Optimal Operation for Photovoltaic-Energy Storage System-Integrated Distribution Network Based on Adaptive Expectation Mechanism

This paper presents an adaptive expectation-based improved genetic algorithm (AE-IGA) for the photovoltaic (PV)-energy storage system (ESS)-integrated distribution network, which helps to improve power quality (PQ) and renewable energy sources consumption. Three optimization objectives are considered in the PV-ESS-integrated distribution network, including maximizing economic benefits, minimizing voltage deviation, and maximizing the power factor. To avoid the complicated decision mechanism, obvious subjectivity and poor applicability in the multi-objective optimization problem (MOPs), the IGA is firstly utilized to obtain the Pareto set (PS) of MOPs. Then, an AE method is designed for selecting the desired nondominated solution from the PS. Finally, simulation results demonstrate that the proposed scheme improves the PV penetration and reduces voltage deviation, and improves the power factor in the distribution network.