Hybrid optimization of EV charging station placement and pricing using Bender’s decomposition and NSGA-II algorithm

Environmental sustainability and carbon free emissions are becoming driving forces to electrify the transportation sector, which have increased the use of electric vehicles (EVs) in city traffic infrastructure. To efficiently utilize the EVs in the transport network, smart and accessible charging infrastructures throughout the city are necessary. In the process of constructing the EV charging infrastructure, the placement of charging stations at affordable consumer pricing is very critical. Based on the above perspectives we formulate the multi-objective optimization problem for EV charging station placement and optimal consumer pricing. The optimization model incorporates the real world data including attraction locations, road network, urban factors, EV penetration, EV factors, EV demand density, utility grid tariffs and consumer charging cost. To solve the non-linear, non-convex optimization problem a hybrid approach based on Bender’s decomposition and NSGA-II algorithm is proposed. To handle the complexity of the problem, the problem is divided into master problem and sub-problems. Bender’s decomposition is utilized for optimal charging station locations. The NSGA-II algorithm is utilized to find Pareto optimal solution while balancing conflicting objectives, i.e., optimal consumer pricing and charging station profitability. The adopted approach ensures the optimal distribution of EV charging stations while reducing congestion at charging stations, increasing coverage for EVs and minimizing re-routing distances for EV consumers at optimal consumer pricing. The simulation results validate the optimal placement of EV charging stations in city with increased coverage of 35 % while meeting increasing charging demand.