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Optimal Operation of Multiple Energy System Based on Multi-Objective Theory and Grey Theory

Author

Listed:
  • Bo Hu

    (State Grid Liaoning Electric Power Co., Ltd., Shenyang 110006, China
    School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Nan Wang

    (State Grid Liaoning Electric Power Co., Ltd., Shenyang 110006, China
    College of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150006, China)

  • Zaiming Yu

    (State Grid Liaoning Electric Power Co., Ltd., Shenyang 110006, China)

  • Yunqing Cao

    (College of Physical Science and Technology, Yangzhou University, Yangzhou 225000, China)

  • Dongsheng Yang

    (College of Information Science and Engineering, Northeastern University, Shenyang 110057, China)

  • Li Sun

    (College of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150006, China)

Abstract

The manufacturing industry consumes electricity and natural gas to provide the power and heat required for manufacturing. Additionally, large amounts of electric energy and heat energy are used, and the electricity cost, amount of environmental pollution, and equipment maintenance cost are high. Thus, optimizing the management of equipment with new energy is important to satisfy the load demand from the system. This paper formulates the scheduling problem of these multiple energy systems as a multi-objective linear regression model (MLRM), and an energy management system is designed focusing on the economy and on greenhouse gas emissions. Furthermore, a variety of optimization objectives and constraints are proposed to make the energy management scheme more practical. Then, grey theory is combined with the common MLRM to accurately represent the uncertainty in the system and to make the model better reflect the actual situation. This paper takes load fluctuation, total grid operation cost, and environmental pollution value as reference standards to measure the effect of the gray optimization algorithm. Lastly, the model is applied to optimize the energy supply plan and its performance is demonstrated using numerical examples. The verification results meet the optimized operating conditions of the multi-energy microgrid system.

Suggested Citation

  • Bo Hu & Nan Wang & Zaiming Yu & Yunqing Cao & Dongsheng Yang & Li Sun, 2021. "Optimal Operation of Multiple Energy System Based on Multi-Objective Theory and Grey Theory," Energies, MDPI, vol. 15(1), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:15:y:2021:i:1:p:68-:d:709000
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    References listed on IDEAS

    as
    1. Changbin Hu & Shanna Luo & Zhengxi Li & Xin Wang & Li Sun, 2015. "Energy Coordinative Optimization of Wind-Storage-Load Microgrids Based on Short-Term Prediction," Energies, MDPI, vol. 8(2), pages 1-24, February.
    2. Maël Riou & Florian Dupriez-Robin & Dominique Grondin & Christophe Le Loup & Michel Benne & Quoc T. Tran, 2021. "Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration," Energies, MDPI, vol. 14(15), pages 1-20, July.
    3. Łukasz Rokicki, 2021. "Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator," Energies, MDPI, vol. 14(19), pages 1-24, October.
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    Cited by:

    1. Shiduo Jia & Xiaoning Kang, 2022. "Multi-Objective Optimal Scheduling of CHP Microgrid Considering Conditional Value-at-Risk," Energies, MDPI, vol. 15(9), pages 1-21, May.

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