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Study on the Optimal Dispatching Strategy of a Combined Cooling, Heating and Electric Power System Based on Demand Response

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Listed:
  • Ye Zhao

    (School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China)

  • Zhenhai Dou

    (School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China)

  • Zexu Yu

    (School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China)

  • Ruishuo Xie

    (School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China)

  • Mengmeng Qiao

    (School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China)

  • Yuanyuan Wang

    (School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China)

  • Lianxin Liu

    (School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China)

Abstract

This paper proposes a combined cooling, heating and electric power (CCHP) system based on demand side response. In order to improve the economy of the system, a two-stage optimal scheduling scheme is proposed with the goal of minimizing the total operating cost of the system and maximizing user satisfaction. The optimal operation of the system was divided into two optimization problems, including the demand side and the supply side. In the first stage, combined with user satisfaction, from the new point of view that users are prone to excessive behavior due to time-of-use electricity prices, the cooling, heating and power load curves are optimized. In the second stage, an economic dispatch model that includes operating costs in terms of energy, maintenance and environment is established. An improved artificial bee colony (IABC) algorithm is used to solve the optimal energy production scheme based on the demand curves optimized in the first stage. Case studies are conducted to verify the efficiency of the proposed method. Compared with the CCHP system that does not consider demand response, this method reduced operation cost on typical days in summer and winter by 5.20% and 5.76%, respectively.

Suggested Citation

  • Ye Zhao & Zhenhai Dou & Zexu Yu & Ruishuo Xie & Mengmeng Qiao & Yuanyuan Wang & Lianxin Liu, 2022. "Study on the Optimal Dispatching Strategy of a Combined Cooling, Heating and Electric Power System Based on Demand Response," Energies, MDPI, vol. 15(10), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3500-:d:812591
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    References listed on IDEAS

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    1. Cui, Qiong & Ma, Peipei & Huang, Lei & Shu, Jie & Luv, Jie & Lu, Lin, 2020. "Effect of device models on the multiobjective optimal operation of CCHP microgrids considering shiftable loads," Applied Energy, Elsevier, vol. 275(C).
    2. Li, Yanbin & Zhang, Feng & Li, Yun & Wang, Yuwei, 2021. "An improved two-stage robust optimization model for CCHP-P2G microgrid system considering multi-energy operation under wind power outputs uncertainties," Energy, Elsevier, vol. 223(C).
    3. Liu, Zuming & Zhao, Yingru & Wang, Xiaonan, 2020. "Long-term economic planning of combined cooling heating and power systems considering energy storage and demand response," Applied Energy, Elsevier, vol. 279(C).
    4. Wang, Jianxiao & Zhong, Haiwang & Ma, Ziming & Xia, Qing & Kang, Chongqing, 2017. "Review and prospect of integrated demand response in the multi-energy system," Applied Energy, Elsevier, vol. 202(C), pages 772-782.
    5. Gao, Lei & Hwang, Yunho & Cao, Tao, 2019. "An overview of optimization technologies applied in combined cooling, heating and power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    6. Wu, Chenyu & Gu, Wei & Xu, Yinliang & Jiang, Ping & Lu, Shuai & Zhao, Bo, 2018. "Bi-level optimization model for integrated energy system considering the thermal comfort of heat customers," Applied Energy, Elsevier, vol. 232(C), pages 607-616.
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    Cited by:

    1. Fan Li & Jingxi Su & Bo Sun, 2023. "An Optimal Scheduling Method for an Integrated Energy System Based on an Improved k-Means Clustering Algorithm," Energies, MDPI, vol. 16(9), pages 1-22, April.

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