Zonal Electricity Supply Curve Estimation with Fuzzy Fuel Switching Thresholds

1. Zonal Electricity Supply Curve Estimation with Fuzzy Fuel Switching Thresholds North American power grid is “the largest and most complex machine in the world” Amin, (2004) Mostafa Sahraei-Ardakani Seth Blumsack Andrew Kleit Department of Energy and Mineral Engineering Penn State University mostafa@psu.edu

2. Motivation How to analyze supply and demand policies considering the transmission constraints ? • Pennsylvania’s Act 129: Energy conservation and peak demand reduction in Pennsylvania. • What would happen to the prices in PA? • What would happen to the prices in other states? • What would happen to the emissions? • Carbon tax: • What would happen to the prices? • What would happen to the emissions?

3. Dispatch Curve Model • What would happen to electricity prices if a CO2 price was imposed? • Engineers • Very complex model • Data may not be publicly available • Policy analysts • Collect marginal cost data from power plants • Collect fuel price data • Form a supply curve by sorting generators from cheap to expensive • Ignore transmission network Each point represents a single power plant Newcomer et al., 2008

4. Different Models • Engineering models • Too complex • Data may not be available • Takes a long time to converge • Econometric models • Estimate prices well • Do not do a good job in estimating fuel mix and emission impacts of policies. • Dispatch curve • Ignores transmission system and how congestion makes prices different. • Our model • Needs no more data than a dispatch curve • Implicitly accounts for transmission constraints

5. Other Approaches • Econometric models • Predict prices well. • Do not do a good job on estimating fuel utilization. • Engineering models: Power Transfer Distribution Factor (PTDF) • Need detailed data which is not publicly available. • They are complex and take a lot of time to converge for large power systems.

6. Our Approach For each zone we want to identify: Thresholds where the marginal fuel changes (Coal, Gas, Oil)  CMA-ES Fixed and variable thresholds The slope of each portion of the overall dispatch curve.  OLS

7. Fuzzy Thresholds Fuzzy Gap qT qT,C/G Variables to be estimated: Relative fuel price threshold for having the fuzzy gap Fuzzy gap width coefficient Fuzzy Thresholds Summer 2008 Deterministic Thresholds Observations Summer 2011 100% Natural Gas 100% Oil 100% Natural Gas 50% Coal, 50% Natural Gas 100% Coal qi GAS qi,G/O ΔC/G COAL

8. Implementation in PJM • Seventeen PJM utility zones • Data: (2006-2009) • Hourly zonal load • Hourly zonal prices • Fuel prices • Insufficient data for nodal level modeling • Robustness Check: • Linear and quadratic curves • Fixed and Variable Thresholds

9. Results: Thresholds Price ($/MWh) Marginal Fuels in PSEG PSEG demand= 5.8 GW PJM demand= 118 GW APS price=80 $/MWh $/MWh Gas Oil Total Load in PJM (GW) Gas-Oil Fuzzy Region Coal – Gas Fuzzy Region PSEG= Public Service Electric and Gas Company Coal Load in PSEG (GW)

10. Results: Supply curve projection Central Pennsylvania and West Virginia Philadelphia • Zonal price differences are captured. • 50 $/ton carbon tax

11. Results: Marginal Fuel Shares • DUQ in western PA is a coal dominated zone. • RECO in northern NJ is a natural gas dominated zone. • Natural gas often sets the prices in PJM. • Another robustness check • Natural Gas often sets the prices.

12. Results: Prices • BGE is in eastern PJM (Baltimore). • DUQ is in western PJM (Pittsburgh). • Our model captures zonal price differences. • 50 $/ton carbon tax would increase prices by about 70%.

13. Application: Pennsylvania Act 129 • Act 129 is a wide-reaching energy policy initiative in Pennsylvania. Among other things, Act 129 requires all Pennsylvania utilities to: • Reduce annual electricity demand by 1% • Reduce “peak” demand (highest 100 hours) by 4.5% • We will estimate the impacts of Act 129 on total electricity costs, fuels utilization and greenhouse gas emissions in the PJM territory, using our model and the “dispatch curve” model that I discussed earlier. We use 2009 as our “base” year.

14. Application: Pennsylvania Act 129 Electricity Cost Savings ($ million): • Savings: 333 million dollars • 253 million dollars in PA • Dispatch Curve: 150 million dollars

15. Application: Pennsylvania Act 129 Shifts in Marginal Fuel (% Increase with Act 129): Emission decreases by 4 million metric tons. Dispatch Curve: 2.3 million metric tons.

16. Conclusions • We have developed an approach to estimating zonal supply curves in transmission constrained electricity markets: • Requires no proprietary data • Can be implemented by analysts without requiring complex engineering calculations • Our approach captures regional effects of policies that “transmission-less” dispatch models do not. Regional impact differences may be important in policy evaluation. • Zonal fuel utilization shift • Zonal price differences

17. Thanks! Mostafa Sahraei-Ardakani Department of Energy and Mineral Engineering Penn State University mostafa@psu.edu Comprehensive Exam

18. Price Increase in DC MC1=P1 10 MW 1 MC1=MC2 P1+P2=10+50 (MW) λ1=MC1=35 ($/MWh) λ1=MC1=20 ($/MWh) Virginia and Washington, DC 35MW 20MW 25$/MWh 25MW Rest of PJM 10MW 20MW 25MW Thermal Capacity =20 MW 50/3 25/3 25/3 5MW 30MW 25MW 50MW MC2=10+P2 2 3 40$/MWh 50/3 λ2=MC2=35 ($/MWh) λ2=MC1=50 ($/MWh) 25MW 45MW 40MW 25MW 30MW