Quantifying Avoided Fuel Use and Emissions from Solar Photovoltaic

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Environ. Sci. Technol. 2009, 43, 226–232

Quantifying Avoided Fuel Use and Emissions from Solar Photovoltaic Generation in the Western United States PAUL DENHOLM* National Renewable Energy Laboratory, 1617 Cole Blvd, Golden, Colorado 80401 ROBERT M. MARGOLIS National Renewable Energy Laboratory JAMES M. MILFORD National Renewable Energy Laboratory

Received May 2, 2008. Revised manuscript received October 14, 2008. Accepted October 17, 2008.

The electric power system in the Western United States was simulated to evaluate the potential of solar photovoltaics (PV) in reducing fossil-fuel use and associated emissions. The simulations used a utility production cost model to evaluate a series of PV penetrations where up to 10% of the region’s electricity is derived from PV. The analysis focused on California, which uses gas for a large fraction of its generation and Colorado, which derives most of its electricity from coal. PV displaces gas and electricity imports almost exclusively in California, with a displacement rate of about 6000-9000 kJ per kWh of PV energy generated. In Colorado, PV offsets mostly gas at low penetration, with increasing coal displacement during nonsummer months and at higher penetration. Associated reductions in CO2, NOx,, and SO2 emissions are also calculated.

1. Introduction Solar PV is being deployed in part to reduce dependence on fossil fuels for electricity use and associated emissions of greenhouse gases and pollutants such as NOx and SO2. Given the time-varying output of PV and the diverse set of electric generators in the power plant fleet, there is considerable uncertainty as to the actual environmental benefits of PV in the U.S. Using simple grid-average emissions and fuel use provides unsatisfactory estimates of the actual benefits, given the potentially significant difference between the average grid and the generators on the margin. For example, the characteristics of power plants that will be backed off in response to the generation of PV electricity are quite different from plants that provide constant baseload power. In this paper, we present selected results of an analysis of the fuel and emissions reductions associated with largescale PV deployment in the western United States (1), specifically the Western Electricity Coordinating Council (WECC) region. We used PROSYM, a production cost model, to simulate the operation of the electric grid in WECC and focus on the response of the power plant fleet to various levels of PV penetration in two states: gas-dominated * Corresponding author phone: 303-384-7488; fax: 303-384-7449; e-mail: [email protected]. 226

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California and coal-dominated Colorado. In 2006, 49% of California’s in-state generation was produced from natural gas, while 72% of Colorado’s in-state generation was produced from coal (2). We examined displaced generation capacity, types and quantity of fuel saved, and emissions of CO2, NOx, and SO2 avoided by deploying varying levels of PV. 1.1. Methods of Evaluating Avoided Emissions. The most basic approach to estimate the displaced fuels and emissions associated with the deployment of renewable energy technologies is to use regional “grid averages”, which assume that any reduction in electricity demand reduces fuel use in proportion to the average mix of fuels used in local electric generation. “Marginal” analysis, which considers the time-varying nature of power plant operation, provides greater accuracy when determining emissions or fuel displacement (3). There are two general methods that can be used to carry out marginal grid analysis: accounting, and modeling (3). Accounting methods use historical generation information to estimate units that would have reduced generation in response to the output from a renewable source such as PV. The primary advantage to this approach is its ability to provide a realistic reflection of the current grid and grid operation. Data sets used in this approach include estimates from individual utilities, various historical plant-level data sets, and the EPA continuous emissions monitoring system (CEMS) database. Accounting methods are particularly well suited for examining the impact of adding a small quantity of PV to the electricity generating system, and several previous studies of avoided emissions using these methods have been performed (4-7). The most significant limitation of accounting methods, however, is the inability to “redispatch” the system based on changes due to the introduction of new technologies, including more than small amounts of renewable energy generation. In contrast, simulation models allow for system redispatch, examination of potential power exchanges between regions, and changes in the use of hydro resources, which may be important when simulating large penetration levels of variable resources. 1.2. System Dispatch Models. Utility system operators use a number of tools, often called “production cost”, “unit commitment and dispatch”, or “chronological dispatch” models to schedule power plant operation and to estimate operational costs, fuel requirements, and emissions. A high quality dispatch model takes into account the variable operating cost as well as the large number of generator and system constraints of all power plants in a utility fleet or an entire region (3, 8). Constraints that can impact the role of PV include ramping limits, minimum up and down times, and minimum loading. An important feature of system dispatch models is the ability to consider power exchanges and regional transmission. Generators in each of the three U.S. grids (Eastern, Western, and Texas) are synchronized, and power can flow from one point to another within each grid, assuming transmission availability. Utilities exchange power and energy from surrounding utilities through open market and bilateral contracts, within the constraints of generation and transmission availability. Using a system dispatch model, likely shifts in energy flows that would result from large scale PV deployment can be captured. 1.3. Study Model. A variety of tools have been used to evaluate unit commitment, production costs, and emissions associated with operation of electric power systems in the U.S. and internationally (9-11). The production-cost model 10.1021/es801216y CCC: $40.75

 2009 American Chemical Society

Published on Web 11/17/2008

FIGURE 1. WECC system topology used by PROSYM. used in this study is PROSYM, by Global Energy Decisions/ Ventyx (12). It includes a database of the U.S. generation fleet, heat rate curves, constraints such as minimum loading levels, and a reduced-form approximation of the transmission system. Since PROSYM is not an open-source model, validation comes primarily from external verification, including the fact that it is widely accepted and used by a significant number of large utilities and energy consultants (3, 9), along with results of “back casting” analysis (13). This model has been utilized for previous analyses of avoided emissions from energy efficiency and renewable energy projects (8, 14). PROSYM has also been been used by a variety of agencies and utilities within the study area (15, 16).

2. Study Scope and Assumptions 2.1. Study Geography. We examined the impact of PV in the WECC region of the U.S. because it will likely be the first region of the country with large-scale PV deployment. Many of the states within WECC, especially California and Colorado, have excellent solar resources and have already implemented significant incentives for PV deployment. The goal of this study was to examine the large-scale impacts of PV on two states within WECC with different generation system characteristics. We chose California, a system dominated by natural gas, and Colorado, a system dominated by coal (17). However, each state, especially California, cannot be isolated, given the significant energy exchanges that occur between individual states and the rest of WECC. It is also not realistic to apply PV in these states without considering the use of PV in surrounding states. As PV is deployed throughout WECC, it will alter the regional “marketplace” into which utilities states buy and sell electricity. As a result, we generated scenarios with various levels of PV deployment throughout the WECC region and analyzed the impact on generator operations specifically within California and Colorado. Figure 1 provides the topology for our analysis. Within PROSYM, WECC is divided into transmission areas, each comprising a load and a number of generators (18). Within each transmission area, load flows are essentially unconstrained. Transmission between regions is modeled with a reduced-form approximation, based on a rated link between each transmission area. Power can flow between transmission areas, limited by path ratings and taking into account line losses. Using this framework, we examined the impacts of PV on two aggregated transmission areas: California, with six transmission areas, and Colorado, with two transmission areas. We also placed PV in the remaining transmission areas to create more realistic simulations of PV impacts.

2.2. Study Time Frame. Since there is considerable correlation between system load, weather, and solar insolation, simulations should employ data for all three variables from the same year. In our analysis, we used solar resource and weather data (discussed in detail in the Supporting Information) and historical load data for WECC from 2003. To accurately represent the current grid, we applied a scaling factor to the load during each hour so the total annual energy consumed was equal to the estimated consumption in 2007. The generation mix and transmission system was modeled as it existed at the beginning of 2007, which represents our base scenario. Two future scenarios (2015 and 2020) were also considered and are discussed in the results section. 2.3. PV Deployment Scenarios. We ran a series of penetration scenarios when PV contributed 0, 2, 4, 6, 8, and 10% of total annual electricity consumption within WECC. The distribution of PV in the scenarios is provided in Table 1. Additional details of the distribution of PV, generation of the hourly PV data, and assumptions related to transmission and distribution losses are provided in the Supporting Information. As shown in Table 1, the majority of PV is assumed to be in California, accounting for roughly 54% of the total installed PV capacity and 55% of PV energy generation. In contrast, we did not assign any PV to several regions in WECC, including the two Canadian provinces, Wyoming, Eastern Idaho, and Montana. Penetration scenarios, as shown in Table 1, were defined to provide 2, 4, 6, 8, and 10% of total annual energy consumption from PV in the entire WECC region. It is important to consider this definition of PV penetration when interpreting the results of this study. For example, in each scenario the level of PV penetration on an energy basis is higher in both Colorado and California than in the WECC overall. Table 2 provides the actual level of PV penetration for California and Colorado in each of the WECC scenarios. 2.4. Study Metrics. The overall impacts of PV were quantified in three primary metrics: (1) reduction in generation (kWh from each generator type per kWh of PV generation), (2)reduction in fuel use (kJ of each fuel type per kWh of PV generation), and (3)reduction in emissions (g of CO2, NOx, or SO2 per kWh of PV generation). The generator types tracked include combined-cycle gas turbines (CC), simple-cycle gas turbines (CT), which actually included gas fired steam turbines and reciprocating engines to capture peaking plants, coal, nuclear, geothermal, hydro, pumped hydro storage (PS), and wind. A relatively small number of plants not fitting into these categories (mostly small thermal plants fired by fuels including wood, waste, landfill gas, petroleum coke, etc.) were placed in the “other” category. Generation, fuel use, and emissions from each type of plant were tracked on an hourly basis within three regions (California, Colorado, and the remainder of WECC). As a result, the actual impact of PV on in-state generation and net imports could be examined. Simulations were performed for various levels of penetration, beginning with a no-PV scenario to establish baseline fuel use and emissions. Then each incremental PV penetration scenario was run, and the resulting changes in operation were quantified. Results were calculated for the total PV deployed to a certain penetration level and the incremental amount of PV deployed from one penetration level to the next.

3. Results 3.1. Load Shape Impacts. Figure 2 illustrates the type and magnitude of load shape impacts created by the various levels of PV penetration in WECC as a whole. The shapes are aggregated, i.e., built up from the load shapes in each of the 22 transmission regions in the model. Three representative VOL. 43, NO. 1, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 1. Distribution of PV Generation fraction of region’s normal fraction of assumed fraction of assumed fraction of load met by PV in the total WECC load (2007) WECC PV capacity WECC PV energy 10% energy scenario

transmission area Arizona California No. California (NP26 + CZP26) San Diego Gas and Electric So. Cal. Edison Los Angeles Dpt. of Water and Power Imperial Irrigation District Nevada Northern Nevada Southern Nevada Idaho (Southwest) New Mexico Utah Northwest (All of WA, OR, and part of MT) Colorado Colorado West Colorado East Remainder of WECC

8.4%

10.0%

11.3%

14.7%

14.3% 2.5% 13.2% 3.5% 0.4%

22.2% 4.0% 21.2% 5.6% 0.6%

21.7% 4.2% 22.2% 5.9% 0.7%

14.2% 17.1% 17.0% 14.1% 18.0%

1.5% 3.4% 1.7% 2.7% 3.7% 17.7%

1.4% 3.1% 1.2% 3.2% 3.9% 15.5%

1.5% 3.5% 1.2% 3.7% 3.8% 11.9%

10.5% 11.0% 6.2% 15.0% 9.5% 5.0%

0.7% 5.5% 19.7%

1.1% 7.0% 0%

1.0% 7.4% 0%

11.1% 13.8% 0%

TABLE 2. WECC and Actual Penetration Levels in California and Colorado WECC penetration scenario

California PV penetration Colorado PV penetration

2%

4%

6%

8%

10%

3.1%

6.3%

9.4%

12.6%

15.5%

2.7%

5.4%

8.1%

10.8%

13.5%

2-day periods (winter, spring minimum, and summer maximum) are used to illustrate the general timing and magnitude of PV deployment impacts. During the winter, PV generation occurs before the evening peak, driven largely by heating and lighting, and thus does not reduce overall peak demand. Spring loads are fairly flat during the daytime given the minimal need for heating or air-conditioning, and sunny days in late spring can create dramatic (and potentially disruptive) changes in load shapes (19). Summertime peak loads are driven by air conditioning demand, which is largely coincident with PV output. The California and Colorado load shapes are very similar to the overall WECC load shapes shown in Figure 2. 3.2. Avoided Generation, Fuels, and Emissions in California. Figure 3 illustrates the representative aggregated impact on individual generator types in California for a representative summer day. Each graph shows the same day, but with six different levels of PV penetration, as defined in table 2. It is important to note that comparing the actual (historical) plant dispatch to the simulated plant dispatch in any given hour, or over very short time periods, is inappropriate. Variations in plant outages, wind availability, and various operational considerations make such a direct comparison of short-term data of limited value. Production cost model simulations can include both scheduled outages and random forced outages that will not match historical outages. An example is the simulated random forced outage of a nuclear plant occurring in the afternoon in Figure 3. As a result, our analysis is intended to evaluate the longer term impacts (seasonal to annual) of PV deployment, not the impact of PV during a specific hour or day. Estimating what would have actually happened (at least as small penetration) on a specific day could be estimated with historical plant dispatch data as discussed in section 1.1. 228

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Figure 3 indicates that the majority of avoided generation on this particular day was from gas-fired generators, with a substantial reduction in electricity imports at high levels of PV penetration. Table 3 provides results for California for the three major PV impact metrics. In each case values are reported for both the incremental amount of PV deployed from one penetration level to the next and the total PV deployed to a certain penetration level. For example in the WECC penetration scenario ALL the PV deployed avoided an average of 7610 kj/kWh of in-state natural gas generation, while the incremental amount of PV deployed raising the WECC penetration from 8 to 10% avoided natural gas at the lower rate of 6070 kj/kWh. The avoided generation numbers in California demonstrate the importance of evaluating WECC as a whole. At high PV penetration levels, it is more cost-effective to reduce imports than to continue reducing in-state gas-fired generation. At the highest increment of PV generation (from 12.6 to 15.5%), nearly 50% of this incremental PV generation in California is offsetting generation outside of the state. It is important to note that the fuel and emissions offset rates in Table 3 apply only to the fraction of avoided generation that occurs in state, since it is not possible to track the origin and destination of every unit of energy in the WECC system. While imports are not identified explicitly, we expect the avoided import fuel mix to be similar to the in-state avoided fuels, starting with high cost gas-fired generation, especially at the lower penetration levels. One important advantage of using a simulation approach is the ability to estimate the impacts of power plant cycling on fuel use and emissions. At high PV penetration, the greater variation in load (as illustrated in the winter and spring load curves in figure 2) will increase the amount of power plant cycling, including part-load operation and increased starts and stops. This can result in increased plant heat rates and corresponding decreases in offset emissions rates. These effects can be quantified, as illustrated in Figure 4. The dotted (decreasing line) indicates the avoided fuel use, which starts high (about 9000 kJ/kWh), due partly to the larger fraction of offsets from natural gas peaking units. Avoided fuel use drops as PV begins to offset more efficient generation units, and due to increased inefficiencies resulting from power plant cycling. The overall heat rate of the entire gas-fired generation fleet (illustrated by the darker line) experiences an overall increase due to the need to follow the variations in PV output.

FIGURE 2. Load Shapes in WECC with Various PV Penetration Scenarios.

FIGURE 3. Simulated Dispatch in California for a Summer Day in 2007 with Various PV Penetration Scenarios.

TABLE 3. Results for California WECC penetration in-state penetration gas imports other gas imports other incremental total incremental total incremental total

2% 3.1%

4% 6% 6.3% 9.4% avoided generation mix (incremental) 89.9% 83.2% 74.0% 6.6% 15.7% 25.7% 3.6% 1.1% 0.3% avoided generation mix (total) 89.9% 86.5% 82.4% 6.6% 11.1% 16.0% 3.6% 2.3% 1.6% natural gas offset rate (kJ/kWh, in state only) 8960 8170 7260 8960 8580 8180 CO2 Offset Rate (g/kWh - in state only) 445 397 354 456 439 419 NOx offset rate (g/kWh, in state only) 0.07 0.07 0.05 0.07 0.07 0.06

The decrease in the fuel savings rate at high levels of PV penetration also translates into a decrease in the avoided CO2 rate from PV, as shown in Table 3. These rates apply only to the portion of PV generation that offsets California generation. In contrast to a declining offset rate for CO2 with increased PV penetration, the NOx offset rate in California actually increases as a function of PV penetration. This is primary due to displacement of several oil-fired base/intermediate load plants at higher penetration levels. SO2 emission benefits were not estimated for California, since it has little coal-

8% 12.6%

10% 15.5%

64.4% 35.6% 0.0%

52.8% 46.9% 0.3%

78.1% 21.0% 0.9%

73.5% 25.8% 0.7%

6690 7860

6070 7610

333 404

306 395

0.13 0.08

0.16 0.09

based electricity generation. However since there is some sulfur in oil, we would expect some SO2 reduction benefit associated with PV deployment in California. 3.3. Avoided Generation, Fuels, and Emissions in Colorado. Compared to California, Colorado imports much less of its electricity and relies more heavily on coal. While Colorado meets most of its baseload demand from coal, gas provides much of the “marginal” fuel that would be offset by PV. This is illustrated in Figure 5, which shows simulated dispatch scenarios for Colorado for a summer day in 2007, with penetration levels as defined in Table 2. VOL. 43, NO. 1, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 4. Average PV gas offset rate and the average heat rates of california natural gas generators resulting from PV deployment.

FIGURE 5. Simulated dispatch for a colorado summer day in 2007 with various PV penetration scenarios.

FIGURE 6. Incremental CO2 emissions displacement rates from PV deployed in colorado and offsetting colorado generation. The negative generation in Figure 5 represents net exports. While the graph implies that coal and wind are being exported, the simulation model does not explicitly track imports and exports at the plant level. Thus exports are really an unspecified blend of various resources. Table 4 provides the results for PV deployed in Colorado. At low penetration, PV displaces mostly gas-fired generation and imports, with greater coal displacement at higher penetration. Offsets of coal generation occur primarily during mid-day periods on winter and spring days, when there is no mid-day peaking demand. During these seasons as PV 230

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penetration increases, its output will begin to reduce the demand met by coal-fired generation. In Colorado when PV penetration increases from 10.8 to 13.5%, about 60% of the incremental PV generation offsets coal-fired generation. As with the California results, the fuel offset rates shown in Table 4 for Colorado apply only to the in-state generation. The shift from offsetting primarily natural gas-fired generation at low PV penetration to an increasing share of coal-fired generation in Colorado at higher penetration levels has a significant increase in the CO2 emissions offset rate. The resulting CO2 displacement rates for Colorado are shown in Figure 6. As in the California case, the introduction of PV results in increased plant cycling, and somewhat decreased efficiency of the power plant fleet. In the base case with no PV, the overall efficiency of the gas plant fleet (both combined cycle and simple cycle units) is about 8500 kJ/kWh, while at the 10% WECC penetration case the heat rate of the gas plant fleet has increased to about 9400 kJ/kWh. The base coal plant efficiency of about 11 400 kJ/kWh is not significantly changed even at the 10% penetration scenario because of the relatively small impact of PV on the overall coal plant fleet. While the 10% PV scenario reduces the overall gas plant output by about 37% relative to the no PV case, coal output is reduced only by about 5%. While the results shown in Table 4 and Figure 6 provide annual values, additional insight is provided by examining

FIGURE 7. Variation in the monthly CO2 emissions offset rate in colorado by WECC PV penetration scenario (see Table 4 for actual PV penetration rates).

TABLE 4. Results for Colorado WECC penetration 2% 4% 6% 8% 10% in-state penetration 2.7% 5.4% 8.1% 10.8% 13.5% avoided generation mix (incremental) gas coal imports other

82.6% 78.5% 67.3% 56.3% 0.1% 4.0% 17.8% 42.1% 17.0% 17.3% 15.0% 1.7% 0.3% 0.2% 0% 0% avoided generation mix (total)

38.3% 60.7% 1.0% 0%

gas 82.6% 80.6% 76.2% 65.6% coal 0.1% 2.0% 7.3% 16.0% imports 17.0% 17.1% 16.4% 12.7% other 0.3% 0.2% 0.2% 0.1% natural gas offset rate (kJ/kWh, in state only)

60.0% 24.9% 10.4% 0.1%

incremental 8060 6960 5240 3580 total 8060 7510 6740 5850 coal offset rate (kJ/kWh, in state only)

2310 5060

incremental 20 570 2360 4770 total 20 290 1000 2060 CO2 offset rate (g/kWh, in state only)

6780 3110

incremental 422 410 477 603 total 422 416 437 484 NOx offset rate (g/kWh - in state only)

718 536

incremental 0.1 0.2 0.4 0.8 total 0.1 0.2 0.3 0.4 SO2 offset rate (g/kWh - in state only)

1.0 0.5

incremental total

1.1 0.4

0.0 0.0

0.1 0.1

0.3 0.1

0.7 0.3

and spring months, and the corresponding CO2 emission offset rates begin to increase significantly. 3.4. Future Scenarios. The results presented represent the penetration of PV into the existing grid, as it existed at the beginning of 2007. Given the fact that it will take some time for PV to reach the levels of penetration evaluated in this work, the future mix of generator types and their operation in response to intermittent generators are important considerations. Model runs were also performed for 2015 and 2020. Future loads are simple linear extractions based on estimated growth rates, and since we kept the relative penetration of PV constant, the only real change between the yearly simulations are changes in the regional generation mix. The generation mix for future years is provided by the software model vendor, based on a business-as-usual scenario, which includes certain state renewable portfolio standards and policies but no aggressive policies toward climate change. The capacity expansion scenario is similar to the Energy Information Administration’s Annual Energy Outlook (20), which assumes a near-term growth scenario dominated by fossil fuels, primarily natural gas. Based on these assumptions, the results for the 2015 and 2020 cases are very similar to the 2007 scenario presented here. Natural gas continues to provide the majority of the marginal fuel in the locations simulated, with avoided fuel and CO2 emissions rates within 10% for all cases.

4. Discussion

the seasonal impact of PV on the displaced generation mix and resulting emission offset rates. The seasonal variation in the CO2 emission displacement rates (g per kWh of PV generation) for the various penetration scenarios is shown in Figure 7. At low penetration levels, PV offsets high emissions peaking units during the summer and more efficient combined-cycle units during the off peak seasons. The summertime incremental emission offset rates drop slightly as PV starts offsetting more efficient units. In contrast, PV begins to offset a greater amount of coal-fired units during the winter

In the Western U.S., natural gas-fired plants are used to meet much of the hourly variation in electric demand and provide the “marginal” fuel during most hours of the year. As a result, any small reduction in demand due to deployment of PV will likely reduce the use of natural gas. Even in states like Colorado where coal provides most of the generation, gas is at the margin during most of the hours in which PV would generate electricity at penetrations foreseeable in the near future. Significant offset of coal generation does not occur in Colorado until PV is providing about 5% of the states’ electricity, which would require around 1.5 GW of generation, equal to about 3 times the total U.S. PV capacity in 2006 (21). Several additions and modifications to this study would provide greater insight to the environmental benefits of PV. VOL. 43, NO. 1, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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These include (1)Estimation of the avoided T&D losses associated with PV that include performing load flow analysis of the various penetration scenarios and estimation of the distribution system losses. (2)Evaluation of “high-renewables” scenarios. The dramatic growth in PV evaluated in this scenario, combined with a business-as-usual growth for both conventional fossil and other renewable sources is highly unlikely. The growth scenario that would allow PV to achieve the penetration described here would probably be due in part to climate-change policies that would also increase the growth rates of other renewable energy sources such as wind, solar thermal, and geothermal. The interaction between these variable sources of energy should be considered. In addition to evaluating the environmental benefits, analysis of the economic and technical impacts of largescale deployment of solar PV is desirable. While some preliminary work has been performed estimating the technical challenges and limits of PV in the grid (19, 22), this work is immature compared to evaluation of the system impacts of wind. The growing body of work and methods associated with wind integration studies (23, 24) should provide direction for studying the impacts of PV on the electric power system.

Supporting Information Available A description of the PV scenarios, simulation of PV output, and assumptions regarding transmission and distribution losses.This material is available free of charge via the Internet at http://pubs.acs.org.

Acknowledgments This work was supported by the National Renewable Energy Laboratory under task number PVB76401. We acknowledge Ray George for assistance in processing the solar radiation data set.

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(7) Connors, S.; Martin, K.; Adams, M.; Kern, E.; Asiamah-Adjei, B. National Assessment of Emissions Reduction of Photovoltaic (PV) Power Systems, 2004. Available at http://www.mtpc.org/ RenewableEnergy/public_policy/dg/resources/2004_PV-AvoidedEmissons_Main-Rept_MIT-Conners-et-al-1.pdf. (8) Keith, G.; Biewald, B. Methods for Estimating Emissions Avoided by Renewable Energy and Energy Efficiency; Synapse Energy Economics: Cambridge, MA, 2005. (9) Analysis and Selection of Analytical Tools to Assess NationalInterest Transmission Bottlenecks; KEMA Consulting: Arnhem, The Netherlands2003. (10) Keith, G.; Biewald, B.; Sommer, A.; Henn, P.; Breceda, M. Estimating the Emission Reduction Benefits of Renewable Electricity and Energy Efficiency in North America; Synapse Energy Economics: Cambridge, MA, 2003. (11) Voorspools, K. R.; D’haeseleer, W. D. The influence of the instantaneous fuel mix for electricity generation on the corresponding emissions. Energy. 2000, 25, 1119–1138. (12) Ventyx. Market Analytics. Available at http://www1.ventyx.com/ analytics/market-analytics.asp (accessed March 20, 2008. (13) Given, G. That’s Our Forecast and We’re Sticking to It! Ventyx’s Forecast Performance since 2001. http://www1.ventyx.com/ pdf/wp07-thats-our-forecast.pdf. (14) La Capra Associates; MSB Energy Associates. Electric Sector Emissions Displaced due to Renewable Energy Projects in New England. Available at http://masstech.org/renewableenergy/ public_policy/DG/resources/2003-09-12_LCA_NE-EmissionsReport-for-MTC.doc, 2003. (15) Tanghetti, A. California Energy Commission’s Electricity Supply Planning Process For 2008-2017. California Energy Commission. Available at www.energy.ca.gov/2007_energypolicy/ documents/2007-03-26_workshop/presentations/TANGHETTI_ ANGELA.PPT, 2007. (16) Warren, A. R. In the Matter of the Application of Public Service Company of Colorado for Approval of its Renewable Energy Standard Compliance Plan. Xcel Energy. Available at www. xcelenergy.com/SiteCollectionDocuments/docs/Warren_Direct_ Final.pdf. 2008. (17) U.S Department of Energy. Electric Power Monthly February 2008. Energy Information Administration: Washington D.C. 2008. (18) Global Energy Decisions. PROSYM User Guide. Software Version 5.5. June 2007. (19) Denholm, P.; Margolis, R. M. Evaluating the Limits of Solar Photovoltaics (PV) in Traditional Electric Power Systems. Energy Policy. 2007, 35, 2852–2861. (20) U.S Department of Energy. Annual Energy Outlook 2007. Energy Information Administration: Washington D.C. 2007. (21) Solar Electric Industry Association. U.S. Solar Industry Year in Review. http://www.seia.org/Year_in_Solar_2006.pdf, 2006. (22) Denholm, P.; Margolis, R. M. Evaluating the limits of solar photovoltaics (PV) in Electric power systems utilizing energy storage and other enabling technologies. Energy Policy 2007, 35, 4424–4433. (23) DeMeo, E. A.; Jordan, G. A.; Kalich, C.; King, J.; Milligan, M. R.; Murley, C.; Oakleaf, B.; Schuerger, M. J. Accommodating wind’s natural behavior. IEEE Power Energy. 2007, 5 (6), 59–67. (24) Ackermann, T.; Abbad, J. R.; Dudurych, I. M.; Erlich, I.; Holttinen, H.; Dristoffersen, J. R.; Sorensen, P. E. European balancing act. IEEE Power Energy 2007, 5 (6), 90–103.

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