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

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  • 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
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    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).

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