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Environment-Friendly Power Scheduling Based on Deep Contextual Reinforcement Learning

Author

Listed:
  • Awol Seid Ebrie

    (Major in Industrial Data Science and Engineering, Department of Industrial and Data Engineering, Pukyong National University, Busan 48513, Republic of Korea)

  • Chunhyun Paik

    (Department of Industrial Management and Big Data Engineering, Dongeui University, Busan 47340, Republic of Korea)

  • Yongjoo Chung

    (Department of Global Marketing, Busan University of Foreign Studies, Busan 46234, Republic of Korea)

  • Young Jin Kim

    (Department of Systems Management and Engineering, Pukyong National University, Busan 48513, Republic of Korea)

Abstract

A novel approach to power scheduling is introduced, focusing on minimizing both economic and environmental impacts. This method utilizes deep contextual reinforcement learning (RL) within an agent-based simulation environment. Each generating unit is treated as an independent, heterogeneous agent, and the scheduling dynamics are formulated as Markov decision processes (MDPs). The MDPs are then used to train a deep RL model to determine optimal power schedules. The performance of this approach is evaluated across various power systems, including both small-scale and large-scale systems with up to 100 units. The results demonstrate that the proposed method exhibits superior performance and scalability in handling power systems with a larger number of units.

Suggested Citation

  • Awol Seid Ebrie & Chunhyun Paik & Yongjoo Chung & Young Jin Kim, 2023. "Environment-Friendly Power Scheduling Based on Deep Contextual Reinforcement Learning," Energies, MDPI, vol. 16(16), pages 1-12, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:5920-:d:1214440
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    References listed on IDEAS

    as
    1. de Mars, Patrick & O’Sullivan, Aidan, 2021. "Applying reinforcement learning and tree search to the unit commitment problem," Applied Energy, Elsevier, vol. 302(C).
    2. Nemati, Mohsen & Braun, Martin & Tenbohlen, Stefan, 2018. "Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming," Applied Energy, Elsevier, vol. 210(C), pages 944-963.
    3. Luis Montero & Antonio Bello & Javier Reneses, 2022. "A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods," Energies, MDPI, vol. 15(4), pages 1-40, February.
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    Cited by:

    1. Ebrie, Awol Seid & Kim, Young Jin, 2024. "Reinforcement learning-based optimization for power scheduling in a renewable energy connected grid," Renewable Energy, Elsevier, vol. 230(C).

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