IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i16p5920-d1214440.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/16/5920/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/16/5920/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhao, Shihao & Li, Kang & Yang, Zhile & Xu, Xinzhi & Zhang, Ning, 2022. "A new power system active rescheduling method considering the dispatchable plug-in electric vehicles and intermittent renewable energies," Applied Energy, Elsevier, vol. 314(C).
    2. 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).
    3. Hao, Ran & Lu, Tianguang & Ai, Qian & Wang, Zhe & Wang, Xiaolong, 2020. "Distributed online learning and dynamic robust standby dispatch for networked microgrids," Applied Energy, Elsevier, vol. 274(C).
    4. Marek Krok & Paweł Majewski & Wojciech P. Hunek & Tomasz Feliks, 2022. "Energy Optimization of the Continuous-Time Perfect Control Algorithm," Energies, MDPI, vol. 15(4), pages 1-13, February.
    5. Jeffrey Christiansen & Brian Dandurand & Andrew Eberhard & Fabricio Oliveira, 2023. "A study of progressive hedging for stochastic integer programming," Computational Optimization and Applications, Springer, vol. 86(3), pages 989-1034, December.
    6. Clarke, Will Challis & Brear, Michael John & Manzie, Chris, 2020. "Control of an isolated microgrid using hierarchical economic model predictive control," Applied Energy, Elsevier, vol. 280(C).
    7. Panda, Debashish & Ramteke, Manojkumar, 2019. "Preventive crude oil scheduling under demand uncertainty using structure adapted genetic algorithm," Applied Energy, Elsevier, vol. 235(C), pages 68-82.
    8. Suroso Isnandar & Jonathan F. Simorangkir & Kevin M. Banjar-Nahor & Hendry Timotiyas Paradongan & Nanang Hariyanto, 2024. "A Multiparadigm Approach for Generation Dispatch Optimization in a Regulated Electricity Market towards Clean Energy Transition," Energies, MDPI, vol. 17(15), pages 1-28, August.
    9. Aguilar, Diego & Quinones, Jhon J. & Pineda, Luis R. & Ostanek, Jason & Castillo, Luciano, 2024. "Optimal scheduling of renewable energy microgrids: A robust multi-objective approach with machine learning-based probabilistic forecasting," Applied Energy, Elsevier, vol. 369(C).
    10. Masoumi, A.P. & Tavakolpour-Saleh, A.R. & Rahideh, A., 2020. "Applying a genetic-fuzzy control scheme to an active free piston Stirling engine: Design and experiment," Applied Energy, Elsevier, vol. 268(C).
    11. Li, Chaoshun & Wang, Wenxiao & Chen, Deshu, 2019. "Multi-objective complementary scheduling of hydro-thermal-RE power system via a multi-objective hybrid grey wolf optimizer," Energy, Elsevier, vol. 171(C), pages 241-255.
    12. McLarty, Dustin & Panossian, Nadia & Jabbari, Faryar & Traverso, Alberto, 2019. "Dynamic economic dispatch using complementary quadratic programming," Energy, Elsevier, vol. 166(C), pages 755-764.
    13. Stennikov, Valery & Barakhtenko, Evgeny & Mayorov, Gleb & Sokolov, Dmitry & Zhou, Bin, 2022. "Coordinated management of centralized and distributed generation in an integrated energy system using a multi-agent approach," Applied Energy, Elsevier, vol. 309(C).
    14. Myada Shadoul & Rashid Al Abri & Hassan Yousef & Abdullah Al Shereiqi, 2024. "Designing a Dispatch Engine for Hybrid Renewable Power Stations Using a Mixed-Integer Linear Programming Technique," Energies, MDPI, vol. 17(13), pages 1-27, July.
    15. Linxin Zhang & Zuobin Ying & Zhile Yang & Yuanjun Guo, 2024. "Dynamic Multi-Energy Optimization for Unit Commitment Integrating PEVs and Renewable Energy: A DO3LSO Algorithm," Mathematics, MDPI, vol. 12(24), pages 1-28, December.
    16. Wang, Wenting & Yang, Dazhi & Huang, Nantian & Lyu, Chao & Zhang, Gang & Han, Xueying, 2022. "Irradiance-to-power conversion based on physical model chain: An application on the optimal configuration of multi-energy microgrid in cold climate," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    17. Rafael Poppenborg & Malte Chlosta & Johannes Ruf & Christian Hotz & Clemens Düpmeier & Thomas Kolb & Veit Hagenmeyer, 2023. "Energy Hub Gas: A Modular Setup for the Evaluation of Local Flexibility and Renewable Energy Carriers Provision," Energies, MDPI, vol. 16(6), pages 1-16, March.
    18. Zhang, Xizheng & Wang, Zeyu & Lu, Zhangyu, 2022. "Multi-objective load dispatch for microgrid with electric vehicles using modified gravitational search and particle swarm optimization algorithm," Applied Energy, Elsevier, vol. 306(PA).
    19. Zheng, Lingwei & Zhou, Xingqiu & Qiu, Qi & Yang, Lan, 2020. "Day-ahead optimal dispatch of an integrated energy system considering time-frequency characteristics of renewable energy source output," Energy, Elsevier, vol. 209(C).
    20. Motamedi Sedeh, Omid & Ostadi, Bakhtiar, 2020. "Optimization of bidding strategy in the day-ahead market by consideration of seasonality trend of the market spot price," Energy Policy, Elsevier, vol. 145(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:5920-:d:1214440. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.