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Deep-Reinforcement-Learning-Based Vehicle-to-Grid Operation Strategies for Managing Solar Power Generation Forecast Errors

Author

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  • Moon-Jong Jang

    (Smart Power Distribution Laboratory of Korea Electric Power Research Institute, Korea Electric Power Corporation, Daejeon 34056, South Chungcheong, Republic of Korea)

  • Eunsung Oh

    (Department of Electrical Engineering, College of IT Convergence, Global Campus, Gachon University, Seongnam-si 13120, Gyeonggi-do, Republic of Korea)

Abstract

This study proposes a deep-reinforcement-learning (DRL)-based vehicle-to-grid (V2G) operation strategy that focuses on the dynamic integration of charging station (CS) status to refine solar power generation (SPG) forecasts. To address the variability in solar energy and CS status, this study proposes a novel approach by formulating the V2G operation as a Markov decision process and leveraging DRL to adaptively manage SPG forecast errors. Utilizing real-world data from the Korea Southern Power Corporation, the effectiveness of this strategy in enhancing SPG forecasts is proven using the PyTorch framework. The results demonstrate a significant reduction in the mean squared error by 40% to 56% compared to scenarios without V2G. Our investigation into the effects of blocking probability thresholds and discount factors revealed insights into the optimal V2G system performance, suggesting a balance between immediate operational needs and long-term strategic objectives. The findings highlight the possibility of using DRL-based strategies to achieve more reliable and efficient renewable energy integration in power grids, marking a significant step forward in smart grid optimization.

Suggested Citation

  • Moon-Jong Jang & Eunsung Oh, 2024. "Deep-Reinforcement-Learning-Based Vehicle-to-Grid Operation Strategies for Managing Solar Power Generation Forecast Errors," Sustainability, MDPI, vol. 16(9), pages 1-18, May.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:9:p:3851-:d:1388386
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    References listed on IDEAS

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    1. Suresh Panchanathan & Pradeep Vishnuram & Narayanamoorthi Rajamanickam & Mohit Bajaj & Vojtech Blazek & Lukas Prokop & Stanislav Misak, 2023. "A Comprehensive Review of the Bidirectional Converter Topologies for the Vehicle-to-Grid System," Energies, MDPI, vol. 16(5), pages 1-33, March.
    2. Srinivasan, Arvind & Wu, Raphael & Heer, Philipp & Sansavini, Giovanni, 2023. "Impact of forecast uncertainty and electricity markets on the flexibility provision and economic performance of highly-decarbonized multi-energy systems," Applied Energy, Elsevier, vol. 338(C).
    3. Michael C. Ferris & Andy Philpott, 2023. "Renewable electricity capacity planning with uncertainty at multiple scales," Computational Management Science, Springer, vol. 20(1), pages 1-40, December.
    4. Zheng, Xidong & Bai, Feifei & Zhuang, Zhiyuan & Chen, Zixing & Jin, Tao, 2023. "A new demand response management strategy considering renewable energy prediction and filtering technology," Renewable Energy, Elsevier, vol. 211(C), pages 656-668.
    5. Bodong, Song & Wiseong, Jin & Chengmeng, Li & Khakichi, Aroos, 2023. "Economic management and planning based on a probabilistic model in a multi-energy market in the presence of renewable energy sources with a demand-side management program," Energy, Elsevier, vol. 269(C).
    6. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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