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Stochastic wind power prediction errors hurt the normal operation of integrated power and natural gas systems (IPGS). First, the data‐driven stochastic chance‐constrained programming method is applied to deal with wind power prediction errors, and its probability distribution is accurately fitted by variational Bayesian Gaussian mixture model with massive historical data. In addition, the data‐driven chance constraints of tie‐line power and reserve capacity of gas turbine are built. Next, to utilize wind power more reasonably, the operational characteristics and optimal commitment of power‐to‐hydrogen devices are considered and modelled in proposed strategy to reflect the actual situation of IPGS. Then, the original complicated dispatch problem is converted into a tractable second‐order cone programming problem via convex relaxation and quantile‐based analytical reformulation techniques. Finally, the effectiveness of the proposed strategy is validated by numerical experiments based on a modified IEEE 33‐bus system integrated with a 10‐node natural gas system and a micro hydrogen system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the proliferation of natural gas combined cycle units, the modern energy infrastructure has evolved into a coupled electricity and natural gas system, which often encompasses multiple large-scale and geographically-diverse energy areas. The similarity in electricity and natural gas consumption profiles in coupled systems gives rise to critical mandates for the coordinated optimization of electricity and natural gas operation. This paper presents a decentralized optimization framework, which considers wind energy and power-to-gas (P2G) technologies in the operation of integrated multi-area electricity and natural gas transmission system (IEGTS). First, a comprehensive optimal dispatch model is established for the multi-area IEGTS in a centralized manner where bi-directional flows of electricity and natural gas among multi-areas are optimized considering the stochastic nature of wind energy. In particular, a linearized transient-state gas flow model under mild assumptions is proposed to represent the dynamic characteristics of the natural gas sector. The centralized optimization problem is decomposed into a set of computationally-tractable subproblems that are solved independently in individual areas. The alternating direction multiplier method is employed to achieve a rapidly convergent solution while preserving the privacy of operation data shared among participating areas. Finally, case studies are carried out on two multi-area IEGTS with multiple wind generation scenarios which demonstrate the computational performance of the proposed method with P2G facilities operating in variable conditions. [ABSTRACT FROM AUTHOR]

Natural gas system that supplies flexible and distributed energy resources can play a significant role in enhancing the resilience of the power distribution system. In this paper, a minimax-regret robust resilience-constrained unit commitment model is proposed for enhancing the resilience of the integrated power distribution and natural gas system (IDGS). First, a tri-level robust cooperation optimization problem is formulated based on the minimax-regret robust criterion to enhance the distribution system resilience against worst N-k contingencies. The multi-stage extreme weather model is considered to obtain the spatial dynamics of extreme weather. Then, the proposed robust model is reformulated as a mixed-integer convex programming model by relaxing nonconvex power and natural gas flow equations into second-order conic (SOC) constraints. The SOC-based column-and-constraint generation algorithm is employed to solve the proposed two-stage robust optimization problem. The effectiveness of proposed robust optimization model is validated using several case studies applied to the 33-bus-6-node and 123-bus-20-node IDGSs. [ABSTRACT FROM AUTHOR]

Energy hubs (EHs) are deployed to supply multi-energy demands economically in the integrated energy systems while considering physical and operational constraints of individual energy systems. This paper presents a decentral framework for the optimal dispatch of integrated power distribution and natural gas system (IDGS) in networked energy hubs. The IDGS operator (IDGSO) is charged with delivering a constrained optimal dispatch while preserving the privacy of participants’ data. First, a comprehensive optimization model is proposed in this paper using the mixed-integer second-order cone programming and sequential second-order cone (SOC) algorithms to guarantee the exactness of SOC relaxation. In this optimization process, we consider advanced control methods, such as remotely controlled switches and static var compensators, and propose two improvements including initial point setting and a special type of cutting plane to obtain a fast and feasible solution. Next, we apply Benders decomposition for decentralizing the decision-making process in accordance with the practical leader–follower relationship between IDGSO and EHOs (EH operators) and preserving private operation data of individual EHOs. Finally, we present numerical case studies, which illustrate the effectiveness of the proposed iterative model and solution algorithm for enhancing the operation of EHs. [ABSTRACT FROM AUTHOR]

This paper proposes a coordinated regional-district operation of integrated energy system (IES) for enhancing resilience in extreme conditions. A bi-directional flow model is established for regional-district IES with the utilization of power-to-gas technologies which shows how regional and district IES can jointly enhance the IES resilience. The regional IES co-optimizes power grid and natural gas networks, while the district IES coordinates the energy conversion and storage facilities. The district IES is modeled as an energy hub which supplies electricity to the regional IES. A tri-level two-stage robust model is established to accommodate random outages caused by natural disasters in both natural gas and electricity generation and delivery systems. A two-level algorithm utilizing Benders decomposition and column-and-constraint generation algorithms is proposed to solve the robust IES problem. Numerical results tested on a 6-bus electrical–6-node natural gas system and a modified IEEE RTS-79 40-node natural gas system show the effectiveness of the proposed model and solution technique for enhancing the IES resilience. [ABSTRACT FROM AUTHOR]

• A two-stage dispatch strategy for IPGS by using stochastic MPC is proposed. • Data-driven chance-constrained dispatch strategy is proposed to prevent adverse the effects of stochastic prediction errors. • A comprehensive evaluation index of the violation rate is proposed, evaluating the violation rate more reasonably. The optimal dispatch of the integrated power and natural gas systems can increase the utilization rate of renewable energy and energy efficiency while decreasing operation costs. The common prediction errors of wind power and electric load have the potential to negatively impact the normal operation of the integrated power and natural gas systems. A two-stage data-driven dispatch strategy is proposed to reduce this effect, consisting of the day-ahead dispatch stage and the intraday rolling dispatch stage using stochastic model predictive control (MPC). In the day-ahead dispatch stage, the data-driven chance constraints of tie-line power and reserve of gas-fired generators are built, and the day-ahead tie-line power is obtained and regarded as input parameters to the intraday dispatch stage. In the intraday dispatch stage, the data-driven chance constraints of tie-line power and reserve of gas-fired generators with the latest rolling prediction data are built, and the remaining control variables are obtained. The distribution characteristics of the stochastic prediction errors of wind power and electric load are captured and described by the variational Bayesian Gaussian mixture model with massive historical data. Then the original stochastic mixed-integer nonlinear programming problem is converted to a tractable deterministic one by the quantile-based analytical reformulation and convex relaxation technique. Finally, the proposed strategy is verified by the numerical experiments based on a modified IEEE 33-bus system integrated with a 10-node natural gas system and a micro hydrogen system. The numerical results demonstrate that the proposed strategy reduces the actual costs and decreases the violation rate caused by the stochastic prediction errors of wind power and electric load. [ABSTRACT FROM AUTHOR]

A robust security-constrained unit commitment (robust SCUC) model is proposed in this paper to enhance the operational reliability of integrated electricity-natural gas system (IEGS) against possible transmission line outages. In this work, adjustable capability of natural gas contract is considered to avoid dramatic changes in real-time gas demand. Distributed natural gas storage is also included to smooth out power system demand curve and further improve operational efficiency at normal state and during contingencies. Nonlinear and nonconvex natural gas flow functions are relaxed into second-order cone (SOC) constraints, then the SOC-based column and constraint generation method is adopted to solve the proposed two-stage robust convex optimization problem. In this iterative method, the security-check subproblem feeds back worst case constraints to the first-stage master problem, until no violated scenarios can be detected. Simulations tested on 6-bus-6-node and 118-bus-10-node IEGS with natural gas storage show that the proposed storage model is effective in the robust SCUC solution for IEGS considering possible N − k contingencies with limited natural gas adjustments. [ABSTRACT FROM AUTHOR]

A large-scale integrated energy system can represent several subsystems representing areas that are tied by electricity and natural gas networks. Accordingly, we propose a decentralized optimal energy flow (DOEF) calculation as compared with a centralized solution method. The proposed approach demonstrates the merits of the decentralized operation and control of a multi-area integrated electricity-natural gas system (IEGS), in terms of large-scale modeling requirements, faster computations, and data management for local sensitivity analyses. Using the proposed decentralized structure, the communication burden is relatively light as individual area operators in a multi-area IEGS will make optimal dispatch decisions independently and the corresponding information is shared with adjacent subsystems. The reformulation of the second-order cone (SOC) is proposed using advanced sequential cone programming (SCP) to handle the nonlinear steady-state natural gas flow, which provides a feasible solution with a high degree of computational efficiency. Furthermore, an iterative alternating direction method of multipliers (I-ADMM) is adopted to manage the nonconvexity of integer variables, which guarantees a satisfactory convergence performance. Case studies on three multi-area IEGS validate the effectiveness of the proposed model in a multi-area IEGS. [ABSTRACT FROM AUTHOR]