Gas Distribution System Research Articles

Development of a Numerical Method for Calculating a Gas Supply System during a Period of Change in Thermal Loads

Nowadays, modern gas supply systems are complex. They consist of gas distribution stations; high-, medium-, and low-pressure gas networks; gas installations; and control points. These systems are designed to provide natural gas to the population, including domestic, industrial, and agricultural consumers. This study is aimed at developing methods for improving the calculation of gas distribution networks. The gas supply system should ensure an uninterrupted and safe gas supply to consumers that is easy to operate and provides the possibility of shutting down its individual elements for preventive, repair, and emergency recovery work. Therefore, this study presents a mathematical calculation method to find the optimal operating conditions for any gas network during the period of seasonal changes in thermal loads. This method demonstrates how the reliability of gas distribution systems and resistance to non-standard critical loads are affected by consumers based on the time of year, month, and day, and external factors such as outdoor temperature. The results in this study show that this method will enable the implementation of tools for testing various management strategies for the gas distribution network.

Low-Carbon Scheduling of Integrated Electricity and Gas Distribution System Considering V2G

With the development of EVs (Electric Vehicles) and the rapidly developing policies on low carbon and environmental protection, electric power systems and natural gas systems become increasingly larger. Under these circumstances, the V2G (Vehicle-to-grid) and the coordinated operation of an integrated electricity–gas distribution system (IEGDS), considering CO2 emissions, can play a part together in the process of energy conservation. Firstly, the V2G model is discussed; this paper presents the cost differences between out-of-order and order for the car. Secondly, the IEGDS model presents coupling constraints of gas turbines and power-to-gas. Lastly, carbon emission is considered in this paper; a carbon capture plant (CCP) captures the CO2 burning by fossil fuel in the power generation process and stores it in a carbon storage tank. This paper also considers trading with the carbon market via a carbon storage warehouse. With the cooperation of various components, a comprehensive model considers the use of V2G to store power in the IEGDS system, with consideration of the carbon trade. Numerical experiments validate the effectiveness of the combination between V2G and IEGDS, considering carbon emissions and carbon trading.

Data-driven modelling for gas consumption prediction at City Gate Stations

City Gate Stations (CGS) are critical elements of Natural Gas (NG) distribution systems, as they connect national high-pressure transmission networks with local low-pressure networks. One of their main tasks is to pre-heat the gas to avoid dangerous sub-cooling due to the Joule-Thompson effect after the pressure reduction stage. For this process, significant amounts of thermal energy are required, usually obtained by burning part of the gas flow rate. This work aims to develop a data-driven model that will serve as a tool to predict and monitor the thermal consumption of the CGS. The plant chosen as a case study for this activity is in a region of central Italy. A Multiple Linear Regression (MLR) model is developed and trained, and its predictive performance is evaluated. The model results achieved an accuracy of over 95% for the coefficient of determination. The method makes it possible to create a baseline consumption model and evaluate the performance of the CGS over time using the CUSUM technique to find variations between actual and modelled gas consumption, being an essential tool for monitoring the effectiveness of the NG preheating system.

Hierarchical Microenergy Hub Sizing and Placement in Integrated Electricity and Natural Gas Distribution Systems

The integration of different energy carriers is a key factor to improve the energy network performance. A micro-energy hub (μEH) concept has recently been proposed in the integrated electricity and natural gas distribution systems (IENGDSs), for generating, converting, storing, and managing different types of energy at the customer level. To improve the performance of μEH, this paper proposes a hierarchical optimization-based model to optimize the sizes of μEH components and locations of μEHs in the IENGDS. The operation and structural sizing constraints of the μEHs are first determined. Then the optimal electricity network buses and natural gas nodes for installing μEHs are determined, considering the operational constraints of IENGDS. The objective in both steps is to minimize operation and investment costs. Moreover, the impacts of seasonal climate changes on energy demands and prices are considered during the planning horizon. Furthermore, the uncertainties in demands and renewable energy generation are quantified with a two-stage stochastic programming framework. The effectiveness of the proposed method is evaluated on an IENGDS that consists of an IEEE 33-bus distribution system and a Belgian 20-node natural gas system. Numerical simulations demonstrate that the optimal hierarchical planning model can improve total line losses and reduce the total purchased electricity from the upstream network and total imported natural gas from gas wells. The simulation results show that if the hierarchical optimization process is not performed in determining optimal placement of μEHs, then IENGDS may operate with electricity network congestion or natural gas pressure violations during the planning horizon.

Low-Cost Representative Sampling for a Natural Gas Distribution System in Transition.

Natural gas distribution systems within municipalities supply a substantial fraction of energy consumed in the United States. As decarbonization of the natural gas system necessitates new modes of operation outside original design purposes, for example, increased hydrogen or biogas blending, it becomes increasingly important to understand in advance how existing infrastructure will respond to these changes. Such an analysis will require detailed information about the existing asset base, such as local soil composition, plastic type, and other characteristics that are not systematically tracked at present or have substantial missing data. Opportunistic sampling, for example, collecting measurements at assets that are already undergoing maintenance, has the potential to substantially reduce the cost of gathering such data but only if the results are representative of the full asset base. To assess prospects for such an approach, we employ a dataset including the entire service line and leak database from a large natural gas distribution utility (∼66,700 km of service pipelines and over 530,000 leaks over decades of observations). This dataset shows that service lines affected by excavation damage produce an approximately random sample of plastic and steel service lines, with similar distributions of component age, operating pressure, and pipeline diameter, as well as a relatively uniform spatial distribution. This means that opportunistic measurements at these locations will produce a first-order estimate of the relative prevalence of key characteristics across the utility's full asset base of service lines. We employ this approach to estimate the plastic type, which is unknown for roughly 80% of plastic service lines in the database. We also find that while 32% of leaks across all components occur in threaded steel junctions, excavation damage accounts for 75% of hazardous grade 1 leaks in plastic service lines and corrosion accounts for 47% in steel service lines. Insights from this sampling approach can thus help natural gas utilities collect the data they need to ensure a safe and reliable transition to a lower-emission system.

Differentiating and Mitigating Methane Emissions from Fugitive Leaks from Natural Gas Distribution, Historic Landfills, and Manholes in Montréal, Canada.

Rapidly reducing urban methane (CH4) emissions is a critical component of strategies aimed at limiting climate change. Individual source measurements provide the details necessary to develop actionable mitigation strategies and are highly complementary to mobile surveys and other top-down methods. Here, we perform 615 individual source measurements in Montréal, Canada, to quantify CH4 emissions from historic landfills, manholes, and fugitive emissions from natural gas (NG) distribution systems. We find that in 2020, historic landfills produced 901 (452 to 1541, 95% c.i.) tons of CH4, manholes emitted 786 (32 to 2602, 95% c.i.) tons of CH4, and NG distribution systems emitted 451 (176-843, 95% c.i.) tons of CH4, placing them all within the top four CH4 sources in Montréal. Methane emissions from both historic landfills and manholes are not accounted for in any greenhouse gas inventory. We find that geochemistry alone cannot positively identify source subcategories (e.g., type of manhole or NG infrastructure) in almost all cases, although C2/C1 ratios can distinguish NG distribution sources from biogenic sources (historic landfills and manholes). Using our individual source measurement data, we show that historic landfills have the greatest potential for CH4 reductions but the highest mitigation costs, unless we target the highest emitting landfills. In contrast, CH4 emissions from manholes can be reduced at low costs, but reduction methods are commercially unavailable. For NG distribution, methods such as increasing repair rates for high-emitting industrial meters can greatly reduce mitigation costs and emissions. Overall, our results highlight the role of individual source measurements in developing actionable CH4 mitigation strategies to meet municipal, regional, and national climate action plans.

Collaborative Resilience to Extreme Weather in Decentralized Co-operation of Electricity and Natural Gas Distribution Systems

• A model to enhance the resilience of the integrated electrical distribution and gas networks. • A min-max robust optimization model considering worst-case scenarios. N-k and wind power generation • A multi-stage and multi-zone model to capture the spatial-temporal dynamic of extreme hurricane. • A hybrid column-and-constraints optimization solution. The flexibility of the power-to-gas (P2G) technologies and natural gas units (NGUs) can enhance the resilience of the electric distribution system (EDS) considering the high penetration of renewable energy resources (RERs). The decentralized collaborative operation (co-operation) of electric distribution systems and natural gas systems (EDSs&NGSs) considering information privacy preserving can enhance the resilience during an extreme hurricane. To address this issue, this paper proposes a three-level hierarchal solution to decompose the centralized co-operation of the EDS&NGS framework in which an independent decision-making strategy and information privacy preserving for both network operators are simultaneously addressed. Furthermore, in this paper, a min–max robust resilience-constrained co-optimization model is presented to enhance the resilience of the integrated EDS&NGS against worst-case N − k contingencies and wind power generation uncertainty under extreme hurricane events. To attain the spatial dynamics of extreme hurricanes, a the multi-zone and multi-time extreme hurricanes model is considered. Then, a column-and-constraints generation (C&CG) algorithm is used to solve the proposed robust resilience-constrained co-optimization model. To verify the effectiveness of the model, we conducted experiments on a modified IEEE-33-bus-10-node/123-bus-20-node EDS&NGS. The numerical results show that the proposed robust resilience-constrained co-operation of EDS&NGS problem is an effective model for enhancing EDS resilience.

Cybersafety: A System-Theoretic Approach to Identify Cyber-Vulnerabilities & Mitigation Requirements in Industrial Control Systems

Recent cyber-physical attacks, such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stuxnet, Triton</i> etc., have invoked an ominous realization about the vulnerability of critical infrastructure, including water, power and gas distribution systems. Traditional IT security-biased protection methods that focus on improving <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cyber hygiene</i> are largely impotent in the face of targeted attacks by advanced cyber-adversaries. Thus, there is an urgent need to analyze the safety and security of critical infrastructure in a holistic fashion, leveraging the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">physics</i> of the cyber-physical system. System-Theoretic Accident Model & Processes (STAMP) offers a powerful framework to analyze complex systems; hitherto, STAMP has been used extensively to perform safety analyses but an integrated safety and cybersecurity analysis of industrial control systems (ICS) has not been published. This paper uses the electrical generation and distribution system of an archetypal industrial facility to demonstrate the application of a STAMP-based method – called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cybersafety</i> – to identify and mitigate cyber-vulnerabilities in ICS. The key contribution of this work is to differentiate the additional steps required to perform a holistic cybersecurity analysis for an ICS of significant size and complexity and to present the analysis in a structured format that can be emulated for larger systems with many interdependent subsystems.

Green ammonia production-enabled demand flexibility in agricultural community microgrids with distributed renewables

Currently, manufactured ammonia relies on natural gas or coal, which releases harmful carbon emissions. We investigate the viability of a small-scale “green” ammonia plant where renewable electricity is used to provide hydrogen via electrolysis and nitrogen via air liquefaction to a Haber–Bosch system to synthesize ammonia. The relatively small scale, and measures such as continuous preheating, are envisioned to provide response times on the order of minutes rather than the days required by prevalent large-scale, fossil fuel-based ammonia plants. A green ammonia plant can serve as a demand-responsive load and long-term energy storage mechanism (through chemical energy storage in ammonia). We investigate its functional and economic viability within an agricultural community microgrid, which is a new contribution to this area of study. This paper proposes a coordinated operational mixed-integer linear programming (MILP) model of an electricity distribution system and an electricity-run, green ammonia plant enabled by the Haber–Bosch process. Case studies in both grid-connected and islanded modes are performed on an agricultural community microgrid based on a modified Pacific Gas & Electric (PG&E) 69-node electricity distribution system coupled with a flexible, small-scale ammonia plant and direct ammonia fuel cell. Results indicate the ammonia plant can adequately serve as a demand response resource and positively impact the distribution locational marginal price (DLMP) by reducing or removing voltage violations and line congestion in the system. Studies showed this coupling decreased electricity costs of the ammonia plant by nearly a third, with ammonia profits increasing 17%. Further, in an emergency, the direct ammonia fuel cell can provide essential power. This cooperation among the food–energy–water (FEW) network offers a unique solution to the challenge of a more efficient interconnection of renewable energy (RE) resources to the grid. Additionally, this work has the potential to reduce grid dependence on fossil fuels and aid in the decarbonization of ammonia production.

Coordinated Planning of Electric Power and Natural Gas Distribution Systems With Refueling Stations for Alternative Fuel Vehicles in Transportation System

The alternative fuel vehicles (AFVs) which include electric vehicles (EVs) and natural gas vehicles (GVs) have gained much attention in recent years for delivering a clean and low-carbon urban mobility. The AFV development is promoting the coordination among electric power, natural gas, and transportation (ENT) networks. This paper proposes a coordinated planning method for investment on and operation of emerging urban energy infrastructures. The sites and sizes of refueling stations are optimized along with the expansion strategies for electric power distribution network (PDN) and natural gas distribution network (GDN). First, AFV refueling demands are analyzed according to the refueling station constraints and options, and transportation network (TN) constraints. Second, the coordinated planning model for PDN and GDN is formulated along with respective operation constraints in PDN, GDN, and TN. An efficient MILP solution method is developed for the proposed optimization problem. Finally, several case studies verify the effectiveness of the proposed model and its solution method. Compared with existing solutions, the proposed panning method offers a more accurate AFV refueling demand model with various refueling options. It is demonstrated that the coordinated planning can reduce investment and operation costs of urban energy infrastructures by exploring the pertinent interactions among constrained PDN, GDN, and TN.

Hydrogen–Natural Gas Blending in Distribution Systems—An Energy, Economic, and Environmental Assessment

Taking into account the international policies in the field of environmental protection in the world in general, and in the European Union in particular, the reduction of greenhouse gas (GHG) emissions, and primarily of carbon dioxide, has become one of the most important objectives. This can be obtained through various renewable energy sources and non-polluting technologies, such as the mixing of hydrogen and natural gas. Combining hydrogen with natural gas is an emerging trend in the energy industry and represents one of the most important changes in the efforts to achieve extensive decarbonisation. The importance of this article consists of carrying out a techno-economic study based on the simulation of annual consumptions regarding the construction and use of production capacities for hydrogen to be used in mixtures with natural gas in various percentages in the distribution network of an important operator in Romania. In order to obtain relevant results, natural gas was treated as a mixture of real gases with a known composition as defined in the chromatographic bulletin. The survey presents a case study for the injection of 5%, 10%, and 20% hydrogen in the natural gas distribution system of Bucharest, the largest city in Romania. In addition to conducting this techno-economic study, the implications for final consumers of this technical solution in reducing greenhouse gas emissions—mainly those of carbon dioxide from combustion—are also presented.

Power-to-Gas in gas and electricity distribution systems: A comparison of different modeling approaches

Power-to-Gas (P2G) has been one of the most frequently discussed technologies in the last few years. Thanks to its high flexibility, it can offer services to power systems, thereby fostering Variable Renewable Energy Sources (VRES) and the electricity demand match, mitigating the issues related to VRES overproduction. The analysis of P2G systems used at the distribution level has only been dealt with in a few studies: however, at this level, critical operation conditions can easily arise, in both the electrical infrastructure and in the gas infrastructure. The choice of appropriate modeling approaches for a P2G plant, as well as for the electricity and gas distribution grids is necessary to avoid overestimating or underestimating the potential flexibility that P2G plants connected to distribution networks can offer. The study presents a methodological analysis on the impact of different simulation approaches when P2G is installed at a distribution system level. The aim of this paper has been to understand the impact of different modeling approaches in order to determine whether, and under what conditions, they could be adopted. An illustrative case study has been developed to perform this analysis. The results show that the flexibility of the P2G technology can also be used at the distribution level; nevertheless, a correct modeling approach is necessary to properly evaluate the potential of this solution. The placement of P2G systems within the electricity network can affect the performance of the plant to a great extent. Therefore, it is necessary to use a model that takes into account the topology and energy flows of the electrical network. It was found, in the analyzed case study, that the use of an inappropriate electricity network model can lead, depending on the conditions, to either an overestimation or an underestimation (of 50 % and 40 %, respectively) of the ability of P2G plants to absorb VRES over-generation. The accuracy of the gas network and of the P2G plant models also plays an important role. In conditions of low gas consumption, it is necessary to consider the gas flows and the line-pack potential of the gas network, as well as the interactions between the components of the P2G plant in order to avoid underestimating the flexibility of the entire system. In the analyzed case study, the use of a simplified model of the gas network led to an underestimation of the accumulation potential of the over-generations of VRES of about 30 %, while the use of a simplified model for the simulation of P2G plants led to a 10 % underestimation of the storage potential.

A new method to forecast multi-time scale load of natural gas based on augmentation data-machine learning model

Gas load forecasting is important for the economic and reliable operation of the city gas transmission and distribution system. In this paper, a nonlinear autoregressive model (NARX) with exogenous inputs, support vector machine (SVM), Gaussian process regression (GPR) and ensemble tree model (ETREE) were used to predict and compare the gas load based on the gas load data in a certain region for past 3 years. The results showed that the prediction errors for most of days were higher than 10%. Further, simulation data were generated by considering the gas load variation trend, which was then combined with historical data to form the augmentation data set to train the model. The test results indicated that the prediction error of daily gas load in one year reduced to below 7% with a machine learning prediction method based on augmentation data. In addition, the model based on augmentation data set still performed better than original data in predicting the monthly gas load in last year as well as daily gas load in last month and week. Therefore, the method based on augmentation data proposed in this paper is a potentially good tool to forecast natural gas load.

A mixed conditional value-at-risk/information gap decision theory framework for optimal participation of a multi-energy distribution system in multiple energy markets

This study provides a unique risk-based hybrid two-stage operational model for a coordinated power and gas distribution system that enables it to engage optimally in the electricity, gas, and thermal markets in order to serve electricity, gas, and heat demands. The suggested hybrid model enables the integrated energy distribution system (IEDS) operator to apply several risk-averse methods concurrently, based on the nature of the uncertainties and the availability of data for unknown parameters. The conditional value-at-risk-based stochastic programming (CVaR-SP) technique is utilized to cover the uncertainty in wind speed, vehicle behavior in a parking lot, day-ahead energy prices, and multi-energy demands. Furthermore, the infromation gap decision theory (IGDT) model is used to control uncertainty in real-time energy prices without requiring the use of a probability distribution function or the creation of scenarios. To provide a high-flexibility structure to meet a variety of demands, the IEDS is outfitted with some local energy resources, including a gas-fired power-only unit, a gas-fired combined heat and power plant, a wind turbine, a gas boiler, an electric vehicle parking lot (EVPL), multi-energy storages (MESs), and integrated demand response (IDR). According to the numerical data, the IEDS operator can lower the risk level by 2% while raising the operating cost by 1.3%. Furthermore, the simultaneous use of MESs, EVPL in vehicle-to-grid mode, and IDR reduce operating costs by up to 17.5%.

Development of a Fast-Acting Locking Device for Gas-Distribution Systems from an Elastic Polymer Material

Development of a Fast-Acting Locking Device for Gas-Distribution Systems from an Elastic Polymer Material

A regional study of the fugitive methane emissions from the gas distribution system in Iran by direct measurement

Global warming is a major concern of today's world that mostly stems from greenhouse gases (GHG) emissions among which methane (CH4) has the second highest potential to cause such a problem. Among various types of discharging CH4 into atmosphere, fugitive emission from the natural gas (NG) supply chain has a significant contribution. In this study, methane emission from the pressure reduction station, distribution pipeline and end user regulating systems (EURSs) was experimentally investigated by a gas detector and a Hi-Flow sampler device. The research was conducted in Mashhad, the second metropolis of Iran; Iran plays a key role in NG reserves and consumptions. The results revealed that the fugitive methane emission from the EURSs is the main source of the GHG emission from the gas distribution network. This source was, then, investigated in detail through the being classified into six components including regulator, union fitting, coupling, locking valve, tee fitting, and elbow fitting. According to the obtained outcomes, the regulator is responsible for the highest emission factor (EF = 754 m3/year) along with the largest contribution (66.62%) in total fugitive methane emission of EURSs. Conversely, the elbow fitting demonstrates the lowest EF (= 134 m3/year) as well as the lowest contribution (0.39%). Additionally, the effect of EURSs lifespan on their EF was studied. The results indicate that the ratio of EF of EURSs with lifespans more than 20 years to mean EF is almost 1.4 while this value is approximately 0.8 for EURSs with lifespans less than 20 years. The results also show that the ratio of EF in different seasons to mean EF decreases as the temperature decreases (colder seasons). At the end, the findings of this research could give useful information about the methane emission from EURSs leading to designing and developing proper mitigation strategies.

Uncertainty propagation analysis of the computed ITER torus effective pumping speed during the dwell phase

At the University of Thessaly the ARIADNE code for modeling complex gas distribution systems operating under any vacuum conditions has been developed by integrating a kinetic database to a typical gas network solver. The ARIADNE code has been successfully implemented to model the ITER primary pumping system providing the torus effective pumping speed, as well as the pressure evolution during the dwell phase. However, the computed results are subject to the input data, which include the pipe network geometry, approximating the real geometry of the ITER primary pumping system and the operating data, such as the torus pressure, gas temperature and cryopump pumping speed. The effect of the aforementioned input quantity uncertainties to the torus effective pumping speed is investigated via an uncertainty propagation analysis by coupling the Monte Carlo method (MCM) with the ARIADNE code. Documenting the propagation of each input parameter uncertainty to the torus effective pumping speed uncertainty is beneficial for judging the accuracy of the modeling and simulation results, as well as for identifying the most important sources of uncertainty. Furthermore, the presented methodology can be used to investigate the uncertainty propagation of any input quantity to any output quantity for vacuum systems of arbitrary complexity.

Efficiency of Using Geoinformation Systems in the Design of Gas Distribution and Gas Consumption Networks

The scientific article is devoted to determining the effectiveness of the use of geoinformation systems (GIS) in the design of gas distribution and gas consumption networks. An experiment was carried out on a laboratory stand modeling ring, dead-end and mixed gas networks. The electronic model of a gas network identical to the laboratory stand in the ZuluGaz 10.0 software package, a module for expanding the GIS functionality for calculating gas supply networks designed to solve various industry. A comparative analysis of the results of an experiment and the results obtained during simulation in GIS is carried out. Based on the analysis, a conclusion is made about the accuracy of the GIS results and the feasibility of using GIS for the design of gas distribution and gas consumption systems.

Study on leakage and explosion consequence for hydrogen blended natural gas in urban distribution networks

It appears to be the most economical means of transporting large quantities of hydrogen over great distances by the existing natural gas pipeline network. However, the leakage and diffusion behavior of urban hydrogen blended natural gas and the evolution law of explosion characteristics are still unclear. In this work, a Computational Fluid Dynamics three-dimensional simulation model of semi-confined space in urban streets is developed to study the diffusion process and explosion characteristics of hydrogen-blended natural gas. The influence mechanism of hydrogen blending ratio and ambient wind speed on the consequences of explosion accident is analyzed. And the dangerous area with different environmental wind effects is determined through comparative analysis based on the most dangerous scenarios. Results indicate that the traffic flow changes the diffusion path of the jet, the flammable gas cloud forms a complex profile in many obstacles, high congestion level lead to more serious explosion accidents. Wind effect keeps the flammable gas cloud near the vehicle flow, the narrow gaps between the vehicles aggravate the expansion of the flammable gas cloud. When the wind direction is consistent with the leakage direction, hydrogen blended natural gas is gathered in the recirculation zone due to the vortex effect, which results in more serious accident consequences. With the increase in hydrogen blending ratio, the higher content of H and OH in the gas mixture significantly increases the premixed burning rate, the maximum overpressure rises rapidly when the hydrogen blend level increases beyond 40%. The results can provide a basis for construction safety design, risk assessment of leakage and explosion hazards, and emergency response in hydrogen blended natural gas distribution systems.

Economic analysis and investment in fuel industry considering risk management by analytical hierarchy process

Natural Gas production and distribution network plans are one of the most common ways to increase energy consumption efficiency. Due to the uncertainty of the economic parameters, the economic justification of this system is very difficult. In this research, the economic risk of investing in urban gas distribution systems has been done with the analytical hierarchy process (AHP). With the development and condensation of urban gas supply networks, accidents caused by gas leaks and other external factors have also increased. Therefore, in recent years, the issue of safety and risk analysis of urban gas networks has received special attention from responsible companies and other urban and safety experts. Gas distribution lines often pass in the vicinity of crowded areas and buildings, so any accident for these lines will lead to a lot of human and financial losses. In the present study, in order to improve the gas supply situation and reduce the risk of failure in Indonesia, urban gas pipelines have been studied to reduce the costs. In this paper, multi-criteria decision making method analysis is used to identify and evaluate the hazards of urban gas pipelines and the risk of occurrence of each hazard is calculated. There was a meaningful relationship based on economic-financial theories between economic factors and investment risk in the gas industry, which emphasizes the greater and more significant effect of exchange rate, failures and inflation rate variables in comparison with other research variables in the matter of controlling investment risk in the gas industry. The effects of changes in energy prices and the economic environment in explaining the behavior of investment risk in this industry have placed the next level of importance. The results showed that with the increase in energy prices, the economics of gas production and distribution risk management plans have improved and the regular trend of energy price increases can guarantee the profitability of fuel consumption optimization plans.