由于风光及用户侧的不确定性，采用传统的优化调度响应不及时会导致日内的供需不平衡。为此，构建一种含风机、光伏、蓄电池和光热的风光储综合能源系统，充分利用可再生能源，并在日前调度的基础上，基于模型预测控制算法并结合能源系统的状态空间方程，建立日内滚动优化数学模型；在Matlab平台上进行典型日的算例仿真，分析系统各设备的功率输出及供能情况，仿真结果显示：在3种典型日下该系统日运行费用较日前分别减少12.3%、7.4%、11.3%，且供电、热不平衡率均有所下降，提高了供能系统运行的经济性和可靠性。

Due to the uncertainties in both the landscape and user demand, traditional optimal scheduling responses can result in imbalances between supply and demand within a single day. In order to address this issue, a comprehensive wind and solar storage energy system is constructed, incorporating fans, photovoltaic panels, batteries, as well as light and heat technologies to fully utilize renewable energy sources. Building upon day-ahead scheduling, a mathematical model for day-rolling optimization is established using the model predictive control algorithm combined with the state space equation of the energy system. A typical daily example simulation is conducted on the Matlab platform. By analyzing the power output and energy supply of each device within the system, simulation results demonstrate that in three typical days there is a reduction of 12.3%, 7.4%, and 11.3% respectively in daily operating costs for the system while also reducing power supply and thermal imbalances rate which enhances both economic efficiency and reliability of this energy supply system.