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Optimal Planning Approaches under Various Seasonal Variations across an Active Distribution Grid Encapsulating Large-Scale Electrical Vehicle Fleets and Renewable Generation

Author

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  • Muhammad Huzaifa

    (US-Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Arif Hussain

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Seoul 16419, Republic of Korea)

  • Waseem Haider

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Seoul 16419, Republic of Korea)

  • Syed Ali Abbas Kazmi

    (US-Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Usman Ahmad

    (US-Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Habib Ur Rehman

    (US-Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

Abstract

With the emergence of the smart grid, the distribution network is facing various problems, such as power limitations, voltage uncertainty, and many others. Apart from the power sector, the growth of electric vehicles (EVs) is leading to a rising power demand. These problems can potentially lead to blackouts. This paper presents three meta-heuristic techniques: grey wolf optimization (GWO), whale optimization algorithm (WOA), and dandelion optimizer (DO) for optimal allocation (sitting and sizing) of solar photovoltaic (SPV), wind turbine generation (WTG), and electric vehicle charging stations (EVCSs). The aim of implementing these techniques is to optimize allocation of renewable energy distributed generation (RE-DG) for reducing active power losses, reactive power losses, and total voltage deviation, and to improve the voltage stability index in radial distribution networks (RDNs). MATLAB 2022a was used for the simulation of meta-heuristic techniques. The proposed techniques were implemented on IEEE 33-bus RDN for optimal allocation of RE-DGs and EVCSs while considering seasonal variations and uncertainty modeling. The results validate the efficiency of meta-heuristic techniques with a substantial reduction in active power loss, reactive power loss, and an improvement in the voltage profile with optimal allocation across all considered scenarios.

Suggested Citation

  • Muhammad Huzaifa & Arif Hussain & Waseem Haider & Syed Ali Abbas Kazmi & Usman Ahmad & Habib Ur Rehman, 2023. "Optimal Planning Approaches under Various Seasonal Variations across an Active Distribution Grid Encapsulating Large-Scale Electrical Vehicle Fleets and Renewable Generation," Sustainability, MDPI, vol. 15(9), pages 1-32, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7499-:d:1138685
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    References listed on IDEAS

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    1. Chandrasekaran Venkatesan & Raju Kannadasan & Mohammed H. Alsharif & Mun-Kyeom Kim & Jamel Nebhen, 2021. "A Novel Multiobjective Hybrid Technique for Siting and Sizing of Distributed Generation and Capacitor Banks in Radial Distribution Systems," Sustainability, MDPI, vol. 13(6), pages 1-34, March.
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    Cited by:

    1. Dai Wan & Miao Zhao & Guidong He & Liang Che & Qi Guo & Qianfan Zhou, 2023. "Fast and Robust State Estimation for Active Distribution Networks Considering Measurement Data Fusion and Network Topology Changes," Sustainability, MDPI, vol. 15(18), pages 1-19, September.
    2. Yi’an Wang & Zhe Wu & Dong Ni, 2024. "Large-Scale Optimization among Photovoltaic and Concentrated Solar Power Systems: A State-of-the-Art Review and Algorithm Analysis," Energies, MDPI, vol. 17(17), pages 1-38, August.

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