Optimization strategies for large scale air conditioning load aggregation clusters under incentive conditions

汤卓凡; 赵建立; 郑庆荣; 赵希超; 石 杰

doi:10.3969/j.issn.1009-1831.2024.03.004

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Optimization strategies for large scale air conditioning load aggregation clusters under incentive conditions

更新时间：2026-03-05

- Optimization strategies for large scale air conditioning load aggregation clusters under incentive conditions
- Vol. 26, Issue 3, (2024)
- 作者机构：
  
  1. 国网上海市电力公司,上海,200030
  2. 上海市智能电网需求响应重点实验室,上海,200030
  3. 国网电力科学研究院有限公司,南京,211106
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2024.03.004
  CLC：
- Published：2024
- 稿件说明：
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汤卓凡, 赵建立, 郑庆荣, et al. Optimization strategies for large scale air conditioning load aggregation clusters under incentive conditions[J]. 2024, 26(3).
DOI：

汤卓凡, 赵建立, 郑庆荣, et al. Optimization strategies for large scale air conditioning load aggregation clusters under incentive conditions[J]. 2024, 26(3). DOI： 10.3969/j.issn.1009-1831.2024.03.004.

摘要

为提高大规模空调负荷聚合精度、提升空调集群可调节潜力，提出了激励条件下的大规模空调负荷聚合集群优化策略。首先建立了大规模空调负荷聚合架构，然后建立单体的二阶空调等值热力参数模型，同时基于蒙特卡洛方法对不同区域的空调负荷进行二次聚合，建立用户满意度与激励水平之间的关系。在此基础上，以实际空调聚合功率与缺口负荷之间的标准差最小、空调负荷聚合商补偿费用最少为优化目标，采用粒子群算法进行求解，最后通过算例说明了本文所提策略的有效性。

Abstract

In order to improve the accuracy of large-scale air conditioning load aggregation and enhance the adjustable potential of air conditioning clusters

an optimization strategy for large-scale air conditioning load aggregation clusters under incentive conditions is proposed.Firstly

a large-scale air conditioning load aggregation architecture is established. Secondly

a second-order equivalent thermal parameter model for individual air conditioners is established

and the air conditioning loads in different regions are secondary aggregated based on the Monte Carlo method. Meanwhile

the relationship between user satisfaction and incentive levels is established. On this basis

optimization objective is to minimize the standard deviation between actual air conditioning aggregated power and the gap load

and to minimize the compensation cost for the air conditioning load aggregator. Particle swarm optimization algorithm is used to solve the problem. Finally

effectiveness of the proposed strategy is demonstrated through numerical examples.

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