Tackling the challenges of the energy transition requires a skilled workforce and advanced educational and training tools. During the previous years, a relatively large number of institutions have acquired Real-Time Simulators, in both academia and industry. However, this is mainly used for practical studies and research, while the use of real-time Hardware-In-the-Loop (HIL) simulation for education and training is rather limited at the moment. In this framework, this work investigates the potential of using HIL simulation for education and training purposes on power and energy topics in both academia and industry. Selected reference experiences of the Task Force members are presented along with learners' feedback to substantiate the effectiveness of the approaches. Particular attention is given to training and education at industrial level, which has not been adequately addressed in the literature, so far. The use of remote laboratories and safety concerns are also addressed.

The response time of grid-forming (GFM) converters under grid disturbances is a critical metric that reflects how fast GFM capabilities can be delivered. In this letter, the analytical expression for the response time under voltage sags is derived using the geometric singular perturbation method. It is revealed that the response speed is naturally faster in weaker grids, and for a wide range of grid conditions, the response time under voltage sags has an inherent minimum limit of a quarter of the fundamental cycle, i.e., 5 ms at 50 Hz. Increasing the voltage-loop integral gain kiv approaches this limit, but an excessively large kiv induces small-signal instability. Hence, a transient reactive current augmentation strategy is proposed to approach and even break the limit while ensuring stability. Moreover, it is derived that current-limiting control reduces the response time. Therefore, the analytical response time constitutes an upper bound on the practical response time with current limiting. The findings suggest potential refinements to grid code requirements for GFM converter response time. The results are validated by hardware-in-the-loop testing.

The increasing penetration of distributed generation (DG) introduces variability in network impedance and harmonic emissions that can degrade the performance of passive harmonic filters designed for nominal operating conditions. This paper develops a reliability-oriented C-type filter synthesis framework that couples the Secretary Bird Optimization Algorithm (SBOA) with MATLAB–PSCAD electromagnetic transient (EMT) co-simulation. Candidate filter parameters are evaluated directly from simulated point-of-common-coupling (PCC) voltage waveforms under Monte Carlo realizations of grid impedance, background harmonic distortion, DG harmonic injection, and component tolerances. Probabilistic constraints are imposed on total and individual voltage harmonic distortion, while a conditional value-at-risk metric and lifecycle cost are considered as optimization objectives. The resulting design is compared with conventional and optimization-based benchmark filters under a common EMT-based evaluation framework. For the investigated medium-voltage distribution system, the optimized filter reduces PCC total voltage harmonic distortion (TVHD) to approximately 2.5%, with no limit violations observed in the considered Monte Carlo samples, while also improving power factor and reducing system and filter losses. The results indicate that incorporating EMT-based scenario evaluation into filter synthesis can improve harmonic-compliance margins under the uncertainties considered in this study.

This study introduces a novel approach using two deep learning agents, trained with the twin-delayed deep deterministic policy gradient (TD3) algorithm, to replace the PI controllers used for the control of grid-connected Archimedes Wave Swing (AWS) wave energy conversion systems. The generator converter's controller has two mandatory objectives: minimizing losses in the stator and maximizing energy extraction from incident sea waves. These goals are achieved by controlling the generator's dq currents using a TD3 agent on the rectifier side. In addition, the grid-side inverter's controller is responsible for regulating both the DC link and the point-of-common-coupling voltages. In the new configuration, two approaches are proposed in this work: either a single deep learning agent replaces the four proportional-integral (PI) controllers on the inverter side, or a hybrid approach combining two PI controllers with a TD3 agent. To verify the reliability of the TD3 agents, the system is analyzed in both steady and transient states under fault conditions. Furthermore, the TD3 agents' performance is benchmarked against the classical PI controller configuration in MATLAB Simulink. The results demonstrate better dynamic and steady-state responses from the hybrid-TD3 agent on the grid side than from the full PI classical configuration.

This paper introduces the Square Shape Slope Index (SSSI), a novel post-optimization multi-criteria decision-making (MCDM) approach for analyzing Pareto fronts generated from bi-objective optimization problems. SSSI leverages multiple Utopia and Nadir points—guided by a user-defined priority scale—to form a dynamic square region around particular segments of the Pareto front. Within this region, slope-based evaluations are used to rank solutions based on user preferences and criteria. The method’s effectiveness is demonstrated through empirical tests on diverse benchmark functions and real-world scenarios, such as energy distribution and portfolio optimization, each encompassing various shapes and patterns of the Pareto front. In addition, SSSI is compared against established decision-making approaches both geometrically and analytically using different aggregation methods. To account for the stochastic nature of evolutionary algorithms, the Non-Dominated Sorting Genetic Algorithm (NSGA-II) is employed to generate Pareto fronts for each test function. Results confirm the robustness and adaptability of SSSI, offering a clear and flexible framework for balancing conflicting objectives in multi-objective decision-making contexts

This paper presents a novel methodology for fault detection, classification, and localization in Multi-Terminal Medium Voltage Direct Current (MT-MVDC) networks. The proposed approach utilizes Singular Spectrum Analysis (SSA) to decompose measured positive and negative pole voltages, isolating the seasonal component that represents the traveling wave. Fault detection is based on comparing this component against a predefined threshold, where minimal fluctuations occur under normal conditions, but significant variations emerge after a fault. Fault classification is achieved by analyzing the rate of change of the line-mode current to distinguish between forward and backward faults. For fault localization, the method leverages traveling wave attenuation and dispersion. The first traveling wave is extracted from the voltage seasonal component, and its spreading behavior over distance is analyzed to compute the curvature rate, enabling precise fault location estimation. The methodology is validated through extensive simulations on an MT-MVDC distribution system using PSCAD/EMTDC. MATLAB is employed for signal processing, and the approach is tested under various fault scenarios, including high fault impedance and extreme external faults. Comparative analysis with existing methods highlights the advantages of the proposed technique in terms of accuracy and robustness.

The saturation of current transformers (CTs) leads to distortion in secondary current, potentially causing malfunction in protective relays within power systems. Detecting the saturated portions in the measured signal and reconstructing the primary reference current are essential to prevent relay malfunctions and ensure sensitivity during faults. Existing methods in the literature still face challenges in improving accuracy, especially in the presence of noise in the measured signal. The proposed method in this paper is more robust under noisy conditions compared to existing signal processing-based methods. It relies on a cross-correlation algorithm that uses an independent target signal. This method is parameter-less and independent of the CT’s specifications. Additionally, no extra hardware equipment is required. The proposed method identifies the saturated portions in the measured secondary current in each cycle, enabling the reconstruction of the saturated current to obtain the reference primary current. A test system has been simulated, and the data are processed using MATLAB. Various test cases are executed, and the results confirm that the proposed method is highly effective in providing fast and accurate detection of CT saturation, with improved robustness against noise.

Ensuring continuity of service is a primary objective in power systems. In grid-connected microgrids (MGs), islanding poses a significant threat to this continuity. Conventional approaches mitigate islanding by disconnecting the MG immediately after separation from the main grid to prevent overload and ensure safety, but this results in service interruption. This study proposes a dynamic islanding management strategy that maintains uninterrupted service using an optimal dynamic neuro-fuzzy–optical microscope algorithm (OMA). The method integrates a convolutional neural network (CNN), fuzzy logic (FL), and the novel OMA optimizer in a two-stage framework. In the first stage, the CNN detects islanding based on active current and voltage measurements at the point of common coupling (PCC) and their dominant harmonic components, obtained from a hybrid MG model. This model is comprising solar panels, wind turbines, a biomass generator, and a storage system. Signal and image processing techniques prepare the measurements for CNN implementation. Upon islanding detection, the second stage is activated, where FL predicts the penalty factor and OMA optimally manages economic power sharing between the grid and the MG. This integration enables safe load coverage without damaging MG components. Performance benchmarking against Quadratic Interpolation Optimization (QIO) and Hunger Games Optimizer (HGO) demonstrates that OMA achieves higher accuracy, faster convergence, and lower execution time. Validation across five scenarios under normal, islanding, and risky operating conditions confirms the method’s effectiveness, reliability, and economic benefits, achieving a 223.7% revenue improvement over the baseline with the shortest execution time. The proposed approach offers a robust and intelligent solution to the islanding problem, ensuring continuous and cost-effective microgrid operation.

The rising usage of power electronic converters linked to renewable energy sources has become a major source of harmonics in power systems. Passive harmonic filters are an excellent solution for addressing this issue. However, these traditional filters have a problem linked to resonance frequency, which needs damping. This paper introduces a Harmonic Blocking Filter (HBF) that consists of a shunt-connected Damped Double-Tuned Passive Filter (DDTF) and a series component. Six different DDTF schemes are investigated: four single-resistor DDTFs (SR-DDTF) and two double-resistor DDTFs (DR-DDTF). This study intends to perform harmonic mitigation and increase PV penetration levels by obtaining parameters for each HBF system using the Autism-Based Optimizer (ABO). The Harmonic Pollution Factor (HPF) is a power quality indicator used to assess and reduce the system’s harmonic content. The findings show that the proposed HBF filter efficiently increases PV penetration in the system while lowering harmonic levels.

Load frequency regulation in hybrid grid is a very crucial issue. This work introduces robust-adaptive control methodology to provide accurate response against any disturbances. Control scheme of two main parts has been introduced. The first one is a novel fractional order model reference adaptive controller where fractional calculus merits and e-modification robust algorithm have been merged with the adaptive skills creating the fractional order model reference robust-adaptive controller. This robust-adaptive controller has utilized output feedback approach that requires only the output state without detailed system model information. The fractional orders have been optimized using a nature-inspired algorithm named Artificial Rabbits. The second part of the control scheme is a disturbance rejection observer that is able to estimate and eliminate external and internal disturbances. For fair comparison, results of the proposed scheme have been compared to that of the most efficient controllers obtained from the literature named fractional order proportional integral derivative controller and integral controller. Furthermore, the integer version of the proposed controller has been tested and compared with the proposed controller to prove the robustness of the fractional modification. To validate the superiority of the proposed controller, five challenging scenarios have been considered, encompassing load fluctuations, integration of renewable energy sources, changes in system parameters and time delay attacks. The proposed controller effectively minimizes area control error achieving stability and showing cost function enhancement of 15% to 152% compared to other tested controllers in all scenarios. Thus, it is strongly recommended for load frequency regulation in multi-area power systems.

Global energy consumption is increasing at a dramatic rate due to the increase in the world's population and the quest for improvement of living standards.Most of our energy comes from fossil fuels which cause the problem of global warming due to the emission of greenhouse gases (GHG).As a result, there are many harmful effects such as rise in sea level, drought in tropical regions near the equator, an increase in hurricanes, tornadoes and floods, and the spread of disease.Renewable energy is the energy generated from natural resources such as solar heat and light, wind, rain, tides, waves, and geothermal heat, which are replenished naturally.From Wikipedia, in 2008, about 19% of global final energy consumption came from renewables, with 13% coming from traditional biomass, used mainly for heating, and 3.2% from hydroelectricity.New renewables (small hydro, modern biomass, wind, solar, geothermal, and bio fuels) accounted for another 2.7% and are growing rapidly.The share of renewables in electricity generation is around 18%, with 15% of global electricity coming from hydroelectricity and 3% from new renewables.This paper highlights in particular the impact of power electronics in solving or mitigating the global warming problem and supporting the generation of renewable energy [1]- [30].

A distribution system's network reconfiguration is the process of altering the open/closed status of sectionalizing and tie switches to change the topological structure of distribution feeders. For the last two decades, numerous heuristic search evolutionary algorithms have been used to tackle the problem of network reconfiguration for time-varying loads, which is a very difficult and highly non-linear efficiency challenge. This research aims to offer an ideal solution for addressing network reconfiguration difficulties in terms of a system for power distribution, to decrease energy losses, and increase the voltage profile. A hybrid Genetic Archimedes optimization technique (GAAOA) has also been developed to size and allocate three types of DGs, wind turbine, fuel cell and PV considering load variation. This approach is quite useful and may be used in many situations. This technique is evaluated for loss reduction and voltage profile on a typical 33-bus radial distribution system and a 69-bus radial distribution system. The system has been simulated using MATLAB software. The findings suggest that this approach is effective and acceptable for real-time usage.

The excessive integration of renewable distributed generation (RDG) and electric vehicles (EVs) could be considered the two most problematic elements representing the greatest threat to the distribution network (DN) technical operation. In order to avoid going beyond technical limitations, the term hosting capacity (HC) was proposed to define the highest permitted amount of distributed generation (DG) or EVs that can be integrated safely into the DN. The connection of RDGs was first brought to the attention of researchers and DN operators since it accounts for the most notable portion of these technical issues. Hence, the phrase ‘DG-HC’ was initially proposed and evolved significantly over the last few years. Currently, EV integration in most DNs worldwide is still low, but given the worldwide support for clean transportation options, expectations are raised for a significant increase. As a result, it is anticipated that over the next years, the effect of EV integration on the DN will be highly noticeable, requiring greater attention from researchers and DN operators to define the accepted limits of EV penetration levels, ‘EV-HC,’ which is expected to pass along the same line of DG-HC. This article provides an in-depth review of both DG-HC and EV-HC. It first analyses how the DG-HC research has grown over the years and then studies the published EV-HC papers, illustrating to what extent there is a similarity between them and, finally, employs these analyses to expect future development in the EV-HC research area. This article includes the different uses of the term HC, the most common performance indices of DG-HC, the various methods for assessing DG-HC, the different techniques for DG-HC enhancement, the effects of integrating EVs on the DG-HC, and finally, calculating and enhancing methods for EV-HC.

The vast diversity of wave energy conversion systems (WECSs) in the literature makes selecting the suitable WECS for wave energy harvest a stubborn process. This work summarizes six of the most widely adopted WECSs used heavily in previous research assessments and practical projects. This includes the Archimedes Wave Swing (AWS), the Wave Dragon (WD), Pelamis Wave Power (PWP), Aquabouy (AB), the Oyster, and the Oscillating Water Column (OWC). The work includes the mathematical modeling of these WECSs and the different projects and prototypes that involve these WECSs. Moreover, the latest research development in each of these WECSs is presented. Also, the wave energy potential in the world is discussed. Besides, the wave energy potential in Egypt, including that of the Mediterranean and the Red Sea, is discussed in detail. Furthermore, the steps required to perform a future feasibility study in Egypt and suggestions for the enhancement of an older study are provided. Finally, some suggestions and required equations are presented to explore the site power density and the most suitable WECS to be utilized in Egypt.

With the increasing number of electric vehicles (EVs), their uncoordinated charging poses a great challenge to the safe operation of the power grid. In addition, traditional individual-EV scheduling models may be difficult to solve due to the increasing number of constraints. Therefore, this paper proposes a cluster-based EV scheduling model. Firstly, electric vehicle clusters (EVCs) are formed based on the charging and discharging preferences of EV users and the expected time for EVs to leave. Secondly, the EVC energy and power boundary aggregation method based on the Minkowski addition algorithm is proposed. Finally, for the sake of reducing user charging cost and distribution network energy loss, and smoothing the daily load curve, an EVC scheduling model for EV participation in grid auxiliary services is proposed. The optimization model includes the reactive-power compensation of EV charging piles. The simulation results show that the proposed EVC scheduling model can greatly reduce the solution time compared to traditional individual-EV scheduling model. The model has high potential to be applied to large-scale EV scheduling. The reactive-power compensation provided by EV charging piles improves the voltage quality of the grid and enables more EVs to be connected to the grid.

Utilizing a static synchronous compensator (STATCOM) in the electrical power grid greatly improves the grid's voltage profile by enhancing voltage stability. This article proposes a novel approach based on Mixed Integer Distributed Ant Colony Optimization (MIDACO) to determine the optimal STATCOM installation in the electrical power grid. This approach has two control variables to optimize: the STATCOM size and location. This optimization aims to enhance voltage stability with minimum cost by minimizing two objectives: the voltage deviation index and the STATCOM cost. Also, this article presents a sensitivity analysis to show the stochastic nature of MIDACO and to explain the effect of MIDACO parameters on the optimization approach and the process of reaching the optimal solution. The proposed method has been evaluated on three standard test systems: IEEE 14-bus, IEEE 57-bus, and IEEE 118-bus. In addition, the MIDACO results are compared to those of the artificial bee colony algorithm, the genetic algorithm, and particle swarm optimization.

This article deals with the black-box modeling of synchronous generators based on artificial neural networks (ANN). The ANN is applied to define the relationship between the excitation and terminal generator voltage values, and the Levenberg–Marquardt algorithm is used for determining the ANN weight coefficients. The relation is made based on generator response on reference voltage step changes. The proposed approach is checked using the experimental results obtained from the measurements on a real 120 MVA generator from a hydroelectric power plant Piva in Montenegro. Furthermore, a fair comparison of the nonlinear autoregression model with the exogenous input (NARX) and Hammerstein–Wiener model is made. For the validation, different experiments were conducted—different values of step disturbances, other controller parameters, and different rotating speeds. Based on the presented results, it can be noted that the proposed ANN model is very accurate and provides a very high degree of matching with the experimental results and outperforms the other considered nonlinear models. Furthermore, the proposed test procedure and model are easy to implement and do not require disconnection of the generator from the grid or additional equipment for experimental realization. Such obtained models can be used for different testing types related to the excitation system.

In this paper, a new fault locator scheme is introduced to address the non-homogeneity of combined transmission lines (Taba-Aqaba Intertie transmission system). To achieve this goal, the faulted side is first determined using a new algorithm for identifying faulted segments. This identification algorithm relies on the voltage's rate of change along the nonhomogeneous lines in the pure positive-sequence circuit, exploiting the inequality of positive-sequence impedances between power cables and overhead lines. The number of outputs generated by the algorithm is one less than the number of line segments being analyzed. For systems with two segments, the output directly identifies the faulted segment. However, for combined lines with three segments, a new voting system is utilized to determine the faulted segment. The fault distance is then calculated based on the identified faulted segment. To evaluate the proposed scheme, various fault scenarios are simulated on the Taba-Aqaba Intertie transmission system, which connects the Egyptian and Jordanian networks, using ATP-EMTP. These tests encompass different fault types and locations, including cases near the interconnection points between the segments. Additionally, the scheme is tested under nonlinear faults.

This paper presents an integrated overcurrent relays coordination approach for an Egyptian electric power distribution system. The protection scheme suits all network topologies, including adding distribution generation units (DGs) and creating new paths during fault repair periods. The optimal types, sizes, and locations of DGs are obtained using HOMER software (Homer Pro 3.10.3) and a genetic algorithm (GA). The obtained values align with minimizing energy costs and environmental pollution. The proposed approach maintains dependability and security under all configurations using a single optimum setting for each relay. The calculations consider probable operating conditions, including DGs and fault repair periods. The enhanced coordination procedure partitions the ring into four parts and divides the process into four paths. The worst condition of two cascaded overcurrent relays from the DGs’ presence viewpoint is generalized for future work. Moreover, a novel concept addresses the issue of insensitivity during fault repair periods. The performance is validated through the simulation of an Egyptian primary distribution network.

Dump load (DL) utilization at low demand hours in highly penetrated islanded microgrid is of great importance to offer voltage and frequency regulation. Additionally, load flow (LF) convergence is vital to optimize the working states of the DL allocation problem. Hence, more analysis is necessary to highlight the significance of DL in power regulation while observing the influence of LF on solution accuracy. This article proposes two LF techniques derived from backward/forward sweep (BFS), viz., general BFS (GBFS) and improved special BFS (SBFS-II). The latter is based on global voltage shared between generating units, while the former has a more general approach by considering generating bus's local voltage. The optimal sizing and sitting of DL with optimum droop sets are determined using the mixed-integer distributed ant colony optimization (MIDACO) with the two new LF methods. The optimization problem was formulated to minimize voltage and frequency deviations as well as power losses. The problem was validated on IEEE 69- and 118-bus systems and compared with established metaheuristics. Results show that DL allocation using MIDACO with SBFS-II and GBFS has improved the solution speed and accuracy, respectively. Furthermore, the enhanced voltage and frequency results highlight DL as an efficient power management solution.