Optimal location and sizing of battery energy storage system using grasshopper optimization algorithm

Abstract

An energy storage system called a battery energy storage system (BESS) collects energy from various sources, builds up that energy, and then stores it in rechargeable batteries for future use. The battery's electrochemical energy can be discharged and supplied to buildings such as residences, electric cars, and commercial and industrial buildings. The advantages of utilizing BESSs, such as minimizing energy loss, improving voltage profile, peak shaving, and increasing power quality, may be reduced if incorrect decisions about the appropriate position and capacity for BESSs are chosen. Furthermore, the optimal position and size for BESSs are critical since deploying a BESS at every bus, particularly in an extensive network, is not a cost-effective option, and installing oversized BESSs would result in higher investment expenses. Hence, this study suggests a proficient method for identifying the most suitable position and the sizes of BESS to save costs. The grasshopper optimization algorithm (GOA) and evolutionary programming (EP) were employed to address the optimization challenge on the IEEE 69-bus distribution test system. The goal of the optimization is to minimize the overall cost. The findings indicate that the GOA has strong resilience and possesses a superior capacity for optimizing cost reduction in comparison to EP.