Optimal allocation of battery energy storage systems to improve system reliability and voltage and frequency stability in weak grids

Integration of renewable energy sources like solar and wind power into the power network has increased significantly in recent years. However, these sources are inherently variable and intermittent, which leads to challenges in maintaining grid stability and reliability. A promising solution to these challenges is the strategic deployment of battery energy storage systems (BESS). The BESS can support improving system voltage and frequency stability and increase system reliability because it can rapidly charge and discharge the grid when needed. To fully explore the advantages of BESS in power systems, it is crucial to determine their optimal allocation. Therefore, this paper presents a technique for optimal allocation of BESS in weak grids to bolster system voltage and frequency stability and enhance system reliability. The proposed method uses the recent adaptive grey wolf optimisation (AGWO) algorithm to identify the optimal capacity and placement of the BESS. The AGWO algorithm is a metaheuristic optimisation algorithm that uses a population of wolves to explore the solution space for the best outcome. The outcomes from the AGWO method are validated using grey wolf optimisation (GWO), beluga whale optimisation (BWO), and sparrow search algorithm (SSA). The efficacy of the proposed methodology is validated in a high renewable distributed generation (DG) penetrated weak IEEE-39 bus system using DIgSILENT PowerFactory software. Simulation findings demonstrate that integrating BESS at the optimal location and size can significantly improve the voltage and frequency stability of the grid and increase its reliability. The proposed methodology can help grid operators and system planners make informed decisions on integrating BESS into the grid.