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Multi-Objective Scheduling of Electric Vehicles in Smart Distribution Network

Author

Listed:
  • Changhong Deng

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Ning Liang

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Jin Tan

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Gongchen Wang

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

Abstract

Due to the energy savings and environmental protection they provide, plug-in electric vehicles (PEVs) are increasing in number quickly. Rapid development of PEVs brings new opportunities and challenges to the electricity distribution network’s dispatching. A high number of uncoordinated charging PEVs has significant negative impacts on the secure and economic operation of a distribution network. In this paper, a bi-level programming approach that coordinates PEVs’ charging with the network load and electricity price of the open market is presented. The major objective of the upper level model is to minimize the total network costs and the deviation of electric vehicle aggregators’ charging power and the equivalent power. The subsequent objective of the lower level model after the upper level decision is to minimize the dispatching deviation of the sum of PEVs’ charging power and their optimization charging power under the upper level model. An improved particle swarm optimization algorithm is used to solve the bi-level programming. Numerical studies using a modified IEEE 69-bus distribution test system including six electric vehicle aggregators verify the efficiency of the proposed model.

Suggested Citation

  • Changhong Deng & Ning Liang & Jin Tan & Gongchen Wang, 2016. "Multi-Objective Scheduling of Electric Vehicles in Smart Distribution Network," Sustainability, MDPI, vol. 8(12), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:12:p:1234-:d:83885
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    References listed on IDEAS

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    Citations

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    Cited by:

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    3. Stojiljković, Mirko M., 2017. "Bi-level multi-objective fuzzy design optimization of energy supply systems aided by problem-specific heuristics," Energy, Elsevier, vol. 137(C), pages 1231-1251.
    4. Zhenshan Zhu & Dichen Liu & Qingfen Liao & Fei Tang & Jun Jason Zhang & Huaiguang Jiang, 2018. "Optimal Power Scheduling for a Medium Voltage AC/DC Hybrid Distribution Network," Sustainability, MDPI, vol. 10(2), pages 1-22, January.
    5. Xingping Zhang & Yanni Liang & Yakun Zhang & Yinhe Bu & Hongyang Zhang, 2017. "Charge Pricing Optimization Model for Private Charging Piles in Beijing," Sustainability, MDPI, vol. 9(11), pages 1-15, November.
    6. Das, Ridoy & Wang, Yue & Putrus, Ghanim & Kotter, Richard & Marzband, Mousa & Herteleer, Bert & Warmerdam, Jos, 2020. "Multi-objective techno-economic-environmental optimisation of electric vehicle for energy services," Applied Energy, Elsevier, vol. 257(C).
    7. Yanni Liang & Xingping Zhang & Jian Xie & Wenfeng Liu, 2017. "An Optimal Operation Model and Ordered Charging/Discharging Strategy for Battery Swapping Stations," Sustainability, MDPI, vol. 9(5), pages 1-18, April.
    8. Gustavo E. Coria & Angel M. Sanchez & Ameena S. Al-Sumaiti & Guiseppe A. Rattá & Sergio R. Rivera & Andrés A. Romero, 2019. "A Framework for Determining a Prediction-Of-Use Tariff Aimed at Coordinating Aggregators of Plug-In Electric Vehicles," Energies, MDPI, vol. 12(23), pages 1-18, November.
    9. Hasan M. Salman & Jagadeesh Pasupuleti & Ahmad H. Sabry, 2023. "Review on Causes of Power Outages and Their Occurrence: Mitigation Strategies," Sustainability, MDPI, vol. 15(20), pages 1-34, October.
    10. Giulia Maesano & Giuseppe Di Vita & Gaetano Chinnici & Gioacchino Pappalardo & Mario D'Amico, 2020. "The Role of Credence Attributes in Consumer Choices of Sustainable Fish Products: A Review," Sustainability, MDPI, vol. 12(23), pages 1-18, November.

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