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Article type: Research Article
Authors: Alagu, Matheswarana; * | Selladurai, Ravindrana | Chelladurai, Chinnadurraib
Affiliations: [a] Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Coimbatore, India | [b] Department of Electrical and Electronics Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India
Correspondence: [*] Corresponding author. Matheswaran Alagu, Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Coimbatore, 641407, India. E-mail: profmathes@gmail.com.
Abstract: The electric vehicle market has surged the consideration of charging station requirements in the commercial and residential areas of the urban regions. The addition of charging stations at the existing power network introduces a greater challenge on voltage stability and losses. The effect of the charging station can be addressed through the optimal integration of Distributed Generation (DG) units into the network. The improper placement of DG units can jeopardize the network stability. These issues are addressed by optimal placement of DG units and charging stations in the network to improve voltage, reduce transmission loss and maximize the charging station capacity. Here the objectives are considered as a multi-objective problem and solved using an enhanced Ant-lion optimization algorithm. The proposed method is implemented and tested over IEEE – 33, 69 and 94 radial bus system in MATLAB R2020a version. In IEEE – 33 bus system, the total loss reduction of 67.63% and the minimum voltage of 0.981 is attained with 2909.2 kW of DG and 1770.7 kW of charging station. The voltage stability index is improved to 0.92. The efficacy of the proposed method is evaluated through comparison with existing methods such as Genetic Algorithm (GA) with VRP method, Harris Hawks Optimization (HHO) and Particle Swarm Optimization (PSO). It is evident that the proposed method gives improved performance than other methods.
Keywords: Charging stations, distributed power generation, optimization, renewable energy sources, smart grids
DOI: 10.3233/JIFS-212401
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 707-719, 2022
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