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Research on Home Energy Management Method for Demand Response Based on Chance-Constrained Programming

Author

Listed:
  • Xiangyu Kong

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

  • Siqiong Zhang

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

  • Bowei Sun

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
    State Grid Liaoning Electric Power Company, Dalian 110006, China)

  • Qun Yang

    (State Grid Liaoning Electric Power Company, Dalian 110006, China)

  • Shupeng Li

    (Tianjin Electric Power Research Institute, State Grid Tianjin Electric Power Company, Tianjin 300384, China)

  • Shijian Zhu

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

Abstract

With the development of smart devices and information technology, it is possible for users to optimize their usage of electrical equipment through the home energy management system (HEMS). To solve the problems of daily optimal scheduling and emergency demand response (DR) in an uncertain environment, this paper provides an opportunity constraint programming model for the random variables contained in the constraint conditions. Considering the probability distribution of the random variables, a home energy management method for DR based on chance-constrained programming is proposed. Different confidence levels are set to reflect the influence mechanism of random variables on constraint conditions. An improved particle swarm optimization algorithm is used to solve the problem. Finally, the demand response characteristics in daily and emergency situations are analyzed by simulation examples, and the effectiveness of the method is verified.

Suggested Citation

  • Xiangyu Kong & Siqiong Zhang & Bowei Sun & Qun Yang & Shupeng Li & Shijian Zhu, 2020. "Research on Home Energy Management Method for Demand Response Based on Chance-Constrained Programming," Energies, MDPI, vol. 13(11), pages 1-27, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2790-:d:365811
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    References listed on IDEAS

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    4. Kong, Xiangyu & Xiao, Jie & Wang, Chengshan & Cui, Kai & Jin, Qiang & Kong, Deqian, 2019. "Bi-level multi-time scale scheduling method based on bidding for multi-operator virtual power plant," Applied Energy, Elsevier, vol. 249(C), pages 178-189.
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    Cited by:

    1. Vasileios M. Laitsos & Dimitrios Bargiotas & Aspassia Daskalopulu & Athanasios Ioannis Arvanitidis & Lefteri H. Tsoukalas, 2021. "An Incentive-Based Implementation of Demand Side Management in Power Systems," Energies, MDPI, vol. 14(23), pages 1-24, November.
    2. Md Mamun Ur Rashid & Majed A. Alotaibi & Abdul Hasib Chowdhury & Muaz Rahman & Md. Shafiul Alam & Md. Alamgir Hossain & Mohammad A. Abido, 2021. "Home Energy Management for Community Microgrids Using Optimal Power Sharing Algorithm," Energies, MDPI, vol. 14(4), pages 1-21, February.
    3. Tostado-Véliz, Marcos & Kamel, Salah & Aymen, Flah & Jurado, Francisco, 2022. "A novel hybrid lexicographic-IGDT methodology for robust multi-objective solution of home energy management systems," Energy, Elsevier, vol. 253(C).

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