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.

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 which utilizes Benders decomposition and column-and-constraint generation algorithms is proposed to solve the proposed 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.

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.

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.

This paper proposes a decentralized strategy for the multi-area optimal transmission switching (OTS) problem. We illustrate the decentralized framework for this problem, which is able to help reduce model scale and communication burden. Each area operator (AO) governs its subsystem and decides the OTS independently. Then each AO coordinates with the adjacent areas by sharing the corresponding information. The augmented Lagrange function (ALF) is utilized to relax and decompose the primal centralized OTS problem into decentralized subproblems for each subsystem. A two-level iterative alternating direction method of multipliers (ADMM) is adopted to co-optimize these subproblems. The upper level is used to fix the binary variables, while the lower level is the standard ADMM method, which guarantees the accuracy of the relaxed subproblem. Numerical tests on a two-area 12-bus system and a modified IEEE RTS-96 system show the effectiveness of the proposed strategy and solution technique.