• Energy Research

Green hydrogen can be produced by consuming surplus renewable generations. It can be injected into the natural gas networks, accelerating the decarbonization of energy systems. However, with the fluctuation of renewable energies, the gas composition in the gas network may change dramatically as the hydrogen injection fluctuates. The gas interchangeability may be adversely affected. To investigate the ability to defend the fluctuated hydrogen injection, this paper proposes a gas interchangeability resilience evaluation method for hydrogen-blended integrated electricity and gas systems (H-IEGS). First, gas interchangeability resilience is defined by proposing several novel metrics. Then, A two-stage gas interchangeability management scheme is proposed to accommodate the hydrogen injections. The steady-state optimal electricity and hydrogen-gas energy flow technique is performed first to obtain the desired operating state of the H-IEGS. Then, the dynamic gas composition tracking is implemented to calculate the real-time traveling of hydrogen contents in the gas network, and evaluate the time-varying gas interchangeability metrics. Moreover, to improve the computation efficiency, a self-adaptive linearization technique is proposed and embedded in the solution process of discretized partial derivative equations. Finally, an IEEE 24 bus reliability test system and Belgium natural gas system are used to validate the proposed method.

© 2025 Elsevier LtdThis paper focuses on the coordinated scheduling problem of integrated electricity–hydrogen systems (IEHS) considering the multiphysics dynamic characteristics of hybrid water and biomass electrolysis. First, a multiphysics-aware hydrogen production model for hybrid water and biomass electrolysis, suitable for the day-ahead or intra-day energy scheduling of IEHS, is presented. The dynamic multiphysics model for alkaline water electrolysis can take advantage of dynamic temperature and hydrogen-to-oxygen impurity crossover processes to optimize the loading range and energy conversion efficiency. The electrochemical model for proton exchange membrane biomass electrolysis can capture operating efficiency and temperature variations to improve the flexibility of hydrogen production. Then, the quasi-steady-state energy scheduling model for IEHS considering the multiphysics dynamics of hybrid water and biomass electrolysis is proposed. A tractable reformulation with multiple convex relaxation techniques, e.g., McCormick envelope, Big-M, outer linear approximation, and binary expansion methods, are utilized to address the highly nonlinear and nonconvex terms arising from the multiphysics-aware electrolysis model and the nonconvex flow quasi-steady-state characteristics of hydrogen network. Numerical results illustrate that the proposed multiphysics-aware electrolysis model can reduce the operating cost by up to 5.74% compared to the constant temperature and constant efficiency model. The solution time is also significantly reduced with a high solution accuracy compared to the original nonconvex and nonlinear model.

Abstract: Due to the autonomous characteristic and heterogeneity of the individual agents in active distribution network (ADN) with multi-microgrids (MMG), this paper proposes a fully decentralized adjustable robust operation framework achieving the coordinated operation between ADN and MMG. The improved linear decision rules (LDRs) based microgrid adjustable robust operation model is proposed to reduce the solution conservatism in dealing with renewable energy uncertainty. The LDRs model is then reformulated as a computationally tractable solution such that the proposed adjustable robust extension of decentralized operation can handle renewable energy uncertainty while reducing the computation burden of decentralized optimization. Then, a tailored fast alternating direction method of multipliers algorithm with a predictor–corrector type acceleration step is developed to improve the convergence rate of decentralized optimization. The effectiveness of the proposed model is validated on a modified IEEE 69-bus distribution system with four microgrids.

Electricity-driven thermostatically controlled loads (TCLs), e.g., air conditioners (ACs), have been widely utilized in demand response (DR) to provide operating reserve for power systems. However, the rebound effects may occur during the recovery process of DR, which can limit the operating reserve quality of ACs or even affect the reliable operation of power systems. With the community-level smart energy hubs (EH), the traditional electricity-driven TCLs can be expanded into multi-energy driven thermostatically controlled loads (MTCLs), e.g., household radiators. Under this circumstance, integrated demand response (IDR) can be exploited to coordinate the operation of MTCLs and provide more operating reserve resources while mitigating rebound effects. To this end, this paper proposes a two-stage IDR strategy to fully excavate the operating reserve provided by MTCLs. The first stage is to coordinate the energy consumption of ACs and household radiators to maximize the end-users’ thermal comfort and mitigate the rebound effects. To quantify the end-users’ thermal comfort, a modified predicted percentage of dissatisfied (PPD) index related to thermal environment parameters is introduced and simplified. Based on the energy consumption determined in the first stage, the energy conversion in EH is optimized in the second stage. Through the optimization in these two stages, a series of indices is established to evaluate the operating reserve in terms of aggregate capacity, duration, ramp rate, and smoothness. The case studies demonstrate that the proposed two-stage IDR strategy can provide high-aggregate-capacity and long-duration reserve resources in power systems while mitigating the rebound effects to maintain supply-demand balance and reliable operation of power systems. The analysis results of the test system show that the reserve capacity and duration obtained by the proposed model are 1.85 and 2.61 times those of the model without considering the multi-energy conversion, respectively.

AbstractWith the adoption of gas‐fired units (GFU), the interaction between the electricity and gas systems has been intensified. The failure of the gas sources may lead to the insufficiency of the gas supply to the GFUs, and further result in the electricity supply shortage, threatening reliabilities of electricity and gas systems. However, compared with the electric power flow, the dynamics of the gas flow are much slower. Most of the existing studies evaluated the reliabilities of integrated electricity and gas systems (IEGS) without considering the slower dynamics of gas flow, which are not fully accurate in the short‐term. This paper proposes a short‐term reliability evaluation technique for IEGS considering the gas flow dynamics. Firstly, the short‐term reliability models of gas sources, GFUs, and gas compressors are developed. Then, the multi‐stage contingency management scheme is proposed, where gas flow dynamics are analysed for determining the time‐varying load curtailments of electricity and gas. Moreover, a time‐sequential Monte Carlo simulation technique is developed with the finite‐difference scheme to tackle the gas flow dynamics during the short‐term reliability evaluation. Finally, the proposed reliability evaluation technique is validated using an integrated IEEE reliability test system and the practical Belgium gas transmission system.

Demand response (DR) is a framework that allows flexible load (FL) to self-schedule, including being curtailed or shifted to maintain system balance between energy supply and demand. With the integration of multi-energy system (MES) and development of information and communication technologies (ICTs), multi-energy infrastructures have expanded the ways FL participates in DR program. FL can shift to another energy carrier without noticeable delay. However, the chronological behavior and economic assessment for such DR methods have not been comprehensively discussed yet. This paper proposed a generalized self-scheduling model for demand side in MES. Firstly, the chronological response potentials for multi-energy FLs are explored. Moreover, the appliance-level economic loss of both load curtailment and shifting are calculated based on customer damage function. The optimization of self-scheduling is formulated as a mixed integer programing problem and solved by genetic algorithm. A test case based on energy hub is formed to illustrate the proposed modeling technique.