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

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? 9/21/2018

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 9/21/2018

Different Models Engineering models Econometric models Dispatch curve 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 9/21/2018

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. 9/21/2018

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 9/21/2018

Fuzzy Thresholds qT Variables to be estimated: Fuzzy Thresholds qi qT ΔC/G 100% Natural Gas 100% Coal 50% Coal, 50% Natural Gas Observations 100% Oil Fuzzy Gap qT,C/G qi,G/O Variables to be estimated: Relative fuel price threshold for having the fuzzy gap Fuzzy gap width coefficient Fuzzy Thresholds Summer 2008 Deterministic Thresholds GAS COAL Summer 2011 9/21/2018

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 9/21/2018

Results: Thresholds Price ($/MWh) Marginal Fuels in PSEG Load in PSEG (GW) Total Load in PJM (GW) Price ($/MWh) Gas Coal Oil Gas-Oil Fuzzy Region Coal – Gas Fuzzy Region PSEG demand= 5.8 GW PJM demand= 118 GW APS price=80 $/MWh $/MWh PSEG= Public Service Electric and Gas Company 9/21/2018

Results: Supply curve projection Central Pennsylvania and West Virginia Philadelphia Zonal price differences are captured. 50 $/ton carbon tax 9/21/2018

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. 9/21/2018

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%. 9/21/2018

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. 9/21/2018

Application: Pennsylvania Act 129 Electricity Cost Savings ($ million): Savings: 333 million dollars 253 million dollars in PA Dispatch Curve: 150 million dollars 9/21/2018

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. 9/21/2018

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 9/21/2018

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

Price Increase in DC 1 2 3 MC1=MC2 P1+P2=10+50 (MW) 10 MW MC1=P1 Rest of PJM Virginia and Washington, DC 10 MW MC1=P1 MC2=10+P2 50MW 1 2 3 λ1=MC1=35 ($/MWh) λ1=MC1=20 ($/MWh) 20MW 35MW 25$/MWh 25MW 25MW 50/3 25/3 25/3 50/3 20MW 30MW 10MW Thermal Capacity =20 MW 5MW 40$/MWh λ2=MC1=50 ($/MWh) λ2=MC2=35 ($/MWh) 25MW 25MW 40MW 45MW 9/21/2018 30MW