IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v315y2022ics0306261922003919.html
   My bibliography  Save this article

Joint control of manufacturing and onsite microgrid system via novel neural-network integrated reinforcement learning algorithms

Author

Listed:
  • Yang, Jiaojiao
  • Sun, Zeyi
  • Hu, Wenqing
  • Steinmeister, Louis

Abstract

Microgrid is a promising technology of distributed energy supply system, which consists of storage devices, generation capacities including renewable sources, and controllable loads. It has been widely investigated and applied for residential and commercial end-use customers as well as critical facilities. In this paper, we propose a joint state-based dynamic control model on microgrids and manufacturing systems where optimal controls for both sides are implemented to coordinate the energy demand and supply so that the overall production cost can be minimized considering the constraint of production target. Markov Decision Process (MDP) is used to formulate the decision-making procedure. The main computing challenge to solve the formulated MDP lies in the co-existence of both discrete and continuous parts of the high-dimensional state/action space that are intertwined with constraints. A novel reinforcement learning algorithm that leverages both Temporal Difference (TD) and Deterministic Policy Gradient (DPG) algorithms is proposed to address the computation challenge. Experiments for a manufacturing system with an onsite microgrid system with renewable sources have been implemented to justify the effectiveness of the proposed method.

Suggested Citation

  • Yang, Jiaojiao & Sun, Zeyi & Hu, Wenqing & Steinmeister, Louis, 2022. "Joint control of manufacturing and onsite microgrid system via novel neural-network integrated reinforcement learning algorithms," Applied Energy, Elsevier, vol. 315(C).
    • Handle: RePEc:eee:appene:v:315:y:2022:i:c:s0306261922003919
    DOI: 10.1016/j.apenergy.2022.118982
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922003919
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.118982?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Petrollese, Mario & Valverde, Luis & Cocco, Daniele & Cau, Giorgio & Guerra, José, 2016. "Real-time integration of optimal generation scheduling with MPC for the energy management of a renewable hydrogen-based microgrid," Applied Energy, Elsevier, vol. 166(C), pages 96-106.
    2. Mehdi Golari & Neng Fan & Tongdan Jin, 2017. "Multistage Stochastic Optimization for Production-Inventory Planning with Intermittent Renewable Energy," Production and Operations Management, Production and Operations Management Society, vol. 26(3), pages 409-425, March.
    3. Ashok, S., 2006. "Peak-load management in steel plants," Applied Energy, Elsevier, vol. 83(5), pages 413-424, May.
    4. Craparo, Emily & Karatas, Mumtaz & Singham, Dashi I., 2017. "A robust optimization approach to hybrid microgrid operation using ensemble weather forecasts," Applied Energy, Elsevier, vol. 201(C), pages 135-147.
    5. Sadegheih, A., 2011. "Optimal design methodologies under the carbon emission trading program using MIP, GA, SA, and TS," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 504-513, January.
    6. Ma, Shuaiyin & Zhang, Yingfeng & Lv, Jingxiang & Ge, Yuntian & Yang, Haidong & Li, Lin, 2020. "Big data driven predictive production planning for energy-intensive manufacturing industries," Energy, Elsevier, vol. 211(C).
    7. Kuznetsova, Elizaveta & Li, Yan-Fu & Ruiz, Carlos & Zio, Enrico & Ault, Graham & Bell, Keith, 2013. "Reinforcement learning for microgrid energy management," Energy, Elsevier, vol. 59(C), pages 133-146.
    8. Ying Ji & Jianhui Wang & Jiacan Xu & Xiaoke Fang & Huaguang Zhang, 2019. "Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning," Energies, MDPI, vol. 12(12), pages 1-21, June.
    9. Yun, Lingxiang & Li, Lin & Ma, Shuaiyin, 2022. "Demand response for manufacturing systems considering the implications of fast-charging battery powered material handling equipment," Applied Energy, Elsevier, vol. 310(C).
    10. Kriett, Phillip Oliver & Salani, Matteo, 2012. "Optimal control of a residential microgrid," Energy, Elsevier, vol. 42(1), pages 321-330.
    11. Sun, Wenqiang & Wang, Qiang & Zhou, Yue & Wu, Jianzhong, 2020. "Material and energy flows of the iron and steel industry: Status quo, challenges and perspectives," Applied Energy, Elsevier, vol. 268(C).
    12. Hawkes, A.D. & Leach, M.A., 2007. "Cost-effective operating strategy for residential micro-combined heat and power," Energy, Elsevier, vol. 32(5), pages 711-723.
    13. Victor Santana-Viera & Jesus Jimenez & Tongdan Jin & Jose Espiritu, 2015. "Implementing factory demand response via onsite renewable energy: a design-of-experiment approach," International Journal of Production Research, Taylor & Francis Journals, vol. 53(23), pages 7034-7048, December.
    14. Zhongwen Li & Chuanzhi Zang & Peng Zeng & Haibin Yu, 2016. "Combined Two-Stage Stochastic Programming and Receding Horizon Control Strategy for Microgrid Energy Management Considering Uncertainty," Energies, MDPI, vol. 9(7), pages 1-16, June.
    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. Ma, Shuaiyin & Huang, Yuming & Liu, Yang & Kong, Xianguang & Yin, Lei & Chen, Gaige, 2023. "Edge-cloud cooperation-driven smart and sustainable production for energy-intensive manufacturing industries," Applied Energy, Elsevier, vol. 337(C).
    2. Zhong, Xiaoqing & Zhong, Weifeng & Lin, Zhenjia & Zhou, Guoxu & Lai, Loi Lei & Xie, Shengli & Yan, Jinyue, 2024. "Localized electricity and carbon allowance management for interconnected discrete manufacturing systems considering algorithmic and physical feasibility," Applied Energy, Elsevier, vol. 372(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. Mohamed Habib Jabeur & Sonia Mahjoub & Cyril Toublanc, 2023. "Sustainable Production Scheduling with On-Site Intermittent Renewable Energy and Demand-Side Management: A Feed-Animal Case Study," Energies, MDPI, vol. 16(14), pages 1-24, July.
    2. Ma, Shuaiyin & Ding, Wei & Liu, Yang & Ren, Shan & Yang, Haidong, 2022. "Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries," Applied Energy, Elsevier, vol. 326(C).
    3. Fontenot, Hannah & Dong, Bing, 2019. "Modeling and control of building-integrated microgrids for optimal energy management – A review," Applied Energy, Elsevier, vol. 254(C).
    4. Ying Ji & Jianhui Wang & Jiacan Xu & Xiaoke Fang & Huaguang Zhang, 2019. "Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning," Energies, MDPI, vol. 12(12), pages 1-21, June.
    5. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    6. Hajo Terbrack & Thorsten Claus & Frank Herrmann, 2021. "Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme," Sustainability, MDPI, vol. 13(23), pages 1-32, December.
    7. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    8. Ahmad Khan, Aftab & Naeem, Muhammad & Iqbal, Muhammad & Qaisar, Saad & Anpalagan, Alagan, 2016. "A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1664-1683.
    9. Zhu, Ziqing & Hu, Ze & Chan, Ka Wing & Bu, Siqi & Zhou, Bin & Xia, Shiwei, 2023. "Reinforcement learning in deregulated energy market: A comprehensive review," Applied Energy, Elsevier, vol. 329(C).
    10. Liu, Shuhan & Sun, Wenqiang, 2023. "Attention mechanism-aided data- and knowledge-driven soft sensors for predicting blast furnace gas generation," Energy, Elsevier, vol. 262(PA).
    11. Zheng, Lingwei & Wu, Hao & Guo, Siqi & Sun, Xinyu, 2023. "Real-time dispatch of an integrated energy system based on multi-stage reinforcement learning with an improved action-choosing strategy," Energy, Elsevier, vol. 277(C).
    12. Grace Muriithi & Sunetra Chowdhury, 2021. "Optimal Energy Management of a Grid-Tied Solar PV-Battery Microgrid: A Reinforcement Learning Approach," Energies, MDPI, vol. 14(9), pages 1-24, May.
    13. Lilia Tightiz & Joon Yoo, 2022. "A Review on a Data-Driven Microgrid Management System Integrating an Active Distribution Network: Challenges, Issues, and New Trends," Energies, MDPI, vol. 15(22), pages 1-24, November.
    14. Ma, Shuaiyin & Huang, Yuming & Liu, Yang & Liu, Haizhou & Chen, Yanping & Wang, Jin & Xu, Jun, 2023. "Big data-driven correlation analysis based on clustering for energy-intensive manufacturing industries," Applied Energy, Elsevier, vol. 349(C).
    15. Kang, Dongju & Kang, Doeun & Hwangbo, Sumin & Niaz, Haider & Lee, Won Bo & Liu, J. Jay & Na, Jonggeol, 2023. "Optimal planning of hybrid energy storage systems using curtailed renewable energy through deep reinforcement learning," Energy, Elsevier, vol. 284(C).
    16. Ahmed Ismail & Mustafa Baysal, 2023. "Dynamic Pricing Based on Demand Response Using Actor–Critic Agent Reinforcement Learning," Energies, MDPI, vol. 16(14), pages 1-19, July.
    17. Wakui, Tetsuya & Kawayoshi, Hiroki & Yokoyama, Ryohei & Aki, Hirohisa, 2016. "Operation management of residential energy-supplying networks based on optimization approaches," Applied Energy, Elsevier, vol. 183(C), pages 340-357.
    18. Ma, Shuaiyin & Huang, Yuming & Liu, Yang & Kong, Xianguang & Yin, Lei & Chen, Gaige, 2023. "Edge-cloud cooperation-driven smart and sustainable production for energy-intensive manufacturing industries," Applied Energy, Elsevier, vol. 337(C).
    19. Khawaja Haider Ali & Marvin Sigalo & Saptarshi Das & Enrico Anderlini & Asif Ali Tahir & Mohammad Abusara, 2021. "Reinforcement Learning for Energy-Storage Systems in Grid-Connected Microgrids: An Investigation of Online vs. Offline Implementation," Energies, MDPI, vol. 14(18), pages 1-18, September.
    20. Agüera-Pérez, Agustín & Palomares-Salas, José Carlos & González de la Rosa, Juan José & Florencias-Oliveros, Olivia, 2018. "Weather forecasts for microgrid energy management: Review, discussion and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 265-278.

    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:eee:appene:v:315:y:2022:i:c:s0306261922003919. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.