Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Fair and Efficient Profit Allocation for Collaborative Operation of Distributed Renewable Energy Operators and Electric Vehicle Charging Stations
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1.State Key Laboratory of Power System Operation and Control, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China;2.Sichuan Energy Internet Research Institute, Tsinghua University, Beijing 100190, China;3.School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Sydney, Australia

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This work was supported in part by the National Natural Science Foundation of China (No. 52122706) and in part by the Tsinghua University Initiative Scientific Research Program.

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    Abstract:

    The difficulty in capital recovery for distributed renewable energy operators (DREOs) and the high charging costs at electric vehicle charging stations (EVCSs) have long been significant challenges in power systems. Collaborative operation of DREOs and EVCSs can effectively address these challenges, yet few studies have approached incentivizing collaboration from the perspective of profit allocation. Therefore, this paper proposes a fair and efficient profit allocation method. Incorporating the Gauss-Legendre quadrature formula into the Aumann-Shapley value (GL-AS) method enables efficient calculation of the profit allocation of cooperative members. However, existing literature only discusses the profit allocation method of conventional power generation units, limiting its applicability. This paper addresses the problem of energy storage system (ESS) switching between charging and discharging in any time interval and the time-varying problem of renewable energy power output, thereby ensuring the efficiency of the solution process. Furthermore, a novel profit allocation adjustment model is provided through the adoption of triangular fuzzy comprehensive evaluation (TFCE). Finally, the effectiveness of the proposed profit allocation method is validated through numerical simulations in various scenarios.

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History
  • Received:May 22,2024
  • Revised:August 30,2024
  • Adopted:
  • Online: February 13,2026
  • Published:
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