Electricity doi: 10.3390/electricity7020044

Authors: Yicheng Li Yue Xiang Tianwen Zheng Cao Wen Wei Wei Jun Tong Haifeng Hu Zhou Sun Tianjin Chen Qian Zhang

To further enhance the active participation of electric vehicles in grid interaction and reduce the decision-making costs for electric vehicle aggregators, this paper addresses the challenges in current EV charging and V2G (Vehicle-to-Grid) management. Considering the owners’ willingness to participate, an optimal charging and V2G model for EV charging stations based on a Deep Q-Network is established. The paper analyzes in detail the mutual influence between the level of EV owner participation and the strategies of EV aggregators. Based on the owners’ willingness and the physical constraints of the EVs, an evaluation metric for EV participation in charging scheduling is developed. The Deep Q-Network is employed to make decisions regarding EV participation, thereby enhancing the decision-making capability of the EV aggregator, reducing the instability of its scheduling plans, and improving the reliability of these plans. Simulation results demonstrate that this method can dynamically consider EV owners’ willingness to participate, adaptively optimize the scheduling margin ratio, make global decisions across multiple time periods, and formulate charging and V2G scheduling plans for the EV aggregator.

Electricity doi: 10.3390/electricity7020043

Authors: Erick C. Jones Erick C. Jones

The rapid proliferation of artificial intelligence (AI) has pushed hyperscale data center rack densities beyond 100 kW, driving facility power requirements to the gigawatt scale. As developers attempt to deploy these massive Zettascale compute loads across US wholesale electricity markets, they encounter severe transmission planning bottlenecks, multi-year interconnection delays, and escalating grid transient stability risks. This paper presents a generalizable techno-economic framework for evaluating grid-tied, behind-the-meter (BTM) energy architectures as a means of bypassing these constraints. The framework is demonstrated through a detailed case study in the Electric Reliability Council of Texas (ERCOT), selected for its rapid data center growth and evolving large-load regulatory environment. Using a scenario-based comparative approach, this study models the feasibility of transitioning from pure-grid reliance to hybrid, on-site generation across a three-phase deployment pathway scaling from 25 MW to 250 MW. Six distinct microgrid configurations are evaluated, integrating baseload technologies—including Enhanced Geothermal Systems (EGSs), Small Modular Reactors (SMRs), and Reciprocating Internal Combustion Engines (RICEs)—with a tiered-performance Battery Energy Storage System (BESS) combining high C-rate lithium-ion units and repurposed electric vehicle batteries. System viability is assessed through two primary metrics: the Levelized Cost of Energy (LCOE) and the Avoided Loss of Load Probability (ALOLP). The results indicate that the blended LCOE scenario ranges from $64.50/MWh (Geothermal + Solar PPA) to $94.20/MWh (SMR-anchored), compared to a $75.00/MWh pure-grid baseline. The 100% Geothermal configuration achieves a scenario-dependent ALOLP exceeding 99.9%, while gas-dependent configurations range from 58.0% to 91.2%. These findings suggest that geographic siting co-optimized with localized generation offers a viable pathway for balancing regulatory compliance, capital cost, and Uptime Tier IV operational resilience in early-stage data center development across constrained grid environments.

Electricity doi: 10.3390/electricity7020042

Authors: Azeddine Bouzbiba Yassine Taleb Roa Lamrani Ahmed Abbou

This article highlights the intrinsic limitations of existing standards, such as EN 50160 and its associated measurement techniques, when applied to the assessment of power quality in high-speed railway traction power supply networks. These networks, characterized by intermittent and non-linear loads, generate disturbances (harmonics, voltage unbalance) that are not always detected or correctly quantified by standardized aggregation methods, leading to an underestimation of the actual impacts and calling into question the credibility of compliance assessments. The study proposes a new evaluation methodology based on synchronizing measurements with train traffic and grouping data by events rather than by fixed aggregation periods. This approach enables a more accurate characterization of negative-sequence voltage unbalance, providing a reliable estimation of both the magnitude and duration of disturbances. Experimental observations from multiple journeys and aggregation scenarios provide quantitative evidence supporting the relevance of the proposed improvements, which will contribute to updating and implementing standards better adapted to the specific characteristics of intermittent networks such as railway traffic, thereby ensuring a reliable, credible, and reproducible power quality assessment.

Electricity doi: 10.3390/electricity7020041

Authors: Stefanos Keskinis Costas Elmasides

This paper presents a unified, data-driven optimization framework for the planning and operation of an arbitrary number N of grid-connected microgrids connected to a distribution feeder. Each microgrid is represented as a controllable energy entity comprising local loads, battery energy storage systems (BESS) modeled through their State of Energy (SOE), and optional local generation. The microgrids are embedded explicitly in a radial distribution network subject to hosting-capacity and ramp-rate constraints at the point of common coupling (PCC). Unlike many existing studies that rely on synthetic or stylized demand profiles, this work employs real, open-access hourly load data from the Electricity Load Measurements and Analysis (ELMAS) dataset (France) to construct heterogeneous residential, commercial, and industrial microgrid instances. A plug-and-play integration rule is formulated at the planning level: the connection of an additional microgrid is admissible if and only if the enlarged optimization problem remains feasible and all reliability, network, and safety-oriented constraints are satisfied. The deterministic formulation is extended to handle uncertainty via scenario-based stochastic modeling of load variability. A comprehensive case study based on real French load profiles illustrates how feeder hosting capacity can be quantified in terms of the maximum number of microgrids that can be safely integrated. The results demonstrate that coordinated planning significantly improves PCC behavior, reduces operational stress, and provides a clear quantitative criterion for plug-and-play microgrid integration in distribution networks.

Electricity doi: 10.3390/electricity7020040

Authors: José Carvalho

The main purpose of electrical transmission and distribution networks is to carry electrical energy from the places where it is produced to the places of consumption, where the energy is used. Electrical energy is produced in power plants by generating units, which convert a form of primary energy into electrical energy. Primary energy comes from a number of sources, such as fossil fuels, nuclear energy, hydropower, wind, and solar. The carbon neutrality targets set by the European Union and several countries around the world have driven a transformation characterized by the gradual replacement of synchronous thermal generation based on fossil fuels with Renewable Energy Sources (RES), such as wind and solar. The energy transition, while necessary to achieve the established targets, introduces significant challenges to the stability of Electrical Power Systems (EPS) and electrical grids, since RES do not yet contribute to stability at levels comparable to the generating units of large thermal power plants, whether in terms of inertia, which has seen a notable reduction in recent years, or in voltage control or short-circuit power. This article presents and discusses solutions to mitigate the effect of this reduction in inertia in power plants using synchronous compensators and synthetic inertia emulation using battery storage.

Electricity doi: 10.3390/electricity7020039

Authors: Jozef Bendík Matej Cenký Žaneta Eleschová

The planning and design of Extra-High Voltage (EHV) overhead power lines require strict adherence to electromagnetic field exposure limits to ensure public safety. This paper presents a comprehensive analysis of the minimum ground clearance required for standard 400 kV transmission towers to comply with international safety guidelines. A review of legislative frameworks across 37 countries indicates a widespread consensus on limiting values of 5 kV/m for the electric field and 100 μT for magnetic flux density. Using analytical methods, the electric and magnetic fields were calculated for four common tower geometries (Cat, Portal, Danube, and Barrel) under varying ground clearances and phase configurations. The results demonstrate that the magnetic flux density is not a limiting factor, as it remains well below safety thresholds even at standard technical clearances. Conversely, the electric field intensity proves to be the critical design constraint, often requiring clearances significantly higher than those dictated by insulation coordination. The study identifies that optimizing the phase sequence in double-circuit towers can reduce the required ground clearance by up to 28%, offering a cost-effective mitigation strategy. These findings provide power line designers with essential decision-making data for the preliminary design phase, enabling the optimization of tower geometry and phase arrangement without the need for computationally intensive simulations.

Electricity doi: 10.3390/electricity7020038

Authors: Rabia Mricha Mohamed Khafallah Abdelouahed Mesbahi

Home Energy Management Systems (HEMS) are increasingly positioned at the center of residential flexibility, particularly as homes integrate photovoltaics, battery storage, electric vehicles, and responsive loads. This systematic review examines recent advances in optimization and multi-asset coordination for HEMS. Searches were conducted in Scopus, Web of Science, IEEE Xplore, and ScienceDirect for studies published between 2020 and 2025; after screening and eligibility assessment, 90 studies were included. The findings indicates that deterministic optimization remains well suited to structured scheduling problems, whereas metaheuristic, hybrid, and learning-based methods are better able to address nonlinearity, uncertainty, and real-time adaptation. Across the reviewed literature, multi-asset integration generally improves cost, peak demand, self-consumption, and, in some cases, user comfort and emissions. Yet the field remains dominated by simulation-based validation. Future progress of HEMS will depend on real-world validation, interoperable system design, explainable control, and stronger alignment with user behavior, communication constraints, and regulatory frameworks.

Electricity doi: 10.3390/electricity7020037

Authors: Gajanan Sarode Mangalkumar Bhatkar Subhadeep Paladhi

The proliferation of renewable energy resources has brought numerous challenges to conventional power systems, as grid integration is predominantly achieved through inverter-interfaced technologies such as photovoltaic (PV) plants and Type-IV wind turbines. Unlike synchronous generators (SGs), inverter-based resources (IBRs) exhibit fundamentally different fault behavior by limiting fault current magnitudes, typically within 1.0–1.2 per unit. Furthermore, the phase angle and sequence composition of the injected fault current are largely dictated by the inverter control strategy rather than by the network impedance. Consequently, distance protection schemes developed under assumptions of system homogeneity, a fixed source-to-impedance ratio (SIR), high fault current contribution, and large inertia may exhibit unreliable performance in inverter-dominated power networks. In this work, the influence of IBRs on key distance protection elements, such as starting elements, fault classification techniques, and impedance calculation with or without inter-feed, is reviewed and evaluated using simulations in PSCAD 5.0 software. Further, reduced grid inertia introduces operational limitations in power swing blocking (PSB) schemes, which are discussed in this paper. This work presents an overview of IBR fault responses and critically summarizes prior work on distance protection in IBR-dominated grids, highlighting key challenges, probable solutions, and the current research status to enhance understanding for further research.

Electricity doi: 10.3390/electricity7020036

Authors: Yining Chen Rovick P. Tarife Jared Jan A. Abayan Sophia Mae M. Gascon Yosuke Nakanishi

Hybrid hydro–solar microgrids offer a practical electrification option for remote and weak-grid communities by combining run-of-river hydropower with photovoltaic generation. However, their performance depends strongly on coordinated decisions across three layers: (i) system sizing and architecture, (ii) turbine selection and rating under variable river flow, and (iii) operational energy dispatch under time-varying solar resource and demand. This paper develops an optimization-driven planning framework for a run-of-river hydro–PV microgrid that co-optimizes component capacities and turbine-related design choices while enforcing time-series operational feasibility. Physics-based component models translate river discharge into hydroelectric output via turbine efficiency characteristics and operating limits, and compute PV generation and storage trajectories under dispatch and state-of-charge constraints. The planning problem is formulated as a multi-objective optimization that quantifies trade-offs among life-cycle cost, supply reliability (e.g., unmet-load metrics), and sustainability indicators (e.g., diesel-free operation or emissions when backup generation is present). A Pareto-optimal set of designs is obtained using a population-based multi-objective algorithm, and representative knee-point (balanced) solutions are selected to illustrate how turbine choice and dispatch strategy interact with seasonal hydrology and solar variability. The proposed approach supports transparent and robust design decisions for hybrid hydro–solar microgrids.

Electricity doi: 10.3390/electricity7020035

Authors: Nicola Collura Fabio Massaro Enrica Di Mambro Salvatore Paradiso Francesco Montana

A replicable methodology for testing the size and placement of Battery Energy Storage Systems connected to high-voltage transmission networks is presented in this study. The proposed approach involves the power flow analysis inside a Renewable Energy Zone, namely a high-renewable area prone to grid congestion during peak generation periods, based on time-series hourly analysis over a critical month. The model includes detailed operational descriptions such as lines ampacity, battery state of charge limits, round-trip efficiency, self-discharge behavior, and ramp rate restrictions. The methodology distinguishes itself by its simplicity, flexibility, and use of open-source tools, making it a valuable asset for supporting future transmission planning in high-renewable-energy scenarios. The model was developed in Python (version 3.12) using the open-source Pandapower library, introducing an innovative constraint management criterion, and validated against real data provided by the national Transmission System Operator. The approach was then applied to a portion of the Sicilian grid with massive wind and solar penetration. Results show that strategic allocation of batteries leads to a significant reduction in line overloads (up to 13 GWh mitigated in one month), improves the dispatch of renewable energy generated within the Renewable Energy Zone and allows a more sustainable exercise of the power system.

Electricity doi: 10.3390/electricity7020034

Authors: Miloš Milovanović Andrijana Jovanović Mladen Banjanin Ilija Vukašinović Branko Gvozdić Aleksandar Žorić Bojan Perović Jovan Vukašinović

This paper presents an experimental investigation of dynamic line rating (DLR) applied to aluminium conductor steel-reinforced (ACSR) overhead line conductors, with a specific focus on wind speed conditions up to 5 m/s. An experimental system was designed and implemented to provide controlled and repeatable cross-flow air conditions along a tested ACSR conductor, enabling direct measurement of wind speed in the immediate vicinity of the conductor surface. Conductor temperature, electrical current, voltage drop per unit length, the phase angle between them, and relevant meteorological parameters were continuously measured under controlled experimental conditions. Based on the measured data, the conductor heat balance was evaluated and the allowable current-carrying capacity was determined. The experimentally obtained conductor temperatures and ampacity values were compared with results calculated using thermal models and correlations recommended by IEEE, CIGRÉ, and IEC standards. The comparison demonstrates that, under low and moderate wind speed conditions, differences between standard-based predictions and experimental results can be significant, leading to deviations in the estimation of allowable current-carrying capacity. The results confirm the high sensitivity of DLR calculations to wind-related assumptions and provide an experimentally validated basis for assessing the applicability and limitations of existing standard thermal models for ACSR conductors under realistic operating conditions.

Electricity doi: 10.3390/electricity7020033

Authors: Amr Refky Eman M. Abdallah Hamdy Shatla Mohammed E. Elfaraskoury

Medium voltage induction motors (MVIM) are a key component of numerous industries, such as water treatment plants, sewage discharge stations, and chilled water systems. The starting process for these MV motors is critical as it is associated with a major impact on both motor lifetime and power grid quality. In this article, a proposed modified and comprehensive starting scheme of MV three-phase induction motors driving pumps for water stations is introduced. Firstly, the starting performance and its impact on power grid quality will be discussed when all motors are normally started with direct on line connection (DOL), which is already the normal established status. A modified starting scheme based on an optimized coordination of motor starting methods in addition to variable voltage variable frequency drive (VVVFD) drive and control implementation will be discussed. A transition between the starting of variant MV induction motors as well as the starting event coordination principle will be discussed to improve the power quality relative to the obligatory time shift required for the operation. The coordination is based on an algorithm implementation which is achieved using different optimization concepts based on artificial intelligence techniques, properly conducting the transition time in addition to the power delivered by the inverter unit rather than determining the number of DOL and VVVF-implemented motors. A comparison between using the optimized VVVFD soft-starting and the proposed modified scheme is performed, focusing on the power quality improvement rather than optimizing the cost function. The modified scheme is simulated using ETAP power station for brief analysis and study of load flow rather than the complete inspection and power quality assessment.

Electricity doi: 10.3390/electricity7020032

Authors: Chhith Chhlonh Marie-Cécile Alvarez-Herault Vannak Vai Bertrand Raison

This paper aims to define the optimal microgrid topology for rural electrification based on the lowest total cost by comparing LVAC and LVDC microgrids across three different scenarios. An LVAC radial topology is first designed using mixed-integer linear programming for phase balancing and the shortest path for connections, then implemented with a genetic algorithm to allocate and size solar home systems, forming an LVAC microgrid. Next, an LVDC topology is then derived from the LVAC structure and integrated with solar home systems under three scenarios: (1) using the same solar home system sizes, locations, and quantities as the LVAC microgrid; (2) using a genetic algorithm to re-determine solar home system sizes and locations, forming an LVDC microgrid; and (3) clustering the LVDC topology into nano-grids, each defined by genetic algorithm for solar home system sizing and placement and connected to the main feeder via bi-directional converters. Finally, all LVAC and LVDC scenarios are simulated over a 30-year planning horizon for analysis. A non-electrified village located in Cambodia has been selected for a case study to validate the proposed methods. The results have been obtained and provide a comparison of performance indicators (i.e., costs, energy production, losses, CO2 emissions, and autonomous energy) among the microgrids (LVAC and LVDC). The LVAC microgrid produced lower total energy losses than the LVDC microgrid in all scenarios. However, when considering environmental impact, LVDC Scenario 2 is preferable. Based on the total cost results, the LVAC microgrid is considered more economical than the LVDC microgrid in each scenario in this study.

Electricity doi: 10.3390/electricity7020031

Authors: Luis Humberto Diaz-Saldierna Julio C. Rosas-Caro Jesus Leyva-Ramos José G. González-Hernández Francisco Beltran-Carbajal Johnny Posada

This paper explores a new switching strategy for a recently proposed quadratic boost converter. The topology under study is a high-step-up DC–DC converter with a configuration that allows a portion of the processed energy to be used in what we call a non-series transfer. This characteristic reduces the amount of power processed redundantly. This converter, called a Quadratic Boost Converter based on Non-Series Energy Transfer (QBC-NSET), also has a non-pulsating input current, which is especially desirable for applications like photovoltaic and fuel-cell sources. This paper proposes a different switching strategy that reduces the output voltage ripple without increasing the switching frequency and without increasing the stored energy (inductance in inductors or capacitance in capacitors). The converter has two transistors, originally operated with synchronized signals; the proposed strategy provides independent switching signals with a phase shift between them. This enables the output capacitor to charge in a different switching state, producing a smaller voltage ripple while preserving the advantages of the topology originally presented. Steady-state analysis and voltage gain derivations confirm that the fundamental conversion characteristics remain unchanged. Experimental results obtained from a laboratory prototype validate the effectiveness of the proposed approach, demonstrating the reduction in the output voltage ripple.

Electricity doi: 10.3390/electricity7020030

Authors: Bo Zhou Yunyang Xu Xinwei Sun Xi Wang Baohong Li Congkai Huang

With the large-scale integration of renewable energy and the increasing use of power electronics, the issue of wide-band oscillations in power grids has become increasingly prominent. The scarcity and uneven distribution of oscillation samples pose significant challenges for training data-driven models, and traditional generative models struggle to ensure fidelity in both time and frequency domains. To address this, this paper proposes a Time–Frequency Fusion Generative Adversarial Network (TFF-GAN) for generating power grid oscillation risk scenarios. The method constructs a dual-path generation and discrimination framework, where the generator decomposes the signal using Short-Time Fourier Transform (STFT), with time-domain features extracted by a convolutional neural network (CNN) and frequency-domain features extracted from the STFT representation by a dedicated spectral network. These features are then fused using a U-Net structure. The discriminator simultaneously evaluates the authenticity of both the time-domain waveform and the frequency-domain spectrum. A composite loss function, incorporating time-domain loss, frequency-domain loss, and adversarial loss, is used for joint optimization. Experimental results demonstrate that the proposed method generates oscillation scenarios with high fidelity in both time-domain waveforms and frequency-domain spectra, effectively supporting power grid oscillation risk assessment and control strategy validation.

Electricity doi: 10.3390/electricity7020029

Authors: Josué D. Builes-Quintero Andrés F. Ángel-Ciro Santiago Bustamante-Mesa Sergio D. Saldarriaga-Zuluaga

The increasing penetration of low-inertia renewable energy sources and distributed generation has significantly reduced system inertia, making frequency stability a critical challenge in modern power systems. Traditional Under-Frequency Load Shedding (UFLS) schemes often fail to adapt to varying operating conditions and load behaviors, leading to either insufficient or excessive disconnections. This paper presents an optimization-based UFLS scheme that integrates dynamic simulations in DIgSILENT PowerFactory with Python programming through the Particle Swarm Optimization (PSO) algorithm. The proposed methodology optimizes key UFLS parameters—frequency thresholds, intentional delays, and load-shedding percentages—under different ZIP load model configurations (constant power, constant current, and constant impedance). Simulation results on the IEEE 39-bus test system demonstrate that the type of load model has a significant impact on frequency recovery performance and the total amount of load shed. The constant power model achieved system stability with the lowest load disconnection, whereas the constant impedance model required a greater amount of shedding to restore nominal frequency. The results validate the effectiveness of the proposed optimization tool and highlight the importance of considering load characteristics in UFLS design to enhance operational reliability and resilience in modern power systems.

Electricity doi: 10.3390/electricity7020028

Authors: Rossano Musca Mariano Giuseppe Ippolito Eleonora Riva Sanseverino

The article presents the development of a large-scale dynamic model of the European power system, including all essential features for wide-area monitoring and control studies. The simulated system includes 3809 nodes, 7343 branches, 618 synchronous machines with 1854 controllers, and 1573 PMUs. The system also integrates inverter-based resources, controlled in either grid-following or grid-forming mode. The model is developed in the phasor-based simulation domain and implemented in MATLAB/Simulink for computation according to a modelling approach that combines vectorized and elementwise operations. The model is publicly available and represents a fundamental tool for investigating transient phenomena and advanced control strategies at a wide-area level. As a demonstration of the possible use of the model, an innovative wide-area damping control is also applied. Numerical experiments are conducted under different configurations, investigating relevant inter-area oscillation phenomena in the European system and assessing the opportunity of the proposed wide-area damping control architectures. The main findings of the case study indicate a definite improvement in the dynamic performance of the system when a wide-area control is applied, leading to a sixfold increase in inter-area oscillation damping, with a reduction of about 80% in the energy involved during the system oscillations.

Electricity doi: 10.3390/electricity7020027

Authors: Jesús Alexander Osorio-Lázaro Ricardo Isaza-Ruget Javier Alveiro Rosero García

Electricity demand forecasting is essential for optimizing energy management and planning in microgrids and institutional contexts. The purpose of this article is to demonstrate how flexibility characterization can serve as a structural foundation for prediction, providing a contextualized framework that surpasses the limitations of traditional approaches. Representative trajectories (A–D), derived from entropy and variability metrics, were consolidated from historical user data and used as the basis for modeling. Two complementary approaches were implemented: ARIMA models, which capture endogenous dynamics, and ARX models, which extend this capacity by incorporating exogenous cyclical variables (hour, day of the week, month) and lagged predictors. A systematic grid search was conducted to identify optimal parameter configurations, followed by validation through rolling forecasts with a 24-h horizon, relevant for operators of microgrids, institutional managers, and energy planners. Performance was evaluated using MAE, RMSE, MAPE, and SMAPE, ensuring comparability across trajectories. Results show that ARIMA consistently achieved lower error rates in stable trajectories (A and C), with SMAPE values around 2.0%, while ARX provided substantial improvements in irregular ones (B and C), reducing SMAPE from 3.7–5.9% to approximately 2.2–2.6%. In highly irregular profiles (D), all models converged to similar accuracy (SMAPE ≈ 9.0%). When applied to individual users, predictive errors varied more widely depending on trajectory assignment: stable users showed SMAPE values around 3–4%, while irregular users exhibited much higher errors, exceeding 18–21%. Unlike conventional methods that treat characterization and prediction as separate processes, this study integrates both into a unified framework, enabling forecasts to capture stability, cyclicity, and adaptability. The methodology was further applied to individual users by assigning them to representative trajectories and adjusting predictions through baseline scaling. Overall, the findings demonstrate that embedding forecasts within characterized trajectories transforms prediction into a contextualized analysis of flexibility, enabling accurate short-term forecasts and supporting practical applications in energy planning, demand management, and economic dispatch. The framework has been designed to support electricity demand forecasting across multiple contexts, from microgrids and institutional systems to larger territorial and national scales. Through contextual calibration, the methodology ensures adaptability and broader relevance for energy forecasting and demand-side management.

Electricity doi: 10.3390/electricity7020026

Authors: Heesu Ahn Jongjin Park Changsoo Ok

The rapid expansion of the high electricity-intensive industries like data center has led to a structural increase in industrial electricity demand, thereby increasing the need for demand response (DR) to enhance power system flexibility. However, in the industrial sector, DR strategies based solely on simple load curtailment can impose productivity losses on participating customers. To address this limitation, this study proposes an SMP-based load shifting linear programming (LP) optimization model that enables DR curtailment to translate into electricity cost reduction through clustered DR resources formed by combining load resources at the industrial complex level. The decision variables representing hourly load shifting are adjusted under constraints defined by the hourly average demand and flexibility of the load resources, and the averages and fluctuations of SMP. The objective function is optimized to minimize the total electricity cost. Since the demand flexibility varies by season, experiments are conducted about various clustered DR resources on a seasonal basis. When resources with similar hourly average demand and flexibility are combined, the resulting load shifting plans are found to yield the greatest electricity cost reduction (Scenario 2—0.79 M KRW). These results confirm that the proposed load shifting LP model can provide a practical approach for DR operation planning.

Electricity doi: 10.3390/electricity7020025

Authors: Juan Pablo Bautista Ríos Dionicio Zocimo Ñaupari Huatuco Franklin Jesus Simeon Pucuhuayla Yuri Percy Molina Rodriguez

This paper introduces a hybrid metaheuristic approach for the reconfiguration of electric distribution networks, integrating Simulated Annealing (SA) and Tabu Search (TS) to accelerate convergence and enhance exploration of the solution space. The method employs a selective mesh-based neighbor generation strategy, which substantially reduces the search space while maintaining operational feasibility (radial topology, voltage, and current limits). The approach was implemented in Python and integrated with DIgSILENT PowerFactory, enabling the direct evaluation of losses, voltages, and currents for reproducible and scalable analysis. Validation on 5-, 16- and 33-bus benchmark systems consistently reached the global optimum across 100 simulation runs, demonstrating robustness and computational efficiency. A real-world application was performed on the 10 kV primary distribution network of Huancayo, Peru, where the proposed method achieved a 10.4% reduction in active losses, improved the minimum voltage from 0.931 to 0.949 p.u., and partially relieved feeder overloads. These results confirm the method’s suitability for both academic benchmarking and practical deployment in Latin American distribution systems.

Electricity doi: 10.3390/electricity7010024

Authors: Aleksey Udovichenko Pavel Sukhanov Dmitry Shtein

Secondary power supplies are an integral part of any complex device that requires power to different circuit nodes. This includes various kinds of telecommunication equipment, the aerospace industry, battery chargers, etc. Secondary power supplies include the most common pulse converters of both the boost, buck, and buck–boost variety, as well as forward, flyback, and push–pull converters. In particular, a galvanic isolation option may be considered for push–pull types. The use of multi–channel secondary power supplies is relevant for the space industry and satellites, where it is necessary to support the operation of many related devices. The efficiency of such devices is high due to their small number of elements and their simplicity of control. PWM (pulse width modulation) controllers can be considered as the last statement. In turn, the presence of radiation-resistant CMOS technology is required in outer space conditions, which is possessed by the PWM controller considered in this paper. Also, high efficiency and small dimensions can be achieved using planar technology. Here, one such secondary power supply, based on the PWM controller 5315EU015 with a power of 10 W, is considered, as well as the proposed design of a planar transformer. A mathematical model obtained from the algebraization of differential equations method, and from the PSIM software v. 22.2 simulation results and experiments is presented.

Electricity doi: 10.3390/electricity7010023

Authors: Yuriy Zhukovskiy Pavel Suslikov Daniil Rasputin

Non-intrusive load monitoring (NILM) can provide actionable feedback for demand response (DR) when direct measurements of device states are unavailable. We propose a reproducible, engineering-oriented pipeline for detecting ON/OFF states of end-use load groups from an aggregated active power time series. The method uses robust hysteresis-based labeling with adaptive thresholds derived from the median and median absolute deviation, followed by compact feature engineering restricted to global active power (GAP). After removing collinear features (|r| > 0.98), permutation importance is used to retain informative predictors. Probabilistic binary classifiers (LGBM, Histogram-based Gradient Boosting, XGBoost, and CatBoost) are trained for each target load, and the decision threshold is optimized via Fβ to balance missed events and false alarms. A post-processing stage stabilizes predictions by smoothing probabilities and suppressing spurious triggers. Model quality is assessed with both sample-wise metrics and event-based metrics that credit the correct detection of switching intervals within a time tolerance. Experiments on the open “Individual Household Electric Power Consumption” dataset (1-min resolution, 2007–2010) demonstrate that lightweight gradient boosting models, particularly LGBM, deliver reliable and interpretable state estimates suitable for practical DR integration and edge deployment.

Electricity doi: 10.3390/electricity7010022

Authors: Oscar Andrés Tobar-Rosero John E. Candelo-Becerra Jhon Montano Luis F. Quintero-Henao Fredy E. Hoyos

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.

Electricity doi: 10.3390/electricity7010021

Authors: Augustine B. Makokha Simiyu Sitati Abraham Arusei

Increasing variability in electricity load patterns, driven by end-use behaviour, grid-related technological changes, and socio-economic factors, calls for more accurate and efficient short-term load prediction (STLP) models. This study evaluates the predictive performance of four hybrid models for short-term Amp-load prediction: Adaptive Neuro-Fuzzy Inference System (ANFIS) combined with Genetic Algorithms (GA) and Particle Swarm Optimisation (PSO), as well as convolutional neural networks (CNN) integrated with long short-term memory (LSTM) and extreme gradient boosting (XGB). The models were developed using hourly Amp-load data collected from a power utility substation in Kenya, together with corresponding meteorological data (temperature, wind speed, and humidity) covering a period from January 2023 to June 2024. Results show that the ANFIS-PSO and ANFIS-GA models outperform the CNN-based models, achieving MAPE values of 4.519 and 4.363, RMSE values of 0.3901 and 0.4024, and R2 scores of 0.8513 and 0.8481, respectively, due to the adaptive nature of ANFIS, which enables effective modelling of the irregular, nonlinear, and complex temporal behaviour of the Amp load. Enhanced prediction accuracy was observed across all models when variational mode decomposition (VMD) was applied to pre-process the input data. This result was corroborated through further analysis of the Amp-load signals using Taylor plots. Among all of the configurations tested, the CNN-LSTM-VMD model exhibited the highest overall prediction accuracy, with MAPE of 2.625, RMSE of 0.1898, and R2 of 0.9702, marginally outperforming the ANFIS-PSO-VMD model, thus making it more suitable for short-term load prediction applications.

Electricity doi: 10.3390/electricity7010020

Authors: Zhengbiao Zhu Meiling Ma

The high penetration of wind power, photovoltaic, and energy storage not only presents opportunities for green and low-carbon development, but also poses significant challenges to frequency regulation. During primary frequency regulation (PFR), improper deadband settings may cause delayed or inadequate frequency responses, thereby exacerbating system frequency fluctuations, reducing renewable energy utilization rates, and compromising grid security and stability. This study proposes a parameter optimization method based on deadband to enhance PFR accuracy and improve overall energy conservation and emission reduction benefits. First, the impact of different deadband settings on system frequency fluctuations is analyzed, and the frequency response process is decomposed to quantify its effects on frequency stability and renewable energy integration capacity. Subsequently, the PFR coefficient is modified and optimized with maximum frequency deviation as the objective, thereby strengthening the frequency response capability of renewable energy. Simulation results demonstrate that the modified PFR coefficients reduce the maximum frequency deviation of wind–solar–storage systems by 0.0026 Hz, 0.0036 Hz, and 0.0034 Hz, respectively. This effectively elevates renewable energy integration levels and enhances the low-carbon stability of power system operations.

Electricity doi: 10.3390/electricity7010018

Authors: Nelson Dhanpal Chetty Gulshan Sharma Ravi Gandhi Amit V. Sant Pitshou N. Bokoro Rajesh Kumar

Traditional thermal power systems are merging with distributed generation and renewable energy sources, resulting in complex interconnected power system networks. This results in operational burdens and complexities in thermal power plants that they were not designed to handle. The role of Automatic Voltage Regulation (AVR) is crucial in maintaining the stability and dependability of these complicated power systems. This research provides a comprehensive review of the AVR control strategies within the last five years, considering operational complexities, changing topologies, and evolving challenges, in contemporary power systems. This review first explores the contemporary control strategies used in voltage regulation. Second, it provides an in-depth evaluation of the traditional Proportional Integral Derivative controllers with various improvements, adaptions, and modifications, followed by an examination of supplementary controllers in the AVR framework. Lastly, this paper reviews various optimisation strategies published in the last five years. This paper enriches our understanding of traditional and advanced control strategies in AVR, providing a comprehensive evaluation of their effectiveness and constraints, and aims to provide a valuable resource for researchers in this field.

Electricity doi: 10.3390/electricity7010019

Authors: Zouhaira Ben Mahmoud Adel Khedher

The use of photovoltaic (PV) water pumping technology offers a viable and sustainable alternative to conventional diesel-driven pumping systems. In PV-based pumping installations, the elimination of bulky transformers significantly reduces the overall system size and weight, which is particularly advantageous for rural and remote irrigation applications. However, removing the transformer can result in high common-mode voltage (CMV) when the induction motor is controlled using a direct torque control (DTC) scheme. This elevated CMV induces leakage currents that may damage the motor, compromise system reliability, and pose potential safety hazards. To ensure a more compact and safer PV pumping system, this paper introduces an improved DTC-based control strategy for induction motors driven by transformerless multilevel PV inverters. The proposed approach effectively suppresses leakage current by mitigating its main source, CMV, while maintaining the simple structure and dynamic performance inherent to conventional DTC. Two new look-up tables (LUTs) are developed to control the stator flux and electromagnetic torque while simultaneously eliminating leakage current. The first method, termed zero-medium vector DTC (ZMV-DTC), employs both zero and medium voltage vectors from the space vector diagram. The second, referred to as medium vector DTC (MV-DTC), utilizes only medium vectors. Numerical simulation results validate the feasibility and superior performance of the proposed algorithms in terms of leakage current suppression. Compared with a conventional DTC (C-DTC) scheme that is designed to limit the CMV, the proposed DTC algorithms achieve a much stronger reduction in the CMV, confining its amplitude to only a few volts, instead of the levels ±Vdc/6 typically produced by the C-DTC. As a result, the leakage current is effectively eliminated, ensuring safer and more reliable operation of the system.

Electricity doi: 10.3390/electricity7010017

Authors: Olanrewaju Lasabi Mohamed Khan Andrew Swanson Leigh Jarvis Anuoluwapo Aluko

The rapid integration of renewable energy sources has accelerated the adoption of DC microgrids as an effective platform for flexible and reliable power generation and management. However, conventional droop-based control suffers from inherent limitations, particularly voltage deviations at the DC bus, which compromise stability, power-sharing accuracy, and overall system performance. To address these challenges, this paper presents a distributed secondary control framework for a standalone PV battery-based DC microgrid that achieves bus voltage regulation, precise power distribution, and state-of-charge (SoC) balancing across multiple energy storage units (ESUs). At the primary level, an adaptive mechanism is introduced that dynamically adjusts droop coefficients in response to the real-time SoC of each ESU, promoting balanced utilization of storage resources. At the secondary level, the strategy leverages limited peer-to-peer communication to exchange only aggregate power information, thereby enabling accurate load sharing while preserving scalability and plug-and-play capability. The control architecture further incorporates voltage and current error compensation, with parameters tuned using a Whale Optimization Algorithm to enhance dynamic response. Validation is carried out through a real-time simulation environment developed in MATLAB/Simulink R2024b and executed on a SpeedgoatTM platform. The results demonstrate robust SoC equalization, improved bus voltage stability, and reliable cooperative coordination, positioning the scheme as a practical solution for next-generation DC microgrids.

Electricity doi: 10.3390/electricity7010016

Authors: Ali Md Ershad Ghamgeen Izat Rashed Zeenab

This study presents an advanced demand-side management framework to optimize energy consumption in smart grids featuring significant intermittent renewable energy integration. The approach leverages real-time data from an advanced metering infrastructure and a predictive model employing a bidirectional long short-term memory network enhanced with attention mechanisms for accurate load and electricity price forecasting. These predictions drive a multi-objective optimization model that harmonizes flexible demands across residential, commercial, and industrial sectors. A novel reference-guided multi-objective particle swarm optimizer is proposed to address the problem’s complexity, promoting improved convergence and diversity in solutions. In benchmarks, RGMOPSO demonstrated superior performance, attaining a fifty-six percent win rate in convergence metrics and a hypervolume of zero point nine three. Simulation results validate the framework’s effectiveness. It achieved a twenty percent reduction in operational costs, a nineteen-point-seven percent lower peak-to-average ratio, and an eighteen percentage point increase in renewable utilization. User-centric benefits included a thirty percent enhancement in comfort and a corresponding reduction in battery degradation. This integrated solution offers a resilient pathway for sustainable smart grid operations amid renewable uncertainties.

Electricity doi: 10.3390/electricity7010015

Authors: Marcela Y. Solorio-García Walter A. Mata-López José Luis Álvarez-Flores Jorge Simón Víctor H. Castillo

Currently, the smart grid concept represents the modern vision of an automated and highly adaptable electrical grid. Supervisory control and data acquisition (SCADA) systems are a fundamental component of a smart grid, enabling communication between field equipment and digital environments. For this purpose, they require industrial frameworks, among which IEC 61850 stands out. IEC 61850 has become a widely adopted standard for substation automation systems (SASs). However, despite its widespread adoption, IEC 61850 faces significant implementation challenges, including the potential complexity of data modeling, which often leads to discrepancies in semantic interpretation and, consequently, different readings among SAS configuration users. A disparity in the semantic interpretation of a process can negatively affect SAS operation, leading to execution errors or interoperability issues. Translating and analyzing SAS configurations can identify and resolve semantic interpretation discrepancies across these systems. The purpose of this research was to determine the degree to which a user interface was perceived as useful to support the translation and analysis of SAS configurations based on the IEC 61850 standard. To this end, a software tool was proposed as the central artifact to address the socio-technical dimension of a custom-built SCADA system at a Latin American state enterprise. The tool serves as the local, intelligent, and real-time operational layer in that system and was rated by users experienced with IEC 61850 as highly usable. The consistently obtained results suggest potential support for those performing the SAS configuration.

Electricity doi: 10.3390/electricity7010014

Authors: Gustavo Arteaga John E. Candelo-Becerra Jhon Montano Javier Revelo-Fuelagán Fredy E. Hoyos

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.

Electricity doi: 10.3390/electricity7010013

Authors: Alisson L. Agusti Giane G. Lenzi Jose M. Balthazar Angelo M. Tusset

LED lamps have not been demonstrating the durability claimed by their manufacturers. One hypothesis is that switching transients may contribute to this. This study investigated switching-induced transients in LED lamps operated through dry contacts: manual switches and contactors. Using an oscilloscope, automated acquisition of waveform records was performed while several lamps were switched on in a 220 VRMS/60 Hz electrical network. LED lamps of different models and manufacturers, one incandescent lamp, and a group of 48 LED lamps, subdivided into six sets of eight lamps, were all switched simultaneously. A total of 56 waveform-record files were obtained from the oscilloscope, comprising 2920 captured screens and 170 measurements. Transient voltage peaks of 380 and 391 V at the supply side, and 357 and 370 V at the lamp side, as well as voltage slew rates of up to 12 and 13 V/µs at the supply side and up to 16 and 19.5 V/µs at the lamp side, were measured, without considering statistical variations, which may indicate values exceeding the ordinary sinusoidal voltage peak (≅311 V) and its typical worst-case slew rate (≅0.12 V/µs). Future studies are suggested, such as tests in real installations, investigations of transient amplification or attenuation within electrical networks, assessment of the effects of wiring and impedance discontinuities, switch bounce, and semiconductor degradation, among others, to continue these studies.

Electricity doi: 10.3390/electricity7010012

Authors: Jairo Blanco-Solano Diego José Chacón Molina Diana Liseth Chaustre Cárdenas

This paper presents an optimization-based method to support distribution system operators (DSOs) in planning large-scale photovoltaic (PV) integration at the medium-voltage (MV) level. The PV hosting capacity (PV-HC) problem is formulated as a mixed-integer quadratically constrained program (MIQCP) without linearizing approximations to determine PV sizes and locations while enforcing operating limits and planning constraints, including candidate PV locations, per-unit PV capacity limits, active power exchange with the upstream grid, and PV power factor. Our method defines two HC solution classes: (i) sparse solutions, which allocate the PV capacity to a limited subset of candidate nodes, and (ii) non-sparse solutions, which are derived from locational hosting capacity (LHC) computations at all candidate nodes, and are then aggregated into conservative zonal HC values. The approach is implemented in a Hosting Capacity–Distribution Planning Tool (HC-DPT) composed of a Python–AMPL optimization environment and a Python–OpenDSS probabilistic evaluation environment. The worst-case operating conditions are obtained from probabilistic models of demand and solar irradiance, and Monte Carlo simulations quantify the performance under uncertainty over a representative daily window. To support integrated assessment, the index Gexp is introduced to jointly evaluate exported energy and changes in local distribution losses, enabling a system-level interpretation beyond loss variations alone. A strategy was also proposed to derive worst-case scenarios from zonal HC solutions to bound performance metrics across multiple PV integration schemes. Results from a real MV case study show that PV location policies, export constraints, and zonal HC definitions drive differences in losses, exported energy, and solution quality while maintaining computation times compatible with DSO planning workflows.

Electricity doi: 10.3390/electricity7010011

Authors: Zeeshan Haider Shehzad Alamgir Muhammad Ali S. Jarjees Ul Hassan Arif Mehdi

The increasing integration of hybrid AC/DC networks in modern power systems introduces new challenges in fault detection and grounding scheme design, necessitating advanced techniques for stable and reliable operation. This paper investigates fault detection and grounding schemes in hybrid AC/DC networks using a machine learning (ML) approach to enhance accuracy, speed, and adaptability. Traditional methods often struggle with the dynamic and complex nature of hybrid systems, leading to delayed or incorrect fault identification. To address this, we propose a data-driven ML framework that leverages features such as voltage, current, and frequency characteristics for real-time detection and classification of faults. Additionally, the effectiveness of various grounding schemes is analyzed under different fault conditions to ensure system stability and safety. Simulation results on a hybrid AC/DC test network demonstrate the superior performance of the proposed ML-based fault detection method compared to conventional techniques, achieving high precision, recall, and robustness against noise and varying operating conditions. The findings highlight the potential of ML in improving fault management and grounding strategy optimization for future hybrid power grids.

Electricity doi: 10.3390/electricity7010010

Authors: Martin Matejko Peter Braciník

A wide range of factors affect the dynamic and complex environment that is the commodity market. The most significant of these are external drivers, such as political decisions and weather conditions, which cannot be directly controlled. Nevertheless, specific characteristics and price behaviors are exhibited by individual commodities, which manifest through seasonal patterns and characteristic fluctuations. This study aimed to analyze the day-ahead electricity market and identify the key factors shaping electricity price formation. Particular focus was given to the role of meteorological variables and the interrelationships between the prices of other commodities, such as natural gas, coal, and oil. The analysis combined empirical techniques, such as Fourier transform and correlation analysis, with a predictive LSTM model using a Seq2Seq architecture to forecast short-term electricity prices. A basic forecast of electricity prices in the day-ahead market was provided by a simple predictive model that was developed based on the findings. The results highlight the interconnectedness of energy markets and confirm that external factors play a crucial role in shaping electricity prices.

Electricity doi: 10.3390/electricity7010009

Authors: Rajae Gaamouche Mohamed Belaid Abdenabi El Hasnaoui Mohamed Lahby

Although interest in tidal energy has increased in recent years, its development remains significantly behind that of other renewable sources such as solar and wind energy. This delay is primarily caused by the complex and harsh ocean environment, which imposes significant constraints on operational systems. This paper proposes a new approach to the design and control of a marine current turbine (MCT) emulator without a pitch mechanism, operating in real time below the rated marine current speed.The emulator control strategy integrates two approaches: predictive control for regulating the speed of the DC machine, and a Linear Quadratic Regulator (LQR) control scheme for maximizing power extraction from the marine current. Our experimental results demonstrate the effectiveness of the proposed hybrid control strategy, which allows precise tracking of reference signals and stable regulation of the direct current machine (DCM) speed, thereby ensuring synchronization with the turbine’s rotational speed. This approach ensures optimal and robust performance over the entire range of marine current variations.

Electricity doi: 10.3390/electricity7010008

Authors: Avelina Alejo-Reyes Julio C. Rosas-Caro Antonio Valderrabano-González Jesus E. Valdez-Resendiz Johnny Posada Juana E. Medina-Alvarez

This paper presents a detection algorithm for identifying when a sinusoidal signal becomes zero, which can provide information about its amplitude. This method can be used to detect voltage interruptions in a single-phase sinusoidal waveform, which may be applied in the rapid recognition of power outages in single-phase electrical systems. The method requires the measurement of a voltage signal. Other analysis methods, like calculating the Root Mean Square (RMS), are based on window sampling and require storing a relatively larger amount of samples in the system memory; an advantage of the proposed method is that it does not require as many samples, but its main advantage is its ability to reduce the detection time compared to other approaches. Techniques like the RMS value or amplitude detection through FFT typically require one full AC cycle to change from a 100% to 0% output signal and then detect a blackout, whereas the proposed method achieves detection within only a quarter cycle without considering additional rate-of-change enhancements, which can be further applied. The algorithm treats the measured single-phase voltage as the α component of an αβ Clarke pair and generates the β component by introducing a 90° electrical delay through a delayed replica of the original signal. The resulting αβ signals are then transformed into the dq reference frame in which the d component is used for outage detection, as it rapidly decreases from 100% to 0% within a quarter cycle following an interruption. This rapid response makes the proposed method suitable for applications that demand minimal detection latency, such as battery backup systems. Both simulation and experimental results validate the effectiveness of the approach.

Electricity doi: 10.3390/electricity7010007

Authors: Elmer Sorrentino

This paper presents the simultaneous maximization of speed and sensitivity in the Optimal Coordination of Directional Over-Current Protections (OC-DOCP), considering that maximum selectivity is maintained in all solutions. Only these three desirable features of the protection system were considered in the multi-objective approach; thus, the problem can be simply formulated as the weighted sum of speed and sensitivity as goals to be maximized, and the Pareto frontiers correlating speed and sensitivity are easily found in this way. These Pareto frontiers had not been shown in the literature about this topic, and they properly show the compromise solutions for the optimal solutions (i.e., speed improvements imply sensitivity deterioration while sensitivity improvements imply speed degradation). The simplest OC-DOCP formulation, applied to a well-known sample system, is taken as an example to show the Pareto frontiers for different time–current curve types. Another OC-DOCP formulation, which considers different topologies and their probability of occurrence, is also solved and the corresponding Pareto frontiers are also shown. The main findings of this work are the following: (a) in general, the results show that the variation in the speed in the Pareto frontier is more notorious for the less inverse curve types, whose optimal solutions are slower; (b) in the case of extremely inverse curves, the optimal solutions are faster and the effect of changes in sensitivity on the protection speed is very low in the Pareto frontiers; (c) it is also herein shown that the knowledge of this topic is also useful to solve some possible cases of unfeasibility related to the upper bound of time dial settings.

Electricity doi: 10.3390/electricity7010006

Authors: Xin Zhou Yanhao Li Shiqin Zhao Xijun Wang Lifan Chen Minyang Cheng Lvwen Huang

To improve the accuracy of cable temperature anomaly prediction and ensure the reliability of urban distribution networks, this paper proposes a multi-scale spatiotemporal model called MSST-Net (MSST-Net) for medium-voltage power cables in underground utility tunnels. The model addresses the multi-scale temporal dynamics and spatial correlations inherent in cable thermal behavior. Based on the monthly periodicity of cable temperature data, we preprocessed monitoring data from the KN1 and KN2 sections (medium-voltage power cable segments) of Guangzhou’s underground utility tunnel from 2023 to 2024, using the Isolation Forest algorithm to remove outliers, applying Min-Max normalization to eliminate dimensional differences, and selecting five key features including current load, voltage, and ambient temperature using Spearman’s correlation coefficient. Subsequently, we designed a multi-scale dilated causal convolutional module (DC-CNN) to capture local features, combined with a spatiotemporal dual-path Transformer to model long-range dependencies, and introduced relative position encoding to enhance temporal perception. The Sparrow Search Algorithm (SSA) was employed for global optimization of hyperparameters. Compared with five other mainstream algorithms, MSST-Net demonstrated higher accuracy in cable temperature prediction for power cables in the KN1 and KN2 sections of Guangzhou’s underground utility tunnel, achieving a coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) of 0.942, 0.442 °C, and 0.596 °C, respectively. Compared to the basic Transformer model, the root mean square error of cable temperature was reduced by 0.425 °C. This model exhibits high accuracy in time series prediction and provides a reference for accurate short- and medium-term temperature forecasting of medium-voltage power cables in urban underground utility tunnels.

Electricity doi: 10.3390/electricity7010005

Authors: Vinicius Gadelha João Soares-Vila-Luz Antonio E. Saldaña-González Andreas Sumper

In this work, a comprehensive literature review on power-routing devices is presented, outlining their current design principles and potential uses. Additionally, a comprehensive loss model for Modular Multilevel Converters (MMCs) in the context of power routers (PRs), a promising technology for enhancing flexibility and efficiency in future smart and hybrid AC–DC grids. Despite their potential, large-scale PR deployment is still limited by the lack of accurate and validated loss models. To address this gap, a detailed analytical model based on the Marquardt approach is proposed, capturing both conduction and switching losses in converter-based PRs. The model is validated through analytical comparison and PLECS simulations, showing strong agreement with theoretical and experimental data. Four case studies are presented to assess the effect of parameters such as power factor, active and reactive power, and the number of submodules on the overall converter losses. The results demonstrate that PR efficiency improves with optimized converter design and proper parameter selection.

Electricity doi: 10.3390/electricity7010004

Authors: Bilali Boureima Cisse Ghamgeen Izat Rashed Ansumana Badjan Hussain Haider Hashim Ali I. Gony Ali Md Ershad

Accurate short-term forecasting of photovoltaic (PV) output is critical to managing the variability of PV generation and ensuring reliable grid operation with high renewable integration. We propose an enhanced hybrid deep learning framework that combines Temporal Convolutional Networks (TCNs), Gated Recurrent Units (GRUs), and Random Forests (RFs) in an optimized weighted ensemble strategy. This approach leverages the complementary strengths of each component: TCNs capture long-range temporal dependencies via dilated causal convolutions; GRUs model sequential weather-driven dynamics; and RFs enhance robustness to outliers and nonlinear relationships. The model was evaluated on high-resolution operational data from the Yulara solar plant in Australia, forecasting horizons from 5 min to 1 h. Results show that the TCN-GRU-RF model consistently outperforms conventional benchmarks, achieving R2 = 0.9807 (MAE = 0.0136; RMSE = 0.0300) at 5 min and R2 = 0.9047 (RMSE = 0.0652) at 1 h horizons. Notably, the degradation in R2 across forecasting horizons was limited to 7.7%, significantly lower than the typical 10–15% range observed in the literature, highlighting the model’s scalability and resilience. These validated results indicate that the proposed approach provides a robust, scalable forecasting solution that enhances grid reliability and supports the integration of distributed renewable energy sources.

Electricity doi: 10.3390/electricity7010003

Authors: Mariajose Giraldo-Jaramillo Carolina Tranchita

The progressive replacement of synchronous machines by inverter-based resources (IBRs) reduces system inertia and short-circuit strength, making power systems more vulnerable to frequency and voltage instabilities. Grid-forming (GFM) inverters can mitigate these issues by establishing voltage and frequency references, emulating inertia and enabling autonomous operation during islanding, while grid-following (GFL) inverters mainly contribute to reactive power support. This paper evaluates the capability of GFM inverters to provide grid support under both grid-connected and islanded conditions at the distribution level. Electromagnetic transient (EMT) simulations in MATLAB/Simulink R2022b were performed on a 20 kV radial microgrid comprising GFM and GFL inverters and aggregated load. Small disturbances, including phase-angle jumps and voltage steps at the point of common coupling, were introduced while varying the GFM share and virtual inertia constants. Also, local variables were assessed during islanded operation and separation process. Results indicate that maintaining a GFM share above approximately 30–40% with inertia constants exceeding 2 s significantly enhances frequency stability, supports successful transitions to islanded operation, and improves overall resilience. The study highlights the complementary roles of GFM and GFL in enabling the stable and resilient operation of converter-dominated distribution systems.

Electricity doi: 10.3390/electricity7010002

Authors: Aleksandr Novozhilov Zhanat Issabekov Timofey Novozhilov Bibigul Issabekova Lyazzat Tyulyugenova

Electrical energy in urban and industrial power supply networks is mainly transmitted through 6–10-kV cable networks with an isolated neutral, where most lines are made as bunches of cables. Up to 75–90% of electrical faults in these cable networks belong to single-phase ground faults (SGFs), which can cause more severe accidents accompanied by significant economic damage. Widely known simple and directional protections against SGFs are relatively selective and, hence, often incapable of properly responding to SGFs in a network with such lines and detecting a cable with SGFs in the bunch of a damaged line. These disadvantages can be eliminated by using new, simple, and inexpensive, absolutely selective protections capable of detecting a cable with SGFs in a damaged line. We suggest the techniques and devices based on zero-sequence current transformers and ring measuring converters for building up such protection systems. The methods for calculating zero-sequence currents in cables of a bunched cable line, depending on the SGF point and the currents in the responding elements, are developed, as well as a procedure for determining a damaged cable and methods for estimating the responding element thresholds and the length of the protection dead zone.

Electricity doi: 10.3390/electricity7010001

Authors: Bouyahi Henda Adel Khedher

Induction motors (IMs) are widely used in variable-speed electric drive systems, where the motor is supplied by a voltage source inverter (VSI). Thus, PWM inverter–IM combination presents several issues that can degrade system performance, particularly overvoltage phenomena when long cables are used. In inverter-fed drive systems, the physical separation between the converter and the motor often requires long motor cables, which can significantly affect voltage stress. As the inverter’s output pulses propagate through the cable, voltage reflections and high-frequency oscillations occur at the motor terminals. We theoretically and experimentally investigate the effect of three PWM methods, namely Space Vector (SVPWM), Selective Harmonic Elimination PWM (SHEPWM), and Random PWM (RPWM) strategies, on overvoltage at the terminals of an induction motor fed by a PWM inverter through a long cable. The simulation results exhibit the validity and efficiency of SVPWM control to reduce overvoltage for different cable lengths. In addition, in order to reduce and eliminate all overvoltage peaks, three filters are proposed and evaluated: an RC filter, an RLC filter, and a compensator. The proposed PWM strategies are assessed using equivalent experimental results obtained on an induction motor fed by a two-level VSI. The experimental tests demonstrate also the efficiency of the SVPWM compared to other strategies.

Electricity doi: 10.3390/electricity6040075

Authors: Rossano Musca Maria Luisa Di Silvestre Liliana Mineo Salvatore Favuzza

Wide-area damping controls, like the wide-synchronization control (WSC), are crucial for power system stability but are vulnerable to communication latencies. This article presents a comprehensive theoretical characterization of the impact of time delays on the WSC. The formal analysis derives mathematical models for both differential and common modes. Two distinct scenarios are investigated: a symmetric condition, where the WSC is applied to both coupled areas, and an asymmetric condition, where it is applied to only one area. A formal stability assessment is conducted to determine stability boundaries and critical delay-induced crossings into unstable regions. Key findings show that under symmetric conditions, the system remains stable for all delays, as latencies only affect the common mode. Conversely, the asymmetric condition introduces a coupling between modes, making the system susceptible to delay-induced instability, especially at high control gains. The work validates the theoretical findings through numerical experiments and evaluates the accuracy of various linear Padé approximant models for representing delays, highlighting how low-order models can fail to predict instabilities, requiring high-order approximants to guarantee adequate accuracy in the analysis.

Electricity doi: 10.3390/electricity6040074

Authors: Vladimir Kopyrin Evgeniy Popov Alexander Glazyrin Yusup Isaev Rustam Khamitov Marina Deneko Maxim Kochetygov

Enhancing the efficiency of mechanized oil production remains a critical objective in the industry. This paper presents a comparative analysis of existing methods aimed at improving the energy efficiency of oil extraction systems, outlining their respective advantages and limitations. A novel approach is proposed, based on the use of a submersible compensator of reactive power to optimize the performance of electric submersible pumps (ESPs). A mathematical model of the ESP’s electrical system is developed to support the proposed method. Theoretical findings are validated by the experimental studies conducted on operational oil wells. Test results demonstrate a reduction in current consumption by 14.5–20% and an improvement in the power factor from 0.62 to 0.96. These outcomes confirm the effectiveness of the proposed solution in enhancing energy efficiency and reducing electrical losses in oil production processes.

Electricity doi: 10.3390/electricity6040073

Authors: Nelson Castañeda-Arias Nelson Leonardo Díaz-Aldana Adriana Luna Hernandez Andres Leonardo Jutinico

Energy management systems (EMSs) are essential for enabling the integration and operation of multiple interconnected microgrids within a microgrid system, especially when the penetration of renewable energy resources is high. As global energy demands rise and the need for sustainable solutions intensifies, microgrids offer a promising path toward enhancing grid resilience and efficiency. This review delves into the state of the art of EMSs in microgrid systems, highlighting the predominant use of optimization algorithms, and artificial intelligence (AI) techniques as the most commonly used strategies in energy management. Despite the advancements in these areas, there is a notable gap in the exploration of bio-inspired strategies that do not rely on traditional optimization approaches. Bio-inspired methods, which mimic natural processes and behaviors, have shown potential in various fields but remain underrepresented in EMS research. This paper provides a comprehensive overview of existing strategies and their applicability to energy management in microgrid systems. The findings suggest that while optimization algorithms and AI techniques dominate the landscape, their combination and integration with other techniques, such as multi-agent systems, are also gaining attention. The document explores how bio-inspired algorithms not only improve the efficiency of existing EMS methods but also enable new paradigms for managing energy in interconnected multi-microgrid systems. Additionally, applications such as vehicle-to-grid (V2G) and the integration of renewable resources are considered in the optimization of operational costs. Bio-inspired approaches could offer innovative solutions for enhancing the performance and sustainability of microgrid systems by defining the interactions between microgrids in a way that mirrors how communities interact; however, bibliometric analysis reveals that those techniques remain under reported, even though they could improve performance and resilience in multi-microgrid systems. This review underscores the need for further investigation into bio-inspired strategies to diversify and improve EMSs in microgrid systems.

Electricity doi: 10.3390/electricity6040072

Authors: Željko N. Popović Neven V. Kovački Marko Z. Obrenić Predrag M. Vidović

This paper proposes a risk-based, multi-objective approach to identify a solution, referred to as the fairness improvement plan, that enhances the fairness of photovoltaic (PV) curtailment, primarily applied to mitigate overvoltage issues in both balanced and unbalanced low-voltage distribution networks with high PV penetration. The proposed approach considers the uncertainty of loads, PV generation, and slack bus voltage. Relative Distance Measure (RDM) interval arithmetic is employed to represent these uncertainties while accounting for correlations among uncertain quantities, and the Pareto Simulated Annealing (PSA) method is used to generate a set of efficient fairness improvement plans. The Hurwicz criterion for measuring risk, which accounts for a decision maker’s risk preference, is incorporated in the interval TOPSIS technique to identify the fairness improvement plan, selected from a set of efficient plans, that minimizes the risk of financial losses and the risk of unfairness of PV’s active power curtailment. The numerical results obtained show that the proposed approach improves the insight and the understanding of the fairness improvement planning under uncertainty. They also highlight the effectiveness of incorporating decision makers’ risk preferences and their trade-off preferences between fairness and cost in developing the optimal fairness improvement plan under uncertainty in low-voltage distribution networks with high PV penetration.

Electricity doi: 10.3390/electricity6040071

Authors: Liyu Dai Xuhui Deng Wujie Chao Junwei Huang Jinke Wang Shengquan Lai Wenyu Qin Xin Chen

Modern power systems face growing challenges in stability assessment due to large-scale renewable energy integration and rapidly changing operating conditions. Data-driven approaches have emerged as promising solutions for real-time stability assessment, yet their performance often degrades under network topology reconfigurations. To address this limitation, the Spatiotemporal Contrastive Graph Convolutional Network (STCGCN) is proposed for the joint task prediction of voltage and transient stability across known and unknown topologies. The framework integrates a graph convolutional network (GCN) encoder to capture spatial dependencies and a temporal convolutional network to model electromechanical dynamics. It also employs supervised contrastive learning to extract discriminative features due to the grid topology variation, enhance stability class separability, and mitigate class imbalance under varying operating conditions, such as fluctuating loads and renewable integration. Case studies on the IEEE 39-bus system demonstrate that STCGCN achieves 89.66% accuracy on in-sample datasets from known topologies and 87.73% on out-of-sample datasets from unknown topologies, outperforming single-task learning approaches. These results highlight the method’s robustness to topology variations and its strong generalization across configurations, providing a topology-aware and resilient solution for real-time joint voltage and transient stability assessment in power systems.

Electricity doi: 10.3390/electricity6040070

Authors: Theofanis Aravanis Polydoros Papadopoulos Dimitrios Georgikos

Accurate prediction of wind turbine power output is essential for optimizing renewable energy generation, enhancing grid integration, and improving the efficiency of wind farms. However, the inherent non-linearities of wind speed–power relationships, combined with abrupt cut-in, rated, and cut-out effects, pose a significant modeling challenge. In this study, we investigate the use of artificial neural networks (ANNs) to model the power curve of a 1kW wind turbine, using an open-access dataset of real operational measurements recorded at 10 min intervals over the course of 2011. In particular, we compare a conventional multilayer perceptron (MLP) trained on raw wind speed inputs with a Fourier-feature-encoded MLP designed to mitigate spectral bias—the tendency of neural networks to favor smooth, low-frequency patterns over sharp, high-frequency variations. Experimental results show that the Fourier-enhanced MLP substantially improves predictive performance, reducing the mean absolute error (MAE) by more than 65% and achieving an R2 score of 0.999. The proposed approach demonstrates that Fourier feature encoding enables neural networks to capture sharp non-linearities in wind-turbine power curves, representing one of the first applications of this technique to wind-turbine power-curve modeling.

Electricity doi: 10.3390/electricity6040069

Authors: Fikri Waskito F. Danang Wijaya Eka Firmansyah

The increasing integration of renewable energy sources is reshaping power systems from centralized, synchronous generator-based architectures to more inverter-dominated and decentralized architectures. This transition, however, results in a significant reduction in system inertia, posing challenges to frequency stability. To address this issue, various control strategies have been proposed to emulate the inertial response of traditional synchronous generators—commonly known as virtual inertia. This study reviews inverter-based virtual inertia and related control strategies that replicate or extend synchronous generator dynamics, covering five main approaches: droop control, synchronverters, virtual synchronous generators (VSGs), the swing equation-based approach, and data-driven grid-forming (GFM) methods. While all approaches enhance frequency nadir and RoCoF, they differ in complexity, robustness, and adaptability. Droop control offers simplicity but lacks true inertia support, whereas synchronverter and swing equation-based controls provide closer emulation of synchronous behavior for grid-forming or islanded systems. VSG offers a more practical grid-following solution, and data-driven GFM introduces adaptability through learning-based mechanisms. Overall, this study contributes to a comprehensive understanding of how these control strategies can be implemented through inverter control to maintain frequency stability in renewable-dominated power systems.

Electricity doi: 10.3390/electricity6040068

Authors: Jhon Montano John E. Candelo-Becerra Fredy E. Hoyos

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.

Electricity doi: 10.3390/electricity6040067

Authors: Hao Wang Wenyue Zhou Yiping Luo

To address the critical issue of low reliability caused by fault impacts in large-scale wind farms transmitting power over long distances via flexible DC transmission systems, this study proposes a collaborative solution. First, a new protection scheme integrating variable quantity differential protection, steady-state quantity differential protection and zero-sequence differential protection is proposed. By establishing a refined model of a wind farm with a flexible DC system, the adaptability of the differential protection for the outgoing lines is checked. Simulation results show that the sensitivity of metallic faults within the protection zone is better than 3.0, and the protection reliably remains inactive for faults outside the protection zone. Second, an innovative fault ride-through strategy combining self-regulating resistor circuits with wind farm MPPT load reduction is proposed. During faults on the receiving grid, the DC voltage fluctuation is controlled within 1.05 p.u. through graded switching of resistor modules and dynamic power regulation. This solution offers both rapid response and smooth fault ride-through characteristics, significantly improving the feasibility and economic viability of wind farm integration via flexible DC transmission.

Electricity doi: 10.3390/electricity6040066

Authors: Andrei Darius Deliu Emil Cazacu Florențiu Deliu Ciprian Popa Nicolae Silviu Popa Mircea Preda

Maritime power systems must reduce fuel use and emissions while improving resilience. We study a shipboard PV–battery subsystem interfaced with a DC–DC converter running maximum power point tracking (MPPT) and curve-scanning GMPPT to manage partial shading. Dynamic average-value models capture irradiance steps and show GMPPT sustains operation near the global MPP without local peak trapping. We compare converter options—conventional single-port stages, high-gain bidirectional dual-PWM converters, and three-level three-port topologies—provide sizing rules for passives, and note soft-switching in order to limit loss. A Fourier framework links the switching ripple to power quality metrics: as irradiance falls, the current THD rises while the PCC voltage distortion remains constant on a stiff bus. We make the loss relation explicit via Irms2R scaling with THDi and propose a simple reactive power policy, assigning VAR ranges to active power bins. For AC-coupled cases, a hybrid EMT plus transient stability workflow estimates ride-through margins and critical clearing times, providing a practical path from modeling to monitoring.

Electricity doi: 10.3390/electricity6040065

Authors: Gilberto Guzman Manuel Madrigal Enrique Melgoza-Vázquez

This paper presents the implementation of the Grid-Forming (GFM) control technique in renewable energy source inverters to synchronize with the grid and provide frequency support. Specifically, the GFM Droop Control technique, based on the Power–Frequency relationship, is employed. The proposed model was developed and validated in the Matlab-Simulink environment. By using electromagnetic transient (EMT) simulations, we were able to precisely monitor and analyze voltage and current waveforms, thereby confirming the approach’s effectiveness in enhancing grid stability and power quality. The implementation of the GFM control technique in islanded mode demonstrated high system frequency stability. In response to sudden load changes up to 5 MW (equivalent to over 30% of the total load), a maximum frequency deviation of 0.04 Hz and a maximum Rate of Change of Frequency (RoCoF) of 4 Hz/s were observed. The system ensured the frequency’s return to its nominal value of 60 Hz, thanks to the virtual inertia and frequency regulation provided by the GFM. The total harmonic distortion (THD) of current and voltage in steady-state operation consistently remained below 1%, thus complying with IEEE 1547 standards. In tests with the GFM interconnected to the grid, the droop+LPF control provided dynamic support to the external system, effectively mitigating both frequency deviations and RoCoF. The GFM contributes to the grid’s frequency stability by providing virtual inertia. The power quality at the point of common coupling (PCC) was excellent, as the voltage distortion was maintained below 0.5%, confirming that the injection of harmonic currents does not violate established limits.

Electricity doi: 10.3390/electricity6040064

Authors: Salah Mokred Yifei Wang

Modern power distribution systems are increasingly stressed as they operate closer to their voltage stability limits, driven by growing electricity demand, complex load behaviors, and the evolving structure of power networks. Radial distribution systems, in particular, are highly susceptible to voltage instability under critical loading conditions, where even minor load increases can trigger voltage collapse. Such events threaten the continuity and quality of power supply and can cause damage to infrastructure and sensitive equipment. While large-scale cascading failures are typically associated with transmission systems, localized cascading effects such as sequential voltage drops, feeder outages, and protective device operations can still occur in distribution networks, especially under high loading. Therefore, reliable and timely voltage stability assessment is essential to maintain system reliability and prevent disruptions. This study presents a comprehensive comparative analysis of four voltage stability indices designed for radial distribution networks. The performance of these indices is evaluated on the IEEE 33-bus and 69-bus test systems under various critical loading conditions and multiple static load models, including Constant Power Load (CPL), Constant Current Load (CIL), Constant Impedance Load (CZL), Composite Load (COML), and Exponential Load (EXL). The analysis investigates each index’s effectiveness in identifying voltage collapse points, estimating critical load levels, and calculating load margins, while also evaluating their robustness across diverse operating scenarios. The findings offer practical insights and serve as a valuable benchmark for selecting suitable voltage stability indicators to support monitoring and planning in modern distribution networks.

Electricity doi: 10.3390/electricity6040063

Authors: Saroj Paudel Jiangfeng Zhang Beshah Ayalew Venkata Yagna Griddaluru Rajendra Singh

The design of battery modules for Electric Vehicles (EVs) and stationary Energy Storage Systems (ESSs) plays a pivotal role in advancing sustainable energy technologies. This paper presents a comprehensive overview of the critical considerations in battery module design, including system requirements, cell selection, mechanical integration, thermal management, and safety components such as the Battery Disconnect Unit (BDU) and Battery Management System (BMS). We discuss the distinct demands of EV and ESS applications, highlighting trade-offs in cell chemistry, form factor, and architectural configurations to optimize performance, safety, and cost. Integrating advanced cooling strategies and robust electrical connections ensures thermal stability and operational reliability. Additionally, the paper describes a prototype battery module, a BDU, and the hardware and software architectures of a prototype BMS designed for a Hardware/Model-in-the-Loop framework for the real-time monitoring, protection, and control of battery packs. This work aims to provide a detailed framework and practical insights to support the development of high-performance, safe, and scalable battery systems essential for transportation electrification and grid energy storage.

Electricity doi: 10.3390/electricity6040062

Authors: Muhammad Talal Khalid Marisol Velapatiño Benito Arin Rzonca Ann-Perry Witmer

The maximum demand payment component (MDPC) of the electricity bill, which reflects the highest level of power demand during a billing period, is a well-recognized barrier to the feasibility of electric vehicle fast-charging facilities (EVFCFs). While several studies have explored control strategies to mitigate demand peaks, they primarily focus on slow-charging facilities and fail to account for maximum demand prices. On the other hand, the few existing EVFCF-particular strategies overlook the diminished user-desired quality of service caused by the additional charging time needed for demand management. Moreover, their implementations under real-world conditions also remain unexplored. To address these issues, this work proposes a managed charging solution that explicitly considers the impact of maximum demand prices while maintaining user-desired quality of service, and implements it under real-world conditions in three different metropolitan areas in the United States. Simulation results indicate that the proposed solution can increase an EVFCF’s operational profits by 5–26% compared with conventional charging methods. The findings also highlight that the outcomes of the proposed solution are significantly influenced by the EVFCF utilization rate, the time between consecutive EV arrivals, the incumbent electric utility-specified maximum demand prices, and the user preferences of selecting the various “alternative fuel-grade options” offered at an EVFCF. These findings could pave the way for a more informed deployment of managed charging solutions at EVFCFs, thereby accelerating equitable transition to transportation electrification.

Electricity doi: 10.3390/electricity6040061

Authors: Renata Nogueira Francisco de Carvalho Erik Eduardo Rego Pamella Elleng Rosa Sangy Simone Quaresma Brandão

Electricity expansion planning is inherently subject to uncertainty, shaped by climatic, regulatory, and economic risks. In Brazil, this challenge is compounded by recurrent crises that have repeatedly reduced electricity demand. This study proposes a complementary decision-support approach to make planning more resilient to such crises. Using Brazil’s official optimization models (NEWAVE), we introduce two analytical elements: (i) a regret-minimization screen for choosing between conservative and optimistic demand trajectories and (ii) a flexibility stress test that evaluates the cost impact of compulsory-dispatch shares in generation portfolios. Key findings show that conservative demand projections systematically minimize consumer-cost regret when crises occur, while portfolios with lower compulsory-dispatch shares reduce total system cost and improve adaptability across 2000 hydro inflow scenarios. These results highlight that crisis-robust planning requires combining cautious demand assumptions with flexible supply portfolios. Although grounded in the Brazilian context, the methodological contributions are generalizable and provide practical guidance for other electricity markets facing deep and recurrent uncertainty.

Electricity doi: 10.3390/electricity6040060

Authors: Raphael I. Areola Abayomi A. Adebiyi Katleho Moloi

The global transition toward sustainable energy has significantly accelerated the deployment of solar power systems. Yet, the inherent variability of solar energy continues to present considerable challenges in ensuring its stable and efficient integration into modern power grids. As the demand for clean and dependable energy sources intensifies, the integration of artificial intelligence (AI) with solar systems, particularly those coupled with energy storage, has emerged as a promising and increasingly vital solution. It explores the practical applications of machine learning (ML), deep learning (DL), fuzzy logic, and emerging generative AI models, focusing on their roles in areas such as solar irradiance forecasting, energy management, fault detection, and overall operational optimisation. Alongside these advancements, the review also addresses persistent challenges, including data limitations, difficulties in model generalization, and the integration of AI in real-time control scenarios. We included peer-reviewed journal articles published between 2015 and 2025 that apply AI methods to PV + ESS, with empirical evaluation. We excluded studies lacking evaluation against baselines or those focusing solely on PV or ESS in isolation. We searched IEEE Xplore, Scopus, Web of Science, and Google Scholar up to 1 July 2025. Two reviewers independently screened titles/abstracts and full texts; disagreements were resolved via discussion. Risk of bias was assessed with a custom tool evaluating validation method, dataset partitioning, baseline comparison, overfitting risk, and reporting clarity. Results were synthesized narratively by grouping AI techniques (forecasting, MPPT/control, dispatch, data augmentation). We screened 412 records and included 67 studies published between 2018 and 2025, following a documented PRISMA process. The review revealed that AI-driven techniques significantly enhance performance in solar + battery energy storage system (BESS) applications. In solar irradiance and PV output forecasting, deep learning models in particular, long short-term memory (LSTM) and hybrid convolutional neural network–LSTM (CNN–LSTM) architectures repeatedly outperform conventional statistical methods, obtaining significantly lower Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and higher R-squared. Smarter energy dispatch and market-based storage decisions are made possible by reinforcement learning and deep reinforcement learning frameworks, which increase economic returns and lower curtailment risks. Furthermore, hybrid metaheuristic–AI optimisation improves control tuning and system sizing with increased efficiency and convergence. In conclusion, AI enables transformative gains in forecasting, dispatch, and optimisation for solar-BESSs. Future efforts should focus on explainable, robust AI models, standardized benchmark datasets, and real-world pilot deployments to ensure scalability, reliability, and stakeholder trust.

Electricity doi: 10.3390/electricity6040059

Authors: Marco Gutierrez Pavel Zuniga Dunstano del Puerto-Flores Felipe Uribe Emilio Barocio

Microgrids have extended their use when connected to or isolated from the grid, where decentralized control architectures are increasingly being used due to their inherent advantages. Among controllers, the non-communicated type allows the problems introduced by the use of communication systems to be avoided; however, these type of controllers are generally limited to performing first-level control actions, precisely due to the lack of information caused by the absence of a communication network. This work proposes an algorithm for a non-communicated controller to (a) identify which of the power sources are connected to a microgrid and (b) calculate the load power; both of these actions only require local measurements and allow the microgrid performance to be improved. The proposal aims at identifying the power sources by analyzing the electromechanical oscillations that occur in microgrids that are fed by inertia-enabled inverters and synchronous generators using droop controllers. This is used to automatically adjust the power sharing ratio between sources based on the generation capacity and load of a microgrid. Numerical simulations that clearly show the advantages are used to support the effectiveness of the proposal.

Electricity doi: 10.3390/electricity6040058

Authors: Fabio A. González Johnny Posada Bruno W. França Julio C. Rosas-Caro

This scoping review analyzes the role of inertia in converter-dominated microgrids, with an emphasis on hybrid AC/DC architectures. Following the PRISMA-ScR methodology, 54 studies published between 2015 and 2025 were identified, screened, and synthesized. The review addresses two key aspects, inertia estimation methods and control strategies for emulating inertia via power converters, emphasizing the role of the interlinking converter (ILC) as a bidirectional interface for inertia support between the AC and DC subsystems. This work addresses several limitations of prior reviews: their narrow scope, often overlooking advanced data-driven approaches such as machine learning; the lack of systematic classifications, hindering a comprehensive overview of existing methods; and the absence of practical guidance on selecting appropriate techniques for specific conditions. The findings show that conventional estimation methods are insufficient for low-inertia grids, necessitating adaptive and data-driven approaches. Virtual inertia emulation strategies—such as Virtual Synchronous Machines, Virtual Synchronous Generators, Synchronverters, and ILC-based controls—offer strong potential to enhance frequency stability but remain challenged by scalability, adaptability, and robustness. The review highlights critical research gaps and future directions to guide the development of resilient hybrid microgrid control strategies.

Electricity doi: 10.3390/electricity6040057

Authors: Morsy Nour Mona Zedan Gaber Shabib Loai Nasrat Al-Attar Ali

Peer-to-peer (P2P) energy trading has emerged as a novel approach to enhancing the coordination and utilization of distributed energy resources (DERs) within modern power distribution networks. This study presents a techno-economic analysis of different DER characteristics, focusing on the integration of photovoltaic (PV) systems and energy storage systems (ESS) within a community-based P2P energy trading framework in Aswan, Egypt, under a time-of-use (ToU) electricity tariff. Eight distinct cases are evaluated to assess the impact of different DER characteristics on P2P energy trading performance and an unbalanced low-voltage (LV) distribution network by varying the PV capacity, ESS capacity, and ESS charging power. To the best of the authors’ knowledge, this is the first study to comprehensively examine the effects of different DER characteristics on P2P energy trading and the associated impacts on an unbalanced distribution network. The findings demonstrate that integrating PV and ESS can substantially reduce operational costs—by 37.19% to 68.22% across the analyzed cases—while enabling more effective energy exchanges among peers and with the distribution system operator (DSO). Moreover, DER integration reduced grid energy imports by 30.09% to 63.21% and improved self-sufficiency, with 30.10% to 63.21% of energy demand covered by community DERs. However, the analysis also reveals that specific DER characteristics—particularly those with low PV capacity (1.5 kWp) and high ESS charging rates (e.g., ESS 13.5 kWh with 2.5 kW inverter)—can significantly increase transformer and line loading, reaching up to 19.90% and 58.91%, respectively, in Case 2. These setups also lead to voltage quality issues, such as increased voltage unbalance factors (VUFs), peaking at 1.261%, and notable phase voltage deviations, with the minimum Vb dropping to 0.972 pu and maximum Vb reaching 1.083 pu. These findings highlight the importance of optimal DER sizing and characteristics to balance economic benefits with technical constraints in P2P energy trading frameworks.

Electricity doi: 10.3390/electricity6040056

Authors: Gintvilė Šimkonienė

Electrical energy supply services are characterised by unpredictable risks that affect both distribution network operators (DSOs) and electricity consumers. This paper presents an innovative cooperative game theory (GT) framework to enhance electric energy supply reliability and demand-side flexibility by aligning the interest of DSOs and consumers. The research investigates the performance of the proposed GT model under different distribution network (DN) topologies and fault intensities, explicitly considering outage durations and restoration times. A cooperation mechanism based on penalty compensation is introduced to simulate realistic interactions between DSOs and consumers. Simulation results confirm that adaptive cooperation under this framework yields significant reliability improvements of up to 70% in some DN configurations. The GT-based approach supports informed investment decisions, improved stakeholder satisfaction, and reduced risk of service disruptions. Findings suggest that integrated GT planning mechanisms can lead to more resilient and consumer-centred electricity distribution systems.

Electricity doi: 10.3390/electricity6040055

Authors: Nouman Liaqat Muhammad Zubair Aashir Waleed Muhammad Irfan Abid Muhammad Shahid

Microgrids are becoming an innovative piece of modern energy systems as they provide locally sourced and resilient energy opportunities and enable efficient energy sourcing. However, microgrid operations can be greatly affected by sudden environmental changes, deviating demand, and unexpected outages. In particular, extreme climatic events expose the vulnerability of microgrid infrastructure and resilience, often leading to increased risk of system-wide outages. Thus, successful microgrid operation relies on timely and accurate outage predictions. This research proposes a data-driven machine learning framework for the optimized operation of a microgrid and predictive outage detection using a Recurrent Neural Network–Long Short-Term Memory (RNN-LSTM) architecture that reflects inherent temporal modeling methods. A time-aware embedding and masking strategy is employed to handle categorical and sparse temporal features, while mutual information-based feature selection ensures only the most relevant and interpretable inputs are retained for prediction. Moreover, the model addresses the challenges of experiencing rapid power fluctuations by looking at long-term learning dependency aspects within historical and real-time data observation streams. Two datasets are utilized: a locally developed real-time dataset collected from a 5 MW microgrid of Maple Cement Factory in Mianwali and a 15-year national power outage dataset obtained from Kaggle. Both datasets went through intensive preprocessing, normalization, and tokenization to transform raw readings into machine-readable sequences. The suggested approach attained an accuracy of 86.52% on the real-time dataset and 84.19% on the Kaggle dataset, outperforming conventional models in detecting sequential outage patterns. It also achieved a precision of 86%, a recall of 86.20%, and an F1-score of 86.12%, surpassing the performance of other models such as CNN, XGBoost, SVM, and various static classifiers. In contrast to these traditional approaches, the RNN-LSTM’s ability to leverage temporal context makes it a more effective and intelligent choice for real-time outage prediction and microgrid optimization.

Electricity doi: 10.3390/electricity6030054

Authors: Murillo Cobe Vargas Mariana Altoé Mendes Oureste Elias Batista Yongheng Yang

This paper investigates the impacts of small-scale distributed energy resources (DERs) on fault currents and overcurrent protection (OCP) coordination in distribution feeders, considering the Brazilian regulatory framework. Changes in fault current levels and OCP coordination are analyzed by focusing on the relationships between DER location, output power, and OCP positioning. Simulations were conducted in Simulink/MATLAB using the IEEE 13-Node Distribution Test Feeder as a case study, considering various DER integration scenarios. The DER model adheres to the Brazilian standard NBR 16149:2013, which governs fault current injection and voltage ride-through behavior. The results indicate that DER integration can disrupt OCP coordination and significantly affect fault current levels, despite their relatively small current contributions during faults. In one scenario, OCP coordination was lost, while in others, coordination time intervals decreased. The findings show that DER location has a minimal influence on fault current changes, whereas output power plays a more critical role. Faults occurring farther from the substation cause greater current variation in installed relays, with deviations nearing ±10%. Additionally, reverse fault currents through relays are identified as a key concern for protection engineers.

Electricity doi: 10.3390/electricity6030053

Authors: Jinxin Ouyang Yaowei Lin Zhiqi Ye Yanbo Diao

High-inertia energy storage synchronous condenser (HI-ES-SC) is operated through rotor-excited variable-speed mechanisms to provide grid power support. Power devices are exposed to alternating electro-thermal stresses, with significant implications for system reliability. Therefore, an electro-thermal modeling approach is developed for the converter of HI-ES-SC during power support operation. Switching dynamics and conduction states are incorporated in the model. A theoretical framework is established to analyze loss mechanisms and junction temperature evolution. A coupled electro-thermal model is constructed, accounting for temperature-dependent thermal network parameters. A numerical solution is proposed to enable co-simulation of condenser–converter systems. The simulation results indicate that the error in thermal parameter estimation remains below 10%. Key findings are summarized as follows: Under active power support, the peak junction temperature is observed to reach 81.69 °C during synchronous speed crossing, accompanied by notable low-frequency thermal accumulation. The derived operational-thermal correlation provides critical guidance for optimal thermal design and device selection.

Electricity doi: 10.3390/electricity6030052

Authors: Leonardo Casey Hidalgo Monsivais Yuniel León Ruiz Julio Cesar Hernández Ramírez Nancy Visairo-Cruz Juan Segundo-Ramírez Emilio Barocio

In this work, a controller hardware-in-the-loop (CHIL) simulation of a grid-connected three-phase inverter equipped with an LCL filter is implemented using a real-time digital simulator (RTDS) as the plant and a digital signal processor (DSP) as the control hardware. This work identifies and discusses the critical aspects of the CHIL implementation process, emphasizing the relevance of the control delays that arise from sampling, computation, and pulse width modulation (PWM), which also adversely affect system stability, accuracy, and performance. Time and frequency domains are used to validate the modeling of the system, either to represent large-signal or small-signal models. This work shows multiple representations of the system under study: the fundamental frequency model, the switched model, and the switched model controlled by the DSP, are used to validate the nonlinear model, whereas the impedance-based modeling is followed to validate the linear representation. The results demonstrate a strong correlation among the models, confirming that the delay effects are accurately captured in the different simulation approaches. This comparison provides valuable insights into configuration practices that improve the fidelity of CHIL-based validation and supports impedance-based stability analysis in power electronic systems. The findings are particularly relevant for wideband modeling and real-time studies in electromagnetic transient analysis.

Electricity doi: 10.3390/electricity6030051

Authors: Muhammad Mukit Hosen Md Shafiul Alam Shaharier Rashid S. M. G. Mostafa

This article presents an optimized approach to battery sizing and economic dispatch in wind-powered microgrids. The primary focus is on integrating battery depth of discharge (DoD) constraints to prolong battery life and ensure cost-effective energy storage management. Because of the intermittent nature of wind energy, wind-powered microgrids require sophisticated energy storage systems to ensure stable operation. This study develops a metaheuristic optimization method that balances power supply, battery lifespan, and economic dispatch in a microgrid. The proposed method optimizes both battery size and dispatch strategy while considering wind energy variability and the impact of DoD on battery lifespan. Case studies conducted on a wind-powered microgrid under varying load conditions show that the developed approach achieves a 40 to 50% reduction in operating costs and cost of electricity (CoE) compared to other approaches. The results also reveal that the inclusion of DoD constraints enhances battery lifespan. The proposed method offers a practical solution for improving the economic and operational efficiency of wind-powered microgrids, providing valuable understanding for energy planners and grid operators in renewable energy systems.

Electricity doi: 10.3390/electricity6030050

Authors: Mahmoud M. Elkholy Ashraf Abd El-Raouf Mohamed A. Farahat Mohammed Elsayed Lotfy

This research focused on determining the technical and economic feasibility of the design of a solar-powered electric vehicle charging station (EVCS) in Cairo, Egypt. Using HOMER Grid, hybrid system configurations are assessed technically and economically to reduce costs and ensure reliability. These systems incorporate photovoltaic (PV) systems, lithium-ion battery energy storage systems (ESS), and diesel generators. A comprehensive analysis was conducted in Cairo, Egypt, focusing on small vehicle charging needs in both grid-connected and generator-supported scenarios. In this study, a 468 kW PV array integrated with 29 units of 1 kWh lithium-ion batteries and supported by time-of-use (TOU) tariffs, were used to optimize energy utilization. This study demonstrated the feasibility of the system in a case of eight chargers of 150 kW each and forty chargers of 48 kW. Conclusions suggest that the PV + ESS has the lowest pure power costs and reduced carbon emissions compared to traditional network-dependent solutions. The optimal configuration of USD 10.23 million over 25 years, with lifelong savings, results in annual savings of tool billing of around USD 409,326. This study concludes that a solar-powered EVC in Egypt is both technically and economically attractive, especially in the light of increasing energy costs.

Electricity doi: 10.3390/electricity6030049

Authors: Adil Rizki Achraf Touil Abdelwahed Echchatbi Rachid Oucheikh

The Economic Dispatch Problem (EDP) plays a critical role in power system operations by trying to allocate power generation across multiple units at minimal cost while satisfying complex operational constraints. Traditional optimization techniques struggle with the non-convexities introduced by factors such as valve-point effects, prohibited operating zones, and spinning reserve requirements. While metaheuristics methods have shown promise, they often suffer from convergence issues and constraint-handling limitations. In this study, we introduce a novel application of Group Relative Policy Optimization (GRPO), a reinforcement learning framework that extends Proximal Policy Optimization by integrating group-based learning and relative performance assessments. The proposed GRPO approach incorporates smart initialization, adaptive exploration, and elite-guided updates tailored to the EDP’s structure. Our method consistently produces high-quality, feasible solutions with faster convergence compared to state-of-the-art metaheuristics and learning-based methods. For instance, in the case of the 15-unit system, GRPO achieved the best cost of USD 32,421.67/h with full constraint satisfaction in just 4.24 s, surpassing many previous solutions. The algorithm also demonstrates excellent scalability, generalizability, and stability across larger-scale systems without requiring parameter retuning. These results highlight GRPO’s potential as a robust and efficient tool for real-time energy scheduling in smart grid environments.

Electricity doi: 10.3390/electricity6030048

Authors: Mehrdad Ghadiri Luca Di Persio

Wind turbine power forecasting is crucial for optimising energy production, planning maintenance, and enhancing grid stability. This research focuses on predicting the output of a Senvion MM92 wind turbine at the Kelmarsh wind farm in the UK using SCADA data from 2020. Two approaches are explored: a hybrid model combining Stochastic Differential Equations (SDEs) with Neural Networks (NNs) and Deep Learning models, in particular, Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and the Combination of Convolutional Neural Networks (CNNs) and LSTM. Notably, while SDE-NN models are well suited for predictions in cases where data patterns are chaotic and lack consistent trends, incorporating stochastic processes increases the complexity of learning within SDE models. Moreover, it is worth mentioning that while SDE-NNs cannot be classified as purely “white box” models, they are also not entirely “black box” like traditional Neural Networks. Instead, they occupy a middle ground, offering improved interpretability over pure NNs while still leveraging the power of Deep Learning. This balance is precious in fields such as wind power prediction, where accuracy and understanding of the underlying physical processes are essential. The evaluation of the results demonstrates the effectiveness of the SDE-NNs compared to traditional Deep Learning models for wind power prediction. The SDE-NNs achieve slightly better accuracy than other Deep Learning models, highlighting their potential as a powerful alternative.

Electricity doi: 10.3390/electricity6030047

Authors: Manuel Seidenath Martin Maerz

Accurately simulating fuses is challenging because the fuse behavior is affected by a variety of thermal and electrical factors. This paper presents a SPICE fuse model and its parameterization procedure. The model mimics the physical behavior of the time–current characteristic including the transition region. For the parameterization only, the time–current characteristic of the fuse, its resistance at room temperature and the melting temperature of the conducting material are needed. The novelty of this SPICE fuse model is the mathematical derivation of a physically based correction factor that considers the temperature dependence of the electrical fuse conductivity. The correction factor is applied to the inverted time–current characteristic. A third-order Foster thermal equivalent network is fitted to the adapted fuse characteristic using a least square algorithm. After a Foster–Cauer transformation, the thermal equivalent network is integrated into the SPICE model. Exemplary LTSpice is used to show and validate the model’s wiring diagram. Comparisons show a very good agreement with data sheet characteristics for a variety of fuse types and current ratings. In the adiabatic and transition region—i.e., at low tripping times—the maximum relative error between the data sheet characteristic and the simulated characteristic was consistently below 15% and thus within the production parameter spread.

Electricity doi: 10.3390/electricity6030046

Authors: Oscar Danilo Montoya César Leonardo Trujillo-Rodríguez Carlos Andrés Torres-Pinzón

This paper presents a hybrid optimization framework for solving the optimal reactive power compensation problem in medium-voltage smart distribution networks. Leveraging Julia’s computational environment, the proposed method combines the global search capabilities of the Chu & Beasley genetic algorithm (CBGA) with the local refinement efficiency of the interior-point optimizer (IPOPT). The objective is to minimize the annualized operating costs by reducing active power losses while considering the investment and operating costs associated with thyristor-switched capacitors (TSCs). A key contribution of this work is the comparative assessment of fixed and time-varying reactive power injection strategies. Simulation results on the IEEE 33- and 69-bus test feeders demonstrate that the proposed CBGA-IPOPT framework achieves annualized cost reductions of up to 11.22% and 12.58% (respectively) under fixed injection conditions. With variable injection, cost savings increase to 12.43% and 14.08%. A time-domain analysis confirms improved voltage regulation, substation reactive demand reductions exceeding 500 kvar, and peak loss reductions of up to 32% compared to the uncompensated case. Benchmarking shows that the hybrid framework not only consistently outperforms state-of-the-art metaheuristics (the sine-cosine algorithm, the particle swarm optimizer, the black widow optimizer, and the artificial hummingbird algorithm) in terms of solution quality but also demonstrates high solution repeatability across multiple runs, underscoring its robustness. The proposed method is directly applicable to real-world distribution systems, offering a scalable and cost-effective solution for reactive power planning in smart grids.

Electricity doi: 10.3390/electricity6030045

Authors: Hugo Alessandro Figueroa-Saavedra Daniel Sanin-Villa Luis Fernando Grisales-Noreña

The transition to decentralized renewable energy systems has highlighted the role of AC microgrids and battery energy storage systems in achieving operational efficiency and sustainability. This study proposes an improved energy management system for AC MGs based on a tuned Parallel Population-Based Genetic Algorithm for the optimal operation of batteries under variable generation and demand. The optimization framework minimizes power losses, emissions, and economic costs through a master–slave strategy, employing hourly power flow via successive approximations for technical evaluation. A comprehensive assessment is carried out under both grid-connected and islanded operation modes using a common test bed, centered on a flexible slack bus capable of adapting to either mode. Comparative analyses against Particle Swarm Optimization and the Vortex Search Algorithm demonstrate the superior accuracy, stability, and computational efficiency of the proposed methodology. In grid-connected mode, the Parallel Population-Based Genetic Algorithm achieves average reductions of 1.421% in operational cost, 4.383% in power losses, and 0.183% in CO2 emissions, while maintaining standard deviations below 0.02%. In islanded mode, it attains reductions of 0.131%, 4.469%, and 0.184%, respectively. The improvement in cost relative to the benchmark exact methods is 0.00158%. Simulations on a simplified 33-node AC MG with actual demand and generation profiles confirm significant improvements across all performance metrics compared to previous research works.

Electricity doi: 10.3390/electricity6030044

Authors: Jinhao Yuan Meiling Ma Yanbing Jia

Large-scale wind power grid-connected systems can trigger the risk of power system instability. In order to enhance the stability margin of grid-connected systems, this paper accurately characterizes the topology of the global boundary of stability domain (BSD) of the grid-connected system based on BSD theory, using the method of combining the manifold topologies and singularities at infinity. On this basis, the effect of large-scale doubly fed induction generators (DFIGs) replacing synchronous units on the BSD of the system is analyzed. Simulation results based on the IEEE 39-bus system indicate that the negative impedance characteristics and low inertia of DFIGs lead to a contraction of the stability domain. The principle of singularity invariance (PSI) proposed in this paper can effectively expand the BSD by adjusting the inertia and damping, thereby increasing the critical clearing time by about 5.16% and decreasing the dynamic response time by about 6.22% (inertia increases by about 5.56%). PSI is superior and applicable compared to traditional energy functions, and can be used to study the power angle stability of power systems with a high proportion of renewable energy.

Electricity doi: 10.3390/electricity6030043

Authors: Nestor Julian Bernal-Carvajal Carlos Arturo Mora-Peña Oscar Danilo Montoya

This paper addresses the economic–environmental dispatch (EED) problem in DC power grids integrating thermoelectric and photovoltaic generation. A multi-objective optimization model is developed to minimize both fuel costs and CO2 emissions while considering power balance, voltage constraints, generation limits, and thermal line capacities. To overcome the non-convexity introduced by quadratic voltage products in the power flow equations, a convex reformulation is proposed using second-order cone programming (SOCP) with auxiliary variables. This reformulation ensures global optimality and enhances computational efficiency. Two test systems are used for validation: a 6-node DC grid and an 11-node grid incorporating hourly photovoltaic generation. Comparative analyses show that the convex model achieves objective values with errors below 0.01% compared to the original non-convex formulation. For the 11-node system, the integration of photovoltaic generation led to a 24.34% reduction in operating costs (from USD 10.45 million to USD 7.91 million) and a 27.27% decrease in CO2 emissions (from 9.14 million kg to 6.64 million kg) over a 24 h period. These results confirm the effectiveness of the proposed SOCP-based methodology and demonstrate the environmental and economic benefits of renewable integration in DC networks.

Electricity doi: 10.3390/electricity6030042

Authors: Hongchun Shu Le Minh Tri Nguyen Xuan Vinh Nguyen Quoc Hung Doan

Transmission lines are constantly exposed to changes in climatic conditions and aging which affect the parameters and change the characteristics of the three-terminal circuit over time. In this paper we propose a fault location algorithm for three-terminal transmission lines to solve this problem. The algorithm utilizes the positive components of the voltage and current signals measured synchronously from the terminals. In this work no prior knowledge of the line parameters was required when calculating the fault location and the use of fault classification algorithms was not necessary. In addition, the proposed method determines the parameters of the line segment and fault location based on a solid mathematical basis and has been verified through simulation results using SIMULINK/MATLAB R2018a software. The fault location results demonstrate the high accuracy and efficiency of the algorithm. Moreover, this method can estimate the characteristic impedance and propagation constants of the transmission lines and determine the location of the fault, which is not affected by different fault parameters including fault location, and fault resistance.

Electricity doi: 10.3390/electricity6030041

Authors: Artur Zaporozhets Vitalii Babak Mykhailo Kulyk Viktor Denysov

This study presents a novel methodology for determining zonal electricity generation and capacity requirements corresponding to forecasted annual production in an integrated power system (IPS). The proposed model combines the statistical analysis of historical daily load patterns with a calibration technique to translate forecast total demand into zonal powers (base, semi-peak and peak). A representative reference daily electrical load graph (ELG) is selected from retrospective data using least squares criteria, and a calibration factor α = Wx/Wie scales its zonal outputs to match the forecasted annual generation Wx. The innovation lies in this combination of historical ELG identification and calibration for accurate zonal power prediction. Applying the model to Ukrainian IPS data yields high accuracy: a zonal power error below 1.02% and a generation error below 0.39%. Key contributions include explicitly stating the research questions and hypotheses, providing a schematic procedural description and discussing model limitations (e.g., treatment of renewable variability and omission of meteorological/astronomical factors). Future work is outlined to incorporate unforeseen factors (e.g., post-war demand shifts, electric vehicle adoption) into the forecasting framework.

Electricity doi: 10.3390/electricity6030040

Authors: Kelly M. Berdugo Sarmiento Jorge Iván Silva-Ortega Vladimir Sousa Santos John E. Candelo-Becerra Fredy E. Hoyos

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.

Electricity doi: 10.3390/electricity6030039

Authors: Oscar Danilo Montoya Brandon Cortés-Caicedo Luis Fernando Grisales-Noreña Walter Gil-González Diego Armando Giral-Ramírez

The optimal integration of photovoltaic (PV) systems and distribution static synchronous compensators (D-STATCOMs) in electrical distribution networks is important to reduce their operating costs, improve their voltage profiles, and enhance their power quality. To this effect, this paper proposes a mixed-integer convex (MI-Convex) optimization model for the optimal siting and sizing of PV systems and D-STATCOMs, with the aim of minimizing investment and operating costs in electrical distribution networks. The proposed model transforms the traditional mixed-integer nonlinear programming (MINLP) formulation into a convex model through second-order conic relaxation of the nodal voltage product. This model ensures global optimality and computational efficiency, which is not achieved using traditional heuristic-based approaches. The proposed model is validated on IEEE 33- and 69-bus test systems, showing a significant reduction in operating costs in both feeders compared to traditional heuristic-based approaches such as the vortex search algorithm (VSA), the sine-cosine algorithm (SCA), and the sech-tanh optimization algorithm (STOA). According to the results, the MI-convex model achieves cost savings of up to 38.95% in both grids, outperforming the VSA, SCA, and STOA.

Electricity doi: 10.3390/electricity6030037

Authors: Vineet Jagadeesan Nair

In this paper, we formulate optimization problems and successive convex relaxations to perform optimal transmission switching (OTS) in order to operate power transmission grids more efficiently. OTS may be crucial in future power grids with much higher penetrations of renewable energy sources, which will introduce more variability and intermittency in generation. Similarly, OTS can potentially help mitigate the effects of unpredictable demand fluctuations (e.g., due to extreme weather). We explore and compare several different formulations for the OTS problem in terms of the computational performance and optimality. In particular, we build upon the literature by considering more complex and accurate power flow formulations for OTS and introducing novel convex relaxations. This allows us to model the grid physics more accurately than prior works and generalize to several different types of networks. We also apply our methods to small transmission test cases as a proof of concept to determine the effects of applying OTS.

Electricity doi: 10.3390/electricity6030038

Authors: Xiaoyuan Luo Weisong Zhu Shaoping Chang Xinyu Wang

Hybrid-energy vessels offer significant advantages in reducing carbon emissions and air pollutants by integrating traditional internal combustion engines, electric motors, and new energy technologies. However, during operation, the high reliance of hybrid-energy ships on networks and communication systems poses serious data security risks. Meanwhile, the complexity of energy scheduling presents challenges in obtaining feasible solutions. To address these issues, this paper proposes an innovative two-stage security optimization scheduling framework aimed at simultaneously improving the security and economy of the system. Firstly, the framework employs a CNN-LSTM hybrid model (WOA-CNN-LSTM) optimized using the whale optimization algorithm to achieve real-time detection of false data injection attacks (FDIAs) and post-attack data recovery. By deeply mining the spatiotemporal characteristics of the measured data, the framework effectively identifies anomalies and repairs tampered data. Subsequently, based on the improved multi-objective whale optimization algorithm (IMOWOA), rapid optimization scheduling is conducted to ensure that the system can maintain an optimal operational state following an attack. Simulation results demonstrate that the proposed framework achieves a detection accuracy of 0.9864 and a recovery efficiency of 0.969 for anomaly data. Additionally, it reduces the ship’s operating cost, power loss, and carbon emissions by at least 1.96%, 5.67%, and 1.65%, respectively.

Electricity doi: 10.3390/electricity6030036

Authors: Ejikeme A. Amako Ali Arzani Satish M. Mahajan

The integration of battery energy storage systems (BESSs) within active distribution networks (ADNs) entails optimized day-ahead charge/discharge scheduling to achieve effective peak shaving.The primary objective is to reduce peak demand and mitigate power deviations caused by intermittent photovoltaic (PV) output. Quasi-static time-series (QSTS) co-simulations for determining optimal heuristic solutions at each time interval are computationally intensive, particularly for large-scale systems. To address this, a two-stage intelligent BESS scheduling approach implemented in a MATLAB–OpenDSS environment with parallel processing is proposed in this paper. In the first stage, a rule-based decision tree generates initial charge/discharge setpoints for community BESS units. These setpoints are refined in the second stage using an optimization algorithm aimed at minimizing community net load power deviations and reducing peak demand. By assigning each ADN community to a dedicated CPU core, the proposed approach utilizes parallel processing to significantly reduce the execution time. Performance evaluations on an IEEE 8500-node test feeder demonstrate that the approach enhances peak shaving while reducing QSTS co-simulation execution time, utility peak demand, distribution network losses, and point of interconnection (POI) nodal voltage deviations. In addition, the use of smart inverter functions improves BESS operations by mitigating voltage violations and active power curtailment, thereby increasing the amount of energy shaved during peak demand periods.

Electricity doi: 10.3390/electricity6020035

Authors: Musawenkosi Lethumcebo Thanduxolo Zulu Rudiren Sarma Remy Tiako

Power systems need to meet the ever-increasing demand for higher quality and reliability of electricity in distribution systems while remaining sustainable, secure, and economical. The globe is moving toward using renewable energy sources to provide electricity. An evaluation of the influence of artificial intelligence (AI) on the accomplishment of SDG7 (affordable and clean energy) is necessary in light of AI’s development and expanding impact across numerous sectors. Microgrids are gaining popularity due to their ability to facilitate distributed energy resources (DERs) and form critical client-centered integrated energy coordination. However, it is a difficult task to integrate, coordinate, and control multiple DERs while also managing the energy transition in this environment. To achieve low operational costs and high reliability, inverter control is critical in distributed generation (DG) microgrids, and the application of artificial neural networks (ANNs) is vital. In this paper, a power management strategy (PMS) based on Inverter Control and Artificial Neural Network (ICANN) technique is proposed for the control of DC–AC microgrids with PV-Wind hybrid systems. The proposed combined control strategy aims to improve power quality enhancement. ensuring access to affordable, reliable, sustainable, and modern energy for all. Additionally, a review of the rising role and application of AI in the use of renewable energy to achieve the SDGs is performed. MATLAB/SIMULINK is used for simulations in this study. The results from the measures of the inverter control, m, VL-L, and Vph_rms, reveal that the power generated from the hybrid microgrid is reliable and its performance is capable of providing power quality enhancement in microgrids through controlling the inverter side of the system. The technique produced satisfactory results and the PV/wind hybrid microgrid system revealed stability and outstanding performance.

Electricity doi: 10.3390/electricity6020034

Authors: Ruoqing Yin Liz Varga

As renewable energy penetration and extreme weather events increase, accurately predicting power system behavior is essential for reducing risks and enabling timely interventions. This study presents a physics-informed learning approach to forecast transient voltage angles in power systems with integrated wind energy under gusty wind conditions. We developed a simulation framework that generates wind power profiles with significant gust-induced variations over a one-minute period. We evaluated the effectiveness of physics-informed neural networks (PINNs) by integrating them with LSTM (long short-term memory) and GRU (gated recurrent unit) architectures and compared their performance to standard LSTM and GRU models trained using only mean squared error (MSE) loss. The models were tested under three wind energy penetration scenarios—20%, 40%, and 60%. Results show that the predictive accuracy of PINN-based models improves as wind penetration increases, and the best-performing model varies depending on the penetration level. Overall, this study highlights the value of physics-informed learning for dynamic prediction under extreme weather conditions and provides practical guidance for selecting appropriate models based on renewable energy integration levels.

Electricity doi: 10.3390/electricity6020033

Authors: Yassine Taleb Roa Lamrani Ahmed Abbou

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Electricity doi: 10.3390/electricity6020032

Authors: Ossama Dankar Mohamad Tarnini Abdallah El Ghaly Nazih Moubayed Khaled Chahine

The growing integration of photovoltaic (PV) energy systems and electric vehicles (EVs) introduces new challenges in managing energy flow within smart grid environments. The intermittent nature of solar energy and the variable charging demands of EVs complicate reliable and efficient power management. Existing strategies for grid-connected PV–battery systems often fail to effectively handle bidirectional power flow between EVs and the grid, particularly in scenarios requiring seamless transitions between vehicle-to-grid (V2G) and grid-to-vehicle (G2V) operations. This paper presents a novel neural network-based model predictive control (NN-MPC) approach for optimizing energy management in a grid-connected PV–battery–EV system. The proposed method combines neural networks for forecasting PV generation, EV load demand, and grid conditions with a model predictive control framework that optimizes real-time power flow under various constraints. This integration enables intelligent, adaptive, and dynamic decision making across multiple objectives, including maximizing renewable energy usage, minimizing grid dependency, reducing transient responses, and extending battery life. Unlike conventional methods that treat V2G and G2V separately, the NN-MPC framework supports seamless mode switching based on real-time system status and user requirements. Simulation results demonstrate a 12.9% improvement in V2G power delivery, an 8% increase in renewable energy utilization, and a 50% reduction in total harmonic distortion (THD) compared to PI control. The results highlight the practical effectiveness and robustness of NN-MPC, making it an effective solution for future smart grids that require bidirectional energy management between distributed energy resources and electric vehicles.

Electricity doi: 10.3390/electricity6020031

Authors: André Ulrich Ingo Stadler Eberhard Waffenschmidt

In the future, higher penetrations of electrical loads in low-voltage distribution grids are to be expected. To prevent grid overload, a possible solution is coordination of controllable loads. Typical examples might be charging of electric vehicles or operation of electric heat pumps. Such loads are associated with specific requirements that should be fulfilled if possible. However, at the same time, a safe grid operation must be ensured. To this end, a corresponding optimal power flow optimization problem might be formulated and solved. This article gives a comprehensive review of the state of the art of optimal power flow formulations. It is investigated which constraint handling techniques are used and how hyper parameters are tuned when solving optimal power flow problems using metaheuristic solvers and how controllable loads and fluctuating renewable production are incorporated into optimal power flow formulations. Therefore, the literature is reviewed for pre-defined criteria. The results show possible gaps to be filled with future research: extended optimal power flow formulations to account for controllable loads, investigation of effects of choosing constraint handling techniques or hyper parameter tuning on the performance of the metaheuristic solver and automated methods for determining optimal values for hyper parameters.

Electricity doi: 10.3390/electricity6020030

Authors: Alessandro Franco Cecilia Pagliantini

Balancing electricity production and distribution remains a central challenge in modern energy systems, especially with the increasing penetration of renewable sources that introduce variability and uncertainty. In this context, accurate forecasting of electricity demand is essential for grid stability and operational efficiency. This study addresses the problem of hourly electricity demand forecasting in Italy using recurrent neural networks (RNNs), particularly long short-term memory (LSTM) models, which are designed to capture complex temporal dependencies in time series data. Utilizing real consumption data from Terna—Rete Elettrica Nazionale S.p.A.—for the years 2022 and 2023, we developed and tested an LSTM model capable of predicting national hourly demand with Root Mean Squared Error (RMSE) consistently below 2%. The model’s forecasts show strong agreement with official data provided by Terna, accurately capturing demand peaks and seasonal trends over both short- and medium-term horizons. In addition to evaluating predictive performance, this work proposes a reproducible methodology applicable to other national contexts or similar forecasting problems. Our findings suggest that, while data-driven models offer robust and replicable results, further improvements may require the integration of system-specific knowledge to address persistent limitations in forecasting extreme events or structural anomalies.

Electricity doi: 10.3390/electricity6020029

Authors: Isidro Fraga-Hurtado Julio Rafael Gómez-Sarduy Zaid García-Sánchez Hernán Hernández-Herrera Jorge Iván Silva-Ortega Roy Reyes-Calvo

This study explores advanced multivariate models that incorporate non-climatic exogenous variables for very short-term photovoltaic energy forecasting. By integrating historical energy data from multiple photovoltaic plants, the research aims to improve the prediction accuracy of a target plant while addressing critical challenges in electric power systems (EPS), such as frequency stability. Frequency stability becomes increasingly complex as renewable energy sources penetrate the grid because of their intermittent nature. To mitigate this challenge, precise forecasting of photovoltaic energy generation is essential for balancing supply and demand in real time. The performance of long short-term memory (LSTM) networks and bidirectional LSTM (BiLSTM) networks was compared over a 5 min horizon. Including energy generation data from neighboring plants significantly improved prediction accuracy compared to univariate models. Among the models, multivariate BiLSTM showed superior performance, achieving a lower root-mean-square error (RMSE) and higher correlation coefficients. Quantile regression applied to manage prediction uncertainty, providing robust confidence intervals. The results suggest that incorporating an exogenous power series effectively captures spatial correlations and enhances prediction accuracy. This approach offers practical benefits for optimizing grid management, reducing operational costs, improving the integration of renewable energy sources, and supporting frequency stability in power generation systems.

Electricity doi: 10.3390/electricity6020028

Authors: Johana-Alejandra Arteaga Yuri Ulianov López Jesús Alfonso López Johnny Posada

This paper analyzes the power quality in a 400 kWp grid-connected solar photovoltaic system with storage (BESS), considering standards IEEE Std 519TM, IEEE Std 1159TM, and IEC 61000-4-30. For system analysis, a photovoltaic array model is developed. Neplan-Smarter Tools software is used for model validation, and experimental measurements are performed on the actual photovoltaic system, recording total harmonic distortion (THDi/THDv). A class B power quality monitor was used to measure three-phase electrical variables: current, voltage, power, power factor, and THD. The THD level was generated at an energy level below 20% of the rated power, resulting in high THDi. The recorded THDv remained below 2.5%, which means that its value is limited by the IEEE 519 standard. When the BESS was connected to the PCC grid, the voltage level remained regulated, and the electrical system appeared to be stable. This paper contributes a methodology and procedure for measurement and power quality assessment, allowing for THD identification and enabling designers to configure better designs and energy system protections when integrating solar photovoltaic energy into an electrical distribution network.

Electricity doi: 10.3390/electricity6020027

Authors: Murilo E. C. Bento Hugo Morais

Power systems are equipped with a set of equipment for the generation, transmission, and distribution of electrical energy to consumption centers in a continuous manner and with quality indices [...]

Electricity doi: 10.3390/electricity6020026

Authors: Saidjon Tavarov Aleksandr Sidorov Natalia Glotova

This article is devoted to the determination of the average daily electric load and the average electric load during the hours of maximum load, taking into account the generalized coefficient Ai, using data on electricity consumption for apartment buildings and individual residential buildings in Chelyabinsk and the cities of Dushanbe and Khorog in the Republic of Tajikistan. The results of modeling the average daily electric load, taking into account the developed generalized coefficient Ai, showed that the specific power values for apartments in apartment buildings and in individual residential buildings in the city of Chelyabinsk and the cities of Dushanbe and Khorog of the Republic of Tajikistan were overestimated, taking into account the applicability in the Republic of Tajikistan of the same standard values of specific electric loads (SELs) for apartments in apartment buildings (ABs) as in the Russian Federation. According to the results of modeling using data on the average monthly electricity consumption for 226 apartments in ABs and for individual residential buildings in Chelyabinsk, and according to the proposed approach, the average daily electric load on days during the month varied in the range of 2–3.5 kW/sq and below, while that for the cities of Dushanbe and Khorog of the Republic of Tajikistan varied in the range of 2–5 kW/sq and below, which did not exceed the SEL given by RB 256.1325800.2016. However, because of the lack of other energy sources (gas supply and hot water supply) in the conditions of the Republic of Tajikistan, on the basis of the obtained maximum load time factor and the generalized coefficient Ai(E), the obtained values of actual capacity exceeded the maximum during peak hours by 1.2–2.5 times the SEL given by RB 256.1325800.2016. To increase the durability and serviceability of power supplies and enhance the effectiveness of forecasting, the authors propose an approach based on the clustering of meteorological conditions, where each cluster has its own regression model. The decrease in mean absolute error due to clustering was 0.52 MW (57%). The use of meteorological conditions allowed the forecast error to be reduced by 0.22 MW (27%). High accuracy in electrical consumption forecasting leads to increased quality of power system management in general, including under such key indicators as reliability and serviceability.

Electricity doi: 10.3390/electricity6020025

Authors: Javier Rodríguez-Herrejón Enrique Reyes-Archundia Jose A. Gutiérrez-Gnecchi Marcos Gutiérrez-López Juan C. Olivares-Rojas

This paper presents a comprehensive analysis of the effects of noise on the detection and localization of faults in transmission lines compensated with a unified power flow controller using traveling wave-based methods. This study focuses on the impact of harmonic and transient noises, which are inherent to power generation, transmission, and UPFC operation. A novel algorithm is proposed combining the Discrete Wavelet Transform and Clarke Transform to detect and localize faults under various noise conditions. The algorithm is tested on a simulated transmission line model in MATLAB/Simulink (Version R2022b) with noise levels of 20 dB, 30 dB, and 40 dB and transient frequencies of 1 kHz, 5 kHz, and 10 kHz. The results demonstrate that the algorithm achieves an average fault localization error of 0.523% under harmonic noise and 0.777% under transient noise, with fault detection rates of 96.3% and 90.75%, respectively. This study highlights the robustness of the traveling wave method in noisy environments and provides insights into the challenges posed by UPFC-compensated lines.

Electricity doi: 10.3390/electricity6020024

Authors: Andre Mrad Jean-François Llibre Yvan Lefèvre Mohamad Arnaout

This study focuses on the experimental determination of the lumped parameters of a Spoke-Type Axial-Flux Permanent Magnet (STAFPM) motor. This type of motor offers high specific torque and is well-suited for transportation applications. The studied STAFPM motor uses Ferrite magnets, which are more environmentally friendly and economical than rare earth magnets. The identification of the lumped electromechanical model parameters is carried out using static torque measurements on a dedicated test bench. The torque measurements are performed in two stages: with and without magnets mounted in the rotor. The no-load flux is determined separately by no-load tests. Together, these tests identify the key parameters of the lumped parameter model, such as self- and mutual inductances, cogging torque, and no-load flux. These parameters are then used to complement the DQ model, commonly used in electric motor analysis. While the DQ model predicts average torque well, it cannot reproduce torque ripples. The lumped parameter model, validated by three-phase DC testing, provides an accurate representation of the motor’s behavior, including torque ripples. This study also applies Maximum Torque Per Ampere (MTPA) control strategies and offers a practical alternative to 3D Finite Element Analysis (FEA), thus aiding the design of STAFPM motors.

Electricity doi: 10.3390/electricity6020023

Authors: Katerina G. Gabrovska-Evstatieva Dimitar T. Trifonov Boris I. Evstatiev

The successful integration of photovoltaic (PV) generators in cities requires careful planning that accounts for possible factors influencing their operation. Numerous authors have extensively studied these factors; however, the urban environment has its unique characteristics. This study aims to conduct a narrative review of the most common and influential urban factors that impact the operation of PV modules and explore potential mitigation strategies. Based on preliminary knowledge on the topic, a methodology was proposed according to which they are classified into two categories: those enhanced by the urban environment and those specific to it. A total of 97 studies, mostly from the last decade, were selected based on the relevance and impact criteria. Shading, soiling, and snow were analyzed in an urban context, followed by different urban-specific factors, such as the urban landscape, pollution, and the limitations of PV mounting spots, which can lead to more than 50% performance losses. The performed review also identified the key and most promising approaches for mitigation of the abovementioned factors, such as electrostatic dust cleaning and forward bias current snow removal. Furthermore, recommendations for urban landscape planning were made in the context of PV integration. This review could also be useful for designers and operators of urban PV facilities by providing them with basic guidelines for their optimization.

Electricity doi: 10.3390/electricity6020022

Authors: Subhranshu Sekhar Pati Umamani Subudhi Sivkumar Mishra

The rapid proliferation of renewable energy sources (RESs) has significantly reduced system inertia, thereby intensifying stability challenges in modern power grids. To address these issues, this study proposes a comprehensive approach to improve the grid stability concerning RESs and load disturbances. The methodology integrates controlled energy storage systems, including ultra-capacitors (UC), superconducting magnetic energy storage (SMES), and battery storage, alongside a robust frequency regulation management system (FRMS). Central to this strategy is the implementation of a novel controller which combines a constant with proportional–integral–derivative (PID) and modified fractional-order (MFO) control, forming 1+MFOPID controller. The controller parameters are optimized using a novel formulation of an improved objective function that incorporates both frequency and time domain characteristics to achieve superior performance. The efficacy of the proposed controller is validated by comparing its performance with conventional PID and fractional-order PID controllers. System stability is further analyzed using eigenvector analysis. Additionally, this study evaluates the performance of various energy storage systems and their individual contributions to frequency regulation, with a particular emphasis on the synergistic benefits of battery storage in conjunction with other storages. Finally, sensitivity analysis is conducted to assess the impact of parameter uncertainties in the system design, reinforcing the robustness of the proposed approach.

Electricity doi: 10.3390/electricity6020021

Authors: Hossein Amini Reza Noroozian

This paper discusses the operation and control of a low-voltage DC (LVDC) isolated distribution network powered by distributed generation (DG) from a variable-speed wind turbine induction generator (WTIG) to supply unbalanced AC loads. The system incorporates a DC-DC storage converter to regulate network voltages and interconnect battery energy storage with the DC network. The wind turbines are equipped with a squirrel cage induction generator (IG) to connect a DC network via individual power inverters (WTIG inverters). Loads are unbalanced ACs and are interfaced using transformerless power inverters, referred to as load inverters. The DC-DC converter is equipped with a novel control strategy, utilizing a droop regulator for the DC voltage to stabilize network operation. The control system is modeled based on Clark and Park transformations and is developed for the load inverters to provide balanced AC voltage despite unbalanced load conditions. The system employs the perturbation and observation (P&O) method for maximum power point tracking (MPPT) to optimize wind energy utilization, while blade angle controllers maintain generator performance within rated power and speed limits under high wind conditions. System operation is analyzed under two scenarios: normal operation with varying wind speeds and the effects of load variations. Simulation results using PSCAD/EMTDC demonstrate that the proposed LVDC isolated distribution network (DC) achieves a stable DC bus voltage within ±5% of the nominal value, efficiently delivers balanced AC voltages with unbalanced levels below 2%, and operates with over 90% wind energy utilization during varying wind speeds, confirming LVDC network reliability and robustness.

Electricity doi: 10.3390/electricity6020020

Authors: Alfredo Abarca Yuri Percy Molina Rodriguez Cristhian Ganvini

This research evaluates the economic feasibility of implementing smart metering (SM) systems in Peruvian electricity distribution companies, prioritizing the maximization of the benefit–cost ratio (BCR). Seven communication architectures were analyzed in four companies, considering variables such as energy losses, meter costs, and per capita consumption. The results, evaluated through economic indicators such as the net present value, internal rate of return (IRR), and BCR showed that Luz Del Sur (LDS) obtained the best results, while ADINELSA (an electrical infrastructure management company), Sociedad Eléctrica Sur Oeste (SEAL), and Electro Sur Este (ELSE) presented the worst. The combination of power line communication and general packet radio service was the most viable architecture, followed by radio frequency mesh. However, this study concludes that a massive deployment of SM in Peru is not yet economically viable because of low per capita consumption and high meter costs. Future research should consider the benefits of distributed generation and demand management, as well as evaluate new communication technologies.