多机制冷系统在线优化控制策略

Online optimal control strategies for multiple-chiller systems

阐述了可应用于大型商业建筑中多台制冷机系统的在线优化控制策略。策略的应用可增强控制鲁棒性并节约运营成本。优化控制策略包括冷冻水供水温度优化、制冷机时序控制、启动优化控制和建筑峰值负荷控制。冷冻水供水温度的优化使得系统在满足负荷的前提下制冷机和冷冻水泵的能耗最小。在时序控制中,3种方法的采用增强了控制的鲁棒性:第一,数据融合以获得可靠楼宇冷负荷的测量;第二,制冷机最大制冷能力简化模型的应用;第三,在线传感器故障诊断(FDD)的应用。在制冷机启动优化控制中,一个基于模型的策略用于最小化早晨启动时期内的系统能耗。这一基于模型的最优启动优化控制策略同时考虑了预冷能力和预冷时间的相互关系。建筑峰值负荷控制以优化月节电费为目标,通过预测合适的每月负荷阈值并依此限制每天的峰值负荷来实现这一目的。通过对香港一幢大型商业建筑内中央制冷系统的动态模拟,以上控制策略得到验证。

This paper presents the online optimal control strategies for multiple-chiller plants in large buildings with enhanced robustness and cost efficiency,including optimization of chilled water supply temperature set-point,chiller sequencing control,optimal start control and electrical demand limiting control.The chilled water supply temperature set-point optimization aims to minimize the total energy consumption of chillers and chilled water distribution pumps.In the chiller sequencing control,three schemes are used to enhance its control robustness,including a data fusion scheme for improved reliability of building cooling load measurement,a simplified adaptive model of maximum chiller cooling capacity,and an online sensor fault detection and diagnosis(FDD).In the chiller optimal start control,a model-based strategy is proposed for minimizing the energy consumption in the morning start period.The model-based optimal start control strategy considers both the recovery ability and the pre-cooling lead time as its optimizing variables.The peak demand limiting control strategy minimizes the monthly electricity bill by predicting a suitable monthly peak demand threshold and restraining the daily peak demand to the threshold.These control strategies are validated using the dynamic simulation of the central chiller plant in a high-rising building in Hong Kong.