Ali Daraeepour - Dalia Patiño-Echeverri Nicholas School of the Environment - Duke University

1. Comparing deterministic and stochastic models for electricity market clearing with high penetration of wind power penetration of wind power Ali Daraeepour - Dalia Patiño-Echeverri Nicholas School of the Environment - Duke University CEDM Annual Meeting, May 24, 2017

2. Motivation: Classic market clearing can be improved High penetration of wind energy resources and market inefficiencies 4 hour ahead • Deviations from day-ahead wind schedules are settled Long Startup time Offer day-ahead expected production Lower forecast error • Day-ahead committed generation can be • Insufficient / inefficient • Costly Adjustments in the Real-time • Real-time Wind Curtailment

3. Two possible improvements to mkt clearing 1. Deterministic + Flexibility Reserve II. Stochastic Market Clearing Wind forecast error Deviation offers Flexibilityreserve requirements Day-ahead energy offers Wind scenarios Day-ahead energy offers Wind forecast Day-ahead Market Stochastic Market Clearing Deviation offers Energy and reserve schedules Energy and flexibility reserve schedules Balancing Market Balancing Market

4. Objective To assess the performance of stochastic market clearing, relative to deterministic + flexibility reserves Environmental benefits Wind energy integration Reduction of air emissions Economic outcomes Reduction in costs of fossil fuels Electricity prices Market efficiency Need for uplift payments Convergence of day-ahead and real-time prices

5. Method for comparing both mkt designs Simulation of hourly operations of both markets over one year under two different scenarios of wind penetration using a Unit Commitment / Economic Dispatch model Production cost based / or assuming perfect competition Transmission constraints not binding Wind power and curtailment offered at no cost Electricity demand is deterministic and inelastic Real time commitment looks two hour ahead Day D DayD+1 • Balancing Market and Operation • UC + EDC • Day-ahead Market Clearing • UC + EDC • Uplift Calculation • Uplift Calculation • Balancing Market and Operation • UC+EDC • Day-ahead Market Clearing • UC + EDC

6. Ensuring the same reliability in both designs 1. Deterministic + Flexibility Reserve II. Stochastic Market Clearing Informed by the same reliability standard = no load shedding in one year Wind forecast error Reserves rule Deviation offers Flexibilityreserve requirements Day-ahead energy offers Wind scenarios VOLL Day-ahead energy offers Wind forecast Day-ahead Market Stochastic Market Clearing Deviation offers Energy and reserve schedules Energy and flexibility reserve schedules Balancing Market Balancing Market

7. Method Making the reserves rule and VOLL consistent • Step 1: Reliability StandardMaximum annual allowable load-shedding equals zero • Step 2: Minimum VOLL  Run system operation with stochastic market clearing and different VOLL • Find the minimum VOLL that ensures reliability across all scenarios • Step 3: Identify the dynamic flexibility reserve requirement rule

8. Method Determining a Dynamic flexibility reserve requirement rule Flexibility Reserve Requirement (d,t)=α× WPSTD (d,t) Wind Production Standard Deviation Proportion of Uncertainty covered by Flexibility reserves • Identify the minimum α that ensures the reliability standard • By trying different values until the minimum requirement for the reliability standard is specified

9. Method Inform both market clearing designs with the same uncertainty characterization 30 scenarios for day-ahead forecast errors • 4 years historical data on day-ahead wind power forecast error • MCMC model 50 scenarios for day-ahead forecast errors • Add to day-ahead forecasts • 50 scenarios for day-ahead hourly wind SynTiSe • Stochastic : • Use scenarios set directly • Deterministic: • Use expected value of wind production scenarios as a day-ahead forecast of wind • Use standard deviation of wind power production scenarios to calculate flexibility reserve requirement

10. Test Grid & Data • 12% scaled version of PJM’s fossil-fired generation mix • heat rate and capacity data from EPA-NEEDS • Installed capacity of thermal resources = 20000 MW • Expected Peak = 17314 MW Reserve margin =15.5% • Fuel prices from Energy Information Administration (EIA) • BPA’ synchronous demand and wind data • Three case studies with different wind penetration levels • Case 1: 6% • Case 2: 12% • Case 3: 21%

11. Results • Fundamental difference between two models is in their DA scheduling of wind • Deterministic always schedules the expected value (i.e., the forecast) • Stochastic schedules different quantities • Sometimes is less than the expected value • Sometimes is a value between the expected value and the maximum • Sometimes is the maximum value Depending on the ratio of expected wind to load At 21% wind penetration, expected wind is more than 50% of load,  Schedules of less than expected value are more common Wind penetraion 21% When expected wind is not much compared to load,  schedule it all 12%

12. Results Wind integration

13. Results Reductions in fossil-fired generation  12% wind penetration

14. Results Cost savings achieved by stochastic clearing from less use of fossil-fuels

15. Results Day-ahead energy prices

16. Results Real-time energy prices

17. Results Generator’s revenue

18. Conclusions 1) Stochastic market clearing increases wind integration, lowers emission, and fossil fuel costs Under case 2, annual costs are reduced by 1.36% (i.e. 500 Million USD) 2) Benefits are mostly due to better day-ahead wind energy schedules 3) Higher wind integration in the stochastic case lowers the day-ahead prices and fossil-fired generation revenues 5) Less flexible resources incur significant losses from implementing the stochastic market clearing 6) Revenues to generators are lower under stochastic market clearing  If not able to recover fixed costs, higher payments from capacity market will be needed