Heterogeneity Modeling and Low-Dimensional Broadcast Cooperative Control of Air Conditioning Load for Power Grid Demand Response

doi:10.11930/j.issn.1004-9649.202504010

Abstract

Abstract:

To address the demand for refined regulation of large-scale heterogeneous air-conditioning loads in power grid demand response, a second-order equivalent thermal parameter model is firstly used to derive the steady-state operating power of air conditioners, and the heterogeneity of air-conditioning parameters is characterized by a Gaussian mixture model to accurately calculate the regulation capacity. Then, an innovative low-dimensional broadcast signal control strategy of "central guidance + local autonomy" is designed. A Markov chain model combined with an augmented Lagrangian function is used to find the optimal temperature rise probability in different temperature intervals, balancing the power grid demand and economic cost constraints. Finally, the proposed method is verified through simulation analysis. The results show that the calculation accuracy of regulation capacity reaches 97.5% in a heterogeneous sample set of 50000 air conditioners, which can stably provide a maximum load reduction of 11188.12 kW. Moreover, the broadcast strategy can dynamically track the grid demand power and effectively alleviate the summer grid peak pressure. The proposed method provides theoretical support and a practical solution for large-scale thermostatically controlled loads to participate in demand response.

Key words: conditioning load, regulation capacity assessment, Gaussian mixture model, Markov chain, Lagrangian function

YU Junyi, LIAO Siyang, KE Deping, ZHANG Jie. Heterogeneity Modeling and Low-Dimensional Broadcast Cooperative Control of Air Conditioning Load for Power Grid Demand Response[J]. Electric Power, 2025, 58(12): 14-26.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202504010

https://www.electricpower.com.cn/EN/Y2025/V58/I12/14

Figures/Tables 13

References 33

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参数		取值
空气等效热阻${R_{\rm a}}$/(℃·(kW)^–1)		$ \mathcal{U}\left( {2,3} \right) $
材料等效热阻${R_{\rm m}}$/(℃·(kW)^–1)		$\mathcal{U}\left( {1,2} \right)$
空气等效热容${C_{\rm a}}$/((kW·h)(℃)^–1)		$\mathcal{U}\left( {4,6} \right)$
材料等效热容${C_{\rm m}}$/((kW·h)(℃)^–1)		$\mathcal{U}\left( {2,4} \right)$
制冷电功率$P$/W		$\mathcal{U}\left( {600,1\;000} \right)$
能效比${\eta _{\rm i}}$		$\mathcal{U}\left( {2.4,3.6} \right)$
温度设定值${T_{\rm set}}$/℃		$\mathcal{U}\left( {18,28} \right)$
允许温度偏差${T_{\rm dev}}$/℃		$\mathcal{U}\left( {0.5,2} \right)$
环境温度${T_{\rm o}}$/℃		$35$

参数		取值
空气等效热阻${R_{\rm a}}$/(℃·(kW)^–1)		$ \mathcal{U}\left( {2,3} \right) $
材料等效热阻${R_{\rm m}}$/(℃·(kW)^–1)		$\mathcal{U}\left( {1,2} \right)$
空气等效热容${C_{\rm a}}$/((kW·h)(℃)^–1)		$\mathcal{U}\left( {4,6} \right)$
材料等效热容${C_{\rm m}}$/((kW·h)(℃)^–1)		$\mathcal{U}\left( {2,4} \right)$
制冷电功率$P$/W		$\mathcal{U}\left( {600,1\;000} \right)$
能效比${\eta _{\rm i}}$		$\mathcal{U}\left( {2.4,3.6} \right)$
温度设定值${T_{\rm set}}$/℃		$\mathcal{U}\left( {18,28} \right)$
允许温度偏差${T_{\rm dev}}$/℃		$\mathcal{U}\left( {0.5,2} \right)$
环境温度${T_{\rm o}}$/℃		$35$

温度区间/℃	1	2	3	4	5
18~19	0.96	0.95	0.88	0.86	0.85
19~20	0.92	0.87	0.79	0.75	0.72
20~21	0.85	0.74	0.68	0.62	0.6
21~22	0.76	0.63	0.56	0.58	0.48
22~23	0.72	0.65	0.62	0.63	0.68
23~24	0.63	0.72	0.65	0.62	0.52
24~25	0.56	0.45	0.42	0.39	0.32
25~26	0.32	0.28	0.25	0.21	0.16
26~27	0.18	0.09	0.09	0.07	0.04
27~28	0.05	0.05	0.04	0.03	0.03

温度区间/℃	1	2	3	4	5
18~19	0.96	0.95	0.88	0.86	0.85
19~20	0.92	0.87	0.79	0.75	0.72
20~21	0.85	0.74	0.68	0.62	0.6
21~22	0.76	0.63	0.56	0.58	0.48
22~23	0.72	0.65	0.62	0.63	0.68
23~24	0.63	0.72	0.65	0.62	0.52
24~25	0.56	0.45	0.42	0.39	0.32
25~26	0.32	0.28	0.25	0.21	0.16
26~27	0.18	0.09	0.09	0.07	0.04
27~28	0.05	0.05	0.04	0.03	0.03

策略	平均负荷差率/%	平均温度偏差/℃
本文方法	1.87	0.52
固定概率	4.12	0.83
无异质性建模	5.45	0.76