Power converters may lose synchronization with the remaining network when integrated in a weak power grid. Such an instability phenomenon [known as grid-synchronization instability (GSI)] features the frequency divergence of phase-locked loop (PLL) and oscillations of the converter's power output. In this paper, we focus on the influences of reactive power control (RPC) methods on GSI. We develop a single-input-single-output model to explicitly reveal how the PLL interacts with the other parts of the converter system in terms of grid synchronization. Then, after deriving the open-loop transfer function and sensitivity function of the entire converter system, we compare the stability margins for different RPC methods. Furthermore, we elaborate on the interactions among RPC, PLL, and voltage feedforward (VFF), and then demonstrate that different design methods of RPC and VFF will lead to different stability margins. The subsequent stability analysis provides insightful guidelines for coordinating the design of multiple control loops, i.e., RPC, VFF and PLL. In particular, we demonstrate how the loop shaping of PLL takes effect in increasing the stability margin and eventually preventing the converter from GSI. The validity of the stability analysis is verified through simulations in MATLAB/Simulink.

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.

In the context of the current sharing economy, the application of shared energy storage (SES) among local integrated energy systems (LIESs) is underexplored. There is an urgent need for developing appropriate modeling and solution methods so as to facilitate the application of SES among LIESs. To this end, this paper proposes a cooperative-game-based day-ahead scheduling model of LIESs with SES. The proposed model can be transformed into a sequential two-step optimization problem for alleviating the computational complexity. More specifically, the subproblem of cooperative alliance profit maximization is established in the first step to obtain the optimal scheduling strategies of each LIES, while the subproblem of SES bargaining is constructed in the second step to obtain the optimal pricing information. Besides, a distributed solution algorithm based on the alternating direction method of multipliers with a warm start and dual update accelerated iteration strategies (ADMM-W-D) is proposed to effectively preserve the privacy of each LIES and improve the convergence performance of traditional ADMM. Case studies show that the proposed model can achieve lower operating costs and higher renewable energy consumption levels of LIESs after considering SES. It is also demonstrated that the proposed algorithm based on ADMM-W-D has stronger convergence and better scalability than traditional algorithms.

Combined harnessing of electrical and thermal energies could leverage their complementary nature, inspiring the integration of power grids and centralized heating systems in future smart cities. This paper considers interconnected power distribution network (PDN) and district heating network (DHN) infrastructures through combined heat and power units and heat pumps. In the envisioned market framework, the DHN operator solves an optimal thermal flow problem given the nodal electricity prices and determines the best heat production strategy. Variate coefficients of performance of heat pumps with respect to different load levels are considered and modeled in a disciplined convex optimization format. A two-step hydraulic-thermal decomposition method is suggested to approximately solve the optimal thermal flow problem via a second-order cone program. Simultaneously, the PDN operator clears the distribution power market via an optimal power flow problem given the demands from the DHN. Electricity prices are revealed by dual variables at the optimal solution. The whole problem boils down to a Nash-type game between the two systems. A best-response decentralized algorithm is proposed to identify the optimal operation schedule of the coupled infrastructure, which interprets a market equilibrium as neither system has an incentive to alter their strategies. Numeric results demonstrate the potential benefits of the proposed framework in terms of reducing wind curtailment and system operation cost.