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Planning and Monitoring of EV Fast-Charging Stations Including DG in Distribution System Using Particle Swarm Optimization

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AI and Machine Learning Paradigms for Health Monitoring System

Part of the book series: Studies in Big Data ((SBD,volume 86))

Abstract

Today the electric vehicles (EVs) market in India is increasing very fast but the availability of efficient charging infrastructure for the EVs is still a big issue. The adoption of EVs is more advantageous and cost-efficient than the internal combustion (IC) engine-based vehicles due to the problem of depletion of convention fuel sources and increasing pollution and offering social as well as technical benefits. Therefore, the adoption of EVs is the alternate source of transportation and efficient replacement in transportation as they emit zero carbon emission, energy-efficient and are economical. For the expansion of EV market, the charging station infrastructure is very important to provide efficient energy for various EVs. This work presents innovative method for the optimal planning of charging stations with distributed generations (DGs) to establish an efficient charging infrastructure by considering the novel parameters such as power losses, voltage, reliability, and economic parameters in the proposed modified radial distribution network of study area. Furthermore, the innovative proposed multi-objective function is utilized for the optimal planning of distributed generations and the electric vehicle charging stations considering the network performance parameters as objectives variables. The minimum value of multi-objective function decides based on the system performance indices as power loss, charging cost, voltage deviation, and reliability indices. The objective function is minimized using particle swarm optimization to obtain the optimal allocation of the EV charging station as well as the placement of DG in the modified IEEE 33-bus radial distribution system. In this work, the case study for Durgapur, WB, India, is presented for particular network section in city center area with modified IEEE 33-bus radial distribution system. The MATLAB/MATPOWER tool is used to solve the power flow through backward and forward method. This work concludes that minimization of power losses, charging cost, voltage deviation, and improved reliability of the system is increased leading to secure and stable EV charging stations with DGs.

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Authors and Affiliations

  1. Electrical Engineering Department, National Institute of Technology (NIT) Durgapur, Durgapur, WB, 713209, India

    Dhiraj Kumar Singh & Aashish Kumar Bohre

Authors
  1. Dhiraj Kumar Singh
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  2. Aashish Kumar Bohre
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Correspondence to Dhiraj Kumar Singh .

Editor information

Editors and Affiliations

  1. National University of Singapore (NUS), BEARS, Singapore, Singapore

    Hasmat Malik

  2. Universiti Sultan Zainal Abidin (UniSZA), Kampung Gong Badak, Malaysia

    Nuzhat Fatema

  3. Wake Forest University, Winston Salem, NC, USA

    Jafar A. Alzubi

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Singh, D.K., Bohre, A.K. (2021). Planning and Monitoring of EV Fast-Charging Stations Including DG in Distribution System Using Particle Swarm Optimization. In: Malik, H., Fatema, N., Alzubi, J.A. (eds) AI and Machine Learning Paradigms for Health Monitoring System. Studies in Big Data, vol 86. Springer, Singapore. https://doi.org/10.1007/978-981-33-4412-9_16

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