Application of Earthworm Algorithm in Battery Size Optimization and Load Schedule of PV-BESS for Residential Houses

Abstract

Due to economic expansion, population growth, and technological improvements contribute to rising energy demand, the self-consumption of locally generated electricity from photovoltaic (PV) systems is becoming an important application area for stationary battery energy storage systems (BESS). The optimized load schedule and battery capacity were employed in this research to reduce the customer's total cost. In many articles, a PV-BESS system was optimized using various optimization approaches. The Earthworm Algorithm (EWA) in MATLAB was used as the optimization method. Based on the result, the EWA BESS size optimized is 20.36 kWh, the total optimized cost is 45% less than the total unoptimized value, and the total EWA optimized cost is 7% less expensive than the total Cuckoo Search Algorithm (CSA) optimized cost. It was proven using statistical tools that the overall cost of an optimized and unoptimized PV-BESS system differs significantly, whereas the total cost of an EWA optimized, and CSA optimized system does not differ much.