Multi-objective Stochastic Optimization Method To Combined Cooling,heating And Power System Integrated Renewable Energy Under Multi-uncertainty

With the gradual reduction of proved reserves of fossil energy and the continuous development of renewable energy application technology,more and more attention has been paid to the structural adjustment of energy supply system.Therefore,how to use traditional energy and renewable energy to meet the needs of users,and realize the comprehensive improvement of energy efficiency,economic and environmental benefits is a burning issue need to be solved urgently.In view of this,this dissertation takes the combined cooling,heating and power(CCHP)system as the main research object,and specifically studies the optimal allocation of the system under multiple uncertainties of renewable energy input and user load demand.The main research contents are as follows:Firstly,this paper constructs a CCHP system considering renewable energy such as wind energy and solar energy,and analyzes the operation characteristics of each equipment and the overall energy balance of the system.Furthermore,according to the idea of complementary utilization of energy,the multi energy operation strategy of the system is briefly described.Secondly,based on the established system model and its multi energy operation strategy,the uncertain factors affecting the safe and stable operation of the system are analyzed,including wind speed,light radiation intensity and user side load demand(electricity,heat,cold and domestic hot water).In view of the fact that all historical data considering uncertainty factors will bring dimens ion disaster in the process of system optimal configuration,this paper uses Monte Carlo sampling and K-means clustering method to fit,sample and randomly combine the uncertainty factors,and then cluster the sampling scenarios to get typical scenarios.Then,in order to get the optimal configuration scheme of the system,this paper considers the annual cost saving rate,primary energy saving rate,carbon emission reduction rate,renewable energy consumption rate,power grid integration level and other performance parameters,constructs a multi-objective optimization model,and uses non dominated sorting genetic algorithm to solve the model.In addition,in order to overcome the defect that the optimization result of genetic algorithm is not unique,a multi-objective decision-making method is applied to screen out the final optimal scheme.Finally,this paper collects relevant data and designs simulation experiments to verify the effectiveness of the proposed system model,multi scenario analysis method,optimization model and its solution method.The research shows that the multi energy complementary CCHP system has better environmental benefits and energy saving benefits than the traditional separate supply system;The multi-scenario method is used to replace the huge real data as the system input,which can avoid the adverse impact of extreme uncertainty on the system configuration,reduce the difficulty of calculation,and get a lower cost configuration scheme;The non-dominated genetic algorithm and multi-objective decision-making method are used to solve the optimization model proposed in this paper,and the configuration scheme that meets the research expectation and is conducive to the stable and efficient operation of the cogeneration system is obtained.