Integrated Electricity And Natural Gas System Research Articles

Fast distributed co-optimization of electricity and natural gas systems hedging against wind fluctuation and uncertainty

The synergistic operation of integrated electricity and natural gas systems (IEGS) in the presence of wind power necessitates a distributed optimization framework that ensures information privacy. However, incorporating wind power penetration leads to distributed optimization problems including uncertainty and unstable distributed algorithm’s convergence. With the increasing proportion of wind power in IEGS, fluctuating wind power penetration highly affects the convergence of distributed optimization solutions, resulting in uncontrollable optimization time of the distributed algorithm. Accordingly, this paper investigates the impact of wind fluctuation and uncertainty on distributed IEGS optimization and proposes a novel fast distributed co-optimization framework. Specifically, an adaptive alternating direction method of multipliers (ADMM) is developed to accommodate wind fluctuation. Based on designed rules, the penalty parameter is updated in every step to maximize the gradient of the optimization objective. The experiments are conducted using real wind power generation data sourced from a wind farm in Australia and a classical IEGS framework composed of the IEEE 24-bus electricity system and a 12-node natural gas system. Compared to original ADMM and residual balancing ADMM, the proposed framework achieves an average reduction of 0–91.4% in the number of iterative steps for multiple solution iterations across various scenarios, with a corresponding decrease in the standard deviation by 13.8%–93.0%.

A data-driven approach towards fast economic dispatch in integrated electricity and natural gas system

Effective economic dispatch for the integrated energy system (IES) can improve energy efficiency and promote renewable energy accommodation. Tradition IES economic dispatch are based on model-based methods that rely on accurate system parameters and uncertainty prediction. This paper proposes a data-driven fast economic dispatch method based on deep reinforcement learning (DRL) in an integrated electricity and natural gas system (IEGS). Unlike other DRL-based studies that spend a lot of computation time to calculate power flow, we employ DNNs to learn the complex nonlinear relationship existing in the IEGS, namely IEGSNet. Then, the economic dispatch problem is formulated as a Markov decision process, and solved by the soft actor–critic​ (SAC) algorithm. Simulation results illustrate that the proposed method achieves the similar operation cost to the tradition DRL-based dispatch method, but takes almost one tenth of the training time. Additionally, the computation time of the proposed method for 10-day dataset is at least two orders of magnitudes shorter that the model-based optimization algorithm.

Asynchronous decentralized adjustable robust operation for multi-area integrated electricity–gas systems considering wind power uncertainty

Modern energy infrastructure has evolved into an integrated electricity and natural gas systems (IEGS), which often encompasses multiple geographically-diverse energy areas. This paper focuses on the decentralized adjustable robust operation problem for multi-area IEGS. Existing distributed algorithms usually require synchronization of all area subproblems, which is hard to scale and could result in under-utilization of computation resources due to the heterogeneity of local areas. To address those limitations, this paper proposes an asynchronous alternating direction method of multipliers (ADMM) based decentralized model for multi-area IEGS. This asynchronous decentralized structure only requires local communications and allows each area to perform local updates with information from a subset of but not all neighbors, where the individual areas’ subproblems are solved independently and asynchronously. Meanwhile, the linear decision rules (LDRs)-based adjustable robust operation model is tailored to combine with the automatic generation control (AGC) systems to fully exploit its potential in dealing with renewable energy uncertainty. Numerical results illustrate the effectiveness of the proposed method.

Carbon-Oriented Expansion Planning of Integrated Electricity-Natural Gas Systems With EV Fast-Charging Stations

Under the pressure of climate change and the energy crisis, electric vehicles (EVs) have experienced a marked growth in many countries recently. However, the environmental benefits of EVs cannot be achieved if the charging power from the electricity grid is still mainly provided by fossil-fueled power plants. How to construct a low-carbon fast-charging system has become an emerging research topic. In this context, a novel carbon-oriented expansion planning model of the integrated electricity and natural gas system (IEGS) with EV fast-charging stations (FCSs) is proposed to determine the optimal alternatives, locations, and sizes for ecofriendly candidate assets, including roof-top photovoltaic (PV) panels and fuel cell (FC) units in each FCS, as well as renewable energy units and carbon capture and storage (CCS) systems in IEGS. To guarantee the carbon reduction performance of the planning scheme, the carbon emission cap of the EV charging system is specified by the planner and optimally allocated among multiple FCSs according to their carbon reduction abilities. Furthermore, the proposed model can not only consider the initial investment and operation costs but also take into account the expected adaptation cost to future scenarios that are generated in line with three uncertainties, namely, the traffic flow levels, conventional load levels, and renewable-based power generation levels. Finally, the proposed expansion planning method is illustrated by conducting numerical experiments in case studies.

Distributed adjustable robust optimal power-gas flow considering wind power uncertainty

The rapid uptake of natural gas-fired units in energy systems poses significant challenges in coordinating the electricity and gas systems. Besides, the uncertainty caused by integrated renewable energy such as wind power raises more requirements on the robustness of the operation for integrated electricity and natural gas system (IEGS). To address these challenges, this paper investigates the distributed adjustable robust optimal power and gas flow (OPGF) model for IEGS. Using linear decision rules (LDRs), we first propose an improved adjustable robust model combining with the automatic generation control systems to fully exploit its potential in dealing with renewable energy uncertainty while utilizing the controllable polyhedral uncertainty set to reduce solution conservatism. This improved LDRs based adjustable robust approach can reduce the computational burden caused by the existing decomposition based robust approach when applied to distributed optimization. Then, to preserve the information privacy and decision-making independence of subsystems, two tailored alternating direction method of multipliers (ADMM) based distributed optimization frameworks for IEGS with and without a central coordinator are presented, respectively. Effectiveness is illustrated through benchmark case studies.

Optimal Energy Reserve Scheduling in Integrated Electricity and Gas Systems Considering Reliability Requirements

With the growing interdependence between the electricity system and the natural gas system, the operation uncertainties in either subsystem, such as wind fluctuations or component failures, could have a magnified impact on the reliability of the whole system due to energy interactions. A joint reserve scheduling model considering the cross-sectorial impacts of operation uncertainties is essential but still insufficient to guarantee the reliable operation of the integrated electricity and natural gas system (IEGS). Therefore, this paper proposes a day-ahead security-constrained unit commitment (SCUC) model for the IEGS to schedule the operation and reserve si-multaneously considering reliability requirements. Firstly, the multi-state models for generating units and gas wells are established. Based on the multi-state models, the expected unserved energy cost (EUEC) and the expected wind curtailment cost (EWC) criteria are proposed based on probabilistic methods considering wind fluctuation and random failures of components in IEGS. Furthermore, the EUEC and EWC criteria are incorporated into the day-ahead SCUC model, which is noncon-vex and mathematically reformulated into a solvable mixed-integer second-order cone programming (MISOCP) problem. The proposed model is validated using an IEEE 30-bus system and Belgium 20-node natural gas system. Numerical results demonstrate that the proposed model can effectively schedule the energy reserve to guarantee the reliable operation of the IEGS considering the multiple uncertainties in different subsystems and the cross-sectorial failure propagation.

Robust Pricing of Energy and Ancillary Services in Combined Electricity and Natural Gas Markets

Rapid growth of gas-fired generators strengthens the interdependency of electricity and natural gas markets. To meet the uncertainty challenge, a robust day-ahead market clearing framework for the integrated electricity and natural gas system (IEGS) is presented. First, a robust security-constrained unit commitment (SCUC) model is built considering the joint dispatch of ancillary services. Regulation-up/down reserve is scheduled to balance electrical load/wind power uncertainties, spinning reserve is to manage N-1 generator outage, and natural gas reserve is to withstand gas load uncertainty and provide fuel for gas-fired electrical reserve. The SCUC model is a two-stage mixed-integer nonlinear optimization problem so that a sequential linear approximation-based Column & Constraint Generation (SLA-based C&CG) algorithm is proposed. Then, according to SCUC solutions, an economic dispatch model is derived to obtain market clearing and pricing results. Based on locational marginal pricing, novel definitions of electrical energy, natural gas and multiple ancillary service prices are proposed, which can reflect network limits and uncertainty effects. Numerical cases on two test IEGSs and a provincial-level IEGS demonstrate the high accuracy of proposed SLA-based C&CG algorithm and the impact of uncertainties on market clearing.

Non-Isothermal Optimal Power and Gas Flow

Isothermal natural gas flow model with constant gas temperature has been widely used in conventional coordinated operations of integrated electricity and natural gas systems (IEGS). However, as a crucial state variable in IEGS, gas temperature varies significantly among nodes in a natural gas system, which can be calculated along with nodal pressures and mass flow rates. This paper proposes a novel non-isothermal optimal power and gas flow (OPGF) model that avoids the isothermal assumptions involved in traditional natural gas network model and improves the accuracy significantly. In the proposed model, partial differential terms with minimal weights are neglected based on the quantitative analysis of the energy conservation equation to further simplify the energy conservation equation. A non-isothermal model of natural gas networks is developed by introducing the finite difference method and laws of thermodynamics to incorporate the gas temperature change into the coordinated operation of IEGS. A start point initialization scheme is developed based on the Newton-Raphson algorithm to accelerate the interior point method solution convergence and improve its solution quality. Numerical experiments presented for two test systems validate the effectiveness of the proposed OPGF model and solution methodology.

A distributed calculation method for robust day-ahead scheduling of integrated electricity-gas systems

The urgency to address the uncertainty in the day-ahead scheduling (DAS) of integrated electricity and natural gas systems (IEGSs) has been highlighted, and the robust optimization has been proven to be an effective method. However, the gas system and electricity system are generally operated by different utilities in practice, i.e., gas system operator (GSO) and electricity system operator (ESO), which poses a considerable challenge to solve this two-stage mixed-integer nonlinear DAS model due to the limit on information sharing. In this paper, a novel distributed calculation method is proposed to solve the robust DAS model of IEGSs in a decentralized way. First, a mixed-integer master problem and a non-convex max–min subproblem can be obtained from the original DAS model by employing the column and constraint generation (CCG) method. Afterwards, based on the limited information exchange between ESO and GSO, an innovative decomposition method is proposed for the master problem to obtain the optimal DAS solution, and then a novel inner CCG algorithm is presented for the subproblem to guarantee the security of IEGSs. By doing so, the robust DAS of IEGS is decomposed into several mixed-integer linear programming (MILP) problems, and GSO and ESO can coordinately solve these MILPs with limited information sharing. Last, numerical tests on two IEGSs verify the superiority of the proposed method in hedging against the wind uncertainty and achieving the solution optimality.

Economic dispatch of multi-area integrated electricity and natural gas systems considering emission and hourly spinning reserve constraints

With the increasing growth of gas-fired units and power-to-gas facilities, the power system and natural gas system become an integrated electricity and natural gas system (IENGS), which brings new opportunities to mitigate the associated environmental issues and deal with various uncertainties from electricity load, renewable energy generation, and gas load. Different geographical locations may have many IENGSs which are connected by electric or gas transmission lines, thus formulating multi-area IENGSs. These multi-area IENGSs are not simple addition of individual IENGSs, since renewable energy may distribute unequally between different IENGSs, and excess renewable energy needs to be transmitted from one area to another, but is limited by potential tie-line congestions. To solve these problems, this paper proposes an economic dispatch model for multi-area IENGSs considering tie-line congestion, maximum allowable emission bounds and hourly spinning reserve constraints. The considered multi-area IENGS consists of multiple single-area IENGSs, among which wind energy distribution varies considerably. The maximum allowable emission bounds of carbon dioxide and nitrogen oxygen are separately set based on their different influences on the environment. The hourly spinning reserves are allocated for both the power system and the natural gas system, which are provided by fossil fuel-fired units, power-to-gas facilities, gas pipelines, and natural gas storage facilities. After that, the presented model is converted into a mixed-integer linear programming problem for higher computing efficiency. Case studies for integrated systems are analyzed to demonstrate the effectiveness of the proposed model.

Distributed optimal dispatch of integrated electricity and natural gas system considering the pipeline storage characteristics

As an important form of multi-energy complementation, the integrated electricity and natural gas system (IEGS) is a new carrier for renewable energy accommodation. Firstly, based on the natural gas pipeline model, the buffer effect of natural gas pipeline storage characteristics in response to natural gas load fluctuations is analyzed. Then, considering the pipeline storage characteristics, a dad-ahead economic dispatch model for IEGSs with a high proportion of wind power is established. And the extreme scenario optimization method is used in this model to deal with the uncertainty of wind power. Furthermore, a distributed solutions method based on the Improved Alternating Direction Method of Multipliers is proposed to accelerate the convergence of distributed solutions. Cases studies are conducted on a modified IEEE 39-bus system and a Belgium 20-node natural gas system to verify the effectiveness of models and algorithms.

Conic Optimal Energy Flow of Integrated Electricity and Natural Gas Systems

In this letter, we propose a market-based bi-level conic optimal energy flow (OEF) model of integrated electricity and natural gas systems (IENGS). Conic AC optimal power flow (ACOPF) is formulated in the upper level model, and the generation cost of natural gas fired generation units (NGFGUs) is calculated based on natural gas locational marginal prices (NG-LMPs). The natural gas system market clearing process is modeled in the lower level model. The bi-level model is then transferred into a mixed-integer second-order cone programming (MISOCP) problem. Simulation results demonstrate the effectiveness of the proposed conic OEF model.

Two-Stage Stochastic Scheduling of Integrated Electricity and Natural Gas Systems Considering Ramping Costs With Power-to-Gas Storage and Wind Power

Power-to-gas (P2G) facilities and natural gas fired power units provide flexibility to integrated electricity and natural gas systems (IENGS) for wind power accommodation and ramp deployment. This paper proposes a stochastic coordinated scheduling model for IENGS considering ramping costs with P2G storage and wind power. The operation model of natural gas system with P2G is presented, and the benefits of P2G integration are analyzed. To address the uncertainties of wind power and energy loads, multiple representative scenarios are generated. The flexible ramping requirements and costs are incorporated and analyzed, and flexible ramp can be provided by P2G in this work. The coordinated scheduling model for IENGS is formulated as a two-stage stochastic programming problem, in which day-ahead scheduling for electricity systems is modeled in the first stage model and scheduling of natural gas systems is carried out in the second stage model. Numerical case studies on a modified PJM 5-bus electricity system with a 7-node natural gas system and the IEEE 118-bus system with a 20-node Belgian natural gas system verify that P2G can help accommodate wind power, provide additional flexible ramping capacities, and reduce the gas supply from gas suppliers and gas load shedding.

Optimal Scheduling of the Integrated Electricity and Natural Gas Systems Considering the Integrated Demand Response of Energy Hubs

Nowadays, integrated electricity and natural gas system (IENGS) stands as one of the most discussed topics among industry and academic sectors. The interdependency issues of electricity and natural gas may limit the operational flexibility of the IENGS and consequently prevent wind power integration. This article aims to solve the problem by incorporating the demand-side flexibility of energy hubs (EHs) into the scheduling of the IENGS. To fully exploit the demand-side flexibility, the detailed integrated demand response (IDR) model is developed for the EHs. Unlike the traditional demand response programs in which only elastic electricity demand is considered, both electricity demand and natural gas demand are considered flexible and controllable in the IDR. Moreover, the IDR is modeled as an optimization problem rather than a price-responsive load bid. The interactions between the EHs' IDR model and the IENGS's scheduling model are achieved based on the two-level optimization framework. Then, the IDR-embedded scheduling of the IENGS is formulated as a complicated two-level mixed integer nonlinear programming (TL-MINLP) problem. A technique is proposed to solve the TL-MINLP problem efficiently to enable the IDR-embedded scheduling model to be used in engineering practice. Case study illustrates the benefits of the proposed IDR-embedded scheduling method.

A convex optimization and iterative solution based method for optimal power-gas flow considering power and gas losses

Optimal power-gas flow (OPGF) is an important problem for the coordinated operation of an integrated electricity and natural gas system (IEGS). However, it is a challenge to solve the OPGF problem due to its non-convexity. This paper proposes a new OPGF method considering both power losses and gas losses based on convex optimization to balance modeling accuracy and tractability. As for power system modeling, a DC power flow model with power losses embedded as equivalent loads is used to approximate the nonlinear AC power flow model. As for gas system modeling, (1) a second-order cone (SOC) relaxation method is proposed to address the non-convex Weymouth equations, and the tightness of the SOC relaxation is guaranteed by introducing a penalty term and an iterative tightening procedure based on the convex envelope. (2) Gas losses of compressors are also modeled as equivalent loads and two more penalty terms on compressors are introduced to improve the accuracy of gas loss modeling. The OPGF problem is finally formulated as a mixed-integer convex programming model with unknown equivalent loads, and an iterative solution approach is then developed to solve the problem. Numerical results validate the effectiveness of the proposed OPGF method.

Decentralized day-ahead scheduling of multi-area integrated electricity and natural gas systems considering reserve optimization

To maximize the benefits of multi-area cooperation and guarantee system reliability, a day-ahead scheduling model for multi-area integrated electricity and natural gas systems (IEGS) considering reserve optimization is proposed in this paper. The reserve scheduling is integrated into the multi-area optimal dispatch model to hedge against the risk caused by the uncertainties in electricity and gas systems. The reserve constraints in electricity and natural gas systems are modeled as probabilistic forms with a predefined confidence level based on chance-constrained programming (CCP). In order to protect the information privacy of each area, the original centralized energy-reserve co-optimization problems are decomposed into several area sub-problems that can be solved independently. Furthermore, the alternating direction method of multipliers (ADMM) with self-adaptive penalty parameters is adopted to solve the proposed model in a decentralized manner. The feasibility and effectiveness of the proposed models and methods are verified in a three-area IEGS.

Cascading Failure Propagation Simulation in Integrated Electricity and Natural Gas Systems

The sharp increase in the total installed capacity of natural gas generators has intensified the dynamic interaction between the electricity and natural gas systems, which could induce cascading failure propagation across the two systems that deserves intensive research. Considering the distinct time response behaviors of the two systems, this paper discusses an integrated simulation approach to simulate the cascading failure propagation process of integrated electricity and natural gas systems (IEGSs). On one hand, considering instantaneous re-distribution of power flows after the occurrence of disturbance or failure, the steady-state AC power flow model is employed. On the other hand, gas transmission dynamics are represented by dynamic model to capture the details of its transition process. The interactions between the two systems, intensified by energy coupling components (such as gas-fired generator and electricity-driven gas compressor) as well as the switching among the operation modes of compressors during the cascading failure propagation process, are studied. An IEGS composed of the IEEE 30-bus electricity system and a 14-node 15-pipeline gas system is established to illustrate the effectiveness of the proposed simulation approach, in which two energy sub-systems are coupled by compressor and gas-fired generator. Numerical results clearly demonstrate that heterogeneous interactions between electricity and gas systems would trigger the cascading failure propagation between the two coupling systems.

Bi-level planning for integrated electricity and natural gas systems with wind power and natural gas storage

Power-to-gas facilities (PtG) are able to convert excess renewable energy into synthetic natural gas that can be stored in natural gas storage units. The coordinated planning of gas-fired generators, PtG and natural gas storage offers the prospect of mitigating energy shortages and accommodating renewable energy within integrated electricity and natural gas systems (IEGS). This paper presents a bi-level planning model for IEGS that minimizes the investment costs of candidate assets, especially wind farms, PtG facilites and natural gas storage units, as well as the operational cost over the planning horizon. A small number of carefully selected scenarios are presented to illustrate the uncertainty of wind power. IEGS operation is also analyzed on the basis of these temporal scenarios, including the operational characteristics and constraints of two systems to assess the potential role of natural gas storage and PtG. The proposed model is a scenario-based planning model that is solved by a bi-level iterative algorithm. The upper-level base-scenario based planning problem determines the candidate assets while the lower-level problem checks the feasibility using the determined components for other scenarios. Two sets of numerical tests are presented to demonstrate the effectiveness of the proposed planning model and associated algorithms.

A Chance-Constrained Decentralized Operation of Multi-Area Integrated Electricity–Natural Gas Systems With Variable Wind and Solar Energy

This article presents a decentralized decision-making strategy for the multi-area integrated electricity and natural gas system (IEGS). Independent system operators (ISOs) in the electric power sector and natural gas system operators (NGSOs) in the natural gas sector operate their respective systems independently with the consideration that a limited set of information is shared, and the information privacy and decision-making independence are preserved among operators. In this article, uncertainties associated with the proliferation of variable wind and solar generation as well as load forecast errors are addressed in a chance-constrained programming framework, and the loss of load probability (LOLP) as well as the transmission line overload probability (TLOP) are introduced to ensure a secure and economic operation of IEGS. The proposed multi-area & multi-layer problem is solved by a hierarchical iterative algorithm in which the inner loop deals with the collaboration between ISO and NGSO in each area, and the collaboration among areas is managed by the outer loop. Finally, case studies are carried out on two multi-area IEGSs with different sizes in which numerical results demonstrate the effectiveness of the proposed methods.

Stochastic dynamic economic dispatch of high wind-integrated electricity and natural gas systems considering security risk constraints

As the proportion of wind power generation increases in power systems, it is necessary to develop new ways for wind power accommodation and improve the existing power dispatch model. The power-to-gas technology, which offers a new approach to accommodate surplus wind power, is an excellent way to solve the former. Hence, this paper proposes to involve power-to-gas technology in the integrated electricity and natural gas systems (IEGSs). To solve the latter, on one hand, a new indicator, the scale factor of wind power integration, is introduced into the wind power stochastic model to better describe the uncertainty of grid-connected wind power; on the other hand, for quantizing and minimizing the impact of the uncertainties of wind power and system loads on system security, security risk constraints are established for the IEGS by the conditional value-at-risk method. By considering these two aspects, an MILP formulation of a security-risk based stochastic dynamic economic dispatch model for an IEGS is established, and GUROBI obtained from GAMS is used for the solution. Case studies are conducted on an IEGS consisting of a modified IEEE 39-bus system and the Belgium 20-node natural gas system to examine the effectiveness of the proposed dispatch model.