Regional Adjustable Capacity Analysis and Synergistic Optimization Based on Air Conditioning Cluster Aggregation Model

Siqiang He; Han Jiang; Chengfeng Wu; Ruijian An

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Regional Adjustable Capacity Analysis and Synergistic Optimization Based on Air Conditioning Cluster Aggregation Model

更新时间：2025-10-29

- Regional Adjustable Capacity Analysis and Synergistic Optimization Based on Air Conditioning Cluster Aggregation Model
- Issue 6, (2025)
- 作者机构：
  
  1. 国网浙江省电力有限公司嘉兴供电公司
  2. 全球能源互联网集团公司
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2025
- 稿件说明：
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Siqiang He, Han Jiang, Chengfeng Wu, et al. Regional Adjustable Capacity Analysis and Synergistic Optimization Based on Air Conditioning Cluster Aggregation Model[J]. 2025, (6).
DOI：

Siqiang He, Han Jiang, Chengfeng Wu, et al. Regional Adjustable Capacity Analysis and Synergistic Optimization Based on Air Conditioning Cluster Aggregation Model[J]. 2025, (6). DOI：

摘要

随着新型电力系统建设的加速推进，挖掘空调负荷的可调能力已成为缓解峰谷矛盾、支撑电网转型的核心议题。该文提出一种数智化赋能的空调集群分层管控架构，借助“云-边-端”三级协同机制实现集群高效调控。在该框架下，首先依据空调一阶热力学方程与一致性算法构建虚拟储能聚合模型，量化集群状态转移特性，继而通过聚合模型评估集群的可调能力。接着，建立以用能成本最优为目标，以用户舒适度与空调运行边界为约束条件的优化运行模型。最后，依托数智化赋能平台，分析了浙江省某市夏季负荷的可调能力，并基于数智化管控框架构建了契合未来高比例可再生能源的微电网。通过对该微电网的优化运行，有效降低了系统用能成本，提高了系统新能源消纳比例，为该地区的数智化用能提供高弹性、低成本的调节路径。

Abstract

With the acceleration of the construction of new power systems

tapping the adjustable capacity of air conditioning loads has become a core issue to alleviate the conflict between peaks and valleys and support the transformation of power grids. In this paper

we propose a numerical intelligence-enabled air conditioning cluster hierarchical control architecture

which realizes efficient regulation and control of the cluster through the “cloud-edge-end” three-level cooperative mechanism. Under this framework

a virtual energy storage aggregation model is first constructed based on the first-order thermodynamic equations of air conditioners and the consistency algorithm to quantify the state transfer characteristics of clusters

and then the adjustable capacity of clusters is evaluated through the aggregation model; then an optimization operation model is established with the optimal cost of energy as the goal

and user comfort and air conditioner operation boundaries as the constraints; finally

relying on the Digital Intelligent Enablement Platform

a practical test is carried out in a city of Zhejiang province

as an example. Finally

relying on the Digital Intelligence Empowerment Platform

a practical test is carried out in a city in Zhejiang Province

which reduces the system energy cost and improves the proportion of new energy consumption in the system

and provides a highly flexible and low-cost regulation path for Digital Intelligent Energy Use in the region.

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