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China has formed a national natural gas pipeline network, and reasonable and accurate pipeline capacity allocation scheme has important significance for improving the utilization rate of pipeline network, improving the income of pipeline network operators and ensuring the gas consumption of downstream users, which poses greater challenges to the formulation of pipeline capacity allocation rules and mechanisms. This paper takes the natural gas pipeline network system as the research object, and studies the rules and mechanisms of pipeline capacity allocation. A capacity allocation model of natural gas pipeline network is proposed with the net present value of pipeline network operator's income as the goal, considering the gas consumption characteristics of downstream users, the dynamic change of gas source node capacity, pipeline hydraulic characteristics, pipeline flow direction, compressor and other constraints. Based on the simulation of the actual pipeline network by the proposed model, seven allocation rules and one mechanism are proposed. Specifically, the user priority of non-interruptible type, the pipeline path adjustable type and the gas source node adjustable type is higher than the corresponding comparison type. In the pipeline capacity allocation mechanism, the amount compensation mechanism for pipeline capacity transactions is as follows: When the pipeline capacity transaction occurs between users, the transaction information is submitted to the pipeline network operator. The pipeline network operator proposes the corresponding amount compensation according to the transaction information, and the transaction parties negotiate the proportion of the amount compensation and submit it to the pipeline network operator. In addition, it is recommended that pipeline network operators vigorously develop LNG receiving stations and downstream interruptible and compressible users in the future. • A capacity allocation model for complex pipeline network is constructed. • The model considers users type, user allocation weight and user purchase plan. • The allocation scheme considers process constraints to ensure technical feasibility. • The rules of pipeline capacity allocation are proposed by analyzing different allocation scenarios. [ABSTRACT FROM AUTHOR]

Third-party damage has become a significant factor leading to pipeline failures and leaks, resulting in severe ecological damage and human casualties. Traditional safety risk analysis methods in the past have focused on severing the causal chain of accidents, while neglecting the unique characteristics of different pipeline segments and environmental factors on their ability to bearing disasters. This paper presents a framework for evaluating the disaster-bearing capacity of natural gas pipeline third-party damage based on probabilistic neural networks. A comprehensive disaster-bearing capacity index system for evaluating third-party damage to natural gas pipelines has been constructed by analyzing the causal factors and potential consequences of third-party damage risk incidents. Next, the cluster analysis and indicator weighting information is used for achieving the comprehensive disaster-bearing capacity assessment. Finally, the data samples are constructed with the index levels and comprehensive disaster-bearing capacity levels of each evaluation unit. An optimization algorithm, Particle Swarm Optimization (PSO), is utilized to improve the parameter selection process of the Probabilistic Neural Network (PNN) model, in order to evaluate the disaster-bearing level of each unit. A typical long-distance natural gas pipeline in Zhejiang Province was selected as the case study, and a total of 200 research units were obtained through pipeline analysis. Furthermore, 14 indicator data were obtained by leveraging multiple sources of data surrounding the pipeline. With the utilization of cluster analysis, it was determined that Gaussian mixed model clustering method ultimately identified a total of 5 different levels of disaster-bearing capacity. The optimal smoothing factor can be accurate from 0.5 to 0.588 by using the PSO-PNN model, and the final accuracy rate is improved from 88% to 98%, which is obviously the introduction of PSO optimization algorithm in the PNN model can improve the accuracy of the model. This represents a significant improvement in accuracy when compared to traditional PNN models. The proposed evaluation method can provide scientific basis for the prevention and control of third-party damage in oil and gas pipelines and pipeline planning. • A third-party damage comprehensive disaster bearing capacity framework for evaluating natural gas pipeline. • Using clustering analysis to eliminate the influence of subjective evaluation. • Evaluation of disaster bearing capacity based on probabilistic neural network. • PSO-PNN optimization model effectively improves the accuracy of evaluation. [ABSTRACT FROM AUTHOR]

• A methodology is proposed for dynamic probability assessment of natural gas pipeline for third-party damage. • Failure factors are identified into two aspects of intentional factors and unintentional factors. • The probabilities of third-party damage failure causations are estimated by Fuzzy set theory and Dempster-Shafer evidence theory. • Fault tree analysis and event sequence diagram are coupled and transformed into dynamic Bayesian network model to eliminate uncertainty. • A real-field case study is used to prove the practicality and advantages of the proposed method. Failure and leakage of natural gas pipelines can lead to serious ecological losses and casualties. Third-party damage has become an important cause of pipeline failure and leakage, which urgently needs an accurate risk assessment method to assess the risk. Conventional qualitative risk analysis methods can only point out the critical events of failure accidents but fails to predict the failure probability. This paper proposes a dynamic risk probability analysis method based on Dynamic Bayesian network (DBN), which is validated by a third-party damage case under uncertainty. First, human factors are taken as the main analysis object in the risk analysis, by which two subcategories of intentional and unintentional factors are classified. A complete risk factor analysis is performed by combining expert recommendations with the fault tree analysis method and developing a coupled model with the event sequence diagram. Second, in order to deal with the uncertainty of risk factors, the coupled model is mapped to a DBN model. The prior probabilities of the input DBN model are obtained by database, fuzzy set theory, and Dempster-Shafer evidence theory. Weibull distribution is applied to construct the probability transfer process between time segments, which better fits the characteristics of third-party disruptive factors in onshore pipelines. Finally, the practicality and advantages of the proposed method are demonstrated by a real case study, which identifies 6 critical events and predicts the probabilistic information in different time slices. Furthermore, the method predicts the probability of failure events and potential consequences by processing the time series information, and it is found that the probability of structural damage and explosion is higher than other consequences. In this way, some risk management countermeasures are proposed in a targeted manner. The results show that compared with the conventional BN model which only performs probabilistic inference once, the DBN model can perform temporal dynamic inference to achieve the prediction of failure probability, and it can effectively achieve the numerical prediction of risk failure probability. [ABSTRACT FROM AUTHOR]

The "dual carbon goal" and "one country network" indicate the greater challenge of balancing supply and demand in natural gas pipeline networks. This paper proposes an optimization method for supply and demand balance of large and complex natural gas pipeline network to respond the demand changes of downstream users by making the peak shaving and flow rate allocation scheme, considering carbon emission targets to improve economic and environmental benefits. This method considers the pipeline network elements including bidirectional pipeline, multiple pipeline structure, compressor station and gas storage so on, and couples the hydraulic characteristics to accurately describe the hydraulic situation of the pipeline under each allocation scheme. Case 1 shows that the economic efficiency is optimized by 15.58% and compensates for the overgrowth demand generated through the initial stage. Compared with TGNET, the average relative error of hydraulic was only 2.89%. Case 2 shows that the optimal flow rate allocation scheme can be further improved in the case of multiple pipelines. Case 3 for a large-scale pipeline network reduces 58% annual carbon emissions by adjusting the flow rate allocation scheme. This study can provide decision support for the allocation and operation of natural gas pipeline network. • A method is proposed to describe the complex natural gas pipeline network system. • A multi-period optimization model of supply and demand balance is proposed. • Economic and environmental benefits are taking as objective function. • The peak shaving and the hydraulic characteristics are considered. [ABSTRACT FROM AUTHOR]

The natural gas pipeline networks play a vital role in Integrated Energy System (IES). Simultaneously, with the increase in the number and types of users, the operational model needs to be transformed from plan-oriented to demand-oriented. Hence, higher requirements are put forward for the flexibility and rapidity of the control methods. In this paper, a novel rapid operation control method for natural gas pipeline networks is proposed based on multi-user demand prediction and pipeline flow state inversion. First, the Nonlinear autoregressive (NAR) neural network is used to analyze the historical consumption of various users and predict each user's demand in the short term. Second, the rapid transient inversion method is derived from the partial differential control equation to realize the inverse of the pipeline flow state. Third, combined with the equipment model, the control method is formed to realize rapid control of the pipeline networks. The control method is applied to classical triangular networks and existing pipeline networks to demonstrate accuracy and effectiveness. The time-consuming ratio of the commercial software and proposed model for the triangular pipeline networks is 1:0.22. For the existing pipeline networks, six users' average demand prediction errors are 5.58%, and the control errors of pressure at three markets are 1.00%, 1.22%, and 0.81%. The results show that the proposed method can respond to users' demands rapidly and provide decision support for the safe and efficient operation of the natural gas pipeline networks. [Display omitted] • A rapid control method is proposed for the natural gas pipeline networks. • Multi-user demand prediction provides advanced guidance for network operation. • An Out-to-In model is derived to inverse the transient flow state of the network. • Two cases are performed to verify the accuracy and effectiveness of the method. [ABSTRACT FROM AUTHOR]