【目的】 针对无储能光伏直驱空调系统在动态电价下面临的弃光率高、供需匹配难问题，提出一种融合电价响应与物理约束的优化控制策略。【方法】 首先，构建卷积神经网络-长短期记忆（convolutional neural network- long short-term memory， CNN-LSTM）预测模型，引入光伏板参数与建筑热容约束提升预测精度；然后，设计以动态成本最小为目标的阈值优化模型；最后，基于优化阈值与滞回比较器实现无储能协同切换。【结果】 以西北某图书馆为实例仿真，结果表明：利用建筑热惯性预冷，高电价时段负荷峰值削减18%，系统日均运行成本降低21.6%。【结论】 所提“预测—优化—控制”闭环框架有效提升了市场化环境下系统的经济性与灵活性，为公共建筑节能降碳提供了解决方案。

[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.