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A Two-Level Optimal Scheduling Strategy for Central Air-Conditioners Based on Metal Model with Comprehensive State-Queueing Control Models

Author

Listed:
  • Yebai Qi

    (Electric Power Planning & Engineering Institute, Beijing 100120, China
    Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Dan Wang

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

  • Yu Lan

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

  • Hongjie Jia

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

  • Chengshan Wang

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

  • Kaixin Liu

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

  • Qing’e Hu

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

  • Menghua Fan

    (State Grid Energy Research Institute, Changping District, Beijing 102249, China)

Abstract

Unlike some thermostatically controlled appliances (TCAs) with small capacities, Central Air-conditioner (CAC) has huge potential for demand response because of its large capacity. This paper presents a new CAC control strategy under multiple constraints. The CAC is modeled by three main modules: CAC central unit, water pumps, and temperature simulation of terminal users. The CAC’s power consumption is mainly determined by users’ load ratio. As the information and communication system have become the central nervous system of the smart grid, big data analysis is of great significance. Assuming that reliable two-way communication systems are preset, an integrated parameter priority list (IPPL) control strategy is used to control and monitor CAC. A new intelligent algorithm, Space Exploration and Unimodal Region Elimination (SEUMRE) algorithm, is introduced for solving the optimization problem of demand response targets generation under multiple constraints with the help of big data analysis. In this paper, influences and constrain factors, such as price and users’ comfortable levels are taken into account to satisfy the need of actual situation. Simulation results show that the proposed approach, when comparing with other typical optimization algorithms, yields better performances and efficiency.

Suggested Citation

  • Yebai Qi & Dan Wang & Yu Lan & Hongjie Jia & Chengshan Wang & Kaixin Liu & Qing’e Hu & Menghua Fan, 2017. "A Two-Level Optimal Scheduling Strategy for Central Air-Conditioners Based on Metal Model with Comprehensive State-Queueing Control Models," Energies, MDPI, vol. 10(12), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2133-:d:122964
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    References listed on IDEAS

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    1. Behrangrad, Mahdi & Sugihara, Hideharu & Funaki, Tsuyoshi, 2011. "Effect of optimal spinning reserve requirement on system pollution emission considering reserve supplying demand response in the electricity market," Applied Energy, Elsevier, vol. 88(7), pages 2548-2558, July.
    2. Wang, D. & Parkinson, S. & Miao, W. & Jia, H. & Crawford, C. & Djilali, N., 2012. "Online voltage security assessment considering comfort-constrained demand response control of distributed heat pump systems," Applied Energy, Elsevier, vol. 96(C), pages 104-114.
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    Cited by:

    1. Wojciech M. Kempa & Dariusz Kurzyk, 2022. "Analysis of Non-Steady Queue-Length Distribution in a Finite-Buffer Model with Group Arrivals and Power Saving Mechanism with Setups," Energies, MDPI, vol. 15(22), pages 1-15, November.

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