Optimal sizing of residential battery energy storage systems for long-term operational planning

Appropriate battery storage capacity plays an important role in the performance and cost of residential energy systems. However, the load demand and renewable energy generation vary seasonally. To address the long-term operational planning problem of battery energy storage, two battery sizing methods are developed based on the consensus alternating direction method of multipliers (C-ADMM). The residential system layout and convex battery model considering cycling aging are first established. Then, for the one-year horizon planning, C-ADMM decomposes the original large-scale problem into 365 sub-problems. The global optimal battery size and operation strategy are obtained by coordinating these sub-problems in parallel until convergence. In addition, to obtain the optimal battery size based on receding horizon control (RHC) strategies in a year, a new bi-level optimization method is developed, which uses the particle swarm optimization (PSO) algorithm in the outer loop and C-ADMM in the inner loop. Thus the inner loop can be accelerated through parallel computing. The results show that the long-term storage planning problem of residential systems can be solved quickly through C-ADMM due to the parallel computing capability. Furthermore, the optimal battery size calculated under the RHC strategy in a year can contribute to more economic benefits than the global battery size when applied online.