Research On Optimization Configuration Of Integrated Energy System Considering Multiple Uncertainties Of Demand Response

The integrated energy system(IES)can realize the organic coordination and optimization of various energy production,conversion,storage and consumption links.It has become an important means to promote energy transition and build a clean,low-carbon,safe and efficient modern energy system.Integrated demand response(IDR)can effectively achieve peak cutting and valley filling and improve economic benefit,which has positive significance for the development of IES.However,due to the large fluctuation of the load in IES and the strong correlation between IDR and user’s electricity consumption behavior,IDR has multiple uncertainties.How to use the complementary relationship between multiple energy sources to optimize the IES configuration and take into account the uncertainty of the IDR has become a key issue in the IES construction.Based on this,this paper proposes an IES optimization configuration method that takes into account IDR uncertainties.First of all,in order to improve energy utilization efficiency,this paper builds an IES based on a combined cooling heating and power(CCHP)system,covering the three links of energy input,energy coupling,and energy consumption.The physical model of the unit equipment in IES is also established.A detailed mathematical model is established for the response characteristics of different types of loads in IDR.At the same time,the uncertainty factors in each IDR model are analyzed,and a method based on evidence theory to deal with multiple uncertainties is proposed.On this basis,an IES optimization configuration model taking into account the electricity price scheme and IDR uncertainty is established.First of all,a strategy for formulating time-of-use electricity prices and an optimization load calculation method based on certain credible constraints are proposed.Then,a two-layer planning model that considers equipment optimization configuration and operation strategy is established.The upper layer selects equipment selection and capacity configuration with the lowest total cost as the goal,and the lower layer optimizes the output of each unit with the lowest operating cost as the objective function.The model uses a differential evolution algorithm and a CPLEX solver based on the YALMIP platform to solve.By comparing the total cost of all electricity price schemes,the optimal electricity price and equipment configuration scheme are finally obtained.In the end,a northern park is taken as an example,and a comparative analysis under multiple scenarios is performed to verify the validity of the model.The results of calculation examples show that the establishment of IES that takes into account IDR can effectively achieve peak clipping and valley filling,and improve system economics.Based on this,the optimal allocation results taking into account the uncertainty of IDR are more resistant to risks.The use of evidence theory can realize the unity of probability and interval,and make more reasonable use of the uncertain information for analysis,which has certain practicability.