Modeling And Optimal Scheduling Of Absorption Solar Heat Pump System Based On Data

The absorption solar heat pump system is made up of solar collector and heat pump system.Its solar collector is used to convert the absorbed solar radiation into heat energy to drive the absorption heat pump system for refrigeration.The absorption solar heat pump system can effectively reduce energy consumption and achieve carbon reduction and pollution reduction,which has prodigious value and latent force of research.However,the promotion of the absorption solar heat pump system is seriously limited by its low thermal efficiency and low energy efficiency.Therefore,how to improve the stability of the absorption heat pump system in continuous operation and improve the energy efficiency of the system is the current research hotspot.Aiming at the operating performance of the absorption heat pump system,the steadystate model of the system and its components was established,and the optimal scheduling strategy of the absorption heat pump system under the given demand load was proposed by using the established pattern.The main research contents were as follows:(1)A modeling method of the absorption solar heat pump system based on SPSO-WNN neural network was proposed.Simple particle swarm operation(SPSO)was used instead of gradient descent to train the parameters of wavelet neural network(WNN),which not only made up for the shortcomings of neural networks that were easy to fall into local optimum,improved modeling accuracy,but also ensured training speed.The proposed SPSO-WNN algorithm was applied to the cooling capacity prediction model of the absorption solar heat pump system.The effectiveness of the method was verified by comparing with the traditional BP algorithm and WNN algorithm.(2)The steady-state model of the main heat transfer components and control components of the absorption solar heat pump system was established.Through the analysis of the working principle of each component of the absorption heat pump system,the input and output structure of each model was determined.Aiming at the complex working mechanism of five main heat exchange components(Generator,Absorber,Evaporator,Condenser,Heat recovery heat exchanger),the model was modeled based on the improved SPSO-WNN algorithm,and the accuracy of the model was verified.For three control components with relatively simple structure(Solution circulation ratio,Electronic expansion valve 1,Electronic expansion valve 2),the empirical modeling method was selected.The established individual component models can be used for the equation constraints in subsequent system optimization scheduling.(3)Based on the established system component model,a system optimization scheduling strategy to meet the heat load demand was proposed.Taking the highest overall energy efficiency ratio of the system as the optimization objective,the steady-state model of each component as the equality constraint,the system boundary condition as the inequality constraint,and the parameter values of the controllable components of the system as the variables to be optimized.Under the given demand load,the particle swarm optimization algorithm was used to optimize the parameter values in the range of 25%-100%load ratio.Through the scheduling and coordination of the internal control components of the system,the object of reducing energy consumption and improving energy efficiency ratio was achieved.By comparing and analyzing the changes of system energy consumption and energy efficiency before and after optimization under different load conditions,the optimal scheduling strategy for absorption solar energy was verified.