Optimal Placement and Capacity of Battery Energy Storage System in Distribution Networks Integrated With PV and EVs Using Metaheuristic Algorithms

In this research, the optimal placement and capacity of battery energy storage systems (BESS) in distribution networks integrated with photovoltaics (PV) and electric vehicles (EVs) have been proposed. The main objective function is to minimize the system costs including installation, replacement, and operation and maintenance costs of the BESS. The replacement cost has been considered over 20 years while the operation and maintenance costs are the costs incurred by transmission line loss, voltage regulation, and peak demand. To solve the problem, three metaheuristic algorithms, namely particle swarm optimization (PSO), african vultures optimization algorithm (AVOA), and salp swarm algorithm (SSA), are employed. The proposed approach is evaluated on the IEEE 33- and 69-bus distribution systems integrated with PV and EVs. The results provided by the considered algorithms are compared in terms of the objective function, system efficiency enhancement, payback period, and statistical analysis. The simulation results show that after the BESS installation, the voltage profile can be improved, transmission loss is reduced, and peak demand is decreased where PSO provides the best objective values and AVOA achieves the fastest payback periods in both systems.

Automated summary

This research proposes the optimal placement and capacity of BESS in distribution networks with PV and EVs. The objective is to minimize system costs, including installation, replacement, and operation and maintenance costs. Three metaheuristic algorithms, PSO, AVOA, and SSA, are employed to solve the problem. The approach is evaluated on IEEE 33- and 69-bus distribution systems integrated with PV and EVs, comparing the results in terms of objective function, system efficiency enhancement, payback period, and statistical analysis. Simulation results show that PSO provides the best objective values and AVOA achieves the fastest payback periods in both systems.