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Genetic Algorithm Based Temperature-Queuing Method for Aggregated IAC Load Control

Author

Listed:
  • Zexu Chen

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Jing Shi

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Zhaofang Song

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Wangwang Yang

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Zitong Zhang

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

In recent years, demand response (DR) has played an increasingly important role in maintaining the safety, stability and economic operation of power grid. Due to the continuous running state and extremely fast speed of response, the aggregated inverter air conditioning (IAC) load is considered as the latest and most ideal object for DR. However, it is easy to cause load rebound when the aggregated IAC load participates in DR. Existing methods for controlling air conditioners to participate in DR cannot meet the following three requirements at the same time: basic DR target, load rebound suppression, and users’ comfort. Therefore, this paper has proposed a genetic algorithm based temperature-queuing control method for aggregated IAC load control, which could suppress load rebound under the premise of ensuring the DR target and take users’ comfort into account. Firstly, the model of the aggregated IAC load is established by the Monte Carlo method. Then the start and end time of DR are selected as the main solution variables. A genetic algorithm is used as the solving tool. The simulation results show that the proposed strategy shows better performance in suppressing load rebound. In the specific application scenario of adjusting the frequency fluctuation of the microgrid, the results of the case show that this strategy can effectively control the frequency fluctuation of the microgrid. The effectiveness of the strategy is verified.

Suggested Citation

  • Zexu Chen & Jing Shi & Zhaofang Song & Wangwang Yang & Zitong Zhang, 2022. "Genetic Algorithm Based Temperature-Queuing Method for Aggregated IAC Load Control," Energies, MDPI, vol. 15(2), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:2:p:535-:d:723281
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    References listed on IDEAS

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    1. Wei, Congying & Wu, Qiuwei & Xu, Jian & Sun, Yuanzhang & Jin, Xiaolong & Liao, Siyang & Yuan, Zhiyong & Yu, Li, 2020. "Distributed scheduling of smart buildings to smooth power fluctuations considering load rebound," Applied Energy, Elsevier, vol. 276(C).
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    Cited by:

    1. Rusi Chen & Haiguang Liu & Chengquan Liu & Guangzheng Yu & Xuan Yang & Yue Zhou, 2022. "System Frequency Control Method Driven by Deep Reinforcement Learning and Customer Satisfaction for Thermostatically Controlled Load," Energies, MDPI, vol. 15(21), pages 1-19, October.
    2. Siyue Lu & Baoqun Zhang & Longfei Ma & Hui Xu & Yuantong Li & Shaobing Yang, 2023. "Economic Load-Reduction Strategy of Central Air Conditioning Based on Convolutional Neural Network and Pre-Cooling," Energies, MDPI, vol. 16(13), pages 1-22, June.
    3. Tetsushi Ono & Aya Hagishima & Jun Tanimoto, 2022. "Non-Intrusive Detection of Occupants’ On/Off Behaviours of Residential Air Conditioning," Sustainability, MDPI, vol. 14(22), pages 1-20, November.

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