Optimal Price Incentive Strategy Based on Chance-constrained Programming for Virtual Power Plants Participating in Demand Response

• Published in: 2021 40th Chinese Control Conference (CCC) pp. 6939 - 6944
• Main Authors: Meng, Hongmin, Zhang, Zhizhi, Xing, Xiaowen, Liu, Di, Liu, Zesan, Xu, Zhenan, Zhang, Wei
• Format: Conference Proceeding
• Language: English
• Published: Technical Committee on Control Theory, Chinese Association of Automation 26.07.2021
• Subjects: Adaptation models; Aggregated resource; Aggregates; Analytical models; Demand response; Energy internet; Optimal price incentive; Power internet of things; Process control; Programming; Uncertainty; Virtual power plant
• ISSN: 2161-2927
• DOI: 10.23919/CCC52363.2021.9549683

Virtual power plants (VPP) aggregate large numbers of direct-controllable resources and indirect-incentive resources. Because of the great difference in the incentive interaction characteristics of IIRs, it brings technical challenges for VPP to participate in the refined operation of grid demand response (DR). This paper designs a VPP full-category aggregated resources (AR) to participate in power grid DR, through analyzing the VPP ARs interactive features of power grid DR, in order to establish uncertain models of AR for DR participation, and put forward optimized price incentive strategy based on chance-constrained programming for VPPs participating in demand response. The proposed strategy adapts to the difference of price incentive level of different ARs, accurately guides the ARs to participate in the DR, ensuring VPPs to meet overall response targets, and maximizing VPP global benefits of participating in the demand response. The simulation example results demonstrate the effectiveness of the proposed strategy.