Ramon Zamora

This paper provides a comprehensive review of recent studies on event-triggered control schemes for DC microgrids. Several event-triggered mechanisms (ETMs) are thoroughly discussed, including static, dynamic, self-triggered, and edge-based algorithms. Considering the strengths and weaknesses of these algorithms, it is found that although such ETMs can decrease communication burden in the system, they are also susceptible to communication delays, Zeno behaviour, sensitivity to control parameter changes in triggering conditions, and inability to adapt to the fluctuating nature of renewable energy sources (RESs). Furthermore, this article examines implementation challenges, including data packet loss, quantisation effects, actuator faults, and a lack of cybersecurity measures, to provide readers with a clear vision of future trends in this field. Based on the main findings of the investigation, this review paper proposes possible areas for future research, highlighting the need for event-triggered control schemes that operate in discrete time, handle delays, and adapt to varying operating conditions. Other concepts, including adaptive control parameters for triggering conditions based on machine learning, the adoption of advanced cybersecurity measures, and data-aware transmission approaches that consider both communication frequency and total data volume, are also discussed. To conduct a comprehensive review of all the above-mentioned ETMs, several databases, including IEEE Xplore, Elsevier, and MDPI, were searched using the main keywords in this field, such as event-triggered, self-triggered, and edge-based ETMs, in conjunction with DC microgrids. This facilitated an in-depth analysis of such control schemes, including their strengths and weaknesses, providing readers with a strong basis for selecting a proper control scheme suited to their future research.

Microgrids are important technologies for increasing the penetration of renewable energy sources (RESs). Compared with AC microgrids, DC microgrids avoid frequency regulation and reactive-power compensation. Moreover, many RES interfaces and energy storage systems (ESSs) are DC or DC-link based; therefore, they can be integrated into DC buses with fewer conversion stages, reducing conversion losses. Consequently, DC microgrids have attracted increasing attention. This paper reviews DC microgrid topologies, hierarchical control methods, and protection schemes. First, the representative topologies are compared from the perspectives of structural features, control implications, protection requirements, and application scenarios. Next, primary, secondary, and tertiary control strategies are analyzed, with emphasis on droop control, virtual impedance, virtual inertia, fractional-order control, communication delay, and energy management. Protection issues, including fault detection, fault interruption, and ground-fault protection, are then discussed with respect to topology–control interactions. Finally, future research trends and challenges for DC microgrids are summarized.

In this paper, we present a reformulation of both the predictive torque and flux control (PTC) scheme and the full-order adaptive observer (FAO) for induction machine drives. The proposed approach is based on a state-space representation expressed exclusively in terms of stator current and stator flux linkage, simplifying the observer structure and removing the explicit dependence on rotor flux variables found in conventional sensorless formulations. This representation is consistently applied within both the FAO and PTC frameworks, and second- and higher-order discrete-time models are derived using Taylor- and Runge–Kutta–based methods to enhance numerical accuracy and dynamic performance. The resulting FAO–PTC scheme is validated through Hardware-in-the-Loop simulations, demonstrating steady-state performance comparable to conventional designs, faster transient response, improved dynamic behaviour, and a reduced state-space order, albeit with slightly higher computational cost. Notably, simply employing a more accurate observer substantially enhances the performance of the sensorless scheme. Among the evaluated discretization strategies, the Taylor-based model provides the highest steady-state accuracy and fastest convergence, with only a modest increase in torque ripple. Overall, the proposed reformulated FAO–PTC framework achieves a balanced trade-off between accuracy, implementation simplicity, and computational efficiency for real-time sensorless induction machine drives.

With increasing concerns about carbon emissions, greenhouse gases, and energy costs, many sustainable development goals and related agreements have been introduced by governing bodies worldwide. As a result, hydrogen-based microgrids integrated with Renewable Energy Sources (RESs) have received significant attention. In such power systems, electrolysers convert excess renewable energy into hydrogen, which is stored and later utilized through fuel cells or hydrogen gas turbines. However, due to the intermittent nature of RES and the unexplored behavior of hydrogen generation, storage, and utilization systems, the combined operation of a power system consisting of both renewable sources and hydrogen energy poses significant challenges, including system stability, energy balancing, safety, and regulation issues. In such situations, Energy Management Systems (EMSs) are employed to achieve stable operation while obtaining various optimal operational solutions. This study presents a detailed illustration of microgrids and hydrogen systems, and provides a comprehensive breakdown of various EMSs for hydrogen technology-based microgrids. Unlike many existing review studies that discuss hydrogen technologies in general terms, this review specifically focuses on the integration and operational strategies of hydrogen techniques, comprising electrolysers, hydrogen storage systems, and fuel cells, within the EMS. By emphasizing the role of hydrogen components in EMS operation and optimization, this paper offers an application-oriented review uniquely focused on the operational integration of hydrogen technologies within EMS frameworks, a gap often overlooked in broader literature.

This paper provides a comprehensive review of Partial Power Converter (PPC) topologies and control methods for fast electric vehicle (EV) charging applications. Partial Power Converters are gaining traction to enhance converter efficiency, reduce power losses, and minimize component sizes by processing only a portion of the total power. This review covers key PPC topologies, including different partial power converters, and highlights their advantages and limitations in the context of EV charging. Various control methods that optimize the performance of these converters are also discussed. The paper presents a comparative analysis between partial power and full power converters. Finally, this review synthesizes the main findings and proposes guidelines for selecting appropriate PPC architectures for future fast EV charging stations.

It is essential for Home Energy Management Systems (HEMSs) to minimize the system operating cost while maintaining the user comfort under forecasting uncertainties of solar and electricity load demand. However, the existing HEMS excessively relies on a single battery system and may not effectively assess user comfort. To this end, a hierarchical HEMS, i.e., system- and local-level, is proposed in this article to coordinate the dispatch of home resources including battery energy storages and supercapacitors (SC). The system-level HEMS consists of long-term (LT) and short-term (ST) optimization based on Model Predictive Control (MPC). The LT optimization optimizes resource dispatch by using forecasted load and solar generation to minimize house operating costs and maximize the user comfort. The ST layer one is proposed to track the optimal power scheduling to minimize the cost error, refine the dispatch of resources and ensure a safe operational level of hybrid energy storage systems including the SC. The SC is employed to compensate the transient power and alleviate the battery degradation effects. The local-level HEMS is used to achieve DC voltage restoration, power sharing, voltage recovery of SC and state of charge (SoC) balance between batteries. The interaction between system- and local-level is also discussed. By using the dataset from NREL and Ameren Illinois Company, the test results show that this methodology can potentially reduce the system operating cost by 4.3500%, 7.7600%, and 37.7253% compared to the other single and multi-layer HEMSs.

The building sector has an important role in the decarbonization of energy. Buildings energy management systems can act on flexible loads to contribute to the integration of renewable energies. This article presents the design, implementation and evaluation of a rule-based energy management systems (RB-EMS) in the frame of a collective self-consumption (CSC) system based on photovoltaic (PV) energy in the Technology Park of Izarbel, in Bidart, France. The RB-EMS acts on the heat ventilation and air conditioning (HVAC) system of one of the buildings involved in the CSC in order to maximize the PV energy self-consumption rate (SCR). Internet of things (IoT) has been developed and implemented in order to retrieve consumption, production and temperature data, and to be able to act on the HVAC. A new 5-step methodology has been developed to design and adjust the RB-EMS. This methodology is mainly based on tests carried out to find the relationship between ON/OFF states of the internal units of the HVAC and the heat pumps’ consumption. Finally, the RB-EMS is tested in heating operating mode. The results show that the RB-EMS allows obtaining a SCR of 89%, and that the users comfort limits are respected.

Increasing the replacement of conventional synchronous machines by non-synchronous renewable machines reduces the conventional synchronous generator (SG) inertia in the modern network. Synthetic inertia (SI) control topologies to provide frequency support are becoming a new frequency control tactic in new networks. However, the participation of SI in the market of RES-rich networks to provide instant frequency support when required proposes an increase in the overall marginal operation cost of contemporary networks. Consequently, depreciation of operation costs by optimizing the required SI in the network is inevitable. Therefore, this paper proposes a flexible SI optimization method. The algorithm developed in the proposed method minimizes the operation cost of the network by giving flexible SI at a given SG inertia and different sizes of contingency events. The proposed method uses Box’s evolutionary optimizer with a self-tuning capability of the SI control parameters. The proposed method is validated using the modified New England 39-bus network. The results show that provided SIs support the available SG inertia to reduce the RoCoF values and maintain them within acceptable limits to increase the network’s resilience.

To ensure reliable power delivery to customers under potential disturbances, the coordination of a microgrid cluster (MGC) is essential. Various control strategies—centralized, decentralized, distributed, and hierarchical—have been explored in the literature to achieve this goal. The hierarchical control method, with three distinct levels, has proven effective in fostering coordination among microgrids (MGs) within the cluster. The third control level, utilizing a time-triggering consensus protocol, relies on a continuous and reliable communication network for data exchange among MGs, leading to resource-intensive operations and potential data congestion. Moreover, uncertainties introduced by renewable energy sources (RESs) can adversely impact cluster performance. In response to these challenges, this paper introduces a new distributed event-triggered consensus algorithm (DETC) to enhance the efficiency in handling the aforementioned issues. The proposed algorithm significantly reduces communication burdens, addressing resource usage concerns. The performance of this approach is evaluated through simulations of a cluster comprising four DC MGs, in each of which were PV and a hybrid Battery-Super capacitor in the MATLAB environment. The key findings indicate that the proposed DETC algorithm achieves commendable results in terms of voltage regulation, precise power sharing among sources, and a reduction in triggering instants. Based on these results, this method can be deemed as a good development in MGC management, providing a more efficient and reliable means of coordination, particularly in scenarios with dynamic loads and renewable energy integration. It is also a viable option for current microgrid systems, due to its ability to decrease communication loads while retaining excellent performance.