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Robust Optimal Scheduling of Microgrid with Electric Vehicles Based on Stackelberg Game

Author

Listed:
  • Jianhong Hao

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Ting Huang

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Qiuming Xu

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Yi Sun

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

With increasing penetration of distributed generators (DG), the uncertainty and intermittence of renewable energy has brought new challenges to the economic dispatch and promotion of environment sustainability of microgrids. Active loads, especially in electric vehicles (EVs), are thought to be an efficient way to deal with the uncertainty and intermittence of renewable energy. One of the most important features of EVs is that their demand will vary in response to the electricity price. How to determine the real-time charging price to guide the orderly charging of EVs and operate with an uncertain renewable energy output represents an important topic for the microgrid operator (MGO). To this end, this paper formulates the optimal pricing and robust dispatch problem of the MGO as a Stackelberg game, in which the upper level minimizes the MGO’s cost, while the lower level minimizes the charging cost of each EV. In the problem, the approximate linear relationship between the node voltage and equivalent load is modeled, and the approximate linear expression of the node voltage security constraint is derived. Using dual optimization theory, the robust optimal dispatch model is transformed into a linear programming model without uncertain variables. Then, the Stackelberg game model is transformed into a mixed integer linear program by using the duality theorem of linear programming. Finally, the effectiveness of the proposed method is proved by simulation within the modified IEEE33-bus system.

Suggested Citation

  • Jianhong Hao & Ting Huang & Qiuming Xu & Yi Sun, 2023. "Robust Optimal Scheduling of Microgrid with Electric Vehicles Based on Stackelberg Game," Sustainability, MDPI, vol. 15(24), pages 1-15, December.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:24:p:16682-:d:1296785
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    References listed on IDEAS

    as
    1. Yuxin Wen & Peixiao Fan & Jia Hu & Song Ke & Fuzhang Wu & Xu Zhu, 2022. "An Optimal Scheduling Strategy of a Microgrid with V2G Based on Deep Q-Learning," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
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    3. Peng Chen & Chen Qian & Li Lan & Mingxing Guo & Qiong Wu & Hongbo Ren & Yue Zhang, 2023. "Shared Trading Strategy of Multiple Microgrids Considering Joint Carbon and Green Certificate Mechanism," Sustainability, MDPI, vol. 15(13), pages 1-15, June.
    4. Lin, Zewei & Wang, Peng & Ren, Songyan & Zhao, Daiqing, 2023. "Economic and environmental impacts of EVs promotion under the 2060 carbon neutrality target—A CGE based study in Shaanxi Province of China," Applied Energy, Elsevier, vol. 332(C).
    5. Muhammad Anique Aslam & Syed Abdul Rahman Kashif & Muhammad Majid Gulzar & Mohammed Alqahtani & Muhammad Khalid, 2023. "A Novel Multi Level Dynamic Decomposition Based Coordinated Control of Electric Vehicles in Multimicrogrids," Sustainability, MDPI, vol. 15(16), pages 1-29, August.
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