电力建设2026，Vol.47Issue(7)：167-177,11.DOI:10.12204/j.issn.1000-7229.2026.07.013
考虑市场化电价信号的光伏直驱空调系统动态优化控制策略
Dynamic Optimal Control Strategy for Photovoltaic Direct-Driven Air Conditioning Systems Considering Market-Oriented Electricity Price Signals
摘要
Abstract
[Objective]To address the challenges of high photovoltaic(PV)curtailment rate and supply-demand mismatch faced by energy storage-free PV direct-driven AC systems under dynamic electricity prices,this paper proposes an optimal control strategy integrating price response with physical constraints to improve economic efficiency.[Methods]First,a convolutional neural network-long short-term memory(CNN-LSTM)forecasting model incorporating PV panel parameters and building thermal capacity constraints is constructed.Second,a threshold optimization model minimizing dynamic operational cost is designed.Finally,energy-storage-free coordinated switching is achieved using optimized thresholds and a hysteresis comparator.[Results]Taking a library in Northwest China as a case study for simulation,the results demonstrate that by leveraging the building's thermal inertia for precooling,the load peak during high electricity price periods is reduced by 18％,and the system's average daily operating cost is lowered by 21.6％.[Conclusions]The proposed closed-loop"forecasting-optimization-control"framework effectively enhances the system's economy and flexibility in a market-oriented environment,providing a viable solution for energy conservation and carbon reduction in public buildings.关键词
光伏直驱系统/市场化电价/动态优化/卷积神经网络-长短期记忆(CNN-LSTM)模型/建筑热惯性Key words
photovoltaic direct-driven system/market-oriented electricity price/dynamic optimization/convolutional neural network-long short-term memory(CNN-LSTM)model/building thermal inertia分类
信息技术与安全科学引用本文复制引用
宫照国,希望·阿不都瓦依提,尹纯亚,李笑竹,陈成林,王旭晖..考虑市场化电价信号的光伏直驱空调系统动态优化控制策略[J].电力建设,2026,47(7):167-177,11.基金项目
This work is supported by National Natural Science Foundation of China(No.52467014) 国家自然科学基金项目(52467014) （No.52467014）
