| With fast development of China's economy and improvement of the people's living levels,the building energy consumption has increased rapidly,which has brought great challenges to China's energy sustainable development.Distract energy station has great advantages because of its massive scale,and has become main form of DHC.But due to the wide range of supply and demand,the traditional PID regulator can't satisfy conservation of resources,which caused a large waste of energy.Therefore,it is of great theoretical significance and practical value to coordinate the equipment management and improve the energy efficiency by designing optimal strategy and verifying its rationality based on mechanism modeling.In order to illustrate the significance of the study,the development status of energy station at home and broad has been introduced.With analysis of the characteristics of station equipment and user load,the model of cooling tower,chiller and pump are presented based on mechanism,user load forecasting model is built by BP neural network.A method combined forgetting factor with restricted memory length is proposed to realize online adaptive parameter identification.Then the model is verified by collected data in the field,and simulation results show that the model can accurately predict the change of system.The parallel operation characteristics of equipment are analyzed,then optimal strategy is obtained,and the overall design is gave on this basis,the optimization objectives and constraints are described in detail,the district cooling system optimization is defined as a multi-object minimum problem with constraints.Then deferent search methods are compared,and the system optimization problem is transformed into an unconstrained minimum problem with two goals by simplifying optimization objective and constraints,then genetic algorithm is used to realize system optimization.The actual data analysis and simulation results show that the cost of cool load is saved about 5%. |
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