A Novel Levy-Enhanced Opposition-Based Gradient-Based Optimizer (LE-OB-GBO) for Charging Station Placement

Abstract

Transportation modes are shifting toward electric vehicles from conventional internal combustion engines to reduce pollution and dependency on conventional fuels. This reduces the fuel cost, while charging stations must be distributed across the locations to minimize range anxiety. Installing charging stations randomly across the distribution system can lead to violation of active power loss, voltage deviation, and reliability parameters of the power system. The problem of the optimal location of charging stations is a nonlinear optimization problem that includes the parameters of the distribution system and road network with their respective constraints. This work proposes a new metaheuristic optimization algorithm, a levy-enhanced opposition-based gradient-based optimizer (LE-OB-GBO), to solve the charging station placement problem. It has a balance between exploration and exploitation and fast convergence rate. The performance of the proposed algorithm was evaluated by solving CEC 2017 benchmark functions and charging station problem. The performance of the proposed algorithm was also compared with that of other state-of-the-art optimization algorithms and was found to outperform 17 out of 29 CEC 2017 functions. Statistical analysis of the charging station placement problem indicates the lowest mean values of 1.4912, 1.4783, and 1.5217 for LE-OB-GBO for considered cases 1 to 3, respectively, thus proving the efficacy of the proposed algorithm.