To address the critical voltage stability of industrial DC microgrids serving sensitive loads, virtual capacitor control is a promising technique for inertia enhancement. However, conventional virtual capacitor control, with its fixed parameters and limited disturbance rejection capability, struggles to maintain qualified voltage quality, threatening the reliable operation of industrial equipment. This paper proposes a novel adaptive virtual capacitor control strategy based on linear active disturbance rejection control (LADRC). The key contribution is a novel control architecture where the virtual capacitor is not predetermined but is adaptively modulated by real-time disturbance estimated by LADRC. This unique feedback mechanism allows the system to proactively counteract both external load changes and internal parameter uncertainties, achieving superior voltage regulation. Furthermore, an integrated sliding time window filter ensures smooth control action by mitigating oscillations from voltage ripple. The proposed strategy's effectiveness in simultaneously enhancing voltage deviation suppression, ripple mitigation, and dynamic inertia support is validated through simulation and hardware-in the-loop (HIL) experiments.

Grid-integrated electrolyzer systems are increasingly deployed for green hydrogen production, which is a promising pathway for energy decarbonization. However, their operation is challenged by insufficiently understood dynamic interactions among the grid-side rectifier, the buck converter, their control loops, and the electrolyzer stack. To address this issue, this paper develops a holistic analytical framework for such systems. A unified model is derived by integrating the rectifier, the buck converter, their control loops, and the electrolyzer stack. Based on this model, eigenvalue, participation-factor, and frequency-response analyses are conducted to systematically quantify stability characteristics, internal dynamic couplings, and parameter sensitivities. For a 2 MW electrolyzer case, the results reveal that excessive rectifier or buck-control bandwidths can independently trigger distinct oscillatory instabilities. On this basis, engineering-oriented controller-tuning guidelines are established, recommending about 10–50 Hz for the phase-locked loop, below about 60 Hz for the DC-link voltage controller, and about 20–150 Hz for the buck power controller. The analysis further shows that properly designed buck bandwidth renders the system-level power response weakly sensitive to slow electrolyzer dynamics dominated by double-layer capacitance, thereby mitigating uncertainty in this capacitance and clarifying the applicability of reduced-order electrolyzer models. These findings are corroborated by PSCAD/EMTDC time-domain simulations, verifying the effectiveness of the proposed analytical model. Additional verifications under frequency and voltage disturbances further confirm the model’s predictive capability, with maximum relative errors of 0.264%–1.486% and 0.153%–5.922%, respectively. Overall, this work offers an efficient analytical tool for stability-oriented control design, model-fidelity selection, and dynamic interaction analysis of grid-integrated electrolyzer systems.

Off-grid power delivery from photovoltaic (PV) systems to electrolyzers serves as a key pathway toward sustainable green hydrogen production, with the PV output voltage adapted to the electrolyzer operating voltage by dc/dc converters. However, a systematic understanding of the performance trade-offs between different converter architectures and their associated control strategies is still lacking, particularly for ensuring robust operation under intermittent solar conditions. This paper presents a systematic comparative study of single- and dual-stage dc/dc converter architectures for standalone PV-electrolyzer (PVEC) systems. The study investigates the fundamental control trade-offs, comparing the single-stage's rigid electrolyzer-following operation with the dual-stage's superior flexibility in providing direct electrolyzer current regulation. To enhance operational resilience, two distinct low power ride-through (LPRT) strategies are proposed and analyzed for the dual-stage configuration, ensuring stable power delivery during significant solar power reductions. The feasibility and performance of the proposed architectures and control strategies are validated through both 5 kW system simulations and experiments on a 200 W GaN-based hardware prototype. The results demonstrate that while the single-stage architecture is viable for small-scale systems, the dual-stage configuration's enhanced control flexibility and scalability are essential for large-scale, storage-ready PVEC applications.

Integrating gigawatt-scale offshore wind-hydrogen energy systems (OWHESs) is pivotal for the energy transition, yet their dynamic interactions and grid-support capabilities remain insufficiently explored. This paper addresses this gap by developing a real-time electromagnetic transient model of a 2 GW OWHES, which is implemented on a commercial real-time digital simulator (RTDS). Furthermore, a novel communication-free coordinated frequency control strategy is proposed, which synergistically harnesses the flexibility of the HVDC system, wind power plants, and electrolyzer plants. Real-time simulation results demonstrate the model's ability to capture the OWHES dynamics. Moreover, results from a significant generation loss scenario demonstrate the proposed control's superiority over existing methods, as it markedly improves the onshore frequency nadir and reduces the rate of change of frequency. This confirms its effectiveness in enhancing onshore frequency stability and showcases the potential of OWHESs as a valuable source of grid ancillary services.

Power-to-hydrogen systems, particularly the most mature alkaline electrolyzers (AELs), are increasingly deployed in modern energy systems due to their pivotal role in green hydrogen production and decarbonization. Proper modeling is vital for optimizing AEL lifecycle decisions, including design, operation, and investment. Despite numerous proposed models, a review focusing on their applications in system-level decision-making (e.g., operation and planning) remains lacking. This paper bridges this gap by reviewing over 100 peer-reviewed articles to offer an in-depth overview of AEL models employed in system-level decision-making. Followed by clarifying modeling requirements across different levels of AEL system analysis, three types of AEL models are classified in system-level decision-making: linear electricity–hydrogen (LEHM), nonlinear electricity–hydrogen (NEHM), and integrated electricity-heat-hydrogen models (IEHHM). This classification is based on representing the AEL with different levels of multi-physics detail and energy conversion assumptions. LEHM assumes a constant electricity-to-hydrogen conversion efficiency of typically about 60%–70%, while NEHM and IEHHM allow modeling of dynamic efficiency variations in the typical range of 60%–80%, where the IEHHM uniquely integrates thermal dynamics. Their modeling principles, characteristics, strengths, and limitations are systematically reviewed, followed by an in-depth overview of their applications and impacts across four applications: economic operation, grid services, heat recovery, and capacity planning. It reveals that LEHM, NEHM, and IEHHM are employed in 35%, 42%, and 23% of these applications, respectively. Finally, a discussion of current modeling limitations and future direction is provided. This paper offers valuable insights and guidance for selecting appropriate AEL models in decision-making studies and identifying pathways for advancing AEL modeling.

Virtual power plant (VPP) serves as an effective solution for maintaining internal power balance and participating in external peak shaving auxiliary services within grid-connected microgrid involved in multi-type flexible resources (FRs). However, with increasing prominence of the feature heterogeneity in response behaviors of diverse FRs and their coupling in peak shaving poses challenges in the accurate decomposition of VPP scheduling commands. This paper proposes a de-aggregation strategy, utilizing discrete choice model and feature matching methods, to dynamically sequence FRs responses while optimizing VPP's peak shaving capability. Initially, heterogeneous features are refined and modeled to characterize the response capability of multi-type FRs in meeting the scheduled demand of grid-connected microgrid (SDGM). Subsequently, a feature difference quantification model and matching priority criterion are formulated to describe the feature mapping relationship and guide dynamic decision-making process. On this basis, the multi-type FRs are co-scheduled in the considered VPP to form a dynamic response sequence achieving peak shaving objectives. Case studies based on real data from a region-connected microgrid demonstrate the proposed strategy's performance in improving return on investment by 6.1 %, reducing peak shaving deviation and power exchange with main grid by 70 % and 13.1 %, respectively, and effectively improve the ability of grid-connected microgrid to balance the power and participate in peaking auxiliary services.

Energy routers present a viable option for harvesting renewable energy sources (RESs) and ensure dependable electricity provision in industrial microgrids. This paper presents a multi-functional, grid-forming energy router (GFMER), accompanied by a hierarchical proactive control approach. The lower-layer controller handles the coordination strategies among photovoltaic (PV) systems, battery energy storage units (BESU), and DC/AC converters. In this layer, an optimized multi-objective droop control mechanism is presented to proactively regulate AC grid-side voltage imbalances and deviations. Meanwhile, the upper-layer control is deployed to maintain the DC bus voltage, and a novel power allocation module has been designed to enhance the dynamic transient support for grids. The effectiveness and practical value of this proposed methodology have been validated through MATLAB/Simulink and hardware-in-the-loop (HIL) experiments.