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OA

[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.