Adaptability assessment method of energy storage working conditions based on cloud decision fusion under scenarios of peak shaving and frequency regulation

Abstract

Abstract Energy storage participating in grid auxiliary services can effectively enhance the regulation capacity of the grid and promote the consumption of renewable energy, and the selection type of energy storage systems is the basis to ensure its safe and economic operation. Starting from the economics and safety of energy storage systems, an adaptive evaluation method of energy storage working conditions based on the cloud decision fusion is proposed. Aiming at strong subjective characteristics of the analytic hierarchy, an adaptability assessment model of energy storage working conditions based on the entropy weight-analysis hierarchy process method is established to obtain the scores of different types of energy storage systems. Aiming at the characteristics of ambiguity and randomness in decision-making indicators, an adaptability assessment model of energy storage working conditions based on the entropy weight-cloud model is established to obtain the scores of different types of energy storage systems. The results of the two scores are fused using Dempster-Shafer evidence theory to get the evaluation result of the best energy storage condition adaptability. In the application scenarios of the peak shaving and frequency regulation, the effectiveness of the proposed method is verified by simulation analysis of performance indicators of the peak shaving and frequency regulation. The simulation results show that the iron phosphate battery has the highest adaptability to work conditions of the peak shaving and frequency regulation, and the Dempster–Shafer evidence theory can eliminate the randomness and qualitative-quantitative doping of decision indicators on the selection type of energy storage systems, which can provide a theoretical basis for the planning of energy storage stations.