Research On Operation Optimization Of Renewable Energy Combined Cooling Heating And Power Systems Based On Model Predictive Control

The world today is facing a serious energy and environmental crisis,China as a major energy consumer,the situation of energy conservation and emission reduction is more serious.Renewable energy combined cold,heat and power supply(CCHP)system is based on the CCHP system to introduce renewable energy,in order to achieve efficient cascade utilization of energy and improve the rate of consumption of renewable energy.The renewable energy CCHP system has numerous equipment,complex working conditions,heterogeneous multi-energy flow coupling,and the uncertainty of renewable energy and cold and hot power load is strong.It is particularly critical to realize the economical and efficient operation of the system,which is also extremely challenging.Aiming at the optimization operation of renewable energy CCHP system,the main work completed in this paper is as follows:Firstly,this paper introduces the composition and energy balance of the traditional supply system.The basic structure and energy flow of renewable energy CCHP system are analyzed.The models of internal combustion generator set,absorption refrigerator and other equipment are established.A method for calculating the evaluation index of renewable energy CCHP system is presented.After that,this paper proposes an optimized operation strategy of renewable energy CCHP system based on model predictive control framework combined with interval optimization.The model predictive control optimization operation framework includes three modules:source and load prediction,rolling optimization and feedback correction.Source and load prediction module:This paper proposes a Gaussian process regression prediction method for ultra-short-term prediction of renewable energy and load,which can predict the predicted value and interval value of wind power,cooling and heating load,and provide data information for optimal operation;rolling optimization module:In this paper,based on the interval optimization theory,the system's uncertainty problems are transformed into deterministic optimization problems.The converted optimization model is used to solve the system equipment output interval rolling optimization,and the scheduling is adjusted in time according to the fluctuation of renewable energy and cooling and heating load demand The instruction guarantees the real-time performance of the optimization;feedback correction module:This article designs error prediction and real-time adjustment links,makes error prediction based on historical data,and corrects the predicted value in real time,reducing the error of the system output.Therefore,based on the optimized operation framework combined with interval optimization,this paper achieves economic,efficient and stable operation of the system.In this paper,the cold and heat load of a hospital in northern my country is taken as an example,and the renewable energy CCHP system is used as the energy supply system for simulation analysis to verify the effectiveness of the above optimized operation strategy.Finally,this paper develops the CCHP system data monitoring and optimization operation platform.The platform can complete the system field data collection and real-time data display,by calling MATLAB software to run the optimization algorithm to solve the equipment output value,provide equipment output reference for system optimization scheduling,combined with CCHP data monitoring system to issue scheduling instructions.In this way,CCHP system data monitoring and optimized operation are realized.