Journal of Modern Power Systems and Clean Energy

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Home > Archive>Volume 12, Issue 5, 2024 >1572-1583. DOI:10.35833/MPCE.2023.000512
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Scenario-based Optimal Real-time Charging Strategy of Electric Vehicles with Bayesian Long Short-term Memory Networks
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Affiliation:

1.College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China,;2.UNSW Business School, The University of New South Wales, NSW 2052, Sydney, Australia;3.Hainan Institute, Zhejiang University, Sanya 572000, China;4.Department of Electrical Engineering, College of Information Science and Engineering, Huaqiao University, Xiamen, China;5.Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guangzhou, China

Fund Project:

This work was supported in part by the National Natural Science Foundation of China (No. U1910216) and in part by the Science and Technology Project of China Southern Power Grid Company Limited (No. 080037KK52190039/GZHKJXM20190100).

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    Abstract:

    Joint operation optimization for electric vehicles (EVs) and on-site or adjacent photovoltaic generation (PVG) are pivotal to maintaining the security and economics of the operation of the power system concerned. Conventional offline optimization algorithms lack real-time applicability due to uncertainties involved in the charging service of an EV charging station (EVCS). Firstly, an optimization model for real-time EV charging strategy is proposed to address these challenges, which accounts for environmental uncertainties of an EVCS, encompassing EV arrivals, charging demands, PVG outputs, and the electricity price. Then, a scenario-based two-stage optimization approach is formulated. The scenarios of the underlying uncertain environmental factors are generated by the Bayesian long short-term memory (B-LSTM) network. Finally, numerical results substantiate the efficacy of the proposed optimization approach, and demonstrate superior profitability compared with prevalent approaches.

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History
  • Received:July 27,2023
  • Revised:November 24,2023
  • Online: September 25,2024

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