Gas Network Research Articles

Prevention and Detection of Coordinated False Data Injection Attacks on Integrated Power and Gas Systems

The growing reliance of integrated power and gas systems (IPGSs) on information and communication technology has left them prone to cyber-attacks. To protect IPGSs against such attacks, having comprehensive knowledge about possible attack strategies and their potential impacts is essential. On this basis, this paper (i) presents a new family of coordinated stealthy false data injection attacks (FDIAs) whose aims are to disconnect gas-fired power plants (GFPPs) by targeting IPGS measurements, (ii) develops a preventive defense strategy against the introduced family of attacks by improving the security of high-risk measurements, and (iii) proposes an online learning-based detection scheme using secured measurements. Initially, from an adversary point of view, a two-stage attack model is developed to construct the FDIA vector. In the first stage, power system measurements are manipulated to maximize the grid reliance on the targeted GFPPs, while bypassing bad data detection schemes. In the second stage, the measurements of gas networks (GNs) are falsified to portray a fake leakage, so forcing the operator to disconnect under-attack pipelines and interrupting the supply of fuel to targeted GFPPs. Each stage is formulated as an attacker-operator bi-level optimization problem, which is solved as mixed-integer linear programming (MILP) by converting the operator level to its corresponding Karush–Kuhn–Tucker (KKT) conditions. Afterwards, based on possible attack scenarios, high-risk measurements are identified to be secured as a preventive defense strategy. Finally, a multilayer perceptron (MLP) model is trained to detect cyber-attacks against IPGS. The performance of the proposed method is corroborated through simulations on IEEE 24-Bus 20-Node IPGS and IEEE 118-bus 135-node IPGS.

Coordinating flexible ramping products with dynamics of the natural gas network

In electricity networks with high penetration levels of renewable resources, Flexible Ramping Products (FRPs) are among the utilized measures for dealing with the potential fluctuations in the net demand. This paper investigates the coordinated operation of Integrated Electricity-Gas Systems (IEGSs) with considering FRPs. To accurately model and reflect the effects of variations in the natural gas fuel demand on the natural gas network, a dynamic Optimal Gas Flow (OGF) formulation is utilized. The non-convex dynamic model of the natural gas system is represented in a convex form via a tight relaxation scheme. An improved distributed optimization method is proposed to solve the coordinated operation problem in a privacy-preserving manner, where the two infrastructures only share limited information. We introduce the Inexact Varying Alternating Direction Method of Multipliers (IV-ADMM) and show that compared with the classic ADMM, it converges considerably faster and in fewer iterations. Through a comparison of day-ahead and real-time operation planning results, it is concluded that without accounting for natural gas network dynamics, the FRP model is not a trustworthy tool in day-ahead planning against uncertainties.

CO2 energy vector in the concept of circular energy

The paper analyzes the conversion of the carbon dioxide (CO2) through recovery and storage into an energy production vector by transforming it into methanol (CH3OH) and carbon monoxide (CO). These gaseous components can produce energy by combustion, and the resulting carbon dioxide is reintroduced into the capture and storage circuit. This type of carbon dioxide transformation into an energy vector in a circular energy system will contribute to the elimination or to the significant reduction of its long-term storage in caves or under the sea water. By eliminating storage, the costs will be significantly reduced, as well as the environmental pollution. The paper considers the methanol use in residential heating instead of liquid hydrocarbons, especially for isolated areas or without a natural gas network, and also in the internal combustion engines in transport, agriculture and construction. The first direction of the research included an experimental phase that involved using a 55kW boiler for medium-sized residential heating. The second direction analyzed the transformation of CO2 into CO (fuel gas) through gasification technology. For this transformation, the fixed bed gasification process was considered. The carbon monoxide can be used in a pure state or mixed with methane or other combustible gases, both in power plants and in internal combustion engines. The current significant results in the methanol production field enables CO2 to become an energy vector, instead of a gas with zero potential and expensive to store. Thus, the research can significantly contribute to the development of power engineering as a circular application in the context of circular economy.

A risk-averse two-stage stochastic model for optimal participation of hydrogen fuel stations in electricity markets

In this paper, a novel stochastic risk-averse mixed-integer linear programming (MILP) model is developed for optimal electricity procurement of hydrogen fuel stations (HFSs) with responsive hydrogen demands. In the developed model, HFS operator may procure its required electricity through day-ahead (DA) market, bilateral contracts, a contract with withdrawal penalty (CWP) and balancing market. The HFS is committed to inject a pre-specified volume of hydrogen into a natural gas network. In the developed stochastic model, the uncertainties in hydrogen demand and DA market prices are characterized as random variables and modeled as scenarios. The developed model is a two-stage model in which the decisions on transactions with bilateral contracts are the first-stage decision variables and the other decision variables belong to the second stage. The risk of suffering from high procurement costs is modeled using the Conditional Value at Risk (CVaR). The results confirm the efficiency of the proposed model both in terms of reduction in HFS expected cost and risk alleviation. The results show that the transaction through bilateral contracts decreases the expected cost and CVaR respectively by 6% and 3%. The results also indicate that the participation in the balancing market decreases the expected cost and CVaR respectively by 6% and 1.6%. As per results, in scenarios in which DA market prices are lower than CWP prices, HFS operator prefers to pay a penalty fee and withdraw from CWP. The sensitivity of HFS profit to the weight factor of risk metric and the participation factor of responsive demands are assessed.

Congestion management for coordinated electricity and gas grids in the presence of multi-energy hubs: A risk-based optimal scheduling

Due to the development of cost-effective and high-efficient infrastructures in electricity and natural gas carriers, the need for optimal operation of multi-carrier energy systems is essential. On the other side, with the increasing penetration of renewable energy resources (RERs), and power demand, the congestion management (CM) of power systems is turning out to be a significant challenge for system operators. Motivated by these, this paper focuses on the congestion management problem for a multi-energy hub (MEH) system incorporated by RERs, flexible energy resources (FERs), and different energy conversion technologies to supply electrical, heat, and cooling demands, aiming to minimize the total operating costs. In current practice, the steady-state Weymouth equation and AC-power flow method for natural gas and electricity networks are implemented to investigate the interdependency of power and gas networks. The renewable energy generation variations are mitigated by the scenario-based stochastic framework. Also, to manage the risk associated with random variables, the downside risk constraint (DRC) model is employed to analyze the changes in the expected cost per several values of the risk parameters. The numerical results demonstrate that the proposed model can significantly utilize the energy hub’s flexibility to alleviate gas and power congestion while decreasing the total operation cost.

Industrial energy hubs with electric, thermal and hydrogen demands for resilience enhancement of mobile storage-integrated power systems

In recent decades, climate change has severely increased the concerns over the resilience of power systems. Extreme events may lead to the failure of a large number of system components and result in interruption of supply to lots of consumers. In this research, the main objective is to use and assess the potential of large industrial energy hubs (EHs) in resilience improvement of power systems. Mobile energy storage and demand response programs are also used to decrease involuntary demand shed in system and enhance system resilience. A stochastic mixed-integer linear programming model is developed considering the uncertainties in damaged transmission lines, hurricane time and repair time. Case study is a modified IEEE 24-bus power system with EHs. The studied EH receives gas from a gas network, exchanges power with power system, includes combined heat and power (CHP), generator, electrolyzer, boiler, thermal storage and hydrogen storage system and feeds electric, heat and hydrogen demands. Expected load not supplied (ELNS) is used as resilience metric. The results approve significant impact of EH in improvement of power system resilience. In scenarios in which the bus connected to EH is isolated, EH supplies the power system demand located at that bus and thereby improves system resilience; on the other hand, in other scenarios, EH typically behaves as an electricity consumer of power system. According to the results, mobile energy storage causes a 2.4% improvement in ELNS. The results also show that demand response program improves ELNS by 7.1%.

Reducing ammonia permeance in poly(acrylonitrile‐co‐butadiene) (NBR)‐based elastomers

AbstractAmmonia (NH3) has emerged as a sustainable future fuel for a low‐carbon future. Ideally, the transportation and storage of ammonia in re‐purposed natural gas networks will dramatically reduce the economic burden associated with converting to ammonia. However, the highly condensable nature and small kinetic diameter of the ammonia means that this fuel readily permeates through the polymer‐based elastomers and gaskets used in natural gas infrastructure. To mitigate this issue, three kinds of additives have been incorporated into the conventional natural gas elastomer poly(acrylonitrile‐co‐butadiene) (NBR) to reduce ammonia permeance. The first is based on increasing the basicity of the elastomer environment, the second provides chemical reactivity with ammonia to limit transport, and the third uses barrier additives to prevent diffusion. The mechanical properties of the modified elastomers; expressed as the tensile stress at yield and Young's modulus were studied. The creation of a basic environment through the addition of amines resulted in moderate changes in ammonia permeability, with up to 50% reduction of ammonia permeability observed from putrescine added NBR. Chemical additives lowered the permeability by up to 20% with limited impact on the tensile properties. The addition of 3% graphene oxide to NBR had the most significant decrease in ammonia permeability, 80%, but also produced a more rigid material. The investigation shows that ammonia permeability can be significantly reduced in NBR elastomers, through the addition of inexpensive additives in tailored strategies.

Resilience-oriented placement of multi-carrier microgrids in power systems with switchable transmission lines

Extreme events such as floods, hurricanes, earthquakes, and wildfires pose significant threats to the uninterrupted supply of electricity to consumers, as they can cause the failure of numerous power system components. The resilience of a power system is defined by its ability to withstand extreme events and continue meeting demand. While the placement of multi-carrier microgrids (MGs) in power systems can impact resilience, the literature lacks research on resilience-oriented placement. This paper aims to address this gap by proposing a resilience-oriented placement strategy for multi-carrier MGs, utilizing switchable transmission lines to enhance the resilience of power systems. The study focuses on a heat-power-hydrogen MG that obtains gas from a gas network, exchanges power with the main power system, and incorporates combined heat and power (CHP), electrolyzer, and thermal storage. The developed model employs a stochastic mixed-integer linear programming (MILP) approach, ensuring the attainment of the global optimum. Resilience is evaluated using the metric of expected load not supplied (ELNS). The results demonstrate that when the bus connected to the MG is isolated, the MG generates electricity through its CHP unit to meet local demand, reducing the total demand shed in the power system and improving system resilience. Specifically, the MG reduces ELNS from 4955.48 MWh to 2356.64 MWh, indicating a remarkable 52% improvement in ELNS. Furthermore, the study shows that transmission line switching further decreases ELNS from 2356.64 MWh to 848.68 MWh. Several experiments are conducted to analyze the sensitivity of power system resilience to the number of MGs, the MG-power system exchange limit, and the limit on gas import from the gas network.

Awareness of biogas increases its social acceptance

Biogas has the potential to be one of the cheapest technologies for decarbonising Australia's gas networks. However, despite biogas being a mature technology in some countries, its production in Australia is limited to only a few hundred plants. If biogas production is to grow to play a role in Australia's future energy system, understanding Australian public perceptions regarding biogas can help to inform the development of suitable policy measures. Through an Australian national survey, we found that lower levels of awareness of biogas were linked to lower social acceptance. After providing some simple information (a definition, a diagram and/or one of three simple messages), we were able to show a change in support for biogas in Australia. The results showed how information provision increased support for biogas from between 5% and 10% compared to the baseline and highlighted the importance of awareness in fostering support for biogas as an Australian decarbonisation mechanism.

Improving natural gas supply chain profitability: A multi-methods optimization study

Developing effective optimization models to improve pipeline network profitability in oil and gas supply chains is one of the most promising research areas in this industry. Because of the substantial advantages natural gas networks’ operations have realized, this industry has become more competitive and eager to develop robust supply optimization decision models. However, although several models and techniques have been developed to reduce natural gas consumption, only very few studies have focused on comparing the performance of these models and the implications of the distinct optimization performances. Consequently, the generalizability of the research in the area is still problematic, representing a research area not sufficiently explored. Taking this into account, this paper compares the fuel consumption values in a French gas pipeline by analyzing the Genetic algorithms (GA), Generalized reduced gradient (GRG), and Ant colony optimization (ACO) models. Overall, our findings show significant differences in gas consumption when the ACO and GA are compared with the GRG technique. Furthermore, the findings indicate that ACOs are competitive with GA and GRG in computational efficiency in finding near-global optimized solutions. The article can assist decision-makers and policymakers in discovering the most profitable operational parameters to minimize gas consumption and increase the profitability of natural gas networks.

Modelling of hydrogen blending into the UK natural gas network driven by a solid oxide fuel cell for electricity and district heating system

A thorough investigation of the thermodynamics and economicperformance of a cogeneration system based on solid oxide fuel cells that provides heat and power to homes has been carried out in this study. Additionally, different percentages of green hydrogen have been blended with natural gas to examine the techno-economic performance of the suggested cogeneration system. The energy and exergy efficiency of the system rises steadily as the hydrogen blending percentage rises from 0% to 20%, then slightly drops at 50% H2 blending, and then rises steadily again until 100% H2 supply. The system's minimal levelised cost of energy was calculated to be 4.64 £/kWh for 100% H2. Artificial Neural Network (ANN) model was also used to further train a sizable quantity of data that was received from the simulation model. Heat, power, and levelised cost of energy estimates using the ANN model were found to be extremely accurate, with coefficients of determination of 0.99918, 0.99999, and 0.99888, respectively.

Improving the performance of artificial neural networks trained on synthetic data in gas spectroscopy – a study on two sensing approaches

Abstract Artificial neural networks (ANNs) are used in quantitative infrared gas spectroscopy to predict concentrations on multi-component absorption spectra. Training of ANNs requires vast amounts of labelled training data which may be elaborate and time consuming to obtain. Additional data can be gained by the utilization of synthetically generated spectra, but at the cost of systematic deviations to measured data. Here, we present two approaches to train ANNs with a combination of comparatively small, measured data sets and synthetically generated data. For the first approach a neural network is trained hybridly with synthetically generated infrared absorption spectra of mixtures of N2O and CO and measured zero-gas spectra, taken with a mid-infrared dual comb spectrometer. This improves the mean absolute error (MAE) of the network predictions from 0.46 to 0.01 ppmV and 0.24 to 0.01 ppmV for the concentration predictions of N2O and CO respectively for zero-gas measurements which was previously observed for training with purely synthetic data. At the same time a similar performance on spectra from gas mixtures of 0–100 ppmV N2O and 0 to 60 ppmV CO was achieved. For the second approach an ANN pre-trained on synthetic infrared spectra of mixtures of acetone and ethanol is retrained on a small dataset consisting of 26 spectra taken with a mid-infrared photoacoustic spectrometer. In this case the MAE for the concentration predictions of ethanol and acetone are improved by 45 % and 20 % in comparison to purely synthetic training. This shows the capability of using synthetically generated data to train ANNs in combination with small amounts of measured data to further improve neural networks for gas sensing and the transferability between different sensing approaches.

Production of town gas from natural gas in Hong Kong for a reduction in cost and greenhouse gas emissions

Hong Kong has a well-established piped town gas network, with 27.7 billion MJ of gas sales in 2021. The feedstock for town gas production was converted from naphtha to a mixed feedstock of 40% naphtha and 60% natural gas (NG) in 2006, resulting in a 21% decrease in greenhouse gas (GHG) emissions and 1.58 billion HKD/year of cost savings. Increasing the proportion of the NG feedstock would potentially lead to more benefits. However, there may be difficulties in meeting the stringent combustion characteristics of town gas and the existing plant capacity may be reduced. Four processes for the production of town gas from NG are proposed in this work. The feasibility of each process was verified via simulation with Aspen Plus V14. A techno-economic evaluation showed that 296 million HKD/year of cost savings and 172224 t CO2-eq/year of GHG emission reduction could be achieved by using the proposed processes to produce town gas from NG. The reformer duties in the new processes were up to 16.6% lower compared to the existing process. There would not be any reduction in plant capacity associated with a limitation in heat transfer area, which is a common bottleneck.

On the numerical treatment of interlaced target values: modeling, optimization and simulation of regulating valves in gas networks

Due to the current and foreseeable shifts towards carbon dioxide neutral energy production, which will likely result in balancing fluctuating renewable energy generation by transforming power-to-gas-to-power as well as building a large-scale hydrogen transport infrastructure, the trading and transport operations of gas will become more dynamic, volatile, and hence also less predictable. Therefore, computer-aided support in terms of rapid simulation and control optimization will further broaden its importance for gas network dispatching. In this paper, we aim to contribute and openly publish two new mathematical models for regulators, also referred to as control valves, which together with compressors make up the most complex and involved types of active elements in gas network infrastructures. They provide direct control over gas networks but are in turn controlled via target values, also known as set-point values, themselves. Our models incorporate up to six dynamical target values to define desired transient states for the elements’ local vicinity within the network. That is, each pair of every two target values defines a bounding box for the inlet pressure, outlet pressure as well as the passing mass flow of gas. In the proposed models, those target values are prioritized differently and are constantly in competition with each other, which can only be resolved dynamically at run-time of either a simulation or optimization process. Besides careful derivation, we compare simulation and optimization results with predictions of the widely adopted commercial simulation tool SIMONE, serving as our substitute for actual real-world transport operations.

Research on the bi-layer low carbon optimization strategy of integrated energy system based on Stackelberg master slave game

With increasing reforms related to integrated energy systems (IESs), each energy subsystem, as a participant based on bounded rationality, significantly influences the optimal scheduling of the entire IES through mutual learning and imitation. A reasonable multiagent joint operation strategy can help this system meet its low-carbon objectives. This paper proposes a bilayer low-carbon optimal operational strategy for an IES based on the Stackelberg master-slave game and multiagent joint operation. The studied IES includes cogeneration, power-to-gas, and carbon capture systems. Based on the Stackelberg master-slave game theory, sellers are used as leaders in the upper layer to set the prices of electricity and heat, while energy producers, energy storage providers, and load aggregators are used as followers in the lower layer to adjust the operational strategy of the system. An IES bilayer optimization model based on the Stackelberg master-slave game was developed. Finally, the Karush-Kuhn-Tucker (KKT) condition and linear relaxation technology are used to convert the bilayer game model to a single layer. CPLEX, which is a mathematical program solver, is used to solve the equilibrium problem and the carbon emission trading cost of the system when the benefits of each subject reach maximum and to analyze the impact of different carbon emission trading prices and growth rates on the operational strategy of the system. As an experimental demonstration, we simulated an IES coupled with an IEEE 39-node electrical grid system, a six-node heat network system, and a six-node gas network system. The simulation results confirm the effectiveness and feasibility of the proposed model.

Techno-economic assessment of H2 extraction from natural gas distribution grids: A novel simulation-based optimization framework for pressure swing adsorption processes

Green H2 plays an important role in the global energy transition. To improve its supply chain economics, the already existing gas grids can be an interim solution for its storage and transportation infrastructure. Injecting H2 into the gas networks seems straightforward from a process design standpoint. However, the extraction, at the other end of the supply chain, poses challenges, and its feasibility from the techno-economic viewpoint is crucial and can support the further development of such programs. This article presents a rigorous techno-economic and optimization model for this process developed in Aspen Adsorption V11 and MATLAB. This holistic model uses the cyclic steady-state approach of Aspen Adsorption V11, which avoids the redundant computations for achieving the steady-state performance in the dynamic simulation approach and allows for the development of a novel simulation-based optimization framework. The proposed optimization model can be used in other pressure swing adsorption applications to improve the rigorousness of the optimal solution and computational time. The results show that H2 extraction from a distribution grid with 30 mol% H2 and 70 mol% CH4 at 12 bar operating pressure costs $0.8064/kg-H2 and $0.9012/kg-H2 for product purities of 99.00 mol% and 99.97 mol%, respectively. Additionally, this study explores the impact of N2 and CO2 presence in natural gas on H2 separation costs. For natural gas containing CH4: CO2: N2 with a molar ratio of 95:2:3 mixed with 30 mol% H2, the separation cost of the 99.00 mol% pure H2 increases by 11.6% to $0.8998/kg-H2; however, based on the simulation, the purity of 99.97 mol% due to the presence of N2 is not technically achievable.

Designing a model for the cost-optimal decommissioning and refurbishment investment decision for gas networks: Application on a real test bed in Austria until 2050

The primary goal of this paper is to investigate the most cost-effective decommissioning and refurbishment investment decision for existing gas networks. An optimization model is developed and tested on a real test bed in an Austrian federal state. The analysis is performed from the network operator’s perspective and depicts different network decommissioning or refurbishment options under the decision of supplying or not supplying available gas demands. Whether or not there is ensured supply, we find that smaller gas networks (in terms of pipeline capacity and network length) are needed in the future. Analyzed shadow prices indicate that a balance/trade-off between the cost-optimal gas network design with and without ensured supply could lead to a robust and economically competitive future for downsized gas networks. The results demonstrate that it is necessary to socialize network operators’ costs among the remaining consumers connected to the network in the future. This adds a cost component to consumers, which needs to be considered when determining the profitability of sustainable alternatives to natural gas.

Method for leak detection and location for gas networks

Method for leak detection and location for gas networks

The Accuracy and Efficiency of a Single-Level Fast Multipole Boundary Element Model for Analyzing Cathodic Protection of Large Pipeline Networks

A single-level fast multipole boundary element method was developed for analyzing cathodic protection (CP) systems of large pipeline networks. This method was obtained by embedding far-field approximation within the traditional single-level fast multipole method. The far-field approximation was used for computing the coefficients for far elements within adjacent cells and determining the moments of the elements within far cells. This approximation reduced the difficulty of the procedures and programming leading to a significant decrease in computational time. The Newton-Raphson method and generalized minimal residual method were combined based on the proposed method to solve the nonlinear boundary conditions due to the polarization curve. Several CP problems were considered to verify and evaluate the method. The calculated potentials of this method were in good agreement with the conventional boundary element method, which was achieved by using pipe elements and quadrilateral elements to mesh the surfaces. Finally, the impressed current CP systems of a large network (more than 100,000 elements) and a complex urban gas network were investigated. The results indicated the capability, efficiency, and precision of the present method for solving large and complicated problems on a common desktop computer.

An Equivalent Dynamic Gas Network Model with Electricity–Gas Systems

An Equivalent Dynamic Gas Network Model with Electricity–Gas Systems