Artificial ecosystem optimization for optimizing of position and operational power of battery energy storage system on the distribution network considering distributed generations

摘要

Installing Distributed Generation (DG) on the Distribution Network (DN) is one of the effective solutions to reduce the cost of electricity supplied from the system. However, the mismatch between DG capacity and load demand may lead to wasted energy. This paper demonstrates the problem of optimizing the location and power of the battery energy storage system (BESS) to reduce energy cost on the DN considering DGs. The survey period is a typical day that is divided into off-peak, normal and peak hours corresponding to different electricity prices. Artificial Ecosystem Optimization (AEO) is first adapted to determine the BESS’s location and power during the survey period. The approach for reducing energy cost and the proposed AEO method are applied to three test DNs integrated photovoltaic and wind turbine DGs. Wherein, the considered types of loads of the DNs are residential, commercial and industrial loads. The performance of the AEO is compared with the well-known Particle Swarm Optimization (PSO) and the recent Harris Hawks Optimizer (HHO). The results show that the BESS placement helps to reduce significantly electricity cost in the survey period for the DN with and without DGs. Furthermore, AEO is the more effective approach than PSO and HHO for the BESS placement problem.