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An Energy Management Optimization Method for Community Integrated Energy System Based on User Dominated Demand Side Response

Author

Listed:
  • Yiqi Li

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Jing Zhang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Zhoujun Ma

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
    State Grid Jiangsu Electric Power Co., Ltd., Nanjing Power Supply Branch, Nanjing 210019, China)

  • Yang Peng

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Shuwen Zhao

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

Abstract

With the development of integrated energy systems (IES), the traditional demand response technologies for single energy that do not take customer satisfaction into account have been unable to meet actual needs. Therefore, it is urgent to study the integrated demand response (IDR) technology for integrated energy, which considers consumers’ willingness to participate in IDR. This paper proposes an energy management optimization method for community IES based on user dominated demand side response (UDDSR). Firstly, the responsive power loads and thermal loads are modeled, and aggregated using UDDSR bidding optimization. Next, the community IES is modeled and an aggregated building thermal model is introduced to measure the temperature requirements of the entire community of users for heating. Then, a day-ahead scheduling model is proposed to realize the energy management optimization. Finally, a penalty mechanism is introduced to punish the participants causing imbalance response against the day-ahead IDR bids, and the conditional value-at-risk (CVaR) theory is introduced to enhance the robustness of the scheduling model under different prediction accuracies. The case study demonstrates that the proposed method can reduce the operating cost of the community under the premise of fully considering users’ willingness, and can complete the IDR request initiated by the power grid operator or the dispatching department.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4398-:d:598447
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    References listed on IDEAS

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    1. Liu, Peiyun & Ding, Tao & Zou, Zhixiang & Yang, Yongheng, 2019. "Integrated demand response for a load serving entity in multi-energy market considering network constraints," Applied Energy, Elsevier, vol. 250(C), pages 512-529.
    2. 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.
    3. Stenner, Karen & Frederiks, Elisha R. & Hobman, Elizabeth V. & Cook, Stephanie, 2017. "Willingness to participate in direct load control: The role of consumer distrust," Applied Energy, Elsevier, vol. 189(C), pages 76-88.
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    Cited by:

    1. 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.
    2. Li, Songrui & Zhang, Lihui & Nie, Lei & Wang, Jianing, 2022. "Trading strategy and benefit optimization of load aggregators in integrated energy systems considering integrated demand response: A hierarchical Stackelberg game," Energy, Elsevier, vol. 249(C).
    3. Álvaro Gutiérrez, 2022. "Optimization Trends in Demand-Side Management," Energies, MDPI, vol. 15(16), pages 1-3, August.
    4. Maher G. M. Abdolrasol & Mahammad Abdul Hannan & S. M. Suhail Hussain & Taha Selim Ustun & Mahidur R. Sarker & Pin Jern Ker, 2021. "Energy Management Scheduling for Microgrids in the Virtual Power Plant System Using Artificial Neural Networks," Energies, MDPI, vol. 14(20), pages 1-19, October.
    5. Lijun Tang & Xiaolong Gou & Junyu Liang & Yang Yang & Xingyu Yuan & Jiaquan Yang & Yuting Yan & Dada Wang & Yongli Wang & Xin Chen & Bo Yuan & Siyi Tao, 2022. "A Two-Stage Planning Optimization Study of an Integrated Energy System Considering Uncertainty," Sustainability, MDPI, vol. 14(6), pages 1-22, March.

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