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.

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.

This article presents a dual-side capacitor tuning and cooperative control strategy for wireless electric vehicle (EV) charging. To improve the efficiency of wireless EV charging across broad output voltages and wide-range load variations, this article introduces a reconfigurable WPT system by incorporating two switch-controlled-capacitors (SCCs) into the double-sided LCC (DLCC) compensation network. Based on the analytical model of the system, optimal capacitor tuning factors are derived to reduce the rms values of the inductor currents and to minimize the turn-off currents across the semiconductors. Furthermore, a dual-side cooperative control strategy is proposed. Through the collaborative control of the inverter, rectifier, and SCCs, the proposed method achieves dual-side optimal zero-voltage-switching (ZVS), wide power regulation, and maximum efficiency tracking simultaneously. Compared with the existing triple-phase-shift (TPS) method, the proposed approach improves the system efficiency across a wide range of dc output voltages and power levels. Experimental results demonstrate that the proposed method achieves a maximum efficiency improvement of up to 1.8% in the boost mode and 1.9% in the buck mode.

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.

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.

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 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.

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.

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.

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.

To tackle the potential grid overloading issue induced by excessive Electric Vehicles (EV) charging demand, a Low Voltage (LV) grid congestion management algorithm with three centralised EV charging management schemes is proposed in this study. The developed algorithm integrates grid information and aims at tackling the foreseen congestion issues by operating on the EV charging processes. This is done through linear programming (LP) or iterative calculations. While the first charging scheme aims at managing the congestion by only affecting the elements with the greatest influence on the congestion, the other two aim at ensuring impartiality towards all users and the overall energy transfer to the EVs, respectively. The simulated results are compared in terms of performance criteria such as grid impact, user satisfaction and fulfilment of charging energy demand. Overall, this study shows that the first scheme brings the best results from a grid perspective. On the other hand, the last scheme leads to competitive results from a grid point of view and the best overall results from a user perspective.

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.

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.

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.

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.

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.

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.

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.

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.