Research on potential user identification and optimal planning of the multiple time scale cloud‐based location sharing energy storage

Abstract

Cloud energy storage is considered a promising application in future power systems. It focuses on optimally leveraging the capacity of centralized large-scale energy storage compared with the requirements of small-scale localized users. In this paper, to satisfy the small-and medium-scale timely energy storage requirement from localized users, the concept of the cloud-based location sharing energy storage is proposed. The modular mobile energy storage system is flexibly configured and deployed at different sites to fulfil the long-term seasonally dynamic transformer capacity increment and short-term daily energy arbitrage based on economic values. To optimize the overall incomes of the energy storage investment, a two-step user potential identification algorithm is proposed to discover the most valuable users at different time scales from the regional power usage profile. Then, the number/capacity optimal planning algorithm is proposed to optimally share the mobile energy storage system among users seasonally and total profits are further increased through daily energy arbitrage at less valuable seasons. Finally, the structure and dispatching strategy of the cloud-based location sharing energy storage management system is illustrated. Case study results prove that the proposed identification algorithm can excavate the most valuable users at different time scales and the optimal planning and operation strategy is able to guarantee the overall income benefits.