Abstract
A new hybrid multi-objective evolutionary algorithm is developed and deployed in the present work for the optimal allocation of Electric Vehicle (EV) charging stations. The charging stations must be positioned on the road in such a way that they are easily accessible to the EV drivers and the electric power grid is not overloaded. The optimization framework aims at simultaneously reducing the cost, guaranteeing sufficient grid stability and feasible charging station accessibility. The grid stability is measured by a composite index consisting of Voltage stability, Reliability, and Power loss (VRP index). A Pareto dominance based hybrid Chicken Swarm Optimization and Teaching Learning Based Optimization (CSO TLBO) algorithm is utilized to obtain the Pareto optimal solution. It amalgamates swarm intelligence with teaching-learning process and inherits the strengths of CSO and TLBO. The two level algorithm has been validated on the multi-objective benchmark problems as well as EV charging station placement. The performance of the Pareto dominance based CSO TLBO is compared with that of other state-of-the-art algorithms. Furthermore, a fuzzy decision making is used to extract the best solution from the non dominated set of solutions. The combination of CSO and TLBO can yield promising results, which is found to be efficient in dealing with the practical charging station placement problem.
Highlights
Road transportation sector is one of the major emitters of greenhouse gases [1]
The charging stations must be positioned on the road in such a way that they are accessible to the Electric Vehicle (EV) drivers and the electric power grid is not overloaded
The combination of CSO and TLBO can yield promising results, which is found to be efficient in dealing with the practical charging station placement problem
Summary
Road transportation sector is one of the major emitters of greenhouse gases [1]. EVs have emerged as an environmentally friendly alternative to traditional Internal Combustion Engine (ICE) driven vehicles, because they have the potential to reduce greenhouse gas emissions. In [12], the authors proposed a placement scheme for public charging stations with cost as the objective function. In [20], the authors proposed a multiobjective framework of charging station placement with the voltage deviation, power losses, and EV flow. In this paper, we strategically address the charging station placement problem by giving the due consideration to the reliability indices simultaneously considering other planning objectives like cost, power loss, voltage deviation, and accessibility. From [8]–[21], it is clear that researchers have applied a large variety of meta-heuristics and classical optimization algorithms for coping with the charging station placement problem. 2. A novel Pareto dominance based CSO TLBO algorithm for the charging station placement problem is proposed. A number of multi-objective benchmark problems and the problem to locate electric vehicle charging stations areattacked by CSO TLBO, and the performance of the proposed algorithm is statistically weighted against the up-to-date algorithms
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