Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm. (15th August 2017)

• Record Type: Journal Article
• Authors: Awasthi, Abhishek; Venkitusamy, Karthikeyan; Padmanaban, Sanjeevikumar; Selvamuthukumaran, Rajasekar; Blaabjerg, Frede; Singh, Asheesh K.
• Is Part Of: Energy. Volume 133(2017)
• Page Start: 70
• Page End: 78
• Publication Date: 2017-08-15
• Subjects: Charging stations -- Electric vehicle (EV's) -- Genetic algorithm (GA) -- Particle swarm optimization (PSO); Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2017.05.094
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: India's ever increasing population has made it necessary to develop alternative modes of transportation with electric vehicles being the most preferred option. The major obstacle is the deteriorating impact on the utility distribution system brought about by improper setup of these charging stations. This paper deals with the optimal planning (siting and sizing) of charging station infrastructure in the city of Allahabad, India. This city is one of the upcoming smart cities, where electric vehicle transportation pilot project is going on under Government of India initiative. In this context, a hybrid algorithm based on genetic algorithm and improved version of conventional particle swarm optimization is utilized for finding optimal placement of charging station in the Allahabad distribution system. The particle swarm optimization algorithm re-optimizes the received sub-optimal solution (site and the size of the station) which leads to an improvement in the algorithm functionality and enhances quality of solution. The genetic algorithm and improved version of conventional particle swarm optimization algorithm will also be compared with a conventional genetic algorithm and particle swarm optimization. Through simulation studies on a real time system of Allahabad city, the superior performance of the aforementioned technique with respect to genetic algorithm and particle swarm optimization in terms of improvement in voltage profile and quality. Highlights: A novel optimal placement strategy of electric vehicles charging station. A hybrid algorithm based on both GA and PSO. Voltage profile shown improvement in the lowest p.u. voltage value. A better performance in terms of quality of solution with lesser number of iterations. The minimum stress on the distribution system.