Abstract: As renewable energy adoption increases globally, the demand for energy storage (ES) has risen accordingly. The sharing economy has emerged as a promising solution to mitigate the high costs associated with ES. However, shared energy storage (SES) faces challenges in balancing efficient energy use and ensuring fair benefit allocation within a SES alliance. This paper addresses these challenges by introducing the Centralized Model for Sharing Storage Decisions (CSSD), which aims to maximize profits for the SES alliance. To solve the CSSD, we propose an enhanced version of the Max-Revenue with a Flow (MRF) Algorithm, called the Greedy Max-Revenue with a Flow (GMRF) Algorithm, which ensures the efficient solution of the centralized problem. Moreover, this paper introduces the concept of capacity exchange cost within the SES alliance. This concept allows ES operators to procure or lease energy storage capacity, generating revenue opportunities. We use linear programming models and inverse optimization to determine the appropriate exchange costs, ensuring that individual profits are maximized while benefiting the entire alliance. The feasibility and effectiveness of these optimization and allocation strategies are validated through simulations conducted in Qingpu district, Shanghai.