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.

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.

High-power flexible dc links employ modular multilevel converters (MMC) for compact active power redirection in medium and high voltage grids. During contingencies, such converters may need to provide an enhanced active power capacity to avoid overload in vulnerable grid locations. This paper achieves this target by using the capacitor voltage ripple margin of the MMC submodules (SM) to enhance the dc voltage beyond the rated value. This voltage enhancement enables the enhanced active power capacity of the MMC while maintaining rated electro-thermal stresses on the components. Moreover, dynamically varying the dc side voltage reduces the MMC's circulating current, improving its operating efficiency. Because the average capacitor voltage is controlled to remain constant, the overall stresses and harmonic performance of the enhanced MMC remain the same as in the base case. In this paper, the analytical expressions for the voltage and power enhancement limits are derived, revealing a dependence on the grid-injected reactive power. Furthermore, a controller is designed to achieve stable operation during transient conditions when the power enhancement is carried out. Finally, the enhancement concept is validated using simulations and experiments with a down-scaled laboratory MMC prototype.

Modular Multilevel Converters (MMCs) offer significant advantages in the medium to high-voltage settings. The modular architecture of MMCs allows for redundant submodules (SMs) to improve overall reliability. These redundant SMs can be deployed using various redundancy strategies, such as Load-Sharing Active Redundancy Strategy (LS-ARS), Fixed-Level Active Redundancy Strategy (FL-ARS), and Standby Redundancy Strategy (SRS). The primary contribution of this paper is the introduction of guidelines for applying Monte Carlo Simulation (MCS) and a comprehensive methodology for its application across various redundancy strategies. This enables precise planning of preventive maintenance and estimation of the number of faulty SMs with a specific lifespan in the MMC. More importantly, MCS is applied to estimate the reliability of the MMC applying Mission Profile for SRS and LS-ARS where analytical solutions are unavailable. An analysis of uncertainty and the applicability of MCS is also presented to demonstrate the advantages of MCS over analytical methods. The computational time required for applying MCS across different redundancy strategies and arm levels is also assessed.

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.

High shares of photovoltaic energy in low-voltage distribution systems lead to voltage limit violations. Deploying energy storage systems in the network can compensate for the mismatch between the generation and the consumption; nevertheless, the mismatch is unevenly distributed throughout the network, suggesting aggregated control strategies as a solution. This paper proposes two coordination control strategies of batteries to address network overvoltage conditions caused by high penetration of photovoltaic systems. The leader–follower coordination strategy determines a battery’s utilization factor by using the node closest to a voltage violation as a reference. The leaderless control uses a shared utilization factor to avoid excessive usage of a particular agent in the network. We tested both approaches in the 18-node CIGRE network for scenarios when not all agents were available and when they had different starting states-of-charge. Our results demonstrate that both strategies are capable of voltage control; however, the leader–follower control leads to uneven storage usage, ultimately leading to short-time failure to comply with the voltage limits under extreme conditions where neighbouring agents must compensate for the unavailable one. Conversely, the leaderless approach presents more balanced use of the agents thanks to the distributed utilization factor, resulting in a more robust control strategy.

The growth of suburbs is a challenge for public transport, and new tools are needed to electrify suburban and intercity bus lines sustainably. In-motion-charging (IMC) buses combine the advantages of both trolleybuses and electric buses. This paper analyses a case of using IMC buses on a 17-km-long intercity route service between Arnhem and Wageningen in the Netherlands. The analysis covers different traction battery technologies, sizes, and charging strategies to find the economically optimal solution. The study was carried out using a numerical model of an IMC bus, which was validated and tuned based on year-round experimental recordings obtained from Arnhem trolleybuses. The model outputs were next used to analyse the batteries ageing under specific charging-discharging current profiles. The analysis shows that the most long-term cost-effective solution for the considered case consists of using merged IMC and opportunity charging as well as a 90 kWh LTO battery, whose expected lifetime would be more than 14 years.

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.

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.

The study presents Electric Vehicle (EV) Point-of-View (POV) Vehicle to Grid (V2G) optimisation, where the system knows the trips and energy demand of the EV. This allows it to be less conservative in the departure State of Charge (SoC) of the EV battery. The peak and cost reduction of Vehicle POV optimisation are compared with state-of-the-art Location Point-of-View optimisation, this is, the car can do V2G but it has to depart each location with at least 80% SoC as the next trip energy demand is unknown. A Mix Integer Linear Programming (MILP) V2G optimisation approach is presented to reduce peak demand and total system cost of a fleet of EVs travelling between several residential locations and one common workplace. Simulations were conducted with 26 EVs unevenly distributed between five residential locations and commuters to a common workplace. The results indicate little differences in the reduction of peak demand between both POVs of V2G optimisation. However, the system cost can be further reduced with Vehicle Point-of-View optimization, saving an additional 200 euros annually per household by transporting cheaper energy from workplaces to residential locations.

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.

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.

Quasi-resonant boundary-conduction mode (QR-BCM) and triangular current mode (TCM) have found widespread use in the literature and industry due to their good performance at relatively low complexity. However, additional control challenges occur when these modulations are applied to the four-switch buck-boost (FSBB) converter, due to a discontinuity in switching frequency in multimode operation. This article presents the first closed-loop operation of a variable-frequency, multimode, quasi-resonant BCM control scheme including smooth mode transitions. The proposed control utilizes feed-forward mode transition techniques, based on software interrupt handlers integrated into the digital control scheme. In contrast to most soft-switching schemes in the literature, the proposed digital control does not imperatively rely on high-frequency current measurements but uses dc measurements and high-frequency voltage measurements instead. A 10 kW prototype is developed with which the proposed modulation is compared with three other soft-switching modulation schemes. Our results indicate that the losses of FSBB converter can be reduced by up to 60% using the proposed modulation. Especially at partial powers and high voltages, significant efficiency gains can be achieved.

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.

Due to the increasing requirement of charging power for electric vehicles, especially heavy-duty electric vehicles (HDEVs), this paper proposes novel matrix converter-based three-phase medium voltage AC (MVAC) grid-connected modular high-power wireless charging systems. The stiff DC-link absent power transfer from low-frequency AC to high-frequency AC is achieved by the full-bridge direct matrix converter (FBDMC). The cascaded FBDMC structure is proposed to achieve the MVAC grid connection. The three-phase coupler is used here to generate the rotating magnetic fields to achieve higher transfer capability and power density. The second and third grid harmonics can be cancelled due to the nature of the FBDMC and three-phase system, which results in significantly less DC-link current ripple compared to single-phase wireless charging systems. Several novel connections between FBDMCs and coils are proposed to provide more flexibility and multiplexity for WPT charging. The topology is verified by the simulation in PLECS and by a down-scale experiment setup.

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.

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.

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.