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.

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.

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.

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.

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.

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.

In this chapter, various electric vehicle (EV) charging technologies are reviewed, including onboard charging, offboard charging, and contactless charging. The focus is on charging power control as well as its converter topology. Because EV charging significantly influences the grid, the power quality of EV charging is thoroughly reviewed in terms of modeling, analysis, and mitigation measures. EV charging, especially overnight, gives a lot of flexibility to instant charging power, which can be used to improve the load flow in the electric grid. Smart charging describes those approaches, which are also reviewed.

In recent years, significant research and adoption of periodic variable-switching frequency pulsewidth modulation (PWM) (P-VSFPWM) have been observed in ac/dc voltage source converters (VSCs). This technique aims to reduce ripple in ac inductor current and dc capacitor voltage, suppress injected current harmonic amplitudes for electromagnetic compatibility (EMC) compliance, and minimize switching losses. However, the presence of overlapped harmonic spectra from different switching harmonic bands can lead to heightened harmonic magnitudes due to the wide-frequency variation, necessitating additional filtering measures. Surprisingly, there is notable absence of harmonic spectra analysis under P-VSFPWM, despite the growing concern over supraharmonics (2-150 kHz) emission injected to the grid from the electric-vehicle (EV) chargers. To address this gap, this article proposes the interleaved P-VSFPWM to mitigate harmonics overlap without deviating from the intended purpose of P-VSFPWM. A fast-acquisition supraharmonics model under arbitrary P-VSFPWM has been proposed based on vectorization, facilitating the subsequent filter design process. Furthermore, this study identifies the optimal P-VSFPWM profile with minimal required filtering inductance based on the spectra analysis. These findings are verified by PLECS simulations and experimental results.

Insulated Gate Bipolar Transistor (IGBT) is the core component of current converter equipment in application scenarios such as flexible DC transmission and flexible AC transmission, and its junction temperature increases significantly with the increase of current level. Therefore, the junction temperature control of IGBT devices has become one of the bottlenecks to further increase the transmission capacity. Al/Diamond material has a much higher thermal conductivity than traditional packaging materials and a smaller coefficient of thermal expansion, so applying Al/Diamond material to IGBT packaging may improve the heat dissipation ability of IGBT and thus reduce the junction temperature. This paper constructs the IGBT model based on Al/Diamond material and the IGBT model based on traditional copper material through COMSOL simulation software, and compares the internal electro-thermal-stress distribution of the two models. In addition, this paper calculates the maximum and average values of temperature, maximum and average values of thermal stress and other parameters of each component inside the model according to the simulation data set. After comparison, it can comprehensively reflect the greater enhancement of heat dissipation capability of IGBT after applying Al/Diamond material to its encapsulation, which provides guidance for the subsequent research on the encapsulation optimization mechanism of high-power IGBT devices based on Al/Diamond material and the actual device fabrication.

This paper concerns the control problem of the active caused by the mismatched impedance in resistive feeders-dominated microgrids. A distributed model predictive control (DMPC) scheme is suggested to regulate the virtual impedance of each involved unit. Moreover, the proposed method benefits resilience to communication failure by designing the communication matrix. Furthermore, it involves propagating information among units in a short period, significantly reducing the communication and computation burden. Finally, the performance of the proposed control scheme is evaluated in terms of its convergence, robustness to communication delay and load variations, resilience to communication failure, and plug-and-play functionality without communication in an inverter-connected system.

Accurately predicting the battery's aging trajectory is required to ensure the safe and reliable operation of electric vehicles (EVs) and is also the fundamental technique toward residual value assessment. As a critical enabler for mainstreaming EVs, fast charging has presented formidable challenges to health prognosis technology. This study systematically compares the performance of features extracted from the multistep charging process in the state of health (SOH) assessment. First, 12 direct features are extracted from the voltage curve, and the degradation mechanisms strongly correlated to these features are analyzed in detail. Integrating the degradation mechanism and correlation analysis, a data feature construction strategy is designed to categorize extracted features into groups. Then, the performance of different features extracted from the fast charging process in the SOH assessment is compared regarding estimation accuracy. Finally, the generalization and feasibility of the optimal data feature are verified with different fast charging protocols and training data sizes. The verification results indicate that the data feature representing fused degradation modes has excellent generalization and feasibility in SOH estimation, and the mean absolute error (MAE) and root-mean-squared error (RMSE) for various cells under different decline patterns are within 0.90% and 1.10% , respectively.

Balancing converters play a pivotal role in bipolar dc grids, and while numerous topologies have been explored, many suffer from drawbacks, such as reliance on bulky passive components, limited soft-switching capabilities, or complex control mechanisms. This article introduces an LC series-resonant balancing converter topology that addresses these issues, featuring smaller passive components, the ability to achieve soft switching across its entire operational range, and simplified control with proper design. By operating in the capacitive region, the converter ensures soft switching for the complete load range, enabling all switches to achieve zero voltage switching (ZVS) turn on. The study reveals that the converter's power flow depends on both the switching frequency (fsw) and the phase shift angle (φ), but notably, when the resonant inductor value is kept sufficiently low, fsw and φ become decoupled, simplifying power flow control. In addition, this article provides detailed design guidelines for the converter's resonant tank and validates the operation and control of the converter through an experimental setup.

This paper concerns the control problem of the active and harmonic power sharing caused by the mismatched impedance in resistive feeders-dominated microgrids. A distributed model predictive control (DMPC) scheme is suggested to regulate the virtual impedance of each involved unit for power sharing based on the neighbor's state. With the distributed philosophy, the central controller is not required. Moreover, the proposed method benefits resilience to communication failure by designing the communication matrix. Furthermore, it involves propagating information among units in a short period, significantly reducing the communication and computation burden. Finally, the performance of the proposed control scheme is evaluated in terms of its convergence, robustness to communication delay and load variations, resilience to communication failure, and plug-and-play functionality without communication in an inverter-connected system.

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 discusses the various operating modes of a series resonant balancing converter for bipolar dc grids. It is shown that the converter can be operated in both the capacitive and inductive regions with respect to the resonant frequency of the LC tank. Furthermore, concerning the pulse width modulation signals to the switches, the converter can either be operated by controlling the phase shift between the converter half bridge legs or the duty cycle of the half bridges. A qualitative comparison of the different modes proves that a) the phase shift modes have better soft switching capabilities, b) the capacitive phase shift mode can show zero voltage switching at switch turn-on in the whole operating range, c) the losses in case of capacitive phase shift mode shows best performance at low load power, d) the inductive region power modes show lower rms current for the same power flow compared with capacitive region modes which lead to lower losses at higher output power. The simulation and experimental results depict the operation of all the modes. Finally, a prototype is designed to validate all operating modes, demonstrating $>$99% system efficiency at 1.75 kW.

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.