Stochastic Economic Dispatch Based Optimal Market Clearing Strategy Considering Flexible Ramping Products Under Wind Power UncertaintiesOACSTPCDEI
High penetration level of renewable energy has brought great challenges to operation of power systems,and use of flexible resources(FRs)is becoming increasingly important.Flexibility of power systems can be improved by changing generation arrangements,but the interests of some market participants may be harmed in the process.This study proposes a stochastic economic dispatch model with trading of flexible ramping products(FRPs).To calculate changes in revenue and reasonably compensate units that provide FRs,multisegmented marginal bidding for energy is simulated by linearizing generation cost,and an optimal market clearing strategy for FRPs is developed according to changes in clearing energy and marginal clearing price.Then,the correlation between prediction errors of wind speeds among different wind farms is determined based on a joint distribution function modeled by the copula function,and quasi-Monte Carlo simulation(QMC)is used to generate wind power scenarios.Finally,numerical simulations of modified IEEE-30 and IEEE-118 bus systems is performed with minimum comprehensive cost as the objective function.This verifies the proposed model could effectively deal with wind variability and uncertainty,stabilize the marginal clearing price of the electricity market,and ensure fairness in the market.
Haoyong Chen;Jianping Huang;Zhenjia Lin;Fanqi Huang;Mengshi Li;
School of Electric Power,South China University of Technology,Guangzhou,510641,ChinaSchool of Electric Power,South China University of Technology,Guangzhou,510641,China Department of Electrical Engineering,The Hong Kong Polytechnic University,Hong Kong,China
动力与电气工程
Flexible ramping productmarginal clearing pricerisk coststochastic economic dispatchwind power correlation
《CSEE Journal of Power and Energy Systems》 2024 (004)
P.1525-1535 / 11
supported by the National Natural Science Foundation of China 51937005;the Natural Science Foundation of Guangdong Province 2019A1515010689.
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