Optimized Scheme for the Combination of Shared Energy Storage Business Models

Abstract

The Stackelberg game model between shared energy storage stations and renewable energy generators optimizes pricing strategies to maximize both parties' profits. The energy storage station, acting as the leader, sets pricing strategies for capacity leasing, peak-valley arbitrage, and frequency regulation services, while the renewable energy generator, as the follower, selects the optimal service package based on the station's pricing schemes. This paper employs a Genetic Algorithm (GA) to optimize the energy storage station’s profits and applies NSGA-II to assist the generators in finding the optimal package combination, thereby improving overall profitability. The study demonstrates that this approach effectively facilitates optimal collaboration under complex market conditions and in the experimental validation, the interactions between the energy storage station and the generators were simulated under different market scenarios. The results show that the proposed game model significantly improves station profitability and optimizes generator strategies. The model’s potential applications and future optimization possibilities are discussed, providing theoretical support for the practical operation of shared energy storage business models.