Cheetah Optimization Algorithm for Simultaneous Optimal Network Reconfiguration and Allocation of DG and DSTATCOM with Electric Vehicle Charging Station

Abstract

The potential of Electric Vehicles (EVs) to decarbonize the transport-ation industry has attracted a lot of attention in recent years in response to growing environmental concerns. Electric Vehicle Charging Stations (EVCSs) need to be properly located for widespread EV integration. The distribution system is facing additional challenges due to inclusion of EVCS. The adverse impacts of EVCS on the Radial Distribution Network (RDN) may be minimized using Distributed Generations (DGs) or Distribution Static Compensators (DSTATCOMs) or by reconfiguring the network. This paper uses a novel optimization technique to solve the problem of simultaneous optimal placement of EVCS with network reconfiguration and optimal planning (siting and sizing) of DGs and DSTATCOMs. The multiple objective functions are considered in order to minimize the active power losses, the voltage deviation, the investment costs for DGs and DSTATCOMs, and to increase the voltage stability of the system. A novel meta-heuristic Cheetah Optimization Algorithm (COA) is used to solve the optimization problem. To examine the effectiveness of the suggested strategy on 33-bus and 136-bus networks, several scenarios of simultaneous incorporation of EVCS, DG, and DSTATCOM installations with network reconfiguration are taken into consideration. The COA results are also compared to the results of grey wolf optimization and genetic algorithms.