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Air conditioner group collaborative method under multi-layer information interaction structure

Author

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  • Wu, Xin
  • Liang, Kaixin
  • Jiao, Dian

Abstract

Demand-side air conditionings are abundant and can function as energy storage. With proper management, it can provide load following service, with application to clean energy. In this paper, an information interaction structure that achieves two-way interactions between a power grid, an aggregator, and loads is established. Based on this structure, a method of group cooperative management for aggregated air conditioning is studied. Appliance companies with cloud platforms can act as aggregators, coordinate the power grid and the demand side, and aggregate the physically dispersed air-conditioning resources into a controllable entity. Using non-intrusive load monitoring terminals, the grid acquires the load information, evaluates the on-grid air conditioning capacity, and assigns consumption tasks to the aggregator. The aggregators control air conditioning groups, and each group cooperates to complete the consumption tasks. This research established a two-way evaluation mechanism between a power grid and an aggregator for air conditioning capacity to serve the overall interests of all parties. The optimization problem of accommodation capacity allocation is constructed on users’ comfort levels. The aggregated air conditionings are controlled by the sliding-mode model. The experiments illustrate that the algorithm can effectively follow the output of clean energy, while intervening less in the air conditioning operation.

Suggested Citation

  • Wu, Xin & Liang, Kaixin & Jiao, Dian, 2019. "Air conditioner group collaborative method under multi-layer information interaction structure," Energy, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:energy:v:186:y:2019:i:c:s0360544219315233
    DOI: 10.1016/j.energy.2019.115851
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    Cited by:

    1. Wu, Xin & Yao, Lijuan & Pi, Tanxin & Liu, Yuhang & Li, Xiang & Gong, Gangjun, 2023. "Virtual-real interaction control of hybrid load system for low-carbon energy services," Applied Energy, Elsevier, vol. 330(PB).
    2. Wu, Hongbin & Wang, Jingjie & Lu, Junhua & Ding, Ming & Wang, Lei & Hu, Bin & Sun, Ming & Qi, Xianjun, 2022. "Bilevel load-agent-based distributed coordination decision strategy for aggregators," Energy, Elsevier, vol. 240(C).

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