Multi-Objective Optimal Allocation of Electric Vehicle Charging Stations in Radial Distribution System Using Teaching Learning Based Optimization

In recent times, the adoption rate of Electric Vehicles (EVs) in the transportation sector has been increased significantly across the world towards sustainability. On the other side, the increasing EV load penetration in an electric power sector can cause for the generation-demand imbalance, real power loss increment, poor voltage profile, and consequently voltage stability margin decrement. To mitigate the impact of increasing EV load penetration on radial distribution systems (RDS), it is essential to integrate EV Charging Stations (CSs) at appropriate locations. In this paper, the teaching-learning based optimization (TLBO) algorithm is applied to determine the optimal locations of EV-CSs considering the objectives minimization of real power loss and average voltage deviation index and maximization of voltage stability index. The simulation studies are performed on standard IEEE 33-bus and 69-bus test systems. The results have highlighted the need for optimal allocation of EV-CSs for maintaining the system performance as better as possible even under increased loading conditions due to EV-CSs. Also, TLBO has shown its ability over other heuristic algorithms namely particle swarm optimization (PSO), ant lion optimizer (ALO), flower pollination algorithm (FPA) and cuckoo search algorithm (CSA) by providing the optimal value consistently in solving the complex non-linear multi-objective optimization problem.