penetration of wind power 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

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

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

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

Day-ahead Market Clearing 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 Day D+1 Day-ahead Market Clearing UC + EDC Balancing Market and Operation UC+EDC Uplift Calculation Balancing Market and Operation UC + EDC Day-ahead Market Clearing Uplift Calculation

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 Day-ahead energy offers Wind forecast Flexibility reserve requirements Day-ahead energy offers Wind scenarios Deviation offers VOLL Day-ahead Market Stochastic Market Clearing Deviation offers Energy and reserve schedules Energy and flexibility reserve schedules Balancing Market Balancing Market

Method Specify a Reliability Standard Making the reserves rule and VOLL consistent Specify a Reliability Standard Estimate the minimum VOLL that ensures this reliability standard Identify the dynamic flexibility reserve requirement rule 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

Flexibility Reserve Requirement (d,t) = α × WPSTD (d,t) 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

Method Inform both market clearing designs with the same uncertainty characterization SynTiSe 4 years historical data on day-ahead wind power forecast error MCMC model 30 scenarios for day-ahead forecast errors 50 scenarios for day-ahead forecast errors Add to day-ahead forecasts 50 scenarios for day-ahead hourly wind 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

Capacity (MW) & share (%) following reserve capability 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) Technology # of units Capacity (MW) & share (%) following reserve capability Quick start Nuclear ST(i) 4 4616 (23%) No Coal ST 19 8727 (44%) Yes NGCC (ii) 14 2996 (15%) Oil CT (iii) 8 631 (3%) NGCT 22 3030 (15%) BPA’ synchronous demand and wind data Three case studies with different wind penetration levels Case 1: 6% Case 2: 12% Case 3: 21%

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% 12% When expected wind is not much compared to load,  schedule it all

Results Wind integration

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

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

Results Day-ahead energy prices

Results Real-time energy prices

Results Generator’s revenue

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