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A Novel Energy Management Optimization Method for Commercial Users Based on Hybrid Simulation of Electricity Market Bidding

Author

Listed:
  • Jidong Wang

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Jiahui Wu

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Yingchen Shi

    (Chengnan District Power Supply Company of State Grid Tianjin Electric Power Company, Tianjin 300201, China)

Abstract

Energy management and utilization for commercial users is becoming increasingly intelligent and refined, fostering a closer and growing connection with the electricity market. In this paper, a novel energy management optimization theoretical framework for commercial users is proposed based on the hybrid simulation of electricity market bidding. The hybrid simulation model based on Multi-Agent Simulation (MAS) with reinforcement learning and System Dynamic Simulation (SDS) is established to solve the problem using a single simulation method: it cannot adjust the clearing price when considering the whole market; considering the uncertainty of Electric Vehicles (EVs) travel and Lighting Loads (LLs), the multi-objective optimization model of energy management for commercial users is constructed to minimize the total energy cost of commercial users, as well as maximize the lighting comfort of indoor office staff, which compensates for the lack of the single-objective optimization of the power consumption for commercial users. A multi-objective optimization model of energy management for commercial users is established based on the hybrid simulation of electricity market bidding. By running the multi-objective optimization model based on hybrid simulation, the results show that the proposed method can realize the optimization of energy management for commercial users considering electricity market bidding.

Suggested Citation

  • Jidong Wang & Jiahui Wu & Yingchen Shi, 2022. "A Novel Energy Management Optimization Method for Commercial Users Based on Hybrid Simulation of Electricity Market Bidding," Energies, MDPI, vol. 15(12), pages 1-24, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4207-:d:833576
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    References listed on IDEAS

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    1. Wang, Jidong & Wu, Jiahui & Che, Yanbo, 2019. "Agent and system dynamics-based hybrid modeling and simulation for multilateral bidding in electricity market," Energy, Elsevier, vol. 180(C), pages 444-456.
    2. Yiqi Li & Jing Zhang & Zhoujun Ma & Yang Peng & Shuwen Zhao, 2021. "An Energy Management Optimization Method for Community Integrated Energy System Based on User Dominated Demand Side Response," Energies, MDPI, vol. 14(15), pages 1-22, July.
    3. Fang, Xichen & Guo, Hongye & Zhang, Xian & Wang, Xuanyuan & Chen, Qixin, 2022. "An efficient and incentive-compatible market design for energy storage participation," Applied Energy, Elsevier, vol. 311(C).
    4. Morcillo, José D. & Franco, Carlos J. & Angulo, Fabiola, 2018. "Simulation of demand growth scenarios in the Colombian electricity market: An integration of system dynamics and dynamic systems," Applied Energy, Elsevier, vol. 216(C), pages 504-520.
    5. Bonomolo, Marina & Zizzo, Gaetano & Ferrari, Simone & Beccali, Marco & Guarino, Stefania, 2021. "Empirical BAC factors method application to two real case studies in South Italy," Energy, Elsevier, vol. 236(C).
    6. Tang, Ruoli & Li, Xin & Lai, Jingang, 2018. "A novel optimal energy-management strategy for a maritime hybrid energy system based on large-scale global optimization," Applied Energy, Elsevier, vol. 228(C), pages 254-264.
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    Cited by:

    1. Edgardo Cayon & Julio Sarmiento, 2022. "The Impact of Coskewness and Cokurtosis as Augmentation Factors in Modeling Colombian Electricity Price Returns," Energies, MDPI, vol. 15(19), pages 1-8, September.
    2. Xian Huang & Zhehan Li, 2023. "A Comparative Analysis of Two Pricing Mechanisms, MCP and PAB, in the Chinese Frequency Regulation Market," Energies, MDPI, vol. 16(6), pages 1-23, March.

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