基于MPC的光电热联合系统建模与控制优化

Modeling and control optimization of photovoltaic-thermal heating system based on MPC

为解决我国北方地区冬季采暖产生的能源消耗与环境污染问题,改善光资源丰富、较丰富地区光能利用率不足的现状,充分利用谷电和日照优势,针对太阳能利用与建筑采暖相结合的分布式能源系统性问题,提出一种基于TRNSYS动态建模与数值建模相结合的光电热联合供暖系统。综合考虑小范围内供暖温度的时滞性以及系统各设备的出力情况,联合Matlab搭建模型预测控制器(MPC),提出一种基于MPC的误差实时校正优化控制策略。分析表明:采用MPC的控制优化,在热负荷跟踪方面,最大误差降低4.16%,平均误差降低2.79%;在室内温度控制方面,最大偏差降低1.2℃,平均偏差降低0.2℃;在太阳能利用占比方面,太阳辐射强度趋近于800 W/m^(2)时,太阳能利用占比差距达最大8.9%。分析结果说明该系统可以更快速、更准确地跟踪建筑热负荷波动,并且有效抑制室内温度波动,提高清洁能源的利用率。

To reduce the energy consumption and fight against environmental pollution crises caused by heating in northern China,and to improve the insufficient light utilization efficiency in areas with abundant or relatively abundant solar resources,a distributed energy system combining solar energy and building heating is proposed,taking the advantages of off-peak electricity and sufficient illumination.The model of the proposed photovoltaic-thermal heating system is built based on TRNSYS dynamic modeling and numerical modeling.Then,considering the time-lag of the heat-supply system for a small area and the output of each device in the system,a model predictive control(MPC)strategy is developed based on Matlab,and an MPC-based optimization control strategy which can realize real-time error correction is made.According to the analysis results:the MPC-based optimization control can keep the maximum error of tracking heat load within 4.16%,and decrease the average error by 2.79%;and the optimization control can keep the maximum deviation of indoor temperature within 1.2℃,which is 0.2℃lower that without the control;under a solar radiation intensity approaching 800 W/m^(2),the difference between solar energy utilization rates with and without the optimization control goes up to a maximum of 8.9%.The results indicate that the MPC can track heat load fluctuations in buildings quickly and accurately,suppress indoor temperature fluctuations effectively and increase the utilization rate of clean energy.