Energy Flow And Optimal Energy Flow Analysis Of Regional Integrated Electricity And Gas System Considering Probabilistic And Possibilistic Uncertainties

With the increase of the installed proportion of gas-fired units,the wide use of electricity-driven compressors,and the development of power to gas(P2G)technology,the coupling of electricity and natural gas systems are getting closer.The regional integrated electricity and gas system(RIEGS)composed by the interaction of the electricity distribution network and gas distribution network has gradually become a typical form of the integrated energy system.Compared with the conventional electricity or gas network,on the one hand,there are also various uncertain factors in RIEGS,such as the change of electricity/gas loads,the fluctuation of electricity/gas sources.On the other hand,due to the existence of the electricity-gas coupling links,these uncertainties and their effects are transmitted between the electricity and gas network,which aggravates the uncertainty and complexity of the operation of the joint system.Therefore,it is necessary to study the above uncertainties and their influence on the operation of RIEGS.Energy flow analysis is a basic tool to study the integrated electricity and gas system(IEGS).At present,a lot of research has been devoted to the uncertainty analysis of energy flow in IEGS.However,there are still many problems and challenges in the modeling and analysis methods of uncertainties.In terms of models,most of the existing studies only consider the probabilistic uncertainty of bus/node injections,and few pay attention to the uncertainty of joint system parameters and their models.In terms of analysis methods,the energy flow analysis of RIEGS is usually a high-dimensional uncertainty analysis problem.How to balance calculation accuracy and efficiency is a difficult problem.How to effectively evaluate the impact of probabilistic and possibilistic uncertainties on the operation of the joint system needs further study.In view of the above problem,this paper takes RIEGS at the distribution network level as the object,and study the model and method of uncertainty analysis in energy flow and optimal energy flow.The main work and achievements are as follows:(1)A probabilistic energy flow analysis method for RIEGS based on the sparse polynomial chaos expansion(sPCE)is proposed.Considering the probabilistic energy flow calculation of RIEGS is a high-dimensional uncertainty analysis problem.In order to balance the calculation accuracy and efficiency,singular value decomposition and principal component analysis are applied to realize the decoupling and dimension reduction of input random variables.On this basis,the output variables approximation model can be solved quickly based on sPCE and orthogonal matching pursuit technology.A RIEGS composed of the IEEE 123 bus electricity distribution network and 11 node gas distribution network is used to verify the correctness and effectiveness of the proposed method.(2)An energy flow analysis method for RIEGS considering both probabilistic and possibilistic uncertainties is proposed.On the basis of considering the probabilistic uncertainty of electricity/gas loads and photovoltaic output,the possibilistic uncertainty of pipeline parameters in the gas distribution network is further taken into account.A two-level cycle solution framework is established to solve the probabilistic-possibilistic energy flow model.The outer loop applied Monte Carlo simulation with Latin hypercube sampling to deal with probabilistic variables,and the α-cuts method is used to deal with possibilistic variables in the inner loop.Finally,the belief and the plausibility functions of the output state variables are obtained based on evidence theory.At the same time,the probabilistic and possibilistic evaluation of energy flow in RIEGS is realized.A RIEGS composed of 33 bus electricity distribution network and 11 node gas distribution network is applied to verify the effectiveness of the proposed method.(3)An optimal energy flow analysis method for RIEGS considering both probabilistic and possibilistic uncertainties is proposed.Firstly,based on the principle of isentropic transformation,the possibilistic model of the pipeline parameters in the gas distribution network is transformed into normal variables.The probabilistic-possibilistic optimal energy flow problem is transformed into a stochastic optimal energy flow problem.On the basis,the chance-constrained programming model of the optimal energy flow for RIEGS is established,and the solution is carried out by the stochastic simulation technology and genetic algorithm.The effectiveness of the proposed method is verified by a RIEGS example composed of 33 bus electricity distribution network and11 node gas distribution network.