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Spatio-Temporal Model for Evaluating Demand Response Potential of Electric Vehicles in Power-Traffic Network

Author

Listed:
  • Lidan Chen

    (School of Electrical Engineering, Guangzhou College of South China University of Technology, Guangzhou 510800, China)

  • Yao Zhang

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Antonio Figueiredo

    (Department of Electronic Engineering, University of York, York YO10 5DD, UK)

Abstract

Electric vehicles (EVs) can be regarded as a kind of demand response (DR) resource. Nevertheless, the EVs travel behavior is flexible and random, in addition, their willingness to participate in the DR event is uncertain, they are expected to be managed and utilized by the EV aggregator (EVA). In this perspective, this paper presents a composite methodology that take into account the dynamic road network (DRN) information and fuzzy user participation (FUP) for obtaining spatio-temporal projections of demand response potential from electric vehicles and the electric vehicle aggregator. A dynamic traffic network model taking over the traffic time-varying information is developed by graph theory. The trip chain based on housing travel survey is set up, where Dijkstra algorithm is employed to plan the optimal route of EVs, in order to find the travel distance and travel time of each trip of EVs. To demonstrate the uncertainties of the EVs travel pattern, simulation analysis is conducted using Monte Carlo method. Subsequently, we suggest a fuzzy logic-based approach to uncertainty analysis that starts with investigating EV users’ subjective ability to participate in DR event, and we develop the FUP response mechanism which is constructed by three factors including the remaining dwell time, remaining SOC, and incentive electricity pricing. The FUP is used to calculate the real-time participation level of a single EV. Finally, we take advantage of a simulation example with a coupled 25-node road network and 54-node power distribution system to demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Lidan Chen & Yao Zhang & Antonio Figueiredo, 2019. "Spatio-Temporal Model for Evaluating Demand Response Potential of Electric Vehicles in Power-Traffic Network," Energies, MDPI, vol. 12(10), pages 1-20, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1981-:d:233722
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    References listed on IDEAS

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    Cited by:

    1. Isaias Gomes & Rui Melicio & Victor Mendes, 2020. "Comparison between Inflexible and Flexible Charging of Electric Vehicles—A Study from the Perspective of an Aggregator," Energies, MDPI, vol. 13(20), pages 1-13, October.
    2. Fuquan Zhao & Fanlong Bai & Xinglong Liu & Zongwei Liu, 2022. "A Review on Renewable Energy Transition under China’s Carbon Neutrality Target," Sustainability, MDPI, vol. 14(22), pages 1-27, November.
    3. Anna Auza & Ehsan Asadi & Behrang Chenari & Manuel Gameiro da Silva, 2023. "A Systematic Review of Uncertainty Handling Approaches for Electric Grids Considering Electrical Vehicles," Energies, MDPI, vol. 16(13), pages 1-25, June.
    4. Sheng Ding & Chengmei Xu & Yao Rao & Zhaofang Song & Wangwang Yang & Zexu Chen & Zitong Zhang, 2022. "A Time-Varying Potential Evaluation Method for Electric Vehicle Group Demand Response Driven by Small Sample Data," Sustainability, MDPI, vol. 14(9), pages 1-21, April.
    5. Aghajan-Eshkevari, Saleh & Ameli, Mohammad Taghi & Azad, Sasan, 2023. "Optimal routing and power management of electric vehicles in coupled power distribution and transportation systems," Applied Energy, Elsevier, vol. 341(C).
    6. Lucian Mihet-Popa & Sergio Saponara, 2021. "Power Converters, Electric Drives and Energy Storage Systems for Electrified Transportation and Smart Grid Applications," Energies, MDPI, vol. 14(14), pages 1-5, July.

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