Optimal Sharing and Fair Cost Allocation of Community Energy Storage

This paper studies an energy storage (ES) sharing model which is cooperatively invested by multiple buildings for harnessing on-site renewable utilization and grid price arbitrage. To maximize the economic benefits, we jointly consider the ES sizing, operation, and cost allocation via a coalition game formulation. Particularly, we study a fair ex-post cost allocation based on nucleolus which addresses fairness by minimizing the minimal dissatisfaction of all the players. To overcome the exponential computation burden caused by the implicit characteristic function, we employ a constraint generation technique to gradually approach the unique nucleolus by leveraging the sparse problem structure. We demonstrate both the fairness and computational efficiency of the method through case studies, which are not provided by the existing Shapley approach or proportional method. Particularly, only a small fraction of characteristic function (less than 1% for 20 buildings) is required to achieve the cost allocation versus the exponential information required by Shapley approach. Though there exists a minor increase of computation over the proportional method, the proposed method can ensure fairness while the latter fails in some cases. Further, we demonstrate both the building-wise and community-wise economic benefits are enhanced with the ES sharing model over the individual ES (IES) model. Accordingly, the overall value(1) of ES is considerably improved (about 1.83 times). (1) The proportion of total electricity bill reduction relative to the ES capital cost.

Automated summary

This paper investigates an energy storage (ES) sharing model cooperatively invested by multiple buildings, considering ES sizing, operation, and cost allocation through a coalition game formulation. The study shows the fairness and computational efficiency of the method, demonstrating enhanced economic benefits for both individual buildings and communities with the ES sharing model compared to individual ES models.