This study presents a two-level hybrid optimization methodology, developed from an edge-computing perspective, to determine the optimal location of multiple Battery Energy Storage Systems (BESSs) and to perform a multiobjective energy management system (EMS) in alternating current (AC) microgrids (MG). At the first level, a Continuous Genetic Algorithm (CGA) is used to define the optimal candidate nodes where the batteries are installed, while at the second level, multiobjective optimization algorithms (MOPSO, MOSSA, MOMVO, and MOGNDO) are integrated to schedule the charge/discharge (SoC) trajectories over a 24-hour horizon. The proposed energy management system simultaneously minimizes energy costs (fixed or variable depending on the scenario), active power losses, and CO2 emissions, ensuring feasibility through power balance constraints, generation and BESS limits, and voltage and current constraints evaluated using hourly power flow and a penalty scheme. The main contribution of this work is the integration of a Continuous Genetic Algorithm (CGA) for locating BESS and Pareto-based multi-objective algorithms for optimizing EMS, evaluating the impact on BESS with daily operation on energy costs, active power losses, and CO2 emissions. The proposed methodology was validated on two modified benchmark feeders, a 27-node rural microgrid (diesel-dominated with PV-DG) and a 33-node urban microgrid (grid-connected with PV-DG), considering fixed and variable energy tariff scenarios. Statistical validation was obtained through 100 global runs, showing consistent improvements compared to the PV base case. In the rural case, the best minimum solution (CGA-MOSSA) achieved reductions of 0.123% in fixed costs, 11.228% in CO2, and 0.124% in losses, whereas CGA-MOMVO was the most stable on average. In the case of an urban network with a fixed cost, CGA-MOMVO dominated with reductions of up to 0.217% in cost, 5.278% in CO2, and 0.214% in losses. With variable tariffs, greater economic reductions (up to 1.694%) were obtained while maintaining compliance with operational constraints. In general, the results confirm that the joint optimization of BESS placement and EMS improves the economic, technical, and environmental performance of urban and rural AC microgrids.

The rapid integration of renewable energy sources and the decentralization of power systems have positioned microgrids as essential for sustainable, resilient energy supply. However, their diverse operating conditions and complex topologies pose challenges for stability, protection, and autonomous control, particularly under fault conditions. This article surveys brain-inspired artificial intelligence (BIAI) models that enable self-healing functions in Microgrids (MGs). It covers structure-driven models, including convolutional, recurrent, and spiking neural networks, alongside behavior-driven approaches such as learning systems (reinforcement, transfer, attention mechanisms, and emotions). A special emphasis is placed on Brain Emotional Learning and its variation, BELBIC, which replicates emotion-driven neural mechanisms to provide adaptive, quick, and less complexity control suited to the nonlinear nature and uncertain behavior of MGs. Evidence reported in the literature suggests that BIAI approaches can improve fault detection, enhance restoration decisions, and support more resilient and adaptive self-healing strategies compared with conventional AI techniques. This review aims to assist researchers and practitioners in developing more robust, adaptive, autonomous self-healing MG architectures. It concludes by highlighting the open challenges and potential future research objectives to accelerate the adoption of BIAI in a dynamic energy environment.

This paper presents a novel edge-computing-based architecture for optimal inverse time overcurrent relays installed to protect mesh microgrids (MGs) with distributed generation. The procedure employs graph theory to automate the detection of network changes, fault locations, and relay pairs in an MG. In addition, an automated process obtains the initial protection settings based on the operating conditions of the MG. Furthermore, the Continuous Genetic Algorithm (CGA), Salp Swarm Algorithm (SSA), and Particle Swarm Optimization (PSO) were implemented to determine the optimal protection settings to obtain better coordination between primary and backup protection relays. These processes were implemented using PowerFactory 2024 Service Pack 5A and Python 3.13.1. The proposal was validated in 68 operating scenarios that considered the islanded and connected operation modes of the MG, charging and discharging cycles of electric vehicle stations, and the presence or absence of photovoltaic generation. The overcurrent protection relays were organized into 100 primary–backup relay pairs to ensure proper coordination and selectivity. The total miscoordination time (TMT) index was used to measure when all pairs of relays were coordinated, with a minimum time close to zero. The results of the graph theory show that all the meshes, fault locations, and relay pairs were identified in the MG. The approach successfully coordinated 100 relay pairs across 68 scenarios, demonstrating its scalability in complex real-world MGs. The automation process obtained an average TMT of 12.2%, while the optimization obtained a TMS of 91.6% with the CGA, and a TMT of 99% was obtained with the SSA and PSO, demonstrating the effectiveness of the optimization process in ensuring selectivity and appropriate fault clearing times.

Mitigating pollution in cities where transportation powered by fossil fuels has a significant impact on human health is a public health priority. Although electric vehicles are one solution to this problem, their high acquisition and maintenance costs have limited their rapid adoption; therefore, other solutions may be useful in supporting reduction efforts. Therefore, this paper proposes a power control system for an Anion Exchange Membrane Fuel Cell (AEMFC) generator powered by hydrogen with the capacity to supply a direct current (DC) motor of 0.75 kW. A mathematical model of the AEMFC was proposed, and the parameters were adjusted to obtain polarization and power curves defining safe operating ranges (12.45-17.9 V). A boost converter was designed to increase the voltage of the cell output to 48 V to meet the requirements of the DC motor. The performance of the power converter was studied by analyzing its small-signal ripple, operating modes, and efficiency. The models and simulations were implemented using MATLAB and PSIM. A cascaded control system with proportional-integral (PI) and proportional-integral-derivative (PID) controllers was implemented to maintain voltage stability in the presence of input and load variation. The results show that the AEMFC is reliable and that the boost converter presents an efficiency higher than 98% in continuous mode. The robustness of the model was validated through simulations and using a prototype.

Economic dispatch in grid-connected microgrids is challenged by the variability of renewable generation, the uncertainty of demand, and the need to simultaneously satisfy technical and economic constraints under different operating conditions. This study proposes an integrated predictive economic dispatch strategy for power grids with interconnected microgrids, structured as a unified optimization framework. The approach integrates nodal electrical modeling, Optimal Power Flow (OPF)-based optimization, multi-scenario analysis, and post-optimization feasibility verification based on performance indicators within a single decision-support structure. The methodology is applied to a modified 14-node power grid interconnected with a microgrid, where simulations are conducted under three representative load scenarios (100%, 70%, and 40%) and two operational configurations (hybrid and renewable-only), enabling a comprehensive assessment of system behavior. Results show that the hybrid configuration consistently outperforms the renewable-only case, achieving loss reductions of up to 7.3 MW, increases in spinning reserve exceeding 50 MW, and a transition from net power import to export of approximately 50 MW under high demand. Additionally, the microgrid plays an active operational role, dynamically switching between import and export modes based on load levels and the generation mix. The proposed framework enables identification of operationally efficient and technically feasible configurations by incorporating bidirectional power exchange, electrical constraints, and reserve requirements. The main contribution lies in integrating technical, operational, and interaction variables within a single deterministic Optimal Power Flow (OPF)-based assessment scheme to support decision-making in interconnected microgrid-based power grids.

Accelerating decarbonization commitments are driving the deployment of renewable-dominant microgrids (MG) to extend reliable, affordable, and clean electricity to remote areas. In power systems with high renewable energy sources penetration, variability in photovoltaic generation (PV) makes energy storage a pivotal resource; this role becomes particularly critical in islanded operation, where its lifetime economics hinge on how battery degradation is modeled inside the Energy Management System (EMS). Second-life batteries (SLB) offer a promising, lower-cost, circular-economy option for stationary storage; however, their heterogeneous aging histories complicate Remaining Useful Life (RUL) estimation and operational planning. This paper presents the development and theoretical implementation of a degradation model for an SLB pack in an isolated MG EMS. The system comprised a PV system, diesel generator, and SLB. An empirical degradation model of SLBs was incorporated into the EMS objective function and compared with a linear model. The empirical model was refined using two additional factors: one to represent thermal heterogeneity at the module level and the other to capture calendar aging and the effect of the average state of charge. The results show that the choice of degradation model directly impacts the costs, dispatch strategy, and estimation of the remaining useful life. Furthermore, it was determined that temperature and internal thermal gradients are the most decisive external factors in the degradation of stationary systems. Finally, it was concluded that SLBs can operate between 6.9 and 11.3 years (equivalent to 2520-4114 full cycles), depending on the severity of the operating conditions and model parameterization.

The increasing digitalization of electrical substations, enabled by IEC 61850-based architectures, has improved operational efficiency while expanding the cyber attack surface. This paper introduces a standards-aligned cybersecurity risk mitigation model specifically designed for digital substations and mapped to representative attack scenarios. The model integrates preventive, detective, and application-level controls derived from NIST SP 800-82r3, IEC 62443, and ISO/IEC 27019, and is validated in a laboratory process-bus environment. A baseline risk assessment identified four high-risk scenarios in the studied digital substation architecture. For validation, a selected subset of controls was experimentally evaluated against two representative attack vectors, namely false data injection (FDI) on GOOSE messages and denial-of-service (DoS) against PTP synchronization. For the remaining scenarios, the post-mitigation effects were reassessed analytically based on control coverage, architectural exposure, and standards-aligned cybersecurity reasoning. The experimental validation demonstrated that both empirically tested high-risk scenarios (FDI on GOOSE and DoS on PTP) were effectively mitigated, reducing their residual risk to moderate and low levels, respectively. For the remaining two scenarios, a post-mitigation analytical reassessment based on control coverage and architectural exposure suggested a consistent risk reduction trend, although without direct experimental confirmation. Under this combined empirical-analytical assessment, the number of high-risk scenarios decreased from four to one, corresponding to a 50% experimentally validated reduction in high-risk exposure, complemented by an analytical reassessment of the remaining scenarios. These results provide quantitative evidence about the effectiveness of the model, even with partial implementation. The scientific contribution of this study lies in integrating multistandard cybersecurity requirements into an operational mitigation model tailored to IEC 61850 substations, combined with experimental risk quantification in a realistic process-bus testbed. The proposed model offers practical guidance for utilities and establishes a scalable foundation for advancing cybersecurity in critical power infrastructure.

This study presents the use of a Battery Energy Storage System (BESS) and a thermal power plant to enhance Primary Frequency Regulation (PFR) in a power system. This integration seeks to mitigate operational challenges, such as the reduction in system inertia and frequency regulation, which are heightened when increasing renewable energy use in power grids with high hydroelectric generation. The proposed solution enables thermal generators to operate at optimal capacity, while the BESS provides a rapid frequency response, thereby enhancing operational efficiency and compliance with national standards. The process was structured in five stages: criteria definition, analysis, design, models, and evaluation. A comprehensive methodological approach was adopted, including dynamic system modeling and BESS sizing based on regulatory parameters. The method was tested with real data from a thermal plant under the conditions of the Colombian electricity market. The simulation results highlight the effectiveness of the proposed BESS, with a response time of approximately 0.6 s and regulation maintenance for over 30 s, reducing mechanical stress and preventing frequency overshoot. The control strategy was designed to maintain the energy neutrality of the BESS, thereby stabilizing its state of charge over the operational horizon. The results show that the BESS targets high-frequency transients and the generator focuses on low-frequency adjustments, managed by an Energy Management System (EMS) with a unified control approach.

This paper presents the analysis, design, and implementation of a two-stage power conversion system consisting of a boost converter and a buck converter. Both converters were controlled using a sliding-mode control based on a washout filter. The system was supplied with an alternating current (AC) voltage source that was rectified using a diode bridge. The main objectives are to improve the power factor (PF) in the boost stage and regulate the output voltage in the buck stage. In the first stage, sliding-mode control is applied to shape the input current according to the rectified voltage, increasing the PF and reducing harmonic distortion. In the second stage, the same control approach is used to maintain a constant output voltage under load variations and disturbances. This study includes the mathematical modeling of both converters, control design, and simulation results in PSIM. The results show that the proposed sliding-mode control strategy effectively enhances energy efficiency, stabilizes the output voltage, and significantly improves the PF, making it suitable for robust and efficient power conversion systems.

This paper presents a new economic and environmental power dispatch approach for the energy management of alternating current microgrids integrated with distributed wind energy resources and battery energy storage systems. This study proposes an algorithm for intelligent energy management that adapts to inherent variations in wind energy resources, state of charge of batteries, and power demand. The problem is formulated to minimize variable and fixed generation costs, network power losses, and CO2 emissions of the microgrid with distributed energy resources, considering the constraints of the network, generation, and energy storage. To solve this problem, four metaheuristic optimization algorithms were implemented: Enhanced Prairie Dog Optimization (EPDO), Salp Swarm Algorithm (SSA), Generalized Normal Distribution Optimization (GNDO), and Crow Search Algorithm (CSA). A particle swarm optimization (PSO) algorithm was used to fine-tune the parameters of each algorithm, eliminating the need for manual adjustments and optimizing the quality and processing time. These algorithms are integrated into the objective functions and evaluated using a 24-hour power flow that incorporates a strict penalty scheme to satisfy the operational constraints. The problem was tested in a 33-node feeder system, and the best solutions found with the algorithms were compared to determine the performance to solve the problem. The results show that the approach ensures technical efficiency while minimizing economic and environmental requirements. The simulation results indicate that the SSA and GNDO algorithms outperform EPDO and CSA, achieving reductions of up to 2.001% in fixed costs, 4.684% in variable costs, 1.214% in CO2 emissions, and 6.084% in energy losses. The SSA stands out for its stability and processing efficiency. This promising model can be applied to urban and rural microgrids, as it offers a robust framework for energy management in alternating current systems.

The sustained growth in energy demand and the penetration of intermittent renewable sources require management systems capable of efficiently coordinating generation, storage, and consumption. This paper proposes a Virtual Power Plant (VPP) that integrates thermal, solar, wind, and battery storage units, with the aim of optimizing energy dispatch under technical constraints. Two control approaches are compared: (i) classic mixed-integer linear programming (MILP), with centralized resolution and daily planning; and (ii) a model-based predictive control (MPC) scheme, with a 24-step prediction horizon and hourly updates. Both models consider the same operating conditions and constraints. The results show that MPC improves coordination between energy storage and trading, achieving a reduction in operating costs of approximately 15.5% compared to MILP, as well as significantly lower computation times than the control interval (1 h), confirming its real-time feasibility. The implementation was carried out in MATLAB using YALMIP and CPLEX, under a common operating scenario for both approaches.

This paper presents an economic–environmental power dispatch approach for a grid-connected microgrid (MG) with photovoltaic (PV) generation and battery energy storage systems (BESSs). The problem was formulated as a multiobjective optimization problem with functions such as minimizing fixed and variable generation costs, power losses, and CO2 emissions. This study addresses the problem of intelligent energy management in microgrids with PV generation and BESSs to optimize their performance based on multiple criteria. This study focuses on optimizing the Energy Management System (EMS) with metaheuristic algorithms to achieve practical implementation with simpler algorithms to solve a complex optimization problem. This study employs four multiobjective optimization algorithms: Nondominated Sorting Genetic Algorithm II (NSGA-II), Harris Hawks Optimization (HHO), multiverse optimizer (MVO), and Salp Swarm Algorithm (SSA), which are classified as robust techniques for obtaining Pareto fronts. The computational resources employed to simulate the problem are presented. The optimal dispatch obtained from the Pareto front achieved reductions of 0.067% in fixed costs, 0.288% in variable costs, 3.930% in power losses, and 0.067% in CO2 emissions, demonstrating the effectiveness of the proposed approach in optimizing both economic and environmental performance. The SSA stood out for its stability and computational efficiency, establishing itself as a promising method for energy management in urban and rural microgrids (MGs) and providing a solid framework for optimization in alternating current systems.

Ensuring stability and power quality is a key challenge in modern power systems, and frequency regulation plays a crucial role in maintaining reliability. Primary Frequency Regulation (PFR) is the first response mechanism to disturbances, restoring frequency within the defined operational limits. In this regard, Battery Energy Storage Systems (BESS) have emerged as a highly effective solution for PFR because of their rapid response times, the ability to provide instant power support, and additional benefits such as equipment lifespan extension and improved operational flexibility. This paper examines the integration of BESS into a thermal power plant in Colombia. The study starts by evaluating various battery technologies to establish the BESS model and proposes a new methodology to enhance the performance of the PFR. Using real-time (RT) simulations, the dynamic response of the system and the feasibility of implementing BESS are evaluated. The results demonstrate that incorporating BESS improves system stability, reliability, and operational security. Furthermore, the proposed approach has significant potential for broader applications, including distributed energy resources and microgrid stabilization.

This study evaluates and compares centralized and distributed reactive power compensation strategies using Static Var Compensators (SVCs) to enhance the performance of a high-voltage transmission system in the Caribbean region of Colombia. The methodology comprises four stages: system characterization, assessment of the uncompensated condition under peak demand, definition of four SVC-based scenarios, and steady-state analysis through power flow simulations using DIgSILENT PowerFactory. SVCs were modeled as Thyristor-Controlled Devices (“SVC Type 1”) operating as PV nodes for voltage regulation. The evaluated scenarios include centralized SVCs at the Slack node, node N4, and node N20, as well as a distributed scheme across load nodes N51 to N55. Node selection was guided by power flow analysis, identifying voltage drops below 0.9 pu and overloads above 125%. Technically, the distributed strategy outperformed the centralized alternatives, reducing active power losses by 37.5%, reactive power exchange by 46.1%, and improving node voltages from 0.71 pu to values above 0.92 pu while requiring only 437 MVAr of compensation compared to 600 MVAr in centralized cases. Economically, the distributed configuration achieved the highest annual energy savings (36 GWh), the greatest financial return (USD 5.94 M/year), and the shortest payback period (7.4 years), highlighting its cost-effectiveness. This study’s novelty lies in its system-level comparison of SVC deployment strategies under real operating constraints. The results demonstrate that distributed compensation not only improves technical performance but also provides a financially viable solution for enhancing grid reliability in infrastructure-limited transmission systems.

This paper presents a robust control strategy for three-phase inverters that combines Sliding Mode Control with a Washout Filter (SMC-w) to achieve low harmonic distortion and high dynamic stability. The proposed approach addresses the critical challenge of maintaining the stability of a high-quality output signal while ensuring robustness against disturbances and adaptability under variable, unbalanced, and nonlinear loads. The proposed hybrid controller integrates the fast response and disturbance rejection capability of SMC with the filtering properties of the washout stage, effectively mitigating low-frequency chattering and steady-state offsets. A detailed stability analysis is provided to ensure the closed-loop convergence of the SMC–w. Simulation results obtained in MATLAB–Simulink demonstrate significant improvements in transient response, total harmonic distortion, and robustness under unbalanced and nonlinear load conditions compared to conventional control methods. The inverter demonstrated rapid tracking of the reference signals with a minimal error margin of 3%, effective frequency regulation with a low steady-state error, and resilience to input disturbances and load variations. For instance, under a load variation from 20 Ω to 5 Ω, the system maintained the output voltage accuracy within a 3% error threshold. In addition, the input perturbations and frequency shifts in the reference signals were effectively rejected, confirming the robustness of the control strategy. Furthermore, the integration of the SMC proved to be highly effective in reducing harmonic distortion and delivering a stable and high-quality sinusoidal output. The integration of the washout filter minimized the chattering phenomenon typically associated with the SMC, further enhancing the smooth response and reliability of the system. This study highlights the potential of SMC–w to optimize power quality and operational stability. This study offers significant insights into the development of advanced inverter systems that can operate in dynamic and challenging environments.

Constant luminous flux lamps are required for ensuring reliable and consistent illumination in various applications, including emergency lighting, outdoor activities, and general use. However, some activities may require maintaining a constant luminous flux, where the design must control the current during the use. This paper presents the design of a portable light-emitting diode (LED) lighting system powered by batteries that maintains constant luminous flux using the zero-average dynamic control (ZAD) and a proportional-integral-derivative (PID) controllers. This system can adapt the current to maintain the luminous flux required for reliable portable lighting applications used in outdoor activities. The results show that the system can provide constant illumination with 12-volt, 18-volt, and 24-volt batteries, and a 12-volt battery with a state of charge of 10%, enhancing usability for outdoor activities, emergency situations, and professional applications.

Digital electrical substations (DESs) represent a significant development for the electrical industry. New communication technologies and architectures have emerged alongside the development of DESs. This paper presents an analysis of typical communication architectures used in DESs. In addition, some optimization alternatives are identified, assessing the possible impacts on companies. Experimental tests were conducted in a laboratory to support the analysis performed in this research. Finally, some emerging topics and technologies supporting infrastructure optimization and technological adaptability are included. This study has revealed various impacts that companies must consider to effectively incorporate these architectures into the design and implementation processes of DESs. Finally, effective cybersecurity and access control policies should be implemented across all components of a DES to ensure successful implementations.

Contribution: Team-based learning (TBL) with a transdisciplinary (TD) approach is applied in one introductory programming course with different cohorts. The approach reduces the failure rate in the course. In addition, the approach helped students understand the application of programming to different engineering professional areas. Background: Programming courses in engineering develop abilities required for professional practice, such as applying concepts to solve complex problems using critical thinking analysis. However, a considerable body of research shows that these courses have significant failure rates because students perceive a high complexity in the topics. Different studies show the effectiveness of using active learning methods such as TBL to improve student performance and perception of the course. Nonetheless, there are few studies regarding active learning with TD in programming courses. Intended Outcomes: The use of TBL with a TD approach can improve student performance through teamwork and discussions including different perspectives. Application Design: The method creates teams of students from different programs and assigns real-life problems related to diverse engineering disciplines. Findings: TBL with TD allows for improving student performance and decreases failure rates. Additionally, a survey shows that students favor the methodology and are committed to developing different activities that integrate several areas of knowledge. Furthermore, the method allows students to visualize the usefulness of the course concepts in their professional field. The students also favor the dynamics of the class and the teamwork.

The increasing global demand for power necessitates a focus on renewable energy sources to reduce environmental impact and diversify energy generation. Estimating power demand in real-time electrical systems is crucial in this context. However, the literature contains various estimation techniques without clear guidance on the most suitable option based on performance and field-specific use. To address this gap, a study compares these techniques and identifies the most effective method for power demand estimation through a training process that assesses the accuracy of predictions against actual values. This comparison is conducted using the JUPYTER platform and historical data from the ENTSO-E power systems database. Results indicate that Linear Regression and Support Vector Machine techniques are commonly used for model evaluation, but the Adaptive Neural Network consistently performs as the most robust estimation method. The choice of technique depends on researcher preference, but it's essential to note that inadequate data preparation can lead to suboptimal results. In summary, as the world's power demand rises, the study identifies the Adaptive Neural Network as a promising tool for accurate power demand estimation, emphasizing the significance of proper data preparation in this process.

This paper presents a new method for the analysis of transient signals in the frequency domain based on the Continuous Wavelet Transform (CWT). The proposed case study involves test signals measured from an electronic switch considering open and close operations. The source is connected to inductive, resistive, and capacitive loads. Resonance behaviors are introduced and compared with the Discrete Fourier Transform (DFT). Multiple factors, such as reliability, repeatability, high noise attenuation, and the smoothing of the analyzed spectrum, are considered in this study. This proposed study highlights the effectiveness of CWT in signal processing, especially in obtaining a detailed spectrum that reveals the behavior of electrical circuits. Resonance behaviors were analyzed, demonstrating that the signal processing performed by CWT is better for spectrum analysis than DFT. This study shows the potential of CWT to analyze transient electrical signals, specifically for identifying and characterizing the behavior of load connections and disconnections.