Capacity optimization of hybrid energy storage system for flexible islanded microgrid based on real-time price-based demand response

• Published in: International journal of electrical power & energy systems Vol. 136; p. 107581
• Main Authors: Li, Bin, Wang, Honglei, Tan, Zhukui
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2022
• Subjects: Demand response; Flexibility insufficiency rate; Flexible islanded microgrid; Hybrid energy storage system; Real-time price; Hybrid energy storage system; Real-time price; Demand response; Flexibility insufficiency rate; Flexible islanded microgrid
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2021.107581

•An incentive mechanism is introduced to improve the responsive load model.•A real-time price mechanism is proposed based on the balance of supply and demand in flexible islanded microgrid (FIMG).•The definitions of FIMG flexibility and flexibility insufficiency rate (FIR) are given, and the importance of FIR in the hybrid energy storage system (HESS) capacity optimization is verified by a case study.•The NSGA-II and the improved TOPSIS are used to obtain the optimal solution. The islanded microgrid (IMG) is universally accepted as an important method to solve the island power supply problem. The optimal capacity of the hybrid energy storage system (HESS) is necessary to improve safety, reliability, and economic efficiency in an IMG. To improve the IMG ability to deal with uncertainty, this paper proposed a flexible islanded microgrid (FIMG) model with real-time price (RTP)-based demand response (DR). Through RTP to guide users’ electricity consumption behaviors more in line with renewable energy sources (RES) output law, the capacity allocation of the HESS was optimized and the system had sufficient flexibility in each scheduling period. In this paper, two decision variables of the HESS, the number of lead-acid batteries and supercapacitors, were determined based on the objective of comprehensive operating cost (COC) and flexibility insufficiency rate (FIR) minimization. Firstly, the non-dominated sorting genetic algorithm II (NSGA-II) was devoted to obtaining the Pareto set that satisfies the FIMG day-ahead scheduling. Then, both the loss of produced power probability (LPPP) and the loss of power supply probability (LPSP) corresponding to each Pareto solution were gotten based on the real-time dispatch strategy. Finally, taking COC, FIR, LPPP, and LPSP as evaluation indicators, the improved Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was applied to select the optimal solution from the Pareto set. In the case study, a FIMG on an island in Guangdong province was analyzed and discussed. By studying typical days in winter and summer of the FIMG, the superiority of the proposed model and methods was verified.