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A low carbon operation optimization model for natural gas pipeline networks is proposed based on different energy structures to reduce carbon emissions. SSA (sparrow search algorithm), WOA (whale optimization algorithm), and AHA (artificial hummingbird algorithm) were compared using test functions. Results show that AHA has better optimization performance. The ShanxiBeijing natural gas pipeline network was taken as an example. A carbon emission optimization model with the goal of minimizing carbon emissions was established. AHA was used to solve the model. Results show as follows. The energy consumption optimization scheme (objective 1 scheme) tends to use natural gas, and the carbon emission optimization scheme (objective 2 scheme) tends to use electricity. The objective 1 scheme reduces energy consumption by 21.75%, while the objective 2 scheme reduces energy consumption by 21.32%. The objective 1 scheme reduces carbon emissions by 21.18%, and the objective 2 scheme reduces carbon emissions by 24.06%. Therefore, the carbon emission optimization scheme minimizes carbon emissions from the pipeline network but cannot minimize energy consumption. In conclusion, the optimization model established in this paper can realize the functions of pipe network energy consumption optimization and carbon emission optimization and help to build a smart pipe network. [ABSTRACT FROM AUTHOR]

Objective Analyzing the applicability of pipeline simulation technology in energy measurement, and examine the deviation between simulation calculation results and actual pipeline measurement values. Methods Using a closed natural gas pipeline network with multiple gas sources will be as an example, a simulation model with functions for natural gas heat generation, pressure, and flow calculation was established. This model is based on the theoretical foundation of pipeline network simulation for energy measurement. Results The deviation between the simulation results of heat generation and the calculated values of the on-site chromatographic analyzer is within 0.3%, which meets the requirements of the maximum allowable error of assigned heat generation in GB/T 18603—2023 Technical requirement of measuring systems for natural gas. The deviation between the pressure simulation calculation result and the recorded value of the on-site pressure transmitter is within 1.0%, and the deviation between the flow simulation calculation result and the measured value of the on-site flow meter is within 2.0%. Conclusions The energy measurement technology based on pipeline simulation is a feasible method for assigning heat generation values to complex multi-gas source pipelines. This method can also be used for real-time tracking or verification of pipeline pressure, flow rate, and natural gas pipeline inventory. [ABSTRACT FROM AUTHOR]

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The natural gas pipeline network, as an important part of the natural gas industry and an indispensable participant in the energy Internet, plays a crucial role in the construction of China's energy industry and in the development of national economy. In this paper, the history of China's natural gas pipeline industry was reviewed from going through extreme hardships in the formative years of the new China after 1949 to leading the world in the construction and operation technologies of high-grade steel, large diameter, high pressure pipelines. And China's technological achievements in natural gas pipeline materials and equipment, design and construction, operation and maintenance were further systematically examined. Based on the current development trend of "One Pipeline Network" for natural gas pipelines and the future development mode of energy Internet, the importance of natural gas pipeline network in achieving the spatial and temporal balance between energy demand and supply by assisting the integrated development of multiple energies was identified under the background of achieving carbon peaking and carbon neutrality and energy transition goals; the role of the natural gas pipeline network was established in energy peak shaving, hydrogen blending transportation, energy conversion and storage. Moreover, the technologies of natural gas energy metering, energy storage and conversion facility construction, simulation of integrated natural gas pipeline network and power grid, and intelligent control of the natural gas pipeline network in energy Internet were systematically analyzed. As a result, this research serves as a valuable reference for determining the direction and ideas of natural gas pipeline network technology research in the context of energy Internet.

In order to overcome the deficiency of the traditional time series prediction method in flow prediction of natural gas pipeline networks, a combined prediction model based on Empirical Mode Decomposition (EMD), Attention and Gated Recurrent Unit (GRU) was proposed. Specifically, the model is to substitute the raw flow data of the natural gas pipeline network with its time series component obtained through Empirical Mode Decomposition (EMD), input the intrinsic mode function component obtained into the GRU neural network, calculate the attention probability weight at different times with the attention integrated into the network, and finally learn in the network and predict the time series of flow in the natural gas pipeline network. The verification results in a natural gas pipeline network show that: the EMD-Attention-GRU combined model demonstrates remarkable performance in flow prediction of natural gas pipeline network, capable of capturing the complex non-linear relationships. Besides, the average absolute percentage error of prediction by the combined model outperforms the single GRU and Attention-GRU models by 6.29% and 5.17%, respectively. Thus, it is indicated that the EMD-Attention-GRU combined model could better address the complexities and dynamic features of flow in natural gas pipeline networks than the conventional time-series prediction methods, with values for promotion and application.

The transient simulation technology of natural gas pipeline networks plays an increasingly prominent role in the scheduling management of natural gas pipeline network system. The increasingly large and complex natural gas pipeline network requires more strictly on the calculation efficiency of transient simulation. To this end, this paper proposes a new method for the transient simulation of natural gas pipeline networks based on fracture-dimension-reduction algorithm. Firstly, a pipeline network model is abstracted into a station model, inter-station pipeline network model and connection node model. Secondly, the pressure at the connection node connecting the station and the inter-station pipeline network is used as the basic variable to solve the general solution of the flow rate at the connection node, reconstruct the simulation model of the inter-station pipeline network, and reduce the equation set dimension of the inter-station pipeline network model. Thirdly, the transient simulation model of the natural gas pipeline network system is constructed based on the reconstructed simulation model of the inter-station pipeline network. Finally, the calculation accuracy and efficiency of the proposed algorithm are compared and analyzed for the two working conditions of slow change of compressor speed and rapid shutdown of the compressor. And the following research results are obtained. First, the fracture-dimension-reduction algorithm has a high calculation accuracy, and the relative error of compressor outlet pressure and user pressure is less than 0.1%. Second, the calculation efficiency of the new fracture-dimension-reduction algorithm is high, and compared with the nonlinear equations solving method, the speed-up ratios under the two conditions are high up to 17.3 and 12.2 respectively. Third, the speed-up ratio of the fracture-dimension-reduction algorithm is linearly related to the equation set dimension of the transient simulation model of the pipeline network system. The larger the equation set dimension, the higher the speed-up ratio, which means the more complex the pipeline network model, the more remarkable the calculation speed-up effect. In conclusion, this new method improves the calculation speed while keeping the calculation accuracy, which is of great theoretical value and reference significance for improving the calculation efficiency of the transient simulation of complex natural gas pipeline network systems.

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Natural gas pipeline system simulation is the core technology for design plan demostration, transportation plan development, operation plan optimization, and emergency security decision-making of pipeline system. Hence, a simulation model applicable to the pipeline network systems in any structures was established based on the principles of conservation of mass, momentum and energy, as well as the characteristic equation of non-pipe elements. An algorithm for solving the simulation model of pipeline network system was formed based on the functional analysis of pipeline mathematical model and the fast LU decomposition under the large sparse matrix compression storage conditions. Besides, large-scale online simulation software for natural gas pipeline systems, comprising the modules of data communication, database storage, data filtering, online simulation and result pushing, was developed with the B/S software architecture, the big data caching technology and the real-time visualization technology for big data extraction. Then, the software was applied in a pipeline network system with a total length of 6 100 km and 45 compressor stations and a large pipeline network system with a total length of 31 000 km and 117 compressor stations. It is indicated that the applicable scale, calculation accuracy and speed of the software have reached the international level of similar commercial software.

As the natural gas pipeline network in China is densified increasingly, its operation becomes more and more complicated, bringing challenges to the research on topology structure and gas supply reliability. In recent years, with the deepening of its research, the complex network theory has been widely applied in the transportation, water, power and other transmission networks. In order to draw on the current progress of the application of complex network theory in similar transmission networks, the main application results in topology structure analysis, transmission reliability evaluation, bottleneck identification, network area division and network flow tracking of were investigated and summarized. Then, according to the status of research on natural gas pipeline network in China, and suggestions were put forward for the development. Specifically, a random sampling method, network flow algorithm and artificial intelligence demand forecasting method for reliability evaluation were developed based on the scale-free characteristics of natural gas pipeline networks. Meanwhile, 3 types of identification methods for bottlenecks were developed, a division algorithm for areas was formed, and different network flow tracking algorithms were proposed for various types of business modes. In addition, analysis on characteristics of topology structure of pipeline networks should be performed, a evaluation method for gas supply reliability should be proposed, and the bottlenecks of pipeline network should be identified with the network flow model, so as to provide basis for the construction of new pipelines. Moreover, the area division should be based on the supply characteristics, and a set of gas flow tracking algorithm should be developed for the settlement of pipeline transportation fees. In general, the research results could provide technical reference to development of natural gas pipeline network.

With the expansion of natural gas pipelines into larger networks, there is a growing concern regarding energy consumption in the operations of pipeline systems. However, the intricate network structure and hydraulic properties pose challenges for operation optimization. In this paper, a Mixed Integer Nonlinear Programming model is proposed to optimize the steady-state operation of natural gas pipeline network, aiming to minimize the energy consumption of compressor units. The model incorporates constraints related to flow direction, flow balance, pressure drop in pipeline sections, and pressure increase at compressor stations. To solve the complex nonlinear model efficiently, a stochastic optimization algorithm that integrates particle swarm optimization and high-fidelity simulation is proposed. The case study is conducted on a large-scale natural gas pipeline network consisting of forty-three pipeline sections and four compressor stations. The optimal operation scheme is calculated, and the outlet pressures of each compressor are determined. The results demonstrate that the stochastic optimization algorithm proposed in this paper can reduce energy consumption by 21.23 % at most and 19.77 % on average during pipeline operation, which can provide guidance for the operation management of large-scale natural gas pipeline network. • Set an integrated framework to optimize the operation of natural gas pipelines. • Propose a stochastic algorithm combining intelligent algorithm and high-fidelity simulation. • The solving efficiency of the non-convex model is guaranteed. • Total energy consumption is reduced by an average of 19.77 %. [ABSTRACT FROM AUTHOR]

The natural gas quality fluctuates in complex natural gas pipeline networks, because of the influence of the pipeline transmission process, changes in the gas source, and fluctuations in customer demand in the mixing process. Based on the dynamic characteristics of the system with large time lag and non−linearity, this article establishes a deep−learning−based dynamic prediction model for calorific value in natural gas pipeline networks, which is used to accurately and efficiently analyze the dynamic changes of calorific value in pipeline networks caused by non−stationary processes. Numerical experiment results show that the deep−learning model can effectively extract the effects of non−stationary and large time lag hydraulic characteristics on natural gas calorific value distribution. The method is able to rapidly predict the dynamic changes of gas calorific value in the pipeline network, based on real−time operational data such as pressure, flow rate, and gas quality parameters. It has a prediction accuracy of over 99% and a calculation time of only 1% of that of the physical simulation model (built and solved based on TGNET commercial software). Moreover, with noise and missing key parameters in the data samples, the method can still maintain an accuracy rate of over 97%, which can provide a new method for the dynamic assignment of calorific values to complex natural gas pipeline networks and on−site metering management. [ABSTRACT FROM AUTHOR]

The fundamental function of a natural gas pipeline network is to transport enough natural gas to users. Therefore, user characteristics should be considered in the formulation of the flow allocation plan of the pipeline network under accident conditions. However, user characteristics have usually not been considered in previous flow allocation models. In this study, a mixed integer linear programming model is developed to determine the optimal flow allocation plan of a large-scale and complex natural gas pipeline network under accident conditions, and the user characteristics are considered as well. The optimization objective is to maximize the weighted sum of the amount of natural gas transported to the consumers under accident conditions, and the weights of the natural gas users are obtained by user characteristics analysis. The model constraints include flow constraints, gas source supply capacity constraints, user demand constraints, pipeline transmission capacity constraints, pressure constraints, and pipeline hydraulic constraint. For the sake of model simplification, the hydraulic constraints are treated piecewise linearly. Furthermore, the model is set into a real-world situation, which is the natural gas pipeline network located in China, and the user characteristics are considered in the optimal flow allocation plan under accident condition. The impact of user characteristics is further investigated by calculating and comparing the flow allocation plan when considering and ignoring user characteristics. The study indicates that when user characteristics are considered, the natural gas pipeline network will tend to give higher priority to those crucial users. [ABSTRACT FROM AUTHOR]

The natural gas pipeline network in North China is well developed, and the demand of natural gas is increasing day by day. However, "1+X" business mode was formed in the intermediate links of transportation by the provincial short-distance pipeline networks due to different operators, scattered distribution, small size and pressure level and low load rate of transportation under the pricing mode of allowable cost + reasonable profits for pipeline transportation, which results in a challenging bottleneck of transportation cost. By analyzing and comparing the difference of natural gas transportation price between the long-distance pipelines in the national pipeline network and the short-distance pipelines in the provincial network in the North China, it was pointed out that increasing the pipeline load rate above the specified starting value could significantly reduce the pipeline transportation price of single cubic meter of gas, and has a significant effect on reducing the cost of gas consumption for the end users. Hence, the following suggestions were put forward: weakening the bottleneck of transportation cost and stimulating the market vitality by reducing the intermediate links of transportation and incorporating the provincial pipeline network into the national network, optimizing the pipeline transportation and distribution path of natural gas, actively promoting the fair opening of provincial short-distance pipelines to the third parties, continuously strengthening the allocating, transportation and supplying of natural gas, and benefiting the users with the interaction of transportation volume and price, so as to facilitate the high-quality development of natural gas industry.

Underground gas storage plays a crucial role in ensuring the supply and demand balance of natural gas pipeline network. However, the peak shaving function of underground gas storage or its injection-withdrawal hydraulic characteristics are often overlooked in the optimization of pipeline network scheduling. This paper develops an integrated scheduling optimization model of complex natural gas pipeline network system with underground gas storage, which responds to multi-period consumer demand changes to balance supply and demand, while considering both economic and environmental benefits. The optimization model comprehensively considers the elements of underground gas storage, including reservoir, injection-withdrawal well, and compressor group. A mixed integer linear programming relaxation method combining the linearization of univariate and bivariate functions and convex relaxation of feasible regions is designed. The case study results show that underground gas storage peak shaving function can effectively compensate for gas supply shortage. Compared to the non-relaxation method, the relaxation method reduced the transportation and injection-withdrawal energy cost by 10.21 % and 15.89 %, and carbon emissions by 20,426 tons. Furthermore, the study analyzed the advantages of the comprehensive underground gas storage model, the coordinated injection-withdrawal strategies of multiple underground gas storages, and the response strategies under system abnormal conditions. • A comprehensive underground gas storage model considering hydraulics is developed. • Pipeline network scheduling strategies integrating gas peak-shaving are proposed. • The carbon emission optimization of gas transportation and storage is considered. • A method combining the linearization and convex relaxation is designed. [ABSTRACT FROM AUTHOR]

With the improvement of natural gas network interconnection, the network topology becomes increasingly complex. The significance of analyzing topology is gradually becoming prominent, and a systematic analysis method is required. This paper selects two typical natural gas pipeline networks: one in Europe, and the other in North China. Based on complex network theory and the nature of natural gas pipelines, topological models for the two typical networks were established and the evaluation indexes were developed based on four factors: network type, overall topological structure characteristics, path-related topological structure characteristics, and topological structure robustness. Using these indexes, the topological structure of the two typical networks is compared and analyzed quantitatively. The comparison shows that the European network topology has more redundancy, higher transmission efficiency, and greater robustness. The topology analysis method proposed in this paper is practical and suitable for the preliminary analysis of natural gas pipeline networks. The conclusions achieved by this method can assist operators in gaining an intuitive understanding of the overall characteristics, robustness, and key features of pipeline network topology, which is useful in the implementation of hierarchical prevention and control. It also serves as a solid theoretical foundation and guidance for network expansion, interconnection construction, and precise hydraulic simulation calculation in the next stage. [ABSTRACT FROM AUTHOR]

The era of big data has brought about great changes in industry. In order to improve the efficiency, security and automation level of industrial production, it is necessary to find out the hidden rules in the massive data by fully mining and utilizing the big data of industry, so as to improve the level of information management in industry, further providing reliable technical support for intelligent monitoring, operation and maintenance. Herein, the application solution and effect of data mining in load forecasting, safety warning, scheduling optimization and performance monitoring of natural gas pipeline network under the context of big data were analyzed with specific cases. Based on that, the development direction of data mining and other relevant technologies was discussed from the perspectives of multi-data fusion, establishment of integrated information platform and full-life-cycle management. Moreover, the application of data mining in natural gas pipeline network was prospected.

Due to the characteristics, such as massiveness, multi-source, diversity and great difference of value density, of the data of natural gas pipeline network, it is of great difficulty to do data analysis and application. With reference to the study results of big data in power grid, supply chain and internet, a big data analysis framework of natural gas pipeline network based on data processing, data mining and comprehensive analysis of multivariate data is put forward. Definitely, the method and function of data processing is illustrated in terms of data cleaning, feature selection and reconstruction. Based on the specific business and scenarios, it is defined that prediction and early warning, model identification, rule learning and deduction are the basis to construct the data mining method of pipeline network. In addition, the comprehensive application of diversified data of pipeline network is discussed. It is also pointed out that the development of multimode learning and federated learning is the key to break the data barrier and to form data intelligence of pipeline network. Through big data analysis of natural gas pipeline network, the big data "ecology" of pipeline network shall be improved continuously, the machine learning method of knowledge in data fusion shall be deeply researched, and a cross-border interpretable and controllable big data analysis method system of pipeline network shall be established, so as to provide theoretical support for development of intelligent pipeline network technologies.

The control technique for linepack of natural gas pipeline network has been studied to ensure the safe and stable operation of gas pipeline network. The classification, control principle and control standard of linepack were elaborated based on the current production and operation experience of natural gas pipeline network, and the control technique for linepack was analyzed in detail from four aspects: selling in spring and autumn, guaranteed supply in winter and summer, gas distribution and prediction of accident pipeline survival time. The results suggest that linepack should be actively controlled for a uniform change according to the weekly inhomogeneity sale in spring and autumn, and it should be raised to the emergencyhigh level or the highest level to ensure the gas supply in winter and summer. The gas from the natural gas pipeline network can be reasonably distributed based on the control area of the linepack, and the survival time of the accident pipeline can be predicted according to the low linepack limit of natural gas pipeline.

As the total mileage of natural gas pipeline network continues to increase, the topological structure of natural gas pipeline network will become more and more complex. The complicated topological structure of natural gas pipeline network is likely to cause inherent structural defects, which have serious impacts on the safe operation of natural gas pipeline network. At present, related researches mainly focused on the safe and reliable operation of natural gas pipeline network, which has become a research hotspot, but few of them considered the complexity of natural gas pipeline network and its potential impacts. In order to understand the complexity of natural gas pipeline network and its behaviors when facing structural changes, this paper studied the robustness of natural gas pipeline network based on complex network theory. This paper drew on the methods and experience of robustness researches in other related fields, and proposed a robustness evaluation method for natural gas pipeline network which is combined with its operation characteristics. The robustness evaluation method of natural gas pipeline network is helpful to identify the key components of the pipeline network and understand the response of the pipeline network to structural changes. Furthermore, it can provide a theoretical reference for the safe and stable operation of natural gas pipeline network. The evaluation results show that natural gas pipeline network shows strong robustness when faced with random disturbances represented by pipeline accidents or component failures caused by natural disasters, and when faced with targeted disturbances represented by terrorist disturbances, the robustness of natural gas pipeline network is very weak. Natural gas pipeline network behaves differently in the face of different types of random disturbances. Natural gas pipeline network is more robust when faced with component failures than pipeline accidents caused by natural disasters.

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The liberalization reform of the natural gas market has decoupled gas trading from storage and transportation services and introduced the new role, namely shippers. The shippers book network capacity from pipeline network operators (PNOs) by signing capacity contracts and nominate the gas of injection and withdraw in the day-ahead market. The PNOs then validate the feasibility of nominations and optimize the operation plan of gas network. How to efficient allocate the network capacity has become a new challenging subject due to the need to coordinate the interests of multiple market participants, including producers, merchants, local distribution companies and others. To address this issue, this paper established a bilevel optimization model for the optimal allocation of network capacity. Two approaches are proposed: (i) a single-level reformulation-based method (KKT reformulation), used as a benchmark, where a tailored outer approximation (OA) method is applied to handle the nonlinear constraints; (ii) a distributed method where a variant of Inexact-ADMM is proposed by exploiting the separable structure of coupling constraints. Case studies are performed on a real-life 352-node gas network. Initially, the proposed method is tested by a simplified 139-node gas network, showing that the solution speed of Inexact ADMM is 27.51 and 28.23 times faster than that of KKT-based method in two typical cases, while the objective value of the whole system only increased by 0.41% and 0.38%. Then, the Inexact-ADMM is successfully implemented in the whole size of the 352-node gas network by further decoupling the upper-level PNOs model into several sub-models. The maximal relative error of hydraulic was <0.4% in Case 1 and 0.7% in Case 2. This study demonstrates that the proposed ADMM-based distributed algorithm can guarantee the pipeline operation, safely and efficiently, by determining the suitable bookable capacities of the shippers. • A bilevel model for the optimal capacity allocation of natural gas network is established. • An ADMM based method is proposed to solve the bilevel model in a distributed way. • The solution speed is >20 times faster than that of KKT method in a 139-node gas network. • The maximal relative error of hydraulic for the 352-node gas network is <1%. [ABSTRACT FROM AUTHOR]

Blending hydrogen into the natural gas pipeline network is an efficient approach for storing and delivering renewable energy. However, the variation in gas quality and the diversity of delivery paths can lead to difficulties in pipeline operation management. In this paper, we propose a bilevel optimization model to understand the influence of hydrogen blending on pipeline capacity allocation and gas transmission tariffs. The lower-level model optimizes the best capacity booking of each shipper, and the upper-level model optimizes the best capacity allocation of the transmission system operator. Detailed transmission paths and gas calorific values at each delivery point are taken into account to calculate the actual gas transmission tariffs. The proposed model is solved by the alternating direction multiplier method and validated by a real-world pipeline network. Results show that: (1) the proposed method can facilitate shippers to meet the energy demand under different hydrogen blending cases, (2) a fair-cost sharing is achieved among all parties, (3) when the hydrogen blending ratio reaches 5 %, the pipeline capacity can be optimally utilized to achieve the maximum pipeline capacity allocation. This approach can guide efficient gas pipeline capacity allocation and facilitate the establishment of trading platforms. • Customize a bilevel optimization model for the operator and shipper. • Determine cost and capacity allocation when hydrogen blending. • Apply alternating direction multiplier method algorithm to solve the model. • Conduct a comprehensive analysis of a real case in China. [ABSTRACT FROM AUTHOR]

Under the background of "One national network", the balance between the natural gas supply side and the demand side is prominent. This paper proposes a supply-demand balance method of natural gas pipeline network coupled with the hydraulic and thermal characteristics, and decides the transportation scheme through flow rate allocation. This paper designs a two-stage relaxation optimization algorithm, coupled the thermal process into the hydraulic calculation to improve the hydraulic calculation accuracy, making the natural gas transportation scheme more applicable to engineering. The result shows the feasibility of transportation decision-making and the accuracy of hydro thermal calculation of the proposed model through a real case. The relative error of thermal calculation and TGNET is below 2%, and the hydraulic calculation error of coupled thermal can also be within 10%. After optimization, the carbon emissions decrease from 418,100 tons to 260,000 tons, reducing by about 37.81%. This study also analyzes the impact of hydraulic calculation on carbon emissions calculation, and further illustrates the necessity of coupled thermal characteristics in transportation scheme. This study can provide decision support for the transportation operation and help the natural gas pipeline network to meet the dual carbon requirements and achieve sustainable development. • A flow rate allocation method combining operation process is proposed. • A convex relaxation optimization algorithm is designed for energy consumption calculation. • Two stage relaxation iteration algorithm is designed to couple thermal and hydraulic characteristics. • Carbon emission objective is added to the optimization model for environmental benefit. • An actual case is used to prove the practicability of the method. [ABSTRACT FROM AUTHOR]

In order to identify the vulnerable links in urban natural gas pipeline network systems, this study established a concept for the vulnerability analysis of the network system, providing a basis for quantitative analysis of vulnerability. The criteria for selecting nodes in the network were determined based on the network composition. Based on the theory of disaster chain, the vulnerability factors were analyzed thoroughly, the hazard factors causing vulnerability were determined and the vulnerable parts of the network system were identified. A model for the calculation of the structural threats from the network itself was established. The first step is to identify the interdicted point of single pipeline sections through the calculation method for friction resistance loss, and the second step is to determine the key nodes with the maximum or minimum vulnerability of the entire network, thereby realizing the point-tonet analysis of the pipeline network. The FIM model was implemented, combining the geological information system ArcGIS, Java programming language, and Lingo optimization software. Using a natural gas pipeline network in Beijing as a case study, the distribution of vulnerable points in the network was plotted and the key nodes with high vulnerability were identified by analyses of vulnerability and importance. [ABSTRACT FROM AUTHOR]

• A resource-side analysis-based method for gas supply reliability is proposed. • The stochastic and time-dependence characteristics of resources is considered. • Uncertainties of resources and consumers, and unit's failure are integrated. • The significance of the resources in the gas supply reliability is elaborated. In this study, a methodology to evaluate gas supply reliability of the natural gas pipeline network is proposed, and the uncertainty of the gas supply capacity of the natural gas resources is considered. The methodology is composed of four parts. Firstly, the gas supply capacity calculation model of natural gas resources considering its stochastic and time-dependent characteristics is described. Secondly, the gas demand prediction model is developed based on the demand-side analysis. Thirdly, the gas supply calculation model of natural gas pipeline network is proposed considering units failure and hydraulic characteristics. Finally, according to the established gas supply reliability index system, the gas supply reliability indicators are calculated based on N (N is a large number) times of Monte Carlo simulation. Furthermore, a real natural gas pipeline network located in China is applied to confirm the feasibility of the methodology, and the gas supply reliability is evaluated based on quantity and time dimensions, and the results are reduced by about 3.9% and 4.1% compared with the results calculated by the method which ignored the uncertainty of resources. At last, the significance of the natural gas resources in the gas supply reliability is investigated by the sensitivity analysis. [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]

• An evaluation method for the economic loss risk on the demand side of a natural gas pipeline network system in a certain period is proposed. • User priorities are considered when there is a shortage of gas in the pipeline network. • The economic consequences on the demand side are quantified in terms of the alternative energy costs. • The effect of supply sequence of users on gas supply reliability is described. Most studies on the natural gas supply reliability evaluation have reflected on whether the volume of gas supply to users is sufficient. However, different types of users suffer from the same natural gas shortage, the consequences of loss are different. The abundance of the supplied gas volumes does not reflect the actual losses incurred by users. Moreover, when a gas shortage occurs, the supply flow of different types of users is redistributed according to the supply order. Therefore, this study proposes an evaluation method for the gas supply reliability based on demand-side economic risk. The user's supply sequence is integrated into the cost matrix to optimize the distribution of the user's demand flow under gas shortage. A calculation model of the economic loss cost is established using alternative energy sources. Finally, a reliability index is established based on economic risk to evaluate the degree of supply security. The results showed that the reliability based on the economic risk assessment was slightly higher than that based on the gas quantity assessment. The reliabilities were 99.988% and 99.991%, respectively. The economic risk is reduced and the reliability is improved for the important users after considering the influence of the demand-side supply order. [ABSTRACT FROM AUTHOR]

The work presents an example of practical application of gas flow modeling results in the network, that was obtained for the existing gas network and for real data about network load depending on the time of day and air temperature. The gas network load in network connections was estimated based on real data concerning gas consumption by customers and weather data in 2010, based on two-parametric model based on the number of degree-days of heating. The aim of this study was to elaborate a relationship between pressure and gas stream introduced into the gas network. It was demonstrated that practical application of elaborated relationship in gas reduction station allows for the automatic adjustment of gas pressure in the network to the volume of network load and maintenance of gas pressure in the whole network at possibly the lowest level. It was concluded based on the results obtained that such an approach allows to reduce the amount of gas supplied to the network by 0.4% of the annual network load. [ABSTRACT FROM AUTHOR]

In North China, natural gas demands vary seasonally. Constraint by LNG receiving station's lowest output capacity, natural gas pipeline encounters inverse peak shaving in summer. Due to the selling price of LNG inversed, company shoulders great operating stress. With the establishment of TS-LNG's BOG pressurization transmission process, combined with re-condensation methods, it will help to solve the inverse peak-shaving in summer as well as improve the network loading capacity and flexibility. This will also benefit overall natural gas transmission and maximize profits, and promote market-oriented reform course. [ABSTRACT FROM AUTHOR]

• Bayesian network approach to optimize the reliability of gas supply is proposed. • Probabilistic safety analysis and prognostic health management are integrated. • Deep reinforcement learning is proposed for decision-making. • Shortage risk eliminations and operational efficiency improvements are achieved. This study proposes a method based on Bayesian networks (BNs) to optimize the reliability of gas supply in natural gas pipeline networks. The method integrates probabilistic safety analysis with preventive maintenance to achieve the targets of minimizing gas shortage risk and reducing maintenance costs. For this, the tasks of unit failure probability calculation, system maximum supply capacity analysis, gas supply reliability assessment and system maintenance planning are performed. A stochastic capacity network model is coupled with a Markov model and graph theory to generate the state space of the pipeline network system. BN, is then, proposed as the modeling framework to describe the stochastic behavior of unit failures and customer gas shortage. The system maintenance problem is converted into a Markov decision process (MDP), and solved by using deep reinforcement learning (DRL). The effectiveness of the proposed method is validated on a case study of a European gas pipeline network. The results show that the proposed method outperforms others in identifying optimal maintenance strategies. The DRL-optimized maintenance strategy is capable of responding to a dynamic environment through continuous online learning, considering the randomness of the unit failures and the uncertainty in gas demand profiles. [ABSTRACT FROM AUTHOR]

The suitable design of natural gas pipeline network in Liaohe oilfield region was studied. Based on the gas consumption characteristics of each oil production plant in Liaohe Oilfield, the pipeline model was established combining with the actual project. The model was used o simulate the pipeline network layout, and study the parameter changes of flow, temperature and pressure. According to the geographical features, the suitable design of the natural gas pipeline network in this region was identified. The results showed that the self-balancing pressure regulation system could solve the problems caused by unbalancing gas consumption and brought by the oil production plant in Liaohe Oilfield. The small directional drilling crossing mode could be used to solve problems of high ground water, more rivers and ditches. The multiple pipeline back-dragging technology in a same hole was used in Liaohe Oilfield for the first time and got good effect. [ABSTRACT FROM AUTHOR]

In the condition of satisfying process requirement, determining the optimum operation schemes of natural gas pipeline network is essential to improve the overall efficiency of network operation. According to the operation parameters of natural gas network, the multi-hierarchy comprehensive evaluation index system is illustrated, and the weights of each index are determined with an improved Analytic Hierarchy Process (AHP). This paper presents a multi-hierarchy grey relational analysis (GRA) method which is suitable for evaluating the multi-hierarchy index system with combining the AHP and grey relational analysis. Ultimately, the industrial application shows that multi hierarchy grey relational analysis is effective to evaluate the nature gas pipeline network operation schemes. [ABSTRACT FROM AUTHOR]

Abstract: A mathematical model of optimal scheduling scheme for gas pipeline network is established, which takes maximum benefits of pipeline network service provider as objective function after comprehensively considered the resources of gas field, downstream gas utilizing conditions, operating parameters of compressor stations and work conditions of pipeline system etc. Evolutionary-based particle swarm algorithm has been investigated, and a new hybrid particle swarm algorithm with embedded chaotic search has been presented to the optimization solution of pipeline network scheduling. Improved algorithm makes use of the ergodicity of chaos to improve the capability of precise search and keep the balance between the global search and the local search, which is able to avoid the premature convergence problem effectively. This paper took a gas network with 17 nodes, 16 pipelines, 2 compressor stations as example, and make the decision on the optimal scheduling scheme for gas pipeline network. The analysis of example demonstrates the effectiveness and applicability of the established model and algorithm. [Copyright &y& Elsevier]

With the increase of natural gas in the world's energy consumption, the efficient and reliable management of natural gas pipeline networks is becoming even more important than before. In recent years, Demand Response (DR) is considered an effective approach for cleaner production and economic strategy, by introducing the participation of customers (CUs). This paper proposes a novel DR method for predictive management in multi-level natural gas markets with different stakeholders. This method is able to make a better trade-off among supplier's profits, gas demand volatility and CU satisfaction. This method includes three parts: dynamic pricing model, intelligent decision making and data-driven demand forecasting. A Markov decision process-based model is developed to illustrate the process of dynamical optimizing energy prices. Then, deep learning and reinforcement learning are integrated to efficiently solve the sequential decision-making problem, based on the physics constraints of natural gas pipeline networks. Besides, to realize the function of predictive optimization, an energy demand forecasting model is developed based on the deep recurrent neural network model. The proposed dynamic pricing method is able to optimize the pricing strategies in accordance to the demand patterns, and dynamically improve the system stability and energy efficiency. Finally, we apply the developed method to a natural gas network with relatively complex topology and different CUs. The results indicate that the proposed method can achieve the targets of peak shaving and valley filling under different pricing periods. Besides, the sensitivity analysis of the critical parameters in the dynamic pricing model is analyzed in detail, which can give a solid criterion for ensuring the effectiveness of this framework. • A novel method is developed for predictive energy management. • An intelligent decision-making algorithm based on deep reinforcement learning is proposed. • An efficient dynamic pricing modeling for natural gas market is developed. • The method can make trade-offs among economic benefit, gas supply reliability and CUs' satisfaction. [ABSTRACT FROM AUTHOR]

• Improved vulnerability analysis method based on risk assessment. • The severity of risk consequences is calculated by utility theory. • Identifying critical components of the pipeline network by combining risk assessment and vulnerability analysis. The crucial part of vulnerability analysis is identifying the critical components of a pipeline network. In this study, we proposed a novel analysis method- "Risk-Vulnerability," which combines the characteristics of risk assessments and vulnerability analyses methods. Risk-Vulnerability identifies the critical components of a pipeline network from three perspectives: pipeline operating status, transmission performance, and network characteristics. The formulas of the importance value of each component were established. Then the component risk indicators were established, and the component risk values were calculated. And a utility theory was introduced to calculate the severity of the consequences. Finally, the component importance and risk values were multiplied to obtain the vulnerability of the component. The feasibility and effectiveness of the method were verified by comparing the identification results of this method to those from the weighted flow capacity rate (WFCR). The Risk-Vulnerability method provides an improved insight into the pipeline criticality compared to the WFCR and improves the calculation formulae based on the identification content, which can not only be used to identify critical components, but also can be used to formulate research on risk reduction measures for pipeline network systems. [ABSTRACT FROM AUTHOR]

• A demand-side analysis-based gas supply reliability evaluation method is proposed. • The fluctuation characteristics and the effect of user importance is considered. • The method is applied to a real natural gas pipeline network located in China. • The significance of the demand side in the gas supply reliability is elaborated. The fluctuation characteristics of the gas demand and the effect of the user importance are usually ignored in previous gas supply reliability research. With the intent of overcoming these deficiencies, an integrated method based on the demand-side analysis is proposed in this study to assess the gas supply reliability of the large-scale and complex natural gas pipeline network. The method is composed of the establishment of the indicator system, the demand-side analysis, the estimation of the unit failure probability, and the calculation of the gas supply. Among them, the demand-side analysis focuses on the market demand forecasting and user importance research. Moreover, the coupling effect of the user importance, the hydraulic and pressure constraints, and the unit failure on the gas supply calculation is considered. Furthermore, a real natural gas pipeline network located in China is applied to confirm the feasibility of the method. According to the evaluation results of the gas supply reliability, the weakest nodes and key links of the natural gas pipeline network are identified, and the suggestions to improve the gas supply reliability are proposed as well. At last, the significance of the demand side in the gas supply reliability is elaborated and validated. [ABSTRACT FROM AUTHOR]

• A method is developed for operation optimization in gas pipeline networks. • A novel method of supply reliability analysis in pipeline networks is developed. • A multi-objective optimization model is developed for gas pipeline optimization. • The uncertainties of both pipeline systems and demand sides are considered. • The model is solved by NSGAⅡ and verified in gas grids with different topologies. Reliable gas supply for minimum risk of supply shortage and minimum power demand for low energy cost are two fundamental objectives of natural gas pipeline networks. In this paper, a multi-objective optimization method is developed to trade-off reliability and power demand in the decision process. In the optimization, the steady state behavior of the natural gas pipeline networks is considered, but the uncertainties of the supply conditions and customer consumptions are accounted for. The multi-objective optimization regards finding operational strategies that minimize power demand and risk of gas supply shortage. To quantify the probability of supply interruption in pipeline networks, a novel limit function is introduced based on the mass conservation equation. Then, the risk of interruption is calculated by combining the probability of interruption and its consequences, measured in utility terms. The multi-objective optimization problem is solved by the NSGA-II algorithm and its effectiveness is tested on two typical pipeline networks, i.e., a tree-topology network and a loop-topology network. The results show that the developed optimization model is able to find solutions which effectively compromise the need of minimizing gas supply shortage risk and reducing power demand. Finally, a sensitivity analysis is conducted to analyze the impact of demand uncertainties on the optimization results. [ABSTRACT FROM AUTHOR]