Active power oscillations (APOs) frequently arise in inverter-dominated power systems with multiple converters operating under virtual synchronous generator (VSG) control, posing risks to system stability and protection coordination. While various mitigation strategies have been proposed, many rely on prior knowledge of system parameters, offer limited damping performance, or involve complex models that lack physical interpretability—making them difficult to apply in practice. To address these challenges, this article first introduces a physically intuitive resistor-inductor-capacitor (RLC) equivalent circuit model to explain the root causes of APOs in both stand-alone (SA) and grid-connected (GC) modes. By mapping inertia, damping, and feeder impedance to capacitive, resistive, and inductive elements, respectively, the model reveals how mismatches among converters lead to interunit oscillations characterized by LC resonance. Building on this insight, we propose two mode-specific mitigation strategies: 1) in SA mode, a graph-theory-based impedance control ensures proportional reactive power sharing and effectively suppresses APOs; and 2) in GC mode, adaptive inertia and damping control with feedforward loop is designed to reshape transient power dynamics while preserving frequency stability. The proposed methods are validated through extensive simulations and real-time hardware-in-the-loop experiments, demonstrating their effectiveness in suppressing oscillations and enhancing the robustness of multiconverter power systems.

This paper introduces a novel control strategy for Modular Multilevel Resonant converters (MMR) in Solid-State Transformer (SST) applications, with a focus on medium-voltage conversion for hydrogen electrolyzers. The article first reviews voltage control methods in MMR, analyzing their operational principles and regulation capabilities. A continuous modulation index control method with double-step staircase waveform modulation is then proposed, simplifying the control scheme to a single control variable while maintaining robust controllability. Meanwhile, the proposed approach maintains comparable power loss and harmonic performance to existing methods under the investigated operating conditions. Simulations and experiments are conducted to verify the feasibility and practical implementation of the proposed approach.

This work presents a downscaled validation of a medium-voltage, medium-frequency transformer (MFT) concept designed for high-current operation on the secondary side using multiple parallel paths. The design is based on a modular winding approach, which simplifies the construction process and conductor placement on the bobbin. A systematic design and optimization procedure is developed, combining analytical calculations and finite-element simulations to explore the mass-efficiency tradeoff and to select a candidate design that meets specified leakage inductance and loss targets. The developed prototype serves as a proof of concept, demonstrating that the electrical, magnetic, and insulation requirements of the full-scale MFT can be effectively verified at reduced power levels. The fabricated prototype is tested under short-circuit and partial discharge conditions. The impedance measurements confirmed the expected resonance behavior, and the partial discharge test results verified sufficient insulation performance under high-voltage stress. The results provide experimental evidence for the scalability and feasibility of the proposed transformer design and offer guidelines for the use of 3D-printed supports, grain-oriented electrical steel cores, and windings in medium-voltage, MFT systems for hydrogen production applications.

This paper presents a practical approach to reduce the size of medium-frequency, medium-voltage dry-type transformers through the innovative use of semiconductive screening. The proposed method minimizes the required air gaps, a critical aspect of dry-type transformer design, particularly for medium-frequency applications. Analytical approaches and Finite Element Method (FEM) simulations in COMSOL are used to demonstrate how to achieve a uniform electric field distribution within the transformers. Experimental investigations by means of partial discharge measurement on a prototype epoxy-based stress cone termination with a semiconductive shield are conducted. The results demonstrate the potential for this method to enhance transformer performance and provide a foundation for further advancements in medium-frequency transformer design.

This study presents a current balancing technique for high-current windings in medium-frequency transformers (MFTs), particularly relevant to solid-state transformer (SST) applications. Handling high currents on the low-voltage high-current winding of MFTs is challenging due to skin and proximity effects. Conventional techniques, such as continuously transposed conductors (CTCs) and parallel winding paths, are applicable but have limitations in medium-and high-frequency applications such as SSTs due to skin and proximity effects. To address these issues, a modular and tunable compensation method is proposed, based on adding small, series-connected inductive elements (compensation toroids) to each parallel winding path. Experimental results from a prototype validate the proposed compensation technique, highlighting its effectiveness in mitigating unbalanced current distribution. Finite element analysis (FEA) and experimental validation across a wide frequency range (1–10 kHz) confirm the effectiveness of the method. The results demonstrate a significant reduction in current imbalance with minimal added losses or system impact.

Active power oscillation (APO) issues may arise during the deployment of multiple parallel converters with Virtual Synchronous Generator (VSG) control in Microgrids. To that end, the equivalent circuit models of a converter with VSG control are proposed, which intuitively reveals the root cause of APOs. Accordingly, a graph-theory-based virtual impedance is introduced to harmonize parameters among involved VSGs, effectively eliminating APOs. Simulation and experimental results verify the improvements of the proposed control.

Partially rated DC interlinking converters are recognized for their high-gain power regulation capabilities, which effectively synergize active power across DC microgrids (DCMGs). Integrating energy storage units (ESUs) to address the intermittent nature of renewables in DCMGs has become an enhanced requirement for these converters. This article proposes a partially rated multiport interlinking converter (PMIC) that incorporates a distributed ESU. The PMIC controls a floating voltage and a bidirectional shunt current on the DC line, ensuring full galvanic isolation for the ESU while operating at a low DC-link voltage. It regulates multidirectional power flow and balances power during peak and off-peak periods. A decentralized droop-based power flow control strategy is proposed for the PMIC, which distributes renewable energy generation, load consumption, and ESU utilization proportionally across the system. The control strategy includes two tailored continuously differentiable activation functions, Sigmoid and Hyperbolic Tangent, to facilitate autonomous global power-sharing and seamless ESU engagement. Simulation and experimental case studies confirm the PMIC's capability to smooth renewable energy fluctuations and enhance the power and voltage profiles of DCMGs.

Highly self-sufficient energy hubs offer a promising solution to mitigate grid congestion in favor of grid operators and to reduce grid fees for the benefit of energy hub operators. Meanwhile, the energy hub's capacity may far exceed the grid connection capacity, creating a weak grid situation. As a result, power quality issues such as voltage fluctuations, frequency deviations, and even instability may occur. In this work, a grid-forming energy storage system (GFM-ESS) is integrated to address these potential problems. A model of the GFM-ESS and energy hub is established based on a 50 kW PV-Hydrogen energy hub demonstrator, where PV-generated power is utilized for green hydrogen production. A trade-off design is proposed to identify the optimal balance between the capacity of the GFM-ESS and the grid connection. The voltage and frequency response at the hub's bus are analyzed to evaluate this trade-off. While experiments with the 50 kW demonstrator are ongoing, simulation results are provided to validate the effectiveness of the proposed design.

Grid-following control (GFL) has been widely implemented as the dominant control method for inverter-based resources (IBR). However, because GFL cannot provide sufficient inertia for frequency regulation, grid-forming control (GFM) is proposed as an alternative solution. However, the impact of grid dynamics characteristics on GFL and GFM control is rarely discussed. Therefore, this paper systematically derives and compares the frequency response of GFL and GFM control, considering the grid dynamics that are emulated by a synchronous generator. The impact of the inertia of GFM is investigated by pole-zero maps.

Distributed secondary control achieves voltage restoration and power sharing through communication among adjacent units but exposes the microgrid to potential cyber-attacks. Traditional mitigation strategies modify the secondary controller after the attack, addressing the issue only postoccurrence. Furthermore, in microgrid planning, the structure of the communication network significantly influences the resilience to attacks, but it remains to be explored. This article presents a proactive defense mechanism by designing a resilient communication network. The proposed method quantifies the impact of attacks and develops a multiobjective optimization algorithm to design the network, considering quantified attacks, convergence, time-delay robustness, and communication costs. The method is validated through OPAL-RT simulations of an islanded microgrid with ten converters.

In line with climate goals and ambitions, the maritime industry is undergoing a technological transformation. Hybrid vessels offer the opportunity to optimize the use of the available distributed (renewable) energy resources on the grid. Hybrid Vessel Energy Management Systems need to make decisions with objectives to reduce fuel consumption, greenhouse gas emissions and maintenance expenditures during operation of the vessel. The verification of the Energy Management System is conducted by creating a Digital Twin of the hybrid vessel on the Typhoon Hardware-In-Loop platform. The model results are achieved in real time, thereby setting the foundation for Controller HIL (C-HIL) projects.

Accurate battery capacity estimation is essential for the effective and reliable operation of lithium-ion battery management systems. Battery impedance is a key parameter that encapsulates electrochemical information, closely correlating with the internal states of batteries. This study proposes a novel capacity estimation framework that effectively balances accuracy, efficiency, and practicality. Firstly, a novel feature extraction method is introduced to extract health features from the imaginary impedance at a single frequency. The extracted feature demonstrates a strong and stable correlation with battery degradation under various operation conditions, while significantly reducing data requirements. To address the impact of diverse degradation patterns on estimation accuracy, an initial adjustment method is applied to precisely retrace the relative degradation paths of batteries. The results show that the mean absolute percentage error of battery capacity estimation decreases from 15.65% to 2.87%. Additionally, a transformer-based capacity estimation model is developed, which integrates a feature fusion unit to explicitly eliminate the influence of temperature on model performance. As a result, the model's accuracy improves by over 28% under various thermal conditions.

Consensus algorithm-based secondary control, such as virtual impedance, is typically used to achieve power-sharing and ensure stable operation in microgrids. The introduction of communication, however, increases the system's vulnerability. Communication failure, e.g., under cyber attack or disruption, can lead to a huge loss. To solve this, this article proposes a signal reconstruction approach for the miscommunicated signals. The power-sharing convergence, both in normal conditions and under communication failure, is analyzed via the Lyapunov method. The feasibility and effectiveness of the proposed approach are validated on a lab-scale microgrid.

The global race toward decarbonization has reached a transformative inflection point as electrification surges across the transportation sector in most of the world. No longer confined to passenger vehicles, electric mobility now spans trucks, ships, and even aircraft, driven by a confluence of environmental mandates, policy momentum, and technological innovation. Nowhere is this shift more pronounced than in Europe, where the Trans-European Transport Network (TEN-T) envisions a seamlessly connected, zero-emissions infrastructure backbone by the midpoint of this century (Figure 1). At the heart of this revolution lies a new breed of ultrafast-charging technologies, electrified highways, and maritime ports—each pushing the limits of energy delivery, grid integration, and power electronics. Yet, as the charging power scales from kilowatts to multimegawatts, and as electric mobility moves from concept to logistics-critical reality, the challenges to the power grid—especially at the distribution level—are becoming clearly visible. This article explores the emerging architectures and innovations required to enable this new era of electric transport, from the Megawatt Charging System (MCS) to medium-voltage (MV) grid integration with solid-state transformers (SSTs) and grid-forming (GFM) battery energy storage systems (BESSs) as key components.

A fast and efficient charging infrastructure has become indispensable in the evolving energy landscape and thriving electric vehicle (EV) market. Irrespective of the charging stations’ internal alternating current (AC) or direct current (DC) bus configurations, the main concern is the exponential growth in charging demands, resulting in network congestion issues. In the context of exponential EV growth and the provision of charging facilities from low-voltage distribution networks, the distribution network may require frequent upgrades to meet the rising charging demands. To avoid network congestion problems and minimize operational expenses (OE) by integrating energy storage systems (ESS) into ultra-fast charging stations (UFCS). This paper presents a techno-economic analysis of a UFCS equipped with a battery ESS (BESS). To reduce reliance on the electric grid and minimize OE, a dual-objective optimization problem is formulated and solved via grid search and dual-simplex algorithms. Analytical energy and physical BESS models are employed to evaluate the optimization matrices. The intricacies of BESS aging are examined to ensure an optimal BESS size with a more extensive lifespan than the corresponding payback period. The integrated BESS significantly reduced reliance on the grid to tackle network congestion while fulfilling charging demands. The dynamic pricing (DP) structure has proven more favorable, as the average per unit cost remains lower than the static tariff (ST). Results illustrate that integrating BESS reduces the OE and peak-to-average ratio (PAR) by 5-to-49% and 16-to-73%, respectively. Moreover, the combination of 70% BESS and 30% grid capacities outperforms the other configurations with a 73% reduction in PAR and a 49% reduction in OE before BESS reaches the end-of-life.

The development of lithium-ion batteries has experienced massive progress in recent years. Battery aging models are employed in advanced battery management systems (BMSs) to optimize the use of the battery and prolong its lifetime. However, Li-ion battery cells often experience fluctuations in battery capacity and performance during cycling, which makes capacity prediction more difficult. Moreover, the reason for the capacity regeneration phenomenon occurring after resting periods is not clear yet, as well as the influence of cycling conditions on capacity regeneration. A relationship between this phenomenon to cycling state of charge (SoC) ranges and current rates was investigated in this article on a battery cell with lithium nickel manganese cobalt (NMC) oxide positive electrode. Experimental results show that the capacity increase is a consequence of decreased internal impedance after the resting period. The experiments also showed that a significant power drop and subsequent power regeneration after a resting period occurs only for specific SoC ranges, and applying a resting period after battery cycling can mitigate this power fading process. The semi-empirical model of battery degradation including capacity regeneration is proposed in this article based on physical processes inside of the cell retaining low computational requirements. The acquired results can be utilized in BMSs for more accurate state of health (SoH) estimation and to prolong battery lifetime.

Accurately evaluating battery degradation is not only crucial for ensuring the safe and reliable operation of electric vehicles (EVs) but also fundamental for their intelligent management and maximum utilization. However, the non-linearity, non-measurability, and multi-stress coupled operating conditions have posed significant challenges for battery health prediction. This paper proposes a battery capacity estimation framework based on real-world operating data. Firstly, a comprehensive feature pool is constructed from the direct external features extracted during multi-step fast charging processes and the quantitative representation of operating conditions. Subsequently, a two-step feature engineering is introduced to select the most relevant features and eliminate the interference components. The battery capacity estimation framework is then implemented using machine learning methods. Validation results demonstrate that the proposed framework achieves superior estimation accuracy with lower computational expense compared to the modelling process without feature engineering. The MAPE and RMSE reach 1.18% and 1.98 Ah, respectively, representing reductions in errors of up to 8.53% and 11.21%. Collectively, the proposed framework paves the foundation for online health prognostics of batteries under practical operating conditions.

This article proposes an analytic approach to design the typical power factor correction (PFC) control of an electric vehicle (EV) charger to ensure small signal stability in weak grid conditions. Compared to the previous works, the proposed method considers the dynamics of all the control loops, i.e., phase-locked loop (PLL), voltage loop (VL), and current loop (CL). The impacts of key influential parameters on stability are analyzed. Furthermore, the upper limits of the PLL and VL bandwidth to ensure small signal stability are derived. Accordingly, the influences of the CL bandwidth, short circuit ratio (SCR), and the filter inductance on the upper limit of the PLL bandwidth and the VL bandwidth are quantified. Consequently, a design procedure that eliminates the need to model the input impedance for tuning the controller to prevent small signal instability is proposed. Simulations and experiments validate the analysis.

This paper presents a control strategy for active power decoupling in a Solid State Transformer (SST) using a cascaded H-bridge (CHB) converter, dual active bridge (DAB) converters, and a high-frequency link (HFL). The strategy balances capacitor voltage and decouples power flow, significantly reducing second harmonic voltage ripple in DC link capacitors and potentially reducing their size.

The communication network used in distributed sec-ondary control (DSC) for microgrid power and voltage regulation is vulnerable to cyber-attacks. Unlike the predominantly resilient research on secondary control, which tends to employ passive defense strategies, this paper presents a proactive defense mecha-nism to design a resilient network for microgrid secure operation. This proposed method involves preparing the resilient scheme before attacks occur and facilitates timely resilience during an attack. First, novel metrics are introduced to effectively quantify the impact of various cyber attacks. Then, a multiobjective optimization method is applied to design the communication graph considering the quantified attacks, convergence, time-delay robustness, and communication cost. Simulations are performed on a microgrid consisting of 10 inverter units under different scenarios to validate the effectiveness of the proposed methodology.