A novel method for optimum sizing and allocation of solar DGs with battery storages, electric vehicle charging stations and capacitors in a practical distribution network using gravitational search algorithm

This paper presents a gravitational search algorithm (GSA) based multi-stage multi-objective optimization methodology for optimum sizing and placement of solar-distributed generation units (DGs) with battery storages (BSSs), capacitors (SCs) and fast charging stations (EVCSs) for electric vehicles (EVs). This methodology also incorporates rooftop solar photovoltaics (PVs) and destination charging for EVs to promote decentralization. Use of EVs is rapidly increasing for decarbonization measures and integrating EVCSs into already overloaded distribution system presents significant challenges, necessitating system upgrades supported by renewable energy-based DGs. Unplanned allocations of EVCSs and DGs can lead to increased power losses and degraded voltage profiles. For this proposed work, the first stage uses GSA for the optimum allocation of solar DGs with BSSs and SCs, aiming to fulfill the multi-objective requirements by minimizing power losses, improving voltage profile and fully relieving the source substation. In the second stage, optimum locations and maximum possible ratings of EVCSs are identified to get accommodated. In the third stage, network is further upgraded to mitigate the impact of the integrated EVCSs. A 52-bus, 11 kV practical network is simulated to validate the effectiveness of the proposed methodology under hourly varying loads and solar irradiance across all seasons.