Efficient operation of battery energy storage systems, electric-vehicle charging stations and renewable energy sources linked to distribution systems

In this paper, distribution systems are optimized to accommodate different renewable energy sources, including PhotoVoltaic (PV) and Wind Turbine (WT) units with existing Electric Vehicles Charging stations (EVCS) con-nected to specific locations of distribution systems. Battery Energy Storage systems (BES) are provided at the exact locations of the PV and WT units. The BES units are optimized to control the power flow inside the dis-tribution systems and enhance their performance by minimizing the proposed fitness functions. Each BES's generated or consumed power depends mainly on its State of Charge (SoC) and system constraints. The Gorilla Troop Optimizer (GTO) algorithm is adopted to solve the optimization problem either as a single-objective or multi-objective optimization. The adopted fitness functions minimize power loss and total voltage deviation (TVD) of the 108-bus distribution system. Two stages are involved in the GTO solution of the optimization problem. The GTO algorithm finds the ideal positions and sizes of the PV and WT units in the first stage. Then the GTO optimizes the BES operation in the second stage after integrating the EVCS into the system, using distinct profiles with two charging levels on weekdays and weekends. The obtained results show that the percentage reductions in the active energy loss with optimal allocations of the PV/WT are 45.29 % and 45.56 % for Level 1 and Level 2 of EVCS charging. The counterpart reductions increase to 48.22 % and 48.33 % when integrating the BES units. Furthermore, with PV/WT integration, utility power consumptions are reduced by 65.74 % and 65.65 % for the two charging levels, while the reductions increase to 70.63 % and 70.54 % when including BES units. These findings emphasize the importance of optimal BES operation in distribution networks equipped with renewable energy sources. The findings demonstrate the superior capabilities of the GTO algorithm during the optimization phase on an equal footing.