Research On Risk Modeling And Fast Evaluation Algorithm Of Gas-Electric Integrated Energy Systems

With the rapid development of economy and society,fossil energy is consumed in large quantities,and the resulting energy depletion and environmental pollution problems have attracted increasing attention.In terms of promoting the comprehensive utilization of multiple energy sources and the large-scale consumption of renewable clean energy,the gas-electric integrated energy system(GEIES)has attracted a lot of interest.Accurate risk assessment of the GEIES is an important prerequisite for early detection of system weaknesses and ensuring the safe and reliable operation of the system.It is also an important foundation for effective risk prevention and risk management of GEIES.The deep coupling between the two heterogeneous energy supply networks of natural gas system and electric power system improves the flexibility of energy supply,utilization and efficiency.However,it also brings multiple uncertainties and complexities,which in turn raise a great challenge to the risk assessment of GEIES.Although some efforts have been conducted in the risk assessment of GEIE,the related research is still at the preliminary stage.To the best of the author's knowledge,little or no work has been done on the probabilistic modelling of gas system components with multiple failure modes and the risk assessment of a GEIES considering the high degree of non-linear operation characteristics of gas system components.To this end,this thesis thoroughly studies the risk assessment of GEIES taking into account the highly non-linear characteristics and multiple failure modes of gas system components,and the rapid risk assessment algorithm.The main work is summarized as follows:(1)In the existing researches in the risk assessment of GEIES,the highly non-linear characteristics of gas system components(such as the nonlinear relationship between the gas discharge/charge rate and the capacity of working gas for gas storage),which do occur in actual operations,are not fully considered.The thesis proposes a risk assessment framework of GEIES that takes into account the highly non-linear characteristics of gas system components.In this framework,various highly non-linear characteristics of gas system components are accurately modeled,including the non-linear relationship between the maximum discharge rate and the current capacity of working gas from gas storages,the non-linearity related to the delay characteristics of gas flow in pipelines,and the nonlinear characteristics between energy consumption and compression ratio in compressors.While the non-linear characteristics of gas network components make the model more realistic,they also produce difficicuties in convergence of energy flow calculations.An adaptive Newton algorithm based on a relaxation idea is proposed to improve the convergence of energy flow calculations.Additionally,compared with an individual power system,a GEIES not only can help improve the reliability of both subsystems(power subsystem and natural gas subsystem),but also can alleviate the problem of wind curtailment.To quantify the risk level and wind power absorption capacity of GEIES,wind power curtailment variables are incoporated into the optimal generalized load reduction model,and several annual risk indices related to wind power and generalized load curtailements are proposed.(2)In the existing researches in the risk assessment of GEIES,a simple two-state pipeline model is usually applied and the impacts of pipeline gas leakage failures are not considered.The thesis proposes a risk assessment framework of GEIES that takes into account the gas pipeline leakage failure modes.A four-state Markov transition model of pipeline is proposed to represent its multiple failure modes.Because statistical data usually only provides failure frequencies of pipelines instead of the transition rates used in the Markov model,the Markov model and the frequency duration method are combined in the thesis to obtain the analytic representations of the transition rates.Virtual gas load variables are incorporated into the proposed model to simulate the impact of pipeline leakages on the risk level of GEIES.Since the virtual gas load nodes are determined by random sampling,which will cause random changes in the network topology,a network model including random network topology changes is presented to represent pipeline leakage failures.(3)In the existing researches in the risk assessment of GEIES,the multiple failure modes of gas compressor stations are not considered.The thesis proposes a risk assessment framework of GEIES that takes into account the multiple failure modes of gas compressor stations.Based on the actual failure behavious of gas compressor stations,the following three failure models of compressor stations are developed: a four-state Markov state transition model for representing the different capacity levels caused by partial or whole failure of a compressor;a two-state common-mode failure model for representing an emergency shutdown of whole compressor station due to a severe gas leakage event or a major failure asscoaited with the compressor station;a power system condition dependent two-state model for an electric-driven compressor station,which represents the availability of the electric supply to the electric-driven compressor station.The by-pass system of compressor station plays an important role and its function is also modeled and simulated in the risk assessment of GEIES.(4)The thesis proposes a multiple time sequencing Monte Carlo simulation method based on cross entropy to improve the computational inefficiency that is caused by the nonlinear characteristics and multiple failure modes of gas network components.The gas-electric integrated energy system that takes into account the line pack and gas storage has a failure delay effect,that is,when a failure event occurs,the line pack and gas storage in the downstream pipeline network can keep the downstream gas load uninterrupted within a certain period of time.A sequential simulation algorithm can simulate the failure delay effect but its simulation efficiency is low.A non-sequential simulation algorithm has relatively high simulation efficiency but cannot simulate the failure delay effect.Although the quasi-sequential simulation algorithm combines the advantages of the sequential algorithm and the non-sequential algorithm,the failure delay effectt of GEIES cannot be simulated beacuase the system component states in this method are still sampled by using the non-sequential algorithm.To tackle this problem,a multiple time-sequencing Monte Carlo simulation method based on cross entropy is established in the risk assessment of GEIES.In the proposed method,a non-sequential algorithm is first used to sample the initial system state of each sequence,then a sequential cross entropy simulation algorithm is used to sequentially evolve the initial system state within the sequence length to consider the failure delay effect,and finally the risk indices are obtained using the multiple time-sequence samples.