| 倪琳娜,孙钢,严华江,黄荣国,陈昱豪,邱剑.考虑响应特性分组的空调聚合商博弈分层优化策略[J].电力需求侧管理,2026,28(4):94-100 |
| 考虑响应特性分组的空调聚合商博弈分层优化策略 |
| Hierarchical game optimization strategy for air conditioning load aggregators considering response characteristic grouping |
| 投稿时间：2026-01-26 修订日期：2026-03-08 |
| DOI：10.3969/j.issn.1009-1831.2026.04.014 |
| 中文关键词: 空调负荷 聚类 非合作博弈 分层优化调度方法 |
| 英文关键词: air conditioning load clustering non-cooperative game hierarchical optimal dispatch method |
| 基金项目:国网浙江省电力有限公司科技项目(5211YF24000D)；国家自然科学基金联合基金重点支持项目(U22B2098) |
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| 中文摘要: |
| 空调负荷作为柔性负荷资源,可由聚合商聚合并参与电力需求响应市场,从而有效缓解夏季高峰期的供电紧张问题。为此,提出一种考虑响应特性分组的空调聚合商博弈分层优化调度策略。首先,研究空调设备特性,推导建立空调在长时间断面内的平均用电功率与温差区间之间的关系方程,挖掘最小平均功率与温差之间的内在关联规律;然后,针对单体参数差异化特征,采用邻近传播聚类(affinity propagation,AP)算法对空调负荷群进行精细化聚类分组;在此基础上,提出空调聚合商博弈优化模型,在求解过程中引入连续性的中间变量,通过分层优化保证博弈的唯一均衡解。仿真结果表明,所提空调运行方程有效挖掘了单体小时级最小运行功率与温差区间的内在关联,降低了变量维度。在聚类基础上,提出的博弈分层优化策略有效利用了空调负荷的响应潜力,降低了仿真系统在调度周期内的运行峰谷差至27.22%。 |
| 英文摘要: |
| As a typical flexible load resource, air conditioning (AC) loads can be aggregated by load aggregator to participate in demand response (DR) markets, effectively alleviating electricity supply pressure during summer peak periods. A hierarchical game-based optimal dispatch strategy for AC aggregators considering response characteristic-based grouping is proposed. First, the characteristics of AC devices are analyzed, and a relationship equation is derived to capture the correlation between average power consumption and temperature difference over long time intervals, thereby revealing the intrinsic relationship between minimum average power and the temperature difference range. Then, considering the heterogeneity of unit-level parameters, an affinity propagation (AP) clustering algorithm is employed to perform fine-grained grouping of AC loads. On this basis, a game-theoretic optimization model is established for AC aggregators. To address solution tractability, continuous intermediate variables are introduced during the solving process, and hierarchical optimization is adopted to ensure the uniqueness of the equilibrium solution. The simulation results demonstrate that the proposed air-conditioning operation equation effectively captures the deep relationship between the minimum hourly operating power of individual units and the temperature-difference interval, thereby reducing the dimensionality of decision variables. Based on the clustering results, the proposed game-theoretic hierarchical optimization strategy exploits the response potential of air-conditioning loads, reducing the peak-to-valley difference of system operation within the scheduling period to 27.22%. |
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