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Many-criteria optimality of coordinated demand response with heterogeneous households

Author

Listed:
  • Zhou, Bin
  • Cao, Yingping
  • Li, Canbing
  • Wu, Qiuwei
  • Liu, Nian
  • Huang, Sheng
  • Wang, Huaizhi

Abstract

This paper proposes a bilevel multi-house energy management (MHEM) framework to coordinate the residential demand response (DR) of heterogeneous households based on many-criteria optimality. In the upper level, the loss of life (LOL) cost of transformers is formulated into the DR cost model, and a stochastic scheduling is implemented to determine the optimum amount of transformer load deferment and curtailment. The lower level aims to optimally allocate the transformer load from the aggregator to individual households, and a many-criteria DR optimality model is proposed to maximize the multi-house benefits from DR while achieving coordination of the DR participation. Furthermore, a hypercube spatial transformation based classification and sorting scheme is developed to form an evolutionary many-objective (EMO) algorithm in order to solve the many-criteria decision making (MCDM) problem of coordinated DR with numerous participants. The performance of the proposed method was benchmarked and validated on different scaled neighborhood systems over a 24-h scheduling horizon, and comparative results demonstrated its superiority and optimality in solving many-house DR problems.

Suggested Citation

  • Zhou, Bin & Cao, Yingping & Li, Canbing & Wu, Qiuwei & Liu, Nian & Huang, Sheng & Wang, Huaizhi, 2020. "Many-criteria optimality of coordinated demand response with heterogeneous households," Energy, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:energy:v:207:y:2020:i:c:s0360544220313748
    DOI: 10.1016/j.energy.2020.118267
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    References listed on IDEAS

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    1. Zhou, Bin & Li, Wentao & Chan, Ka Wing & Cao, Yijia & Kuang, Yonghong & Liu, Xi & Wang, Xiong, 2016. "Smart home energy management systems: Concept, configurations, and scheduling strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 30-40.
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    6. Hou, Qingchun & Zhang, Ning & Du, Ershun & Miao, Miao & Peng, Fei & Kang, Chongqing, 2019. "Probabilistic duck curve in high PV penetration power system: Concept, modeling, and empirical analysis in China," Applied Energy, Elsevier, vol. 242(C), pages 205-215.
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    1. Ildar Daminov & Rémy Rigo-Mariani & Raphael Caire & Anton Prokhorov & Marie-Cécile Alvarez-Hérault, 2021. "Demand Response Coupled with Dynamic Thermal Rating for Increased Transformer Reserve and Lifetime," Energies, MDPI, vol. 14(5), pages 1-27, March.
    2. Zheng, Ling & Zhou, Bin & Cao, Yijia & Wing Or, Siu & Li, Yong & Wing Chan, Ka, 2022. "Hierarchical distributed multi-energy demand response for coordinated operation of building clusters," Applied Energy, Elsevier, vol. 308(C).
    3. Haider, Haider Tarish & Muhsen, Dhiaa Halboot & Al-Nidawi, Yaarob Mahjoob & Khatib, Tamer & See, Ong Hang, 2022. "A novel approach for multi-objective cost-peak optimization for demand response of a residential area in smart grids," Energy, Elsevier, vol. 254(PB).
    4. Rajaa Naji EL idrissi & Mohammed Ouassaid & Mohamed Maaroufi & Zineb Cabrane & Jonghoon Kim, 2023. "Optimal Cooperative Power Management Framework for Smart Buildings Using Bidirectional Electric Vehicle Modes," Energies, MDPI, vol. 16(5), pages 1-22, February.

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