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Active and Reactive Power Collaborative Optimization for Active Distribution Networks Considering Bi-Directional V2G Behavior

Author

Listed:
  • Gang Xu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Bingxu Zhang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Le Yang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Yi Wang

    (Kunming Engineering Corporation Limited, Kunming 650051, China)

Abstract

Due to their great potential for energy conservation and emission reduction, electric vehicles (EVs) have attracted the attention of governments around the world and become more popular. However, the high penetration rate of EVs has brought great challenges to the operation of the Active Distribution Network (ADN). On the other hand, EVs will be equipped with more intelligent chargers in the future, which supports the EVs’ high flexibility in both active and reactive power control. In this paper, a distributed optimization model of ADN is proposed by employing the collaborative active and reactive power control capability of EVs. Firstly, the preference of EV users is taken into account and the charging mode of EVs is divided into three categories: rated power charging, non-discharging, and flexible charging–discharging. Then, the reactive power compensation capacity of the plugged-in EV is deduced based on the circuit topology of the intelligent charger and the active–reactive power control model of the EV is established subsequently. Secondly, considering the operation constraints of ADN and the charging–discharging constraints of EVs over the operation planning horizon, the optimization objective of the model is proposed, which consists of two parts: “minimizing energy cost” and “improving voltage profile”. Finally, a distributed solution method is proposed based on the Alternating Direction Method of Multipliers (ADMM). The proposed model is implemented on a 33-bus ADN. The obtained results demonstrate that it is beneficial to achieve lower energy cost and increase the voltage profile of the ADN. In addition, the energy demand of EV batteries in their plugin intervals is met, and the demand preference of EV users is guaranteed.

Suggested Citation

  • Gang Xu & Bingxu Zhang & Le Yang & Yi Wang, 2021. "Active and Reactive Power Collaborative Optimization for Active Distribution Networks Considering Bi-Directional V2G Behavior," Sustainability, MDPI, vol. 13(11), pages 1-26, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:6489-:d:570328
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    References listed on IDEAS

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    Cited by:

    1. Yuxuan Wang & Bingxu Zhang & Chenyang Li & Yongzhang Huang, 2022. "Collaborative Robust Optimization Strategy of Electric Vehicles and Other Distributed Energy Considering Load Flexibility," Energies, MDPI, vol. 15(8), pages 1-22, April.
    2. Hamidreza Mirtaheri & Piero Macaluso & Maurizio Fantino & Marily Efstratiadi & Sotiris Tsakanikas & Panagiotis Papadopoulos & Andrea Mazza, 2021. "Hybrid Forecast and Control Chain for Operation of Flexibility Assets in Micro-Grids," Energies, MDPI, vol. 14(21), pages 1-22, November.

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