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Aggregated large-scale air-conditioning load: Modeling and response capability evaluation of virtual generator units

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  • Wang, Jingjie
  • Qiu, Rujia
  • Xu, Bin
  • Wu, Hongbin
  • Tang, Longjiang
  • Zhang, Mingxing
  • Ding, Ming

Abstract

In order to address the behavioral characteristics of air conditioning users and help load aggregators avoid high penalties for breach of contract caused by insufficient response capacity, it is essential to develop a practical and effective method for evaluating the response capability of aggregated air conditioning. This paper proposes a method for screening users with similar load characteristics through primary clustering and for conducting secondary clustering of air conditioning with similar adjustable characteristics using the self-organizing map. By aggregating all user devices with similar attributes and adjustable characteristics within a given region, a highly accurate virtual generator unit model is developed for air conditioning loads, with the external characteristic parameters of equivalent generator units calculated. To account for the probability of user dropout due to equipment failure or changes in willingness to respond, a response capability evaluation method for air conditioning virtual generator units is proposed. The results demonstrate that the proposed model can conveniently calculate the response capacity indices of virtual generator units under different task targets, and provide decision-making basis for dispatching centers or load aggregators to regulate their internal loads.

Suggested Citation

  • Wang, Jingjie & Qiu, Rujia & Xu, Bin & Wu, Hongbin & Tang, Longjiang & Zhang, Mingxing & Ding, Ming, 2023. "Aggregated large-scale air-conditioning load: Modeling and response capability evaluation of virtual generator units," Energy, Elsevier, vol. 276(C).
    • Handle: RePEc:eee:energy:v:276:y:2023:i:c:s0360544223009647
    DOI: 10.1016/j.energy.2023.127570
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    1. Xiong, Yongkang & Zeng, Zhenfeng & Xin, Jianbo & Song, Guanhong & Xia, Yonghong & Xu, Zaide, 2023. "Renewable energy time series regulation strategy considering grid flexible load and N-1 faults," Energy, Elsevier, vol. 284(C).
    2. Liu, Xin & Li, Yang & Wang, Li & Tang, Junbo & Qiu, Haifeng & Berizzi, Alberto & Valentin, Ilea & Gao, Ciwei, 2024. "Dynamic aggregation strategy for a virtual power plant to improve flexible regulation ability," Energy, Elsevier, vol. 297(C).

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