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Orderly Charging Strategy Based on Optimal Time of Use Price Demand Response of Electric Vehicles in Distribution Network

Author

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  • Hui Hwang Goh

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Lian Zong

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Dongdong Zhang

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Wei Dai

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Chee Shen Lim

    (Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, 111 Ren’ai Road Suzhou Industrial Park, Suzhou 215123, China)

  • Tonni Agustiono Kurniawan

    (College of Environment and Ecology, Xiamen University, Xiamen 361102, China)

  • Kai Chen Goh

    (Department of Technology Management, Faculty of Construction Management and Business, University Tun Hussein Onn Malaysia, Johor Bahru 86400, Johor, Malaysia)

Abstract

In order to manage electric vehicles (EVs) connected to charging grids, this paper presents an orderly charging approach based on the EVs’ optimal time-of-use pricing (OTOUP) demand response. Firstly, the Monte Carlo approach is employed to anticipate charging power by developing a probability distribution model of the charging behavior of EVs. Secondly, a scientific classification of the load period is performed using the fuzzy clustering approach. Then, a matrix of demand price elasticity is developed to measure the link between EV charging demand and charging price. Finally, the charging scheme is optimized by an adaptive genetic algorithm from the distribution network and EV user viewpoints. This paper describes how to implement the method presented in this paper in an IEEE-33-bus distribution network. The simulation results reveal that, when compared to fixed price and common time-of-use pricing (CTOUP), the OTOUP charging strategy bears a stronger impact on reducing peak–valley disparities, boosting operating voltage, and decreasing charging cost. Additionally, this paper studies the effect of varied degrees of responsiveness on charging strategies for EVs. The data imply that increased responsiveness enhances the likelihood of new load peak, and that additional countermeasures are required.

Suggested Citation

  • Hui Hwang Goh & Lian Zong & Dongdong Zhang & Wei Dai & Chee Shen Lim & Tonni Agustiono Kurniawan & Kai Chen Goh, 2022. "Orderly Charging Strategy Based on Optimal Time of Use Price Demand Response of Electric Vehicles in Distribution Network," Energies, MDPI, vol. 15(5), pages 1-25, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1869-:d:763315
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Yang, So Young & Woo, JongRoul & Lee, Wonjong, 2024. "Assessing optimized time-of-use pricing for electric vehicle charging in deep vehicle-grid integration system," Energy Economics, Elsevier, vol. 138(C).
    2. Qi Huang & Aihua Jiang & Yu Zeng & Jianan Xu, 2022. "Community Flexible Load Dispatching Model Based on Herd Mentality," Energies, MDPI, vol. 15(13), pages 1-18, June.
    3. António Gomes Martins & Luís Pires Neves & José Luís Sousa, 2023. "Electricity Demand Side Management," Energies, MDPI, vol. 16(16), pages 1-3, August.
    4. Tonni Agustiono Kurniawan & Mohd Hafiz Dzarfan Othman & Xue Liang & Muhammad Ayub & Hui Hwang Goh & Tutuk Djoko Kusworo & Ayesha Mohyuddin & Kit Wayne Chew, 2022. "Microbial Fuel Cells (MFC): A Potential Game-Changer in Renewable Energy Development," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    5. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    6. Olga Bogdanova & Karīna Viskuba & Laila Zemīte, 2023. "A Review of Barriers and Enables in Demand Response Performance Chain," Energies, MDPI, vol. 16(18), pages 1-33, September.
    7. Pramote Jaruwatanachai & Yod Sukamongkol & Taweesak Samanchuen, 2023. "Predicting and Managing EV Charging Demand on Electrical Grids: A Simulation-Based Approach," Energies, MDPI, vol. 16(8), pages 1-22, April.
    8. Du, Wenyi & Ma, Juan & Yin, Wanjun, 2023. "Orderly charging strategy of electric vehicle based on improved PSO algorithm," Energy, Elsevier, vol. 271(C).
    9. Liwen Zhu & Jun He & Lixun He & Wentao Huang & Yanyang Wang & Zong Liu, 2022. "Optimal Operation Strategy of PV-Charging-Hydrogenation Composite Energy Station Considering Demand Response," Energies, MDPI, vol. 15(16), pages 1-23, August.

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