Optimal Scheduling Strategy of Virtual Power Plant Using Fuzzy Satisfaction Function
Virtual power plant (VPP), as an aggregation of various distributed energy resources, face many challenges about management and optimization.In order to obtain the optimal trade-off between economy and reliability, this paper presents a fuzzy chance constrained programming approach to the day-ahead scheduling of.Uncertain factors in VPP are characterized by fuzzy parameters and reserve requirements are formulated as chance constraints,in which VPP reliability requirements are to be satisfied with a presumed confidence level of high probability.Aiming at the difficulty to decide the optimal confidence level, a synthetic satisfaction function is developed to depict satisfaction degree of VPP under different probabilities.The satisfaction function can also reflect the decider”s distinct attitudes toward risk and profit.A matrix real-coded genetic algorithm combined with Monte Carlo simulation is used to solve the model developed in this paper.To reduce calculation burden, the fuzzy chance constraint is converted into its crisp equivalent by utilizing credibility theory.Numerical tests are performed in a typical VPP system, and the results show the sensitivity of the decider”s satisfaction as the confidence level changes.The best confidence level can also be determined through comparing VPP”s satisfaction degree under different cases,which prove the validity of the proposed model and algorithm.
Day-ahead scheduling fuzzy chance constraints satisfaction function uncertain factors virtual power plant
Songli Fan Qian Ai
School of Electronic information and Electrical Engineering,Shanghai Jiao Tong University,Minhang District,Shanghai 200240,China
国内会议
广州
英文
439-451
2015-05-01（万方平台首次上网日期，不代表论文的发表时间）