).  Enhanced geothermal was stated as contributing 6%, but it’s commercial yet and may never be.  Offshore wind was put at 4%, but corrosion, a short 15-year-lifespan, enormous cost, extreme intermittency, and other factors make offshore wind unlikely to contribute 4% of power.

In the real world, only the best resources are likely to be developed at a few locations in the darkest colored states.

USA GIS utility-scale RURAL solar PV

 

 

 

 

 

 

 

 

58.2% Figure 3. Total estimated technical potential for rural utility-scale SOLAR PV in the United States

USA GIS CSP

 

 

 

 

 

 

 

 

24.1% Figure 5. Total estimated technical potential for concentrating solar power (CSP) in the United States. NOTE THE LACK OF CSP in most of the eastern half of the USA. In a grid with mostly renewable power, CSP with thermal storage is the only renewable energy that can balance intermittent wind power/ solar and fill in for solar PV when the sun goes down or wind dies, aside from very limited amounts of biomass (which at best can contribute .1% of overall TWh assuming it all goes to generating electricity and not for biofuels or chemicals).  How can Jacobson possibly claim that solar CSP can contribute 10% of renewable power in New York?

The only likely CSP plants to be built are in areas with at least class 5 potential because they are incredibly expensive  (i.e. Ivanpah which cost $2.2 billion but only produces 100 MW and has NO STORAGE because there’s so little water in the desert — this document does not address the water issue with CSP in the 6 states with the best CSP potential).

USA GIS CSP class 5 potential

 

 

 

 

 

 

USA GIS onshore wind power

 

 

 

 

 

 

 

 

6.8% of total TWh. Figure 6. Total estimated technical potential for onshore wind power in the United States.  4 of these states are in the WESTERN (WECC) connection and Texas is on its own, so these 5 states are not going to be sending their wind to the eastern connection, which is severely lacking in wind (and solar) power.

Despite Jacobson and other cornucopians (*) proposing hydrogen storage and power, about 3/4 of the power generated would be needed just to break the bonds of hydrogen and oxygen in water, leaving little power to compress hydrogen (the only way to give it power since it has none itself), store, and deliver it, all of which require a massive infrastructure that doesn’t exist.

(*) A cornucopian is a futurist who believes that continued progress and provision of material items for mankind can be met by similarly continued advances in technology. Fundamentally they believe that there is enough matter and energy on the Earth to provide for the ever-rising population of the world, and that there are no limits to growth.

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In a world without fossil fuels, concentrated solar power WITH THERMAL STORAGE is ESSENTIAL

CSP with storage is essential to keeping the electric grid up 24 hours and to balance intermittent wind and solar since there’s not enough geothermal, hydropower, biomass, or utility-scale energy storage to do that. Solar PV peaks at noon, but the highest demand is morning and late afternoon/early evening.  The wind may not be blowing at all.  The power will have to come from CSP with storage.  The United States is distant enough from the equator that the amount of CSP generated in the winter is not so great. So even if the West shares CSP with the East, where peak demand is in the winter, it is not going to be as much power as needed.  There is also a limit to the amount of pure salts from Peru which will limit the number of CSP plants that can be built.  CSP is also limited by cost – it is very expensive, takes up enormous amounts of land with less than 3% slopes to provide relatively little power.

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.

These 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.

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 six 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.

Concentrating Solar Power: Prospects and Challenges By Michele Boyd

CSP requires a high level of clear-sky solar radiation, called Direct Normal Irradiance (DNI), in order to produce electricity at the lowest price. In the US, high DNI can be found in Arizona, California, Colorado, Nevada, New Mexico, Texas and Utah.  Outside of the US, this type of intense sun can be found in the Middle East, North Africa, South Africa, Spain, Chile, China, India, Australia and Mexico.

Within areas of high DNI, there are three requirements for building CSP plants: suitable land, transmission lines, and financing. CSP requires large tracts of relatively flat land with minimal environmental or cultural conflicts, such as endangered species or native artifacts.  The Department of Interior has identified 17 solar energy zones (SEZs), totaling approximately 285,000 acres on public lands in six Southwestern states.  Most of the identified SEZs, however, do not have available transmission lines or capacity to move the electricity to cities or other demand centers.  Purchasing and building on private land remains an option, but in some states, large tracts of contiguous private land appropriate for CSP plants are difficult to find.

Large CSP projects are capital-intensive. Thus long-term, low-interest loans are necessary in order to make these projects financially viable. Since the global banking system collapsed in 2008, banks are only offering short-term loans for very limited amounts per loan, which is insufficient to finance these large power projects. The cost of CSP is coming down, but it is less developed than PV and has not yet come as far down the learning curve.  Research and development can help bring down the cost of CSP, but it is also necessary to build new plants to decrease costs through improving construction methods, cultivating a trained workforce, and developing a competitive supply chain.

Transmission is one of the most complex challenges to developing CSP projects in the US. Numerous federal, state and tribal agencies are involved in permitting transmission lines, which often cross multiple jurisdictions, including private lands.

EIA. 2015. Table 6.7.B. Capacity Factors for Utility Scale Generators Not Primarily Using Fossil Fuels, January 2008-November 2014. U.S. Energy Information Administration.

 

 

 

 

 

 

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Critiques of Mark Jacobson’s ideas to run the world on renewable energy

Jacobson is a scientist at Stanford telling people what they want to hear.   But there are too many problems with his “solutions” to list – here are a few off the top of my head:

  • He promotes solutions that are far from commercial  and are unlikely to ever be commercial (hydrogen, wave and tidal power, flywheels,  electric vehicle-to-grid)
  • Is totally unaware of energy returned on energy invested concepts that have been around for decades and have too low  an EROI to sustain society (solar PV)
  • Does not address the many issues of wind power
  • Doesn’t take into account the fossil fuel energy to replace billions of existing diesel engines that last up to 40 or more years that power trucks, trains, ships, and equipment or explain how we could possibly electrify them, let alone produce enough power to electrify them
  • Or the fossil fuel energy to make the alternative energy contraptions themselves which typically last only half as long as fossil plants — about 20 years for onshore wind and solar, 15 years for offshore wind due to corrosion
  • Doesn’t understand how powerful oil is and why it’s so hard to replace
  • Is unaware of  how the electric grid works (not his field) and the need to balance intermittent energy with baseload power and generation that can ramp up and down quickly to balance intermittent, unpredictable, and unreliable wind, solar, and wave power (the only renewable that can do that is biomass which he rejects for it’s greenhouse gas contributions, though he ought to be rejecting biomass for the teensy amounts produced by photosynthesis every year and tremendous ecological harm and negative EROEI).
  • Above all, energy storage is needed, mainly utility-scale batteries for which there isn’t enough material or energy to construct because there aren’t enough (pumped) hydro storage or compressed air energy storage locations. Geothermal is baseload and can’t ramp up and down to balance wind and solar.  The idea of hydrogen storage is so preposterous that it ought to make any reasonable person question his other “solutions” as well.

Alice Friedemann at energyskeptic.com

This entire website (especially the energy menu items) and my booklist have thousands of peer-reviewed references that refute Jacobson’s claims.  Below are some other critiques:

Critique of the 100 Percent Renewable Energy for New York Plan by Edward Dodge

A Critique of Jacobson and Delucchi’s Proposals for a World Renewable Energy Supply by Ted Trainer

There’s not enough first-rate (or even second-rate) solar, wind, geothermal, hydropower, or wave/tidal power in all of the United States to replace fossil fuels (and the east coast where 70% of the population lives, is particularly deficient):

Not enough wind, solar, geothermal to replace fossil and nuclear power in the 11 western states of the WECC.   California, Oregon, Utah, and Washington have already developed most (if not all) of their prime-quality in-state resources by Alice Friedemann

U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis by Anthony Lopez, National Renewable Energy Laboratory.

My comment: this document shows that the solutions for New York state in Jacobson’s 2013 paper “Examining the feasibility of converting New York State’s  all-purpose energy infrastructure to one using wind, water ,and sunlight” in Energy Policy is physically impossible because New York does not have enough Solar PV, Solar CSP, geothermal, hydropower, or wind power/capacity/potential. Nor do any of the other 36 states in the Eastern Interconnection (Alabama, Arkansas, Connecticut, Delaware, Florida, Georgia,  Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Nebraska, New Hampshire, New Jersey, New York, North Carolina, North Dakota, Ohio, Pennsylvania, Rhode Island,  South Carolina,   Oklahoma, South Dakota, Tennessee, West Virginia, Wisconsin, Vermont, Virginia (and Canada: Ontario, Quebec, Nova Scotia and New Brunswick, Saskatchewan, Manitoba)).

Comments on Jacobson et al.’s proposal for a wind, water, and solar energy future for New York State by Nathaniel Gilbraith et al.  

A Reality Check on a Plan for a Swift Post-Fossil Path for New York By Andrew C. Revkin, New York Times

 

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Drawbacks of a distributed generation electric grid

Gardiner, B. October 8, 2013. Bypassing the Power Grid. New York Times.

Small, decentralized generators are mostly inefficient, costing far more per unit of output than conventional power or even utility-scale renewable energy, like big solar farms.  Making haphazard changes to a system as complex as the electrical grid could have unintended consequences

Between 1 percent and 2 percent of American power is now generated by decentralized, renewable sources.

Renewable-energy technologies like solar and wind power have begun to shake up the mix of energy sources, and are now challenging the traditional distribution system. Advocates of a decentralized approach, known as distributed generation or distributed energy, envision a day when grids will no longer be one-way systems. Thousands of small generators, including rooftop solar panels and facilities that extract energy from garbage or sewage, could feed into the system, replacing or complementing big coal, nuclear or natural gas plants, they say.

Some energy experts say a less centralized system would be better suited to the diverse mix of energy sources that is likely to be needed to reduce climate-warming carbon emissions. It could also be less vulnerable to hits from stormy weather, demand overload and other difficulties that have sometimes knocked out traditional systems.

Without careful pricing and regulation, an overexpansion of distributed generation could drive up electricity prices and unfairly shift costs to customers who cannot afford to produce their own electricity.  Even homes with rooftop solar panels rely on the grid when the sun is not shining.

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Peter Dykstra: Last Tango for Nuclear?

Dykstra, Peter.  Feb 4, 2015. Last Tango for Nuclear? energycollective.

There is some promise for nuclear: Projects in Georgia, South Carolina and Tennessee may yield the first new nuclear plants in decades.  But these new nukes are falling behind schedule and soaring over budget, making an already-jittery Wall Street even more skeptical. Earlier this week the builders of 2 new reactors at Georgia’s Plant Vogtle disclosed additional delays and overruns, potentially making the project over a billion dollars in the hole and three years late.

Wisconsin, California, Florida and Vermont (Vermont Yankee nuclear power plant in Vernon) are closing aging nuclear plants, and some planned new ones have been shelved in Maryland, New York, Texas and Florida.

Closing and decommissioning isn’t cheap — usually a billion dollars or more.

As many as seven reactors in Illinois (the 2-reactor complexes at Quad Cities and Byron, and the Clinton single reactor site), Ohio (Davis-Besse reactor near Toledo) and New York (Ginna plant near Rochester, New York) could close this year if not rescued by ratepayers.

Nukes also have been getting their lunch eaten in the deregulated electricity marketplace, mostly by cheaper natural gas. 

The demise of Yucca Mountain means there’s nowhere for the industry to permanently store its waste. And just when you thought it was safe to atomically boil the water, Fukushima provided the first nuclear mega-disaster since Chernobyl a quarter-century earlier, fairly or unfairly reviving public unease about nuclear energy’s safety in the U.S.

And it didn’t help when the longtime CEO of America’s biggest nuclear player stuck the financial fork in shortly after his retirement.

John Rowe, a longtime nuclear booster and former CEO of Exelon, the Chicago-based offspring of mergers between Commonwealth Edison of Illinois, Philadelphia’s PG&E and Baltimore-based Constellation, oversaw 23 reactors. “I’m the nuclear guy,” Rowe told a gathering at the University of Chicago two weeks after his 2012 retirement. “And you won’t get better results with nuclear. It just isn’t economic, and it’s not economic within a foreseeable time frame.

Rowe was commenting on plans for newly built reactors. But old ones, including up to 6 of Exelon’s fleet, may be on the block.

States to the Rescue

In the 1990s, the federal government and many states moved to deregulate electricity. Leaving every potential power source free to marketplace dynamics, it was reasoned, would serve ratepayers well and promote competition among generators. The biggest boosters of deregulation were heavy industries looking to reduce their enormous power bills, and an up-and-coming energy trader called Enron, which thrived for a few years before collapsing in scandal.

The industry’s embrace of deregulation isn’t universal, though. In at least four states, nuclear utilities have sought state government assistance to benefit nuclear plants, if not keep them alive and running.

Officials at Chicago-based Exelon say the free market may soon kill off several of its nukes. Exelon’s CEO has been outspoken about its opposition to subsidies for its wind industry, but the company is not shy about seeking short-term help for its own financially troubled nuclear plants.

Illinois may be nuclear’s short-term ground zero. Exelon operates nukes at 6 sites in the state and acknowledged that 3e — the two-reactor complexes at Quad Cities and Byron, and the Clinton single reactor site — may have priced themselves out of the market.

While Exelon CEO Chris Crane insists that the company is not seeking a bailout, and Exelon spokesman Adams said that all “energy technologies should compete on their own merits,” Crain’s Chicago Business and other publications have reported that the company is pushing state regulators to restructure power markets in a way that critics say could stack the deck for their beleaguered nukes. Exelon senior vice president Kathleen Barron told the Illinois Commerce Commission last September that the company needs rate increases that would bring in $580 million in additional revenue to keep its nukes afloat. That extra cash would come from ratepayers, particularly at times of peak power usage.

Another Exelon nuke, the Ginna plant near Rochester, New York, is on the brink. Facing a deadline on power purchases from the 45 year-old plant’s biggest buyer, Rochester Gas & Electric, Ginna will close without a rate hike, according to Exelon. The plant’s license doesn’t expire till 2029.

Ohio is considering rate hikes to save  the Davis-Besse reactor near Toledo. FirstEnergy, operator of the trouble-plagued Davis-Besse, calls for an estimated $117 million “power purchase agreement” for its ratepayers. Longtime energy activist Harvey Wasserman called the potential rate hikes a “pillaging” of Ohio.

The utilities have spiced up the battle by resisting efforts to disclose financial data that could shed light on the plants’ financial health, and the need for a rate hike.
A shifting regulatory environment is providing a boost to Florida’s Turkey Point nuclear facility.

Florida also has pitched in to help the industry: In mid-January, Florida’s Department of Environmental Protection drew fire from conservationists when it loosened oversight over hot water discharges from the Turkey Point energy complex south of Miami. Turkey Point’s two reactors and three fossil-fuel plants dump heated water into a four-decade-old network of cooling canals, where algae blooms and rising salinity are believed to threaten to coastal waters, public drinking water wells and Everglades recovery. In writing a new permit for the plant, the DEP cut local water officials out of the regulatory process, leaving the state agency in sole command of the canal field, a radiator-like matrix of 165 miles of waterways extending south from Turkey Point.

Unlike the reactors in Ohio, Illinois and New York, there’s no talk of imminent financial demise at Turkey Point. In fact, Florida Power and Light has state approval to build two more, larger reactors at the site, and is awaiting a green light from the Nuclear Regulatory Commission, expected in 2016.

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Robert Hirsch on the Saudis and repercussions of low oil prices

Commentary: Déjà Vu With a Twist? By Robert L. Hirsch, Feb 2, 2015, ASPO USA Peak Oil Review.

The recent world oil supply/price decline situation looks very much like what happened in 1985-86, when the Saudis dramatically increased oil production, causing world oil prices to crater. That Saudi action was the result of their having acted as swing producer in OPEC, which under those circumstances caused a progressive loss of their oil market share. Back then, I was at ARCO and was provided the unwanted “opportunity” of reengineering ARCO’s upstream R & D program – Read, “fire a number of very good people.”

In those years, the Saudis maintained high production, and it took roughly a decade for oil prices to regain their previous levels. Other producers refused to cut production, and it took increasing world oil demand to bring a supply/demand balance that eventually resulted in increasing oil prices.

What motivates the current Saudi action to crater oil prices? Analysts suggest a number of possibilities, including a desire to cripple US light tight oil (shale) production; an effort to hurt Iran with which Saudi is in conflict; and/or a desire to hurt ISIL, Russia, and Syria. Or it could be all of the above.

As in the mid-1980s, it is conceivable that Saudi might maintain high oil production for many years, forcing oil prices to remain around $40-50 per barrel, possibly lower. If that were to happen, the US and world tight light oil enterprise would be decimated, a number of deep-water and expensive frontier projects would be suspended or canceled, and heavy oil production in Canada and Venezuela would falter, to name just some of the obvious. Rigs would be idled and could eventually be scrapped; a large number of service contracts would be canceled; people throughout the industry would be laid off and seek employment outside the industry; and royalties would be lost. US GDP would be negatively impacted, and U.S. oil imports would increase, negatively impacting the U.S. balance of payments.

Outside the US, prolonged low oil prices would significantly damage Iran, Russia, and other oil exporters, possibly even to the point of civil unrest. ISIL and Syria would be impacted but their situation is so complicated that it is uncertain how they might farep.

The current reduced oil price environment is different from the mid-1980s in significant ways. First, the Saudi “neighborhood” is especially unsettled, because of the “Arab Spring,” widespread terrorism, and Iranian adventurism. MENA OPEC countries are under significant, unusual pressure, so meaningful rebellion within OPEC is possible.

Secondly, there is a “twist’ this time. A number of analysts believe that the world is close to the onset of world oil production decline, often called “peaking.” In a worst case scenario, the onset of decline could start after 2015, when U.S. and world high-cost oil production capability will have been significantly degraded, making catch-up much more difficult than it might have been just a year ago.

Finally, that so-called “tax cut” that U.S. consumers are supposed to have received because of lower oil prices would not only evaporate, but a sudden increase in oil prices could cause a recession and immediate inflation.

“May you live in interesting times”!

PS. After this note was drafted, I received the latest issue of World Oil in which Kurt Abraham made the same connection to the mid-1980s situation in an editorial entitled “Numbers help explain Saudi mindset on global production.” January 2015.

PPS. Rarely in human history has one country had the ability to inflict such a large impact on the economies of so many other countries with just the turn of a valve.

PPS. What might happen if the house of Saud is overthrown and unfriendlies take charge of the Saudi oil valve? Before the onset of decline? After the onset of decline?

PPPS. What can we learn from this event to help us understand how Saudi might behave after the onset of world oil production decline?

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