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Brookhaven Science Associates U.S. Department of Energy Energy-Economic-Environmental Analysis of Photovoltaics in the US V.M. Fthenakis Environmental.

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Presentation on theme: "Brookhaven Science Associates U.S. Department of Energy Energy-Economic-Environmental Analysis of Photovoltaics in the US V.M. Fthenakis Environmental."— Presentation transcript:

1 Brookhaven Science Associates U.S. Department of Energy Energy-Economic-Environmental Analysis of Photovoltaics in the US V.M. Fthenakis Environmental Sciences Department Brookhaven National Laboratory

2 Brookhaven Science Associates U.S. Department of Energy The MARKAL model was developed as an energy and environmental systems model in the 1976-81 period in an effort involving analysts from 17 nations and two international organizations. The model is currently used for energy and environmental planning in over 35 countries. The US Energy Information Administration has chosen a version of MARKAL for projections in the International Energy Output. MARKAL is a demand-driven, multi-period, linear programming model optimization model (Fishbone and Abilock, 1981).

3 Brookhaven Science Associates U.S. Department of Energy MARKAL establishes a competitive market to supply energy demands. All energy resources and both supply and demand technologies compete in this market in an even-handed manner.

4 Brookhaven Science Associates U.S. Department of Energy Study Design Study was based on the MARKAL version of the 2001 EIA Annual Energy Outlook. PV, Wind, Advanced Combined Cycle plants and Microturbines were released from growth constraints in the AEO model. Cost and performance data for PV, wind and solar thermal technologies were drawn EPRI (1997). These three MARKAL analyses were constrained to growth rates of 25%/year, 30%/year and 50%/year.

5 Brookhaven Science Associates U.S. Department of Energy Study Design These three analyses were compared among themselves and to the 25%/year Roadmap. This allows us to compare the Roadmap with a sophisticated energy systems model under different circumstances.

6 Brookhaven Science Associates U.S. Department of Energy Study Design In addition, The potential role of PV on peak load was explored. MARKAL is designed to produce the cost of electricity endogenously. We tricked the model by decreasing the length of the summer day, which forces the price of electricity to increase. We achieved a price of $287/MWh, within the range of peak prices found by Sioshansi (2000) in the Pacific Northwest.

7 Brookhaven Science Associates U.S. Department of Energy PV 50%/y growth constraint

8 Brookhaven Science Associates U.S. Department of Energy 50 %/y Competing Technologies Note: Wind 5-7 Limited to 100 GW Resource Limit

9 Brookhaven Science Associates U.S. Department of Energy 50%/y Carbon Displacement

10 Brookhaven Science Associates U.S. Department of Energy PV 30%/y Growth Constraint

11 Brookhaven Science Associates U.S. Department of Energy 30%/y Growth Constraint - Competing Technologies Wind 5-7constrained to 100GW resource limit.

12 Brookhaven Science Associates U.S. Department of Energy 30%/y Carbon displacement

13 Brookhaven Science Associates U.S. Department of Energy PV: 25%/y growth

14 Brookhaven Science Associates U.S. Department of Energy 25%/y Growth limit - Competing technologies Wind 5-7 limited to 100 GW resource

15 Brookhaven Science Associates U.S. Department of Energy 25% Carbon displacement

16 Brookhaven Science Associates U.S. Department of Energy Compare PV among three scenarios

17 Brookhaven Science Associates U.S. Department of Energy Carbon Displacement for three PV scenarios

18 Brookhaven Science Associates U.S. Department of Energy Potential Role of PV in peaking power SD10 compresses summer day to achieve $288/MWh peak.

19 Brookhaven Science Associates U.S. Department of Energy Implications of Peaking for PV In a deregulated market, electric prices can be high during peak hours, especially when there is a shortage of capacity. Peak periods usually coincide with hot sunny days. MARKAL characterizes the electric load curve in three seasons (winter, summer, intermediate), night and day, and peak. The amount the peak exceeds the average summer day (assuming a summer peak) is determined exogenously. To determine the potential role of PV for peaking, we artificially narrowed the time-period for the summer day, inducing a high cost peak ($200-$300 per MWh.)

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