Modular multilevel converters (MMCs) are widely used in various applications due to their scalability, efficiency, and fault-tolerant capabilities. This article proposes a fault-tolerant methodology tailored for full-bridge (FB) submodules (SMs) in MMCs to enhance system reliability under open-circuit faults (OCFs) in insulated-gate bipolar transistors (IGBTs). The method adopts a hybrid approach, using control logic adjustments to reconfigure faulty SMs into half-bridge (HB) configuration for T2/T3 faults while employing redundant SMs for T1/T4 faults. Accurate fault detection and localization are achieved through established methods, such as state observers and voltage comparisons. It is shown using MCS that the proposed method can improve the 17 kV 10 MVA converter reliability by almost 25% over solely redundancy-based solution for given lifetime requirements. Finally, using a lab-scale FB MMC prototype, it is experimentally shown that the proposed reconfiguration technique can successfully localize the fault and revert to normal operating requirements by shifting from FB to HB SM configuration in approximately 20 ms of fault initiation.

This article presents an extended hybrid modulation (EHM) technique to achieve multistage constant-current (MSCC) charging of electric vehicles using wireless power transfer (WPT) technology. Although most research focuses on constant-current constant-voltage charging, MSCC charging offers key advantages, such as lower temperature rise, decreased charging time, and prolonged battery lifespan. However, the existing phase-shift-modulation (PSM) method encounters substantial circulating reactive power and significant efficiency drops in MSCC charging. To overcome this, an EHM strategy is proposed to expand the modulation range of PSM. By applying EHM to both the inverter and active rectifier, the proposed method provides up to 16 operating modes to facilitate multiple CC outputs. Furthermore, an optimal mode trajectory, specifically designed for the MSCC charging, is developed. By implementing this trajectory across different charging stages, zero-voltage-switching is achieved for all power switches, and the overall power loss of the system is minimized. Finally, a WPT prototype was developed to validate the proposed approach. Experimental results demonstrate that the proposed approach effectively enables the MSCC charging while notably enhancing transmission efficiency, achieving dc-to-dc efficiencies between 92.45% and 95.67% across a power range of 231 to 3.015 kW.

The increasing number of electric vehicles (EVs) means both a challenge and an opportunity for the electric grid. Different charging algorithms have been proposed in the literature to tackle these specific challenges and make use of the potential services that EVs can provide. However, to properly investigate the conflicting objectives, a multi-objective approach is paramount. These algorithms provide a family of solutions instead of just one, so the decision-maker can see the connection and trade-offs between the objectives. This paper proposes a highly customisable multi-objective framework based on an expanded version of the augmented ε-constraint 2 method. Together with a mixed integer linear programming (MILP) formulation, it is used to solve a charging station scheduling problem. An energy management system (EMS) executes the calculated schedules to show the effect on the individual EVs. Numerical simulations based on market and EV data from the Netherlands demonstrate the adaptability and effectiveness of the proposed algorithm.

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.

Electric aircraft represent a promising low-emission alternative to fuel-powered aviation. As the energy source, the battery pack must guarantee key performance metrics such as energy density, power density, lifetime, and safety. Among these, energy density is particularly critical as it directly impacts the range and payload capacity. Additionally, the battery thermal management system (BTMS) of the battery pack is essential to maintain safety, efficiency, and lifetime. Hence, to design a battery pack with improved energy density and optimized thermal and aging performance, a complete electro-thermal-aging (ETA) model at both cell and pack levels is developed to predict pack behavior under operational conditions. Simulations demonstrate that the proposed model achieves an accurate thermal prediction accuracy within 0.87°C during an example all-electric aircraft (AEA) mission profile. Optimization based on the proposed model is conducted, focusing on geometric configurations of the battery pack and coolant flow parameters, including channel wall thickness (L_Al), inlet width (W_cl), cell spacing (D_cell), package wall thickness (L_enc), inlet flow temperature (T_cl,in), and flow velocity (U_cl,in). An optimized liquid-cooled battery module using Samsung 18650-35E cells is designed with [L_Al, W_cl, D_cell, L_enc] = [0.4, 1.6, 20, 0.5]mm, T_cl,in =35°C, and U_cl,in = 0.05ms^-1 during cruise and 0.02ms^-1 during takeoff, climb, and descent. This configuration achieves a maximum temperature of 41.76°C and a maximum cell-to-cell temperature difference of 3.11°C, improving thermal uniformity. The lifetime performance also demonstrates a 5.51% improvement in state-of-health (SOH) after 180 cycles. Based on the module-to-pack structure analysis, the battery pack exhibits energy densities of 227.01Wh kg^-1 gravimetrically and 353.67Wh L^-1 volumetrically. This study facilitates the guideline for compact and lightweight liquid-cooled battery pack design with improved thermal and aging performance for AEA applications.

Targeting a climate-neutral maritime sector drives the adoption of the all-electric ship (AES). While AESs can utilize both ac and dc shipboard power systems (SPS), a dc system offers advantages in efficiency, power density, and source synchronization. However, the enhanced network complexity of dc grids combined with the high penetration of power electronic devices and harsh environmental conditions can compromise the system's reliability. Therefore, this article provides an overview of the reliability aspect of dc-SPSs, addressing the power system design, adequacy assessment, and reliability improvement. First, the performance tradeoffs associated with the SPS design are examined, revealing how changes in the power system topology and dc bus structure impact the vessel's reliability along with other performance parameters. Second, a hierarchical reliability model framework is proposed for the adequacy assessment of dc-SPSs, considering the reliability from the component level up to the system level. To determine the system-level reliability, multiple probabilistic methods, including simulation and analytical models, are compared using a propulsion subsystem example. Finally, an overview of the reliability improvement strategies is provided, addressing methods at the system, device, and component level. These three topics combined aim to provide guidance in the design of future reliable dc-SPSs.

Owing to the intermittent characteristics of renewable energy sources (RESs) and the unpredictability of load demand, integrating multiple RESs and energy storage systems (ESSs) has become imperative. Modular Multiport Converters (MMPC) have emerged as a viable solution to meet this need, offering superior performance, efficiency, and reliability compared to multiple SISO DC/DC converters. To this end, this paper presents a comprehensive model of an MMPC, which is bidirectional and capable of operating in both step-up and step-down modes. Following the derivation of the converter model, a robust μ-controller using the D − G − K iterative procedure is designed. This controller addresses the cross-coupling challenges inherent in MIMO systems and effectively overcomes the parametric uncertainties associated with the converter. Finally, hardware-in-the-loop (HIL) test results derived from OPAL-RT 4610, and experimental results from a prototype are used to validate this control approach.

This article implements a real-time digital twin (RTDT) of a 10 kW Dual Active Bridge (DAB)-electrolyzer system. The electrical model of a 10 kW alkaline electrolyzer is presented to understand its I–V characteristics. A sensitivity analysis is performed to assess the impact of various parameters on the electrolyzer’s electrical characteristics. The series inductance, crucial for power transfer within a DAB converter, is examined using PLECS software to study the impact of the electrolyzer load on the peak and RMS currents. Based on this, the value of series inductance is optimized, resulting in a minimum overall RMS current throughout the operating power range. RTDT of the DAB electrolyzer system is developed using an OP4610XG real-time simulator to validate the presented model and simulation parameters. A comparison with the PLECS simulation results shows that the developed RTDT accurately operates within the 10 kW alkaline electrolyzer’s electrical characteristics. Thus, this setup exhibits the potential to evaluate power electronics converter designs without a physical electrolyzer system.

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.

This article proposes a transformerless, capacitively isolated converter based on a full-bridge topology with phase shift control. Compared to the existing capacitively isolated converters, the proposed design offers the advantage of zero mean voltage across the isolating capacitors, thereby reducing voltage stress. A comprehensive analytical model is developed to describe the converter’s operation in both continuous and discontinuous conduction modes. The model is validated through an experimental prototype tested at power levels up to 6 kW and switching frequencies up to 500 kHz, achieving a peak conversion efficiency exceeding 98%. The experimental results confirm the accuracy of the theoretical model and waveforms.

The energy transition encourages using heat pumps at the residential level, which results in a multi-carrier energy system when combined with PV and battery storage. Optimally controlling such systems has proven challenging. The numerous constraints required, different response times per energy carrier, and the need for forecasting methods also increase the complexity and computational cost. We propose an adaptable energy management system strategy for any system architecture with a reduced number of constraints using genetic algorithms with a discrete-continuous approach for the power setpoints. Using random forest regression, we also created short-term estimation models for the PV generation and electric and thermal demand, with error distributions centred near 0 %. Our results demonstrate that the strategy can solve the power allocation problem in the order of 1 s, including forecasting 60 minutes, minimizing electric costs, and ensuring thermal comfort.

Properly addressing uncertainties in reliability analysis is essential for realistic lifetime predictions of power devices. This paper investigates parameter uncertainties on the lifetime estimation of power devices using an empirical lifetime model and Monte Carlo simulations. Key parameters such as junction temperature swings (ΔT _j), minimum junction temperature (T _{j, min}), and lifetime model constants are analyzed for their impacts on lifetime outcomes. Sensitivity analysis reveals significant effects from variations in parameters like β ₁ and ΔT _j on the expected lifetime and its variability. Simultaneous variations across all parameters further highlight the dominant influence of β ₁ on lifetime predictions. The analysis suggests that a 5 % uncertainty margin appears to offer a balanced trade-off between realistic lifetime estimations and predictability. This Study underscores the importance of considering parameter uncertainties for precise reliability evaluations. It addresses a critical gap by examining the rationale behind commonly assumed 5 %, and 10 % uncertainty margins in lifetime modeling. By systematically evaluating these margins’ impacts on key reliability parameters, the study provides a framework for selecting reasonable assumptions based on physical insights and variability analysis, advancing the reliability modeling of power devices.

This study investigates the techno-economic impacts of various pricing policies on a photovoltaic (PV) system combined with battery energy storage (BES) as a single integrated system within a Dutch residential building. With the increasing adoption of PV systems, managing reverse power flow and grid stability becomes crucial. The study evaluates different scenarios, including net metering, feed-in tariffs (FiT) with time-of-use (TOU), RTP pricing, and subsidised BES. Using a multi-objective genetic algorithm, the optimal size and charging/discharging patterns of the PV-BES system were determined. The optimisation simultaneously minimises the Net Present Cost (NPC) and maximises the Self-Consumption Rate (SCR), to determine the PV-BES size that achieves an optimal balance between economic and technical performance. Results indicate that RTP pricing significantly enhances SCR. While the levelised cost of electricity (LCOE) and payback periods (PBP) are initially higher in the RTP pricing scenario, subsidising BES can mitigate these disadvantages. Additionally, incorporating price limit control variables into the energy management system (EMS) optimises the charging/discharging cycles, extending BES lifetimes and potentially increasing future revenues. These findings provide insights for policymakers to balance economic benefits and grid technical requirements through effective PV-BES integration.

The inclusion of PV and heat pumps in residential low-voltage distribution systems is a fundamental component of the energy transition. Nevertheless, adoptions below 40% can already cause voltage conditions incompliant with the standard EN50160 during winter. Aggregated storage systems have been proposed as a solution; however, the literature generally assumes full observability and controllability of the assets, which is unrealistic in many cases. This paper evaluates the potential of aggregated single- and multi-carrier storage systems to maintain voltage stability in low voltage networks, considering separated controllers for the prosumer and the aggregator. We used a real 301-node residential distribution network in the Netherlands as case study. Our results demonstrate that aggregated multi-carrier energy storage can ensure the voltage conditions established in the standard EN50160 for energy transition adoptions up to 80%, while aggregated single-carrier storage can reach 60% and centralized storage only 40%. We concluded that aggregation of storage assets increases the utilization of the existing grid infrastructure, reducing reinforcement costs for the DSOs. However, the energy storage assets’ high investment costs lead to unattractive conditions for single- and multi-carrier storage, compared to a case with only PV and heat pumps. Considering the current market conditions, using storage for voltage support would require economic compensations. These findings provide DSOs valuable insight on alternative solutions to grid reinforcement and centralized storage to address the challenges of the energy transition.

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.

Low-carbon technologies (LCTs) such as Electric vehicles (EVs), heat pumps (HPs), and PV systems increase phase unbalance due to uneven phase distribution. Phase unbalance can lead to overheating, suboptimal capacity utilization, and power losses. This work analyzes the unbalance impact inflicted by the grid integration of PVs, HPs, and EVs under different combinations and penetration levels. The main novelty of this study is the use of different types of real-world distribution grids (rural, suburban, and urban) for the LCT unbalance impact comparison, while simultaneously considering the influence of several unbalance factors; LCT phase connections and grid distributions, the seasonal effect, and the power and consumption levels, the latter of which have been evaluated as mitigation strategies. The results showed that the combined integration of PVs, EVs, and HPs can cause high voltage unbalance, especially in grids with high existing loading. The seasonal effect was the most impactful unbalance factor, intensifying unbalance by the integration of PVs-HPs and PVs-EVs combinations during Winter and Summer, respectively. Furthermore, reductions in the power and consumption levels of the LCTs decreased the unbalance total violation duration in a range between 11% and 25% for all distribution grids. Reducing the LCT consumption levels also decreased the unbalance magnitude, which reached up to 15% for the urban grid under 100% HP and PV penetration. Finally, it was found that consumption duration enhances unbalance, such as the peak power levels, because it increases the simultaneity of technologies operating in different phases.

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.

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.

This paper presents the design and control of 12 kW medium voltage Modular Multilevel Converter (MMC) prototype, providing a general overview on both the component and system levels. A top level functional overview of the sub-module (SM) converter design including features like semiconductor temperature monitoring and protections for over-voltage, overcurrent, and over-temperature to enhance reliability. The paper also offers a comprehensive system overview, utilizing OPALRT as a high-level controller. To demonstrate the effectiveness of the proposed design, a 12-kW, three-phase MMC prototype was constructed, consisting of four full-bridge (FB) SMs in each converter arm. The paper explains communication management and the integration of analog and digital signals between the physical system and the user interface controller. Finally, the system's output under various operating conditions is analyzed and presented.