Natural Gas Systems Research Articles

Analysis of Dynamic Operation Characteristics of the Integrated Energy System

Gas turbines make the coupling between the natural gas system(NGS) and the power system tighter. So Natural gas systems and power systems are interdependent in security and stability, and only paying attention to the security of the power system itself can not guarantee the security and stability of the system. To meet the problem and challenge, in this paper, a technical route based on the method of characteristic (MOC) is proposed to analyze the interaction of integrated energy systems (IES). It can calculate the transient process of the active and reactive power output of GT after the fluctuation of the NGS. Then, according to the dynamic math model of the natural gas in the pipeline(line-pack), the dynamic change of the line-pack is calculated, and the maximum interruption time of the gas source can be calculated according to the line-pack calculation result. Because of the intrinsic mechanism advantages of the MOC, the time step in the simulation calculation can be very small, which does not affect the calculation efficiency. It can calculate the dynamic change of GT output power, identify weak links in the operation of the system, and provide an important decision-making basis for taking safety control measures. Finally, based on an IES case, it is verified that the method proposed in this paper is of great value to analyze the security and stability of the IES.

Reliability Evaluation for Integrated Electricity-Gas Systems Considering Hydrogen

Regarded as the cleanest and a versatile energy carrier, green hydrogen generated with renewable energy is receiving increased attention in the transition to a carbon-neutral society. On the other hand, the integration of hydrogen tightens the coupling between power systems and natural gas systems, which highlights the importance of reliability evaluation of the integrated electricity-gas systems (IEGSs) with hydrogen. This paper proposes a reliability evaluation model for the IEGSs considering the effects of hydrogen. The power to hydrogen and methane process is proposed to convert the surplus renewable energy to hydrogen and methane, which are then mixed into natural gas systems. A novel optimal energy shedding model is proposed to explicitly account for the impact of hydrogen on the energy flow. To consider the temporal feature of renewable energy, a sequential Monte Carlo simulation approach is applied to evaluate the reliability of the IEGSs. The numerical simulations are performed on the integrated IEEE 24-bus and 20-node energy systems and the integrated IEEE 72-bus and 40-node energy systems to verify the effectiveness of the proposed model.

Metal Doping to Control Gate Opening and Increase Methane Working Capacity in Isostructural Flexible Diamondoid Networks.

Adsorbed natural gas (ANG) systems involve using porous materials to increase the working capacity and/or reduce the storage pressure compared to compressed natural gas (CNG). Flexible metal-organic materials (FMOMs) are particularly interesting in this context since their stepped isotherms can afford increased working capacity if the adsorption/desorption steps occur within the proper pressure range. We report herein that metal doping in a family of isostructural FMOMs, ML2 (M=Co, Ni or Nix Co1-x , L=4-(4-pyridyl)-biphenyl-4-carboxylic acid), enables control over the gate opening between non-porous (closed) and porous (open) phases at pressures relevant to methane storage. Specifically, methane-induced phase transformations can be fine-tuned by using different Ni/Co ratios to enhance methane working capacity. The optimal working capacity from 5 to 35 bar at 298 K (153 cm3 cm-3 ) was found for Ni0.89 Co0.11 L2 (X-dia-1-Ni0.89 Co0.11 ), which is greater than that of benchmark rigid MOFs.

Analysis of a tiered top-down approach using satellite and aircraft platforms to monitor oil and gas facilities in the Permian basin

As energy systems transition towards decarbonization, natural gas is expected to play an important role as a cleaner hydrocarbon fuel source to bridge the gap between renewable energy production and increasing energy demand. Regulations aim to reduce methane emissions from natural gas systems to ensure the total environmental impacts of natural gas production and consumption are indeed lesser than for other fossil fuels. GHGSat Inc., has developed a tiered top-down remote sensing approach utilizing satellite and airborne instruments to monitor methane emissions from industrial sites globally. This satellite-aircraft hybrid system was demonstrated in the Permian basin for detecting and quantifying methane emissions from oil and gas (O&G) facilities. A heavy-tail distribution of methane emissions was observed, with 12% of emitting targets (total sample size of 106) accounting for over 50% of total detected methane emissions. Traditional leak detection and repair (LDAR) programs often incorporate multiple annual screening campaigns with ground-based optical gas imaging (OGI) technologies to detect methane leaks from O&G infrastructure. Three alternative LDAR programs incorporating GHGSat's satellite and airborne monitoring technologies were modelled using the Arolytics AroFEMP methane model. Model parameters were derived from GHGSat's 2021 Permian basin data to evaluate their equivalency and costs against traditional LDAR monitoring. When compared to the required practice of performing OGI surveys four times per year, the proposed alternative LDAR programs were modelled to have at least equivalent methane reduction performance while being substantially more cost-effective.

Optimizing Renewable Injection in Integrated Natural Gas Pipeline Networks Using a Multi-Period Programming Approach

In this paper, we propose an optimization model that considers two pathways for injecting renewable content into natural gas pipeline networks. The pathways include (1) power-to-hydrogen or PtH, where off-peak electricity is converted to hydrogen via electrolysis, and (2) power-to-methane, or PtM, where carbon dioxide from different source locations is converted into renewable methane (also known as synthetic natural gas, SNG). The above pathways result in green hydrogen and methane, which can be injected into an existing natural gas pipeline network. Based on these pathways, a multi-period network optimization model that integrates the design and operation of hydrogen from PtH and renewable methane is proposed. The multi-period model is a mixed-integer non-linear programming (MINLP) model that determines (1) the optimal concentration of hydrogen and carbon dioxide in the natural gas pipelines, (2) the optimal location of PtH and carbon dioxide units, while minimizing the overall system cost. We show, using a case study in Ontario, the optimal network structure for injecting renewable hydrogen and methane within an integrated natural gas network system provides a $12M cost reduction. The optimal concentration of hydrogen ranges from 0.2 vol % to a maximum limit of 15.1 vol % across the network, while reaching a 2.5 vol % at the distribution point. This is well below the maximum limit of 5 vol % specification. Furthermore, the optimizer realized a CO2 concentration ranging from 0.2 vol % to 0.7 vol %. This is well below the target of 1% specified in the model. The study is essential to understanding the practical implication of hydrogen penetration in natural gas systems in terms of constraints on hydrogen concentration and network system costs.

Single-blind validation of space-based point-source detection and quantification of onshore methane emissions

Satellites are increasingly seen as a tool for identifying large greenhouse gas point sources for mitigation, but independent verification of satellite performance is needed for acceptance and use by policy makers and stakeholders. We conduct to our knowledge the first single-blind controlled methane release testing of satellite-based methane emissions detection and quantification, with five independent teams analyzing data from one to five satellites each for this desert-based test. Teams correctly identified 71% of all emissions, ranging from 0.20 [0.19, 0.21] metric tons per hour (t/h) to 7.2 [6.8, 7.6] t/h. Three-quarters (75%) of quantified estimates fell within ± 50% of the metered value, comparable to airplane-based remote sensing technologies. The relatively wide-area Sentinel-2 and Landsat 8 satellites detected emissions as low as 1.4 [1.3, 1.5, 95% confidence interval] t/h, while GHGSat’s targeted system quantified a 0.20 [0.19, 0.21] t/h emission to within 13%. While the fraction of global methane emissions detectable by satellite remains unknown, we estimate that satellite networks could see 19–89% of total oil and natural gas system emissions detected in a recent survey of a high-emitting region.

A Multi-Variable Analytical Method for Energy Flow Calculation in Natural Gas Systems

This letter presents a novel multi-variable analytical method to quickly calculate the energy flows in natural gas systems (NGSs). The solution, which does not require the traditional iterative method, will be applicable to the multi-scenario analysis. Case studies on several test systems verify the effectiveness of the proposed method.

A Parallel Fast-Track Service Restoration Strategy Relying on Sectionalized Interdependent Power-Gas Distribution Systems

In the distribution networks, catastrophic events especially those caused by natural disasters can result in extensive damage that ordinarily needs a wide range of components to be repaired for keeping the lights on. Since the recovery of system is not technically feasible before making compulsory repairs, the predictive scheduling of available repair crews and black start resources not only minimizes the customer downtime but also speeds up the restoration process. To do so, this article proposes a novel three-stage buildup restoration planning strategy to combine and coordinate repair crew dispatch problem for the interdependent power and natural gas systems with the primary objective of resiliency enhancement. In the proposed model, the system is sectionalized into autonomous subsystems (i.e., microgrid) with multiple energy resources, and then concurrently restored in parallel considering cold load pick-up conditions. Besides, topology refurbishment and intentional microgrid islanding along with energy storages are applied as remedial actions to further improve the resilience of interdependent systems while unpredicted uncertainties are addressed through stochastic/information gap decision theory (IGDT) method. The theoretical and practical implications of the proposed framework push the research frontier of distribution restoration schemes, while its flexibility and generality support application to various extreme weather incidents.

Nested Bilevel Energy Hub Bidding and Pricing With Price-Responsive Demand

The increasing interdependence among different energy systems has led to the development of the integrated energy system (IES), where the energy hub (EH) is a primary market player in the distribution-level energy market. In an EH, subsystems such as electric, natural gas, and heating systems coexist and are interdependent. This paper proposes an innovative bilevel strategic decision-making framework of an EH that maximizes profit in the energy market and simultaneously reallocates profit among EH subsystems through a dynamic pricing mechanism. In the upper level, the EH acts as a price maker in the day-ahead distribution electricity market for profit maximization. In the lower level, Nash-Bargaining-based profit redistribution is realized through bilateral pricing between energy subsystems in the EH, where the fairness and interests of individual subsystems are respected. The price-responsive load couples the profit maximization and profit reallocation problems. Case studies demonstrate that the proposed framework can effectively solve the bidding and pricing problem of an EH in a day-ahead market, and the cooperation within energy subsystems contributes to higher overall profit and better fairness for the EH subsystems.

Optimal dispatch of HCNG penetrated integrated energy system based on modelling of HCNG process

Hydrogen is injected into the existing natural gas network to form hydrogen-rich compressed natural gas (HCNG), effectively addressing the high cost of hydrogen transmission. However, the traditional IES model cannot be used due to the hydrogen injection's effect on gas properties and the vague characteristics of the transport and separation processes. Therefore, this paper proposes an HCNG penetrated integrated energy system (HPIES) optimal dispatching method by comprehensively modelling the injection, transmission, and separation processes of HCNG. An HCNG mass flow rate model considering variable mixing ratio and unknown beginning flow direction is developed to describe the effect of hydrogen injection. Furthermore, the hydrogen separation model is established by introducing a combined membrane and pressure swing adsorption separation process. The tightening McCormick algorithm is proposed to solve quickly HPIES optimal dispatch problem with an acceptable feasibility check. Finally, case studies on the HPIES consisting of IEEE 39-bus power system and 20-node natural gas system validate the effectiveness of the algorithm and model. The results show that the average error is 0.031% for the bilinear term constraint.

Multi-objective optimization of integrated energy systems based on demand response

ABSTRACT The implementation of demand response (DR) in the operation optimization of the integrated energy system has become one of the research hotspots worldwide with the rapid development of the energy network. Integrated gas-electricity system (IGES) operators can allocate different energy carriers more rationally and decrease energy cost and exergy input considering the DR scheme. However, DR was mainly applied to electric power systems in the past, while natural gas systems were less considered. In this paper, based on the theory of price-elasticity, and simultaneously with the consideration of the DR for end-user (EDU) of electricity and natural gas, the optimization model of the IGES is established, which includes two objective functions, the minimum operating cost of the integrated system and the maximum EDU satisfaction. The constraints include compressors and gas storages in the natural gas system, coal and gas generator units in the electric power system, Pow to gas units, and EDU gas/electric load changes. Subsequently, the proposed model is employed for an integrated 7-node natural gas system and 6-node power system with the sensitivity analysis for operating cost. The results demonstrate that the implementation of DR brings specific economic benefits to the operation of the entire system, the total operating cost is reduced by 16.6%, and the natural gas load is reduced by 89.9%. The implementation of DR balances the 24-hour load, reduces the fluctuation of the EDU load, increases the efficiency of the load shifting, and alleviates the instability of upstream production and transportation.

Reinforcement Learning-Based Bi-Level strategic bidding model of Gas-fired unit in integrated electricity and natural gas markets preventing market manipulation

Due to its efficient operation and environment-friendly characteristic, gas-fired unit (GFU) plays a more and more important role in electric power systems and natural gas systems. To investigate the performance of GFU's participation in integrated electricity and natural gas markets, a bi-level strategic bidding model considering price and quantity factors is proposed. With the increasing participation of consumers in electricity markets, demand response (DR) management is implemented in the electricity market clearing process and user comfort level (UCL) is considered in the market clearing model. Since GFU participates in both the electricity market and the natural gas market, a local marginal price penalty (LMPP) variable is defined in this paper to prevent potential market manipulation (MM) of GFU. Then a modified reinforcement learning (RL)-based method is proposed to solve the model, combining deep deterministic policy gradient (DDPG) algorithm with autocorrelated noise. Test results on an integrated electricity-gas system show that the proposed method can reflect the strategic behaviors of GFU effectively. The proposed method has better performance than traditional DDPG algorithm with Gaussian noise and the Deep Q-Network (DQN) algorithm. And electricity markets with LMPP can save about 3.03% in generation cost by preventing MM of GFU.

Strategic bidding of gas-fired units with tanks in coupled electricity and natural gas markets

With the increasing interdependence between the electricity and natural gas systems in economy and physics, this paper addresses the problem of developing optimal offering strategies in synchronized electricity–gas markets with step-wise energy offer formats. The gas-fired generators act as price makers in electricity markets and interruptible loads in the natural gas markets. The proposed model considers not only the impact of electricity clearing prices on gas bidding behaviors but also the influence of fuel cost variation on developing optimal electricity bidding curves. A price update method is proposed with the help of gas tanks to suppress price fluctuation. A two-stage trading mechanism is designed for gas-fired generators to optimize bids in electricity and gas markets via an iterative method. These bilinear terms in objectives and constraints are tackled by primal–dual equality conditions and binary expansion method. Then, the mathematical program with equilibrium constraints is converted to the mixed-integer linear program. The diagonalization algorithm is nested in the proposed trading mechanism if there are many leaders in the market instead of just one. Case studies validate the proposed methodology in detail.

Nonlinear Dynamic Energy Flow Calculation for Integrated Gas and Power System Based on Method of Lines

The rapid development of gas turbine (GT) and power-to-gas (P2G) technology promotes the integration of power systems (PS) and natural gas systems (NGS). To accurately describe the state distribution, this paper first introduces the model of integrated gas and power system (IGPS) governed by nonlinear partial differential-algebraic equations (PDAEs). A third-order method of lines (MoL) is proposed to solve the nonlinear partial differential equations (PDEs) in the natural gas system. The distribution of mathematical equations and variables in the energy flow model is then analyzed. The solution strategy for nonlinear dynamic energy flow calculation (EFC) in integrated gas and power system is then developed, including the equation set formulation, solvability analysis, and initial guess selection method. Finally, the accuracy and efficiency of the proposed method are verified through simulation in two different cases.

Effect of hydrogen blending on the energy capacity of natural gas transmission networks

In this paper the effects of hydrogen on the transport of natural gas-hydrogen mixture in a high-pressure natural gas transmission system are investigated in detail. Our research focuses on the decrease in transferable energy content under identical operating conditions as hydrogen is blended in the gas transmission network. Based on the extensive literature review the outstanding challenges and key questions of using hydrogen in the natural gas system are introduced. In our research the transmissible energy factor - TEF - is defined that quantifies the relative energy capacity of the pipeline caused by hydrogen blending. A new equation is proposed in this paper to find the value of TEF at specific pressure and temperature conditions for different hydrogen concentrations. This practical equation helps the natural gas system operators in the decision-making process when hydrogen emerges in the gas transmission system. In this paper the change of the compression power requirement, which increases significantly with hydrogen blending, is investigated in detail.

An improved correlation for dry pressure losses across static mixers at high Reynolds numbers

AbstractCorrugated (SMV‐style) static mixers are industrially important for process intensification and can promote gas–liquid mass transfer in processes such as sour gas sweetening. Current correlations for pressure loss are limited to Reynolds numbers (Re) below 40 000, far below the ranges encountered in natural gas systems (105 < Re < 107). An experimental and numerical study of pressure drop across multiple corrugated mixers in the range of 104 < Re <2 × 105 encompassing different configurations (aligned, rotated), pipe diameters (1–4 in.), and sand grain surface roughness values (10–5000 μm) is reported here. Our previous correlation for pressure loss across a single corrugated element is shown to be extendable to multiple corrugated mixing elements. Through the inclusion of mixer tortuosity (τ), porosity (ε), and macro‐scale (geometric) wall roughness (e), the correlation also matches historical pressure drop data (at different τ, ε) reported in literature, thereby demonstrating the utility of these variables as parameters that can help optimize mixer performance. Experiments and computational fluid dynamics (CFD) modelling revealed that the rotated configuration increased the residence time by up to 13% in comparison to the aligned configuration. This may have implications on the selective absorption of sour gas components that are based on fast kinetics. In addition, the role of wall roughness (both pipe housing and mixer) was demonstrated to be significant in this study (accounting for 55% of the pressure losses) and must be accurately accounted for when scaling laboratory measurements.

The Planning Method of Township Regional Low-carbon Energy System Based on Demand Side Response

To solve the problem of environmental pollution caused by rising energy demand and explore the impact of demand-side resources on system optimal scheduling, based on the EH model, it is of great significance to study the integrated demand response strategy, carbon trading mechanism and low-carbon energy uncertainty for the operation of IES low-carbon economy. Compared with the traditional load response model, this model has better flexibility with the adjustment response. According to the thermal inertia and certain time delay characteristics of the heat load, the demand response model of heat load is established. Then, according to the application of the multienergy price demand response theory in natural gas system, the optimization model of gas load response is constructed. Three different optimization scenarios are set for simulation and comparative analysis, which verifies that the township comprehensive demand response model considering electric and thermal gas load has a significant effect on improving the system economy and the absorption capacity of low-carbon energy.

Techniques for Creating Synthetic Combined Electric and Natural Gas Transmission Grids

This paper presents a methodology for the creation of a synthetic combined electric and natural gas transmission network, along with representative benchmark results. The systems do not contain actual, confidential network data, but are synthetic, meaning they are built to capture the behavior of a combined network that is geographically constrained. First, natural gas loads are placed in a selected area. Work already done in building synthetic electric grids aids in this process, where the natural gas-powered generators are modeled as loads in the natural gas system. Publicly available data is then used to place the remaining gas loads and the gathering plant. Next, a method is introduced to construct a pipeline network connecting the loads and processing plant, which acts as the source. The combined electric-gas system is then solved for the nodal pressures, pipeline flow rates, and electric state variables. A 51-node gas test case with 49 pipes, 23 loads, 2 compressor stations and a loop is constructed and solved in combination with a 173-bus electric system, designed to aid with developing and validating analysis techniques for combined electric-gas systems.

Resilience Enhancement of Integrated Electricity-Gas-Heat Urban Energy System With Data Centres Considering Waste Heat Reuse

In this paper, a novel co-optimization model is proposed for integrated electricity-gas-heat urban energy systems to improve resilience during extreme events. This paper modelled and analysed data centres’ optimal operation, workload redistribution, and waste heat reuse. In addition to guaranteeing power supply as critical loads, data centres also provide the generated waste heat to customers via the urban heating network. Meanwhile, the proposed model considers complex interactions of multiple energy systems and the recovery decisions of the power distribution system. Energy conversion devices and electric-driven coupling components are modelled. Even more importantly, the dynamic characteristics of natural gas and heating systems, which cannot be neglected, are also considered and discussed in detail. Case studies verify the effectiveness of the proposed co-optimization model. The results indicate that the resilience of urban energy systems requires to be improved by collaboration between multiple sectors. Considering interdependencies and coupling among multiple energy systems can enhance their response and resistance to extreme events.

A Data-Driven State Estimation Framework for Natural Gas Networks With Measurement Noise

The large-scale coverage of natural gas makes the composition structure and operation mode of natural gas network more complex, higher requirements are put forward for the effectiveness and accuracy of state estimation. The existing methods for state estimation of natural gas network with noise are all modeled after processing the data with noise, leading to the real data being distorted to a certain extent. With that in mind, a data-driven method is presented in this paper. While solving the problem of state estimation for natural gas network with measurement noise in the input data, filtering and denoising are unnecessary during state estimation, retaining the complete information of real data. It avoids destruction of real data induced by separating noise from measured data owing to different methods and intensities of noise processing. According to the gas flow characteristic equation of natural gas system, the original problem is converted into a weighted low-rank approximation problem, the search space is shrunk to an orthogonal complement space. The selection of initial values is not merely unrestricted but there will be no accumulation and transmission of iteration error. The effectiveness of the proposed method is demonstrated through simulating 10-node natural gas network. Compared with the Newton’s method, the data-driven method has superior performance, the RMSE achieves 0.2268 and the MAPE achieves 1.63%.