A Stackelberg game model with cloud energy storage operators: A multi-user, multi-scenario analysis, adopting the time-based pricing strategy

Abstract

This study establishes a Stackelberg game model with Cloud Energy Storage Operators (CESO) as the leader, collaborating with industrial park users to achieve mutual benefit. CESO and industrial park user. The cloud energy storage (CES) effectively addresses the high self-investment costs and underutilization of resources in the energy internet context. This study proposes a time-based pricing strategy for CES leasing services. CESO determines the hourly capacity and power leasing prices over 24 h The aim is to minimize the discrepancy between user declarations and actual usage through penalty measures. Industrial Park users determine leased energy storage capacity and charging/discharging power based on CESO's prices, their own loads, and renewable energy availability. This study proposes an improved snow ablation optimizer (ISAO) to obtain the global optimal solution, i.e., the optimal price for the time-based pricing of CES leasing services. In a multi-user, multi-scenario analysis, adopting this strategy increased CESO's benefits by 44.80 % and users' benefits by 6.76 %. Users increased the electricity sales during peak hours by 51.53 % and reduced the electricity purchases during valley hours by 19.9 %.