Heterogeneous Aggregation and Stable Control for Hybrid Systems of Regional Distributed Thermostatically Controlled Loads

HUANG Qifeng; ZHUANG Zhong; SU Huiling; DUAN Meimei; YANG Shihai

doi:10.19725/j.cnki.1007-2322.2024.0161

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Heterogeneous Aggregation and Stable Control for Hybrid Systems of Regional Distributed Thermostatically Controlled Loads

更新时间：2026-10-19

- Heterogeneous Aggregation and Stable Control for Hybrid Systems of Regional Distributed Thermostatically Controlled Loads
- Vol. 43, Issue 5, Pages: 865-876(2026)
- 作者机构：
  
  国网江苏省电力有限公司营销服务中心,江苏省,南京市,210019
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2024.0161
  CLC： 5
- Published：2026
- 稿件说明：
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HUANG Qifeng, ZHUANG Zhong, SU Huiling, et al. Heterogeneous Aggregation and Stable Control for Hybrid Systems of Regional Distributed Thermostatically Controlled Loads[J]. 2026, 43(5): 865-876.
DOI：

HUANG Qifeng, ZHUANG Zhong, SU Huiling, et al. Heterogeneous Aggregation and Stable Control for Hybrid Systems of Regional Distributed Thermostatically Controlled Loads[J]. 2026, 43(5): 865-876. DOI： 10.19725/j.cnki.1007-2322.2024.0161.

摘要

需求侧温控负荷物理分布分散，只有在同一区域电网内具有物理连接的负荷，才具备提供有效功率转移的能力。为满足功率转移需求，需尽可能聚合区域内所有可调温控负荷。然而，由于负荷参数的差异性，聚合后的负荷系统呈现随机、无序的混杂特性，难以直接统一控制输出稳定响应。针对功率转移容量的有效性问题和异质聚合模型的稳定可控问题，提出区域分布式温控负荷混杂系统的异质聚合和稳定控制模型。首先，构建基于Kohonen神经网络的混杂温控负荷聚类算法，根据温控负荷地理和物理参数的相似性，将区域混杂负荷聚合形成多个异质集群，以异质集群为聚合单元进行协同管控；其次，构建温控负荷异质集群面向控制的广义异质聚合模型，针对温控负荷物理参数的异质性，基于增广卡尔曼滤波构建聚合参数估计模型，进而基于参数估计结果构建广义异质聚合模型，保证异质聚合模型的稳定可控；最后，构建面向异质集群协同的混杂系统控制模型，实现对温控负荷的统一管控，输出稳定的功率转移容量。仿真部分以某光伏发电数据作为跟踪目标，基于提出的算法，不同场景下的清洁能源出力跟踪误差在±0.03%以内，验证提出算法的准确性和适用性。

Abstract

The thermostatically controlled loads (TCLs) on the demand side are dispersed across physical locations. Only the loads physically connected within the same regional power grid can provide effective power transfer. To meet the power transfer requirements

it is essential to aggregate as many adjustable TCLs within a region as possible. However

due to the variations in load parameters

the aggregated load system exhibits hybrid characteristics of randomness and disorder. It is challenging to directly and uniformly control the aggregated load system to output a stable response. Aiming at the issues of power transfer capacity effectiveness and the stable control in heterogeneous aggregation models

we propose a heterogeneous aggregation and stable control model for the regional distributed TCL hybrid system. Firstly

a hybrid TCL clustering algorithm based on the Kohonen neural network is developed. According to the similarity of geographical and physical parameters of TCLs

regional hybrid loads are aggregated into multiple heterogeneous clusters

which are taken as the aggregation units for collaborative control. Secondly

a control-oriented generalized heterogeneous aggregation model for TCLs is established. For the heterogeneity of TCL physical parameters

an aggregation parameter estimation model is constructed utilizing an augmented Kalman filter. Based on the parameter estimation results

a generalized heterogeneous aggregation model is constructed to ensure the model’s stability and controllability. Finally

a hybrid system control model for heterogeneous cluster collaboration is built

enabling unified control and ensuring stable power transfer capacity. In the simulation

the photovoltaic power generation data are used as the tracking target. By employing the proposed algorithm in this paper

the tracking errors of clean energy output in different scenarios are maintained within ±0.03%

which verifies the accuracy and applicability of the proposed algorithm.

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