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Active Participation of Air Conditioners in Power System Frequency Control Considering Users’ Thermal Comfort

Author

Listed:
  • Rongxiang Zhang

    (Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, 17923 Jingshi Road, Jinan 250061, China)

  • Xiaodong Chu

    (Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, 17923 Jingshi Road, Jinan 250061, China)

  • Wen Zhang

    (Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, 17923 Jingshi Road, Jinan 250061, China)

  • Yutian Liu

    (Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, 17923 Jingshi Road, Jinan 250061, China)

Abstract

Air conditioners have great potential to participate in power system frequency control. This paper proposes a control strategy to facilitate the active participation of air conditioners. For each air conditioner, a decentralized control law is designed to adjust its temperature set point in response to the system frequency deviation. The decentralized control law accounts for the user’s thermal comfort that is evaluated by a fuzzy algorithm. The aggregation of air conditioners’ response is conducted by using the Monte Carlo simulation method. A structure preserving model is applied to the multi-bus power system, in which air conditioners are aggregated at certain load buses. An inner-outer iteration scheme is adopted to solve power system dynamics. An experiment is conducted on a test air conditioner to examine the performance of the proposed decentralized control law. Simulation results on a test power system verify the effectiveness of the proposed strategy for air conditioners participating in frequency control.

Suggested Citation

  • Rongxiang Zhang & Xiaodong Chu & Wen Zhang & Yutian Liu, 2015. "Active Participation of Air Conditioners in Power System Frequency Control Considering Users’ Thermal Comfort," Energies, MDPI, vol. 8(10), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:10:p:10818-10841:d:56504
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    References listed on IDEAS

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    1. Dehghanpour, Kaveh & Afsharnia, Saeed, 2015. "Electrical demand side contribution to frequency control in power systems: a review on technical aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1267-1276.
    2. Pedro Faria & Zita Vale & José Baptista, 2015. "Demand Response Programs Design and Use Considering Intensive Penetration of Distributed Generation," Energies, MDPI, vol. 8(6), pages 1-17, June.
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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. Ying-Yi Hong, 2016. "Electric Power Systems Research," Energies, MDPI, vol. 9(10), pages 1-4, October.
    3. Yuchun Li & Yinghua Han & Jinkuan Wang & Qiang Zhao, 2018. "A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort," Energies, MDPI, vol. 11(12), pages 1-20, December.

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