Battery Energy Storage Sizing and Operational Strategy for Microgrid with State of Charge Scenarios

Microgrids (MGs) are a key component in the growing renewable energy integration with the utility grid, which is necessary to fulfil the increasing load demand. MG and its energy management systems can focus on developing optimal operation plans to maximise the use of renewable sources, including battery energy storage (BES), at the lowest cost. The BES capacity with its initial charge supports the optimal operation. The difficulties of MG economic operation are a large number of variables and constraints, such as the satisfying energy demands, the BES capacity, the operating reserve, the charge/discharge efficiency of BES, and the distributed generator capacity. Thus, this challenge is complex and needs an efficient approach to achieve MG goals with optimal BES capacity, considering zero renewable power curtailment. In this study, cost-based problem formulation has been done to determine the optimal BES size with the minimisation of operating cost by considering different scenarios under defined constraints. Here, the formulated problem is complex. It is solved using meta-heuristic algorithms such as arithmetic optimisation algorithm, grey wolf optimisation (GWO), and particle swarm optimisation to determine the BES size. Its performance comparison is also carried out. In comparison to others, the GWO provides the lowest MG operation cost with the optimal BES size. Furthermore, results show that using BES reduces MG operation costs by 64% compared to a scenario without BES.