Stochastic Programming Of Integrated Energy System Considering Multi-scale Correlated Uncertainties

The integrated energy systems are mainly composed of energy production,energy exchange,energy storage,energy usage and others.Through the multi-energy coupling systems,the mutual coupling and complementary advantages of different energy sources can be realized,carbon emissions can be reduced,meanwhile,energy supply efficiency can be improved.However,with the large-scale integration of distributed renewable energy sources such as wind power,in order to meet the thermal load demand,CHP’s power generation has met most of the electricity demand,which limits the consumption of renewable energy generation in the system.In order to deal with the uncertainty of wind power output,the system needs lots of power generation reserves to meet the load demand which will increase the cost of power system dispatch.Therefore,exploring how to effectively solve the problem of consumption and utilization of renewable energy in CHP-based integrated energy system is of great theoretical value and engineering significance.Therefore,by encouraging end-users to participate in demand response,the initiative and flexibility of the demand side are brought into play to promote the economic and efficient operation of the integrated energy system.In order to analyze the potential value of demand-side resource allocation in system planning and operation,explore the impact of multi-scale uncertainties and the correlation between uncertainties on the planning results and investment returns of the integrated energy system,this paper stablishes a stochastic planning model of integrated energy systems from both supply side and demand side,and introduces scenarios and related technologies to deal with the multi-scale uncertainties,and further introduces a comprehensive scenario reduction method based on optimal clustering to reduce the computational burden.First,based on the overview of uncertainty in the integrated energy system,the probability density function is used to model the multi-scale uncertainties in the model.A series of related scenario processing technologies are used to generate a set of relevant deterministic scenarios.In order to reduce the deviation of correlation between uncertainties before and after scenario reduction,a comprehensive scenario reduction method based on the optimal clustering is further introduced.Secondly,the elastic model is used to establish a price-based demand response model,and on this basis,a comprehensive energy system planning framework for source-load coordination is proposed.This model takes minimizing the total planning-operation cost as the objective function to jointly optimize the planning and operation of power generation unit and advanced metering infrastructure to achieve the optimization of the economic and environmental benefits of the system.Finally,in order to verify the effectiveness of the proposed model,simulation analysis is performed on a standard example and a real electric-heat-gas coupling system.The simulation results show that the source-load coordinated model proposed in this paper can effectively analyze the potential benefits of demand response to the system operators,and reveal the impact of uncertainty and correlation in the system on the planning results,which has guiding significance for the planning and operation of the integrated energy systems.