Modeling And Optimization Of Adaptive Optimal Control System For Regional Energy Station

With the rapid development of economy, people's requirement for the environment has become much higher, and people spend most of their time indoor. Therefore, the quality of indoor environment has become more and more important, the emphasis on building energy consumption optimization control has also increased. Now, in order to meet the needs of users and reduce the energy consumption of Heating?Ventilation and Air Conditioning system, the optimization of control method is also a developing trend.The management and optimal control of cold-heat system is one of the most important aspects. The model includes three parts: the parameter estimation model, the device model and the optimization model.Two kinds of modeling methods was used,the pure mechanism modeling method and the method of combining mechanism modeling method with identification method to establish the device model. The energy station system is divided into winter and summer mode. The hot water pump is modeled by the mechanism method, and the other equipment is modeled by the method of combining mechanism modeling method with identification method.On the basis of the device model, the parameter estimation model is established. The NLLS(Nonlinear Least Squares Method) is used in the estimates of the scale mode model parameters of chiller energy consumption parameters, The RLS(Recursive Least Square) is used in the estimates of the pump energy consumption and lift model and the cooling tower inlet and outlet water temperature model parameters.Based on the parameter identification of the system model, the optimization model of the system is determined. For the optimization model, the penalty function is constructed based on the objective function and the penalty condition, and the penalty function is used to calculate the degree of fitness. The genetic algorithm is used in the optimization control algorithm, and it is the most widely used process optimization algorithm.Finally, the results of the optimization are showed according to the historical data and the optimization data, based on the data, 5% to 10% energy saving is reached.