Electricity, Volume 7, Issue 3 (September 2026) – 44 articles

Photovoltaic fault detection and maximum power point tracking are commonly treated as separate functions, although both depend on irradiance, temperature, electrical state and data quality. This study develops a physics-guided framework that links measured fault detection to diagnosis-dependent supervisory MPPT while keeping measured diagnostic evidence separate from control-software evidence. The Lahore dataset contains 217,196 timestamped records and 19,452 unique physical fault-event groups from one grid-tied inverter with two monitored MPPT channels. Fault events are separated chronologically at the physical-event level, and calibration and threshold selection use Training + Validation data only. The HGB detector using the complete operational representation achieved 99.46% Test accuracy and macro-F1, with MCC 0.9892 and AUROC 0.9998. Permutation importance showed strong dependence on sequence-availability descriptors; a residual-free and quality-free design using only logged electrical, environmental and grid channels achieved 88.79% Test accuracy. In a separate static P–V software benchmark, the diagnosis-dependent supervisory P&O controller achieved 99.96% mean tracking efficiency and 0.193 V mean simulated voltage-reference oscillation on a restricted 240-event subset. The control experiment contains no converter dynamics or physical time base and the measured diagnosis evidence is limited to one PV installation. External plant validation and converter-level testing are therefore required before broader operational transfer. Full article

The frequent occurrence of extreme high-temperature events has significantly affected the operating characteristics and distributed photovoltaic (PV) hosting capacity of distribution networks. However, existing hosting capacity assessment methods rarely consider the accumulated heat effect caused by sustained high temperatures. To address this issue, this paper proposes a coordinated planning method for enhancing distributed PV hosting capacity under extreme high-temperature scenarios. First, an accumulated heat load model is developed to characterize the temporal cumulative influence of sustained high temperatures on temperature-sensitive loads. Meanwhile, the uncertainties associated with PV output fluctuations and load demand variations are considered to represent the stochastic characteristics of source-side generation and load-side consumption. Subsequently, a multi-objective source–network–load coordinated planning model is established to maximize distributed PV hosting capacity while minimizing the hosting capacity enhancement cost. A multi-objective artificial bee colony (MO-ABC) algorithm incorporating Sobol sequence-based quasi-Monte Carlo sampling (Sobol-MC) and a constraint domination-based constraint handling strategy are further developed to solve the proposed model efficiently. Simulation results on the modified IEEE 33-bus distribution system show that the proposed method increases distributed PV hosting capacity by 69.52% under extreme high-temperature scenarios through coordinated optimization of PV inverter reactive power control, VAR compensation, and Incentive-based Demand Response (IDR). Full article

In high-speed drive applications, including drone propulsion systems and high-speed spindle drives, switching frequency is often limited by thermal constraints or cost considerations when the adoption of wide-bandgap power devices is not economically justified. Under these conditions, natural-sampled PWM offers significant advantages over regular-sampled PWM, particularly at low switching-to-fundamental frequency ratios, by improving output waveform quality and reducing control-loop delay. This paper proposes an adaptive modulation strategy for two-level three-phase voltage-source inverters, enabling a seamless transition from space-vector PWM (SVPWM) to generalized discontinuous PWM (GDPWM). The proposed approach preserves the number of switching events by synchronizing the discontinuities of the modulation signals with the corresponding carrier peaks, thereby ensuring a consistent switching pattern while exploiting the benefits of discontinuous modulation. Full article

Current electrical systems are undergoing a profound transformation due to the massive integration of renewable energy sources and power electronics, exceeding the capabilities of traditional simulation tools. Digital Twins (DTs) have emerged as a strategic solution by enabling a bidirectional and real-time connection between physical assets and their virtual replicas. This paper presents a conceptual design proposal for a DT laboratory for electrical networks, developed from the analysis of twelve representative case studies of internationally recognized DT laboratories. The analyzed laboratories were evaluated using a technological integration scale to identify common technological trends and experimental capabilities. The results indicate that most of the selected laboratories operate at Hardware-in-the-Loop (HIL) and Power Hardware-in-the-Loop (PHIL) maturity levels, with a predominant focus on smart grids and distribution systems. Based on these findings, a conceptual laboratory architecture organized into four functional stages is proposed as a scalable cyber–physical environment for technology validation and advanced training in the electrical sector. Full article

This research presents a comparative performance evaluation of two advanced maximum power point tracking (MPPT) methodologies, namely sliding mode control (SMC) and the Kalman filter (KF), specifically applied to a standalone photovoltaic water pumping system (PVWPS). To achieve economic viability, the system is designed for storage-less operation, driving a three-phase induction motor (IM) via a high-dynamic direct torque control (DTC) scheme and a three-level inverter. The core technical contribution addresses the critical challenge of maximizing energy yield under partial shading conditions (PSCs). PSCs result in a complex, non-convex power–voltage (P−V) characteristic, containing multiple peaks, where conventional MPPT algorithms fail to consistently locate the global maximum power point (GMPP). To overcome this deficiency, we implemented the SMC-based MPPT algorithm to exploit its inherent robustness and rapid dynamic response, and compared it with the Kalman filter MPPT, which relies on stochastic state estimation to achieve accurate tracking and effective disturbance rejection. MATLAB/Simulink analysis compares the proposed techniques with the perturb and observe (P&O) MPPT method. The comparison considers tracking efficiency, convergence speed, and steady-state ripple under various shading conditions to identify the most effective control strategy for improving PVWPS performances. The reported performance evaluations are based on numerical simulations conducted within the MATLAB/Simulink environment, using a validated system model. Full article

This paper presents an approach for optimal operation of self-healing networked microgrids (NMGs) under both normal operation and emergency conditions using the pufferfish optimization algorithm (POA). The proposed methodology is based on an energy management system (EMS) with two levels and independent functions. The lower-level is designed for normal operation, where the local controller of each microgrid (MG) performs the optimal dispatch of power from the dispatchable sources. During an emergency case in any MG, the higher-level EMS is activated, and the global controller is brought into operation. Physically, the NMGs are connected by tie-lines, while cyber links are established to exchange information and control signals for coordinated operation. Each MG operates to supply its local demand during normal operation conditions, resulting in no electrical power exchange between MGs. İn case of generation deficiency or a fault leading to generation outage, electrical power can be exchanged through the existing interconnections, enabling the affected microgrid to receive support from neighboring MGs. The main objective of POA is to minimize the total operating cost, in which the economic impact of network power losses is incorporated into the single objective function. Simulation studies were conducted using MATLAB and DIgSILENT software over one day. The performance of POA was compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO) under the same computational settings. Statistical and convergence analyses show that POA achieves the lowest mean operating cost across all studied cases, with low run-to-run variability and favorable convergence behavior. The results demonstrate the effectiveness of the proposed approach in improving the economic operation of NMGs under both normal and emergency conditions. Full article

Lithium-ion battery (LIB)-powered locomotives have emerged as a promising alternative to conventional rail electrification by reducing dependence on overhead catenary systems, lowering infrastructure costs, and improving operational flexibility. However, train failures involving power loss or mechanical faults present significant challenges for battery-powered rail systems, particularly on single-track corridors where alternate route options are limited. Consequently, the implementation of a reliable limp-home strategy is more complex than in road electric vehicles (EVs), requiring consideration of battery availability, rescue logistics, and track accessibility. This study investigates limp-home operation and rescue planning for a battery-powered historic trolley operating on a 20 km heritage route in North Carolina. The trolley is powered by a dedicated LIB trailer and supported by battery-charging (BC) infrastructure based on inductive power transfer (IPT) technology. A comprehensive framework is developed that integrates time–space analysis, cellular automata (CA)-based failure-risk modeling, and battery-energy assessment to evaluate train-failure scenarios and recovery strategies. The results identify critical failure regions along the route and demonstrate the benefits of strategically deploying additional LIB rescue trailers and wireless power transfer (WPT) infrastructure. A revised rescue strategy incorporating two additional LIB trailers, together with static and dynamic WPT systems, substantially reduces recovery time and improves the likelihood of maintaining scheduled excursions. Emergency energy analysis further shows that WPT-assisted operation can significantly extend limp-home capability under low state-of-charge conditions. Based on the integrated analysis, a tiered limp-home decision framework is developed to support operator decision-making during train failures. The proposed methodology provides a practical approach for enhancing the resilience, recoverability, and operational reliability of battery-powered heritage rail systems and can serve as a foundation for future battery-electric rail applications. Full article

This paper presents a novel master–slave stochastic optimization framework for the optimal siting and sizing of fixed-step capacitor banks in medium-voltage distribution networks, explicitly addressing the inherent variability of load demand that is typically neglected in conventional deterministic approaches. The proposed methodology integrates a scenario-based stochastic optimization model with a Chu and Beasley genetic algorithm (CBGA) as the master stage, which handles discrete placement decisions, and a successive-approximation power flow method (SAPF) as the slave stage, which evaluates the technical and economic performance of each candidate solution under multiple load scenarios. To capture demand uncertainties, 365 daily load realizations are generated using independent Gaussian noise with a relative standard deviation of 10% applied to each load point. These are subsequently reduced to ten representative scenarios via k-means clustering, reducing the number of power-flow evaluations per candidate solution from 365 to 10 (a 36.5-fold reduction); the reduced scenarios exhibit a low mean absolute error (MAE: <2%) with respect to the original mean, indicating faithful representation of the average load behavior, while the silhouette score is modest (approximately 0.25), consistent with the unimodal nature of the generated data and implying that the clusters are not well separated. Extensive simulations on a 33-bus test feeder considering three energy-cost-escalation scenarios (0%, 10%, and 20%) over a 20-year planning horizon demonstrate that both the deterministic and stochastic approaches reduce the total net present cost by 16.52% to 17.34% compared to the uncompensated network; the stochastic approach consistently delivers solutions that are either superior or comparable to deterministic planning (yielding up to approximately 0.16% additional cost reduction) while offering enhanced robustness against load variability. The stochastic framework offers distinct advantages, including robust solutions across a wide range of operating conditions, an inherent ability to adjust investment levels in response to probabilistic load distributions, and the ability to quantify uncertainty in decision making, with the most significant benefits observed when energy costs are low and load variability is high. The convergence of both approaches at a 20% escalation level further validates the reliability of high-resolution deterministic modeling when economic factors strongly dominate the optimization objective. This study underscores the importance of probabilistic modeling for modern distribution network planning, providing a practical and computationally efficient decision-support tool for utility planners to enhance grid resilience and operational efficiency in the context of increasing demand variability and renewable energy integration. Full article

The availability of reliable digital models of low-voltage (LV) networks is a prerequisite for operation, planning, hosting-capacity, and asset-management studies. In practice, however, utility geodatabases often contain geometric discontinuities, implicit relationships among assets, and incomplete connectivity, which hinders their direct use as topological models for electrical studies. This work proposes a reproducible framework for topological-quality and fitness-for-use screening, applied to a real LV network derived from a utility geographic information system (GIS). The methodology converts geospatial layers into a graph representation, audits load-to-pole and endpoint-to-known-node distances, evaluates structural sensitivity to the endpoint connection tolerance, computes a load-level screening confidence score, characterizes transformer-level coverage under an explicit demand scenario, and evaluates structural sensitivity under synthetic perturbations of the line geometry. The case study comprises 38 urban blocks, 155 poles, 13 distribution transformers, 429 loads, 777 overhead LV segments, and 23 underground LV segments. The mean load-to-pole distance is 10.91 m and the mean endpoint-to-known-node distance is 4.39 m. The fixed 30 m load-to-pole assignment rule retains 416 loads (96.97%) throughout the tolerance sweep, while the number of connected components decreases from 245 to 82 and the number of virtual nodes from 817 to 439 as the endpoint tolerance increases from 0.5 to 5.0 m. The confidence score classifies 32.40% of loads as High, 59.44% as Medium, 5.13% as Low, and 3.03% as Review/Unassigned. Under the stated residential screening assumptions, four transformers exceed the adopted 80% loading threshold. Synthetic perturbations leave the load-to-pole control metrics unchanged by construction, because only line-segment geometry is perturbed, but the number of connected components varies from 78 to 236, with a maximum relative change of 187.80% with respect to the 5 m base graph. The contribution is therefore a transparent GIS quality-screening procedure, not an exact reconstruction or electrical validation of the physical LV topology. Full article

The increasing penetration of variable renewable energy sources into electric power systems requires advanced optimization tools to address the complexity of hybrid hydrothermal scheduling while minimizing generation costs and carbon emissions. This study investigates the application of four deep learning architectures—Kolmogorov–Arnold networks (KANs), long short-term memory (LSTM), gated recurrent unit (GRU), and deep feedforward (DFF)—to solve the hydrothermal scheduling problem in hybrid power systems that incorporate wind power generation and pumped-storage hydropower (PSH) plants. The methods were evaluated on a 10-generator test system over a 24-h planning horizon in three objective-weighting scenarios, considering economic dispatch only, pure emission minimization only, and balanced objectives. All architectures successfully solved the integrated problem and satisfied the system constraints. This study reports the first application of the KAN to the hydrothermal scheduling problem, demonstrating its viability and interpretability potential for future applications in electric power systems. Full article

Power quality degradation in low-voltage (LV) distribution networks remains insufficiently documented in many rapidly urbanizing African cities despite its significant impact on electrical equipment, service reliability, and network operation. This study investigates the following research question: To what extent does the power quality of an urban low-voltage distribution network comply with international standards, and which network characteristics are most strongly associated with the observed disturbances? To address this question, an extensive field measurement campaign was conducted from October 2024 to February 2025 on five radial feeders supplied by the Babemba medium-voltage/low-voltage (MV/LV) substation in Lubumbashi, Democratic Republic of the Congo. Electrical parameters were monitored using a Class B Chauvin Arnoux Qualistar C.A. 8331 power quality analyzer and evaluated against internationally recognized power quality standards. The measurements revealed persistent power quality degradation characterized by chronic under-voltage, with prolonged voltage levels below 207 V, typical deviations ranging from −20% to −30%, and voltage dips reaching 70–80% of the nominal voltage during peak loading conditions. Power supply continuity was also severely affected, with a System Average Interruption Frequency Index (SAIFI) of 7.85 interruptions/year and a System Average Interruption Duration Index (SAIDI) of 491 min/year, while a medium voltage outage lasting approximately 48 h highlighted the limited resilience of the distribution system. Additional disturbances included phase voltage imbalance reaching 18%, neutral currents up to 327 A, and short-term flicker values (Pst) approaching 1.5, exceeding the recommended comfort threshold. Overall, the observed disturbances were associated with heterogeneous feeder loading conditions, network configuration, non-standard electrical connections, and documented physical deterioration of the infrastructure. This study provides a comprehensive field-based assessment of power quality and service continuity in an urban LV distribution network in the Democratic Republic of the Congo and offers a quantitative basis for prioritizing feeder reinforcement, phase balancing, infrastructure rehabilitation, and the establishment of continuous local power quality monitoring. Full article

Distribution networks face low supply reliability and limited fault-recovery capability. Flexible interconnection devices enable looped operation of AC feeders and facilitate optimal power-flow control, which helps address these operational challenges. This paper proposes a novel medium-voltage flexible interconnection device with a star-connected topology, together with a compact power-module structure and its main-circuit topology. Because distribution networks often contain high background harmonics, a control scheme is developed for the device, comprising a phase-locked loop, hierarchical capacitor-voltage control, and an inner current-control loop. A harmonic-current compensation control strategy is further designed to suppress current harmonics. A hardware-in-the-loop (HIL) testing platform is built to validate the proposed strategy. The results show that the proposed topology enables flexible interconnection of distribution networks, and the proposed control strategy operates stably under high background harmonics while effectively compensating current harmonics. Full article

Optimal distribution network reconfiguration modifies feeder topology through switching actions to improve electrical performance and service continuity. However, integrated and reproducible approaches that explicitly combine Pareto-based loss–reliability trade-off analysis, AC electrical evaluation, and clearly defined feasibility criteria remain comparatively limited, particularly in studies extending beyond standard benchmark systems. This paper presents a Pareto-based computational framework for radial distribution network reconfiguration that combines an NSGA-II-type evolutionary search with AC power-flow evaluation in OpenDSS^®. The search retains non-dominated sorting, crowding-distance-based diversity preservation, and elitist environmental selection, while offspring are generated through a graph-aware close–open branch-exchange mutation adapted to radial DNR. Accordingly, the evolutionary component is used as a problem-oriented search strategy rather than as a new canonical NSGA-II variant. Active power losses and SAIDI are minimized simultaneously, while SAIFI is reported as a derived reliability indicator and ENS as a complementary metric. Connectivity, radiality, and power-flow convergence define feasibility, while voltage compliance and load supply are subsequently assessed to characterize the operational status of each configuration. The framework is evaluated on a synthetic 5-node system, the IEEE 33-node benchmark, and an anonymized planning-oriented model of a real 13.2 kV Colombian distribution network. For the IEEE 33-node system, the minimum-loss configuration reduces losses by 26.03%, whereas the compromise solution achieves a 25.88% loss reduction and a 21.52% improvement in SAIDI. Across 20 paired runs, both the graph-aware strategy and a canonical NSGA-II baseline with topology repair recovered the same five-point empirical non-dominated set, including the classical minimum-loss topology without explicit seeding. For the real system, the operational compromise reduces active power losses by 38.88%, improves SAIDI by 23.11%, and increases the minimum voltage from 0.826 to 0.915 p.u. These results show that minimum-loss operation does not necessarily provide the most balanced trade-off between electrical efficiency and expected service continuity, supporting the use of explicit Pareto analysis for planning-oriented distribution network reconfiguration. Full article

The rapid deployment of distributed photovoltaic (PV) systems has increased renewable energy generation while introducing operational challenges for distribution networks, particularly surplus PV export during periods of high solar production. This study proposes a measurement-driven, rule-based smart electric vehicle (EV) charging framework to enhance the local utilization of surplus PV generation using real operational measurements acquired from a Siemens PAC3200T power quality analyzer installed at the point of common coupling (PCC) of the Far’ata–Immatain distribution feeder in Palestine. The proposed framework coordinates EV charging based on representative surplus PV operating states and PCC operating conditions while considering user charging requirements and network operational limits. Four MATLAB-based simulation scenarios representing increasing EV penetration were evaluated. The results demonstrate progressive reductions in reverse active power export together with corresponding improvements in local PV utilization as EV penetration increases. Under the highest investigated charging scenario, up to 200 kW of the investigated surplus PV generation was locally utilized, and reverse active power export was eliminated under the investigated operating conditions. Overall, the proposed framework provides a practical and scalable approach for improving renewable energy utilization in data-limited distribution networks without requiring comprehensive feeder models or immediate network reinforcement. Full article

With the increasing conflict between economic and environmental objectives in power systems, the economic emission dispatch (EED) problem has become a highly constrained, nonlinear, and strongly non-convex multi-objective optimization problem due to valve-point effects and nonlinear constraints such as network losses. To address this challenge, this paper proposes an improved multi-objective Grey Wolf Optimizer (IMOGWO). The proposed method enhances search performance through four strategies: a hybrid initialization scheme combining circle chaotic mapping and Latin hypercube sampling to improve population diversity, a dream-inspired group perturbation mechanism to strengthen global exploration, a nonlinearly decreasing convergence factor to dynamically balance exploration and exploitation, and a hybrid update strategy incorporating Lévy flight to avoid local optima. Experimental results demonstrate that IMOGWO can effectively balance the trade-off between generation cost and pollutant emissions while exhibiting competitive performance in terms of convergence behavior, solution quality, and stability. Full article

False data injection attacks (FDIAs) pose a growing threat to distributed energy resource (DER) aggregators because a compromised aggregator can expose and affect a number of enrolled DERs. However, DER aggregators only have access to limited measurements from DERs and the systems. This makes existing FDIA detection methods ineffective in this setting due to two main limitations: (1) they are grounded in full observability of a microgrid or distribution system and therefore are incompatible with the limited observability of a DER aggregator; (2) they can only either detect anomalies or explain why a deviation occurs, but not both simultaneously. To address these limitations, we propose a physics-guided fusion-based cyberattack detection method specifically designed for DER aggregators. This approach integrates two complementary modules: a forecasting-assisted residual method for rapidly anomaly detection, and a PV-aware sensitivity-based method to diagnose and explain their underlying physical causes. A gradient boosting machine (GBM) is then leveraged to fuse these outputs, optimizing the precision–recall tradeoff. The proposed method is tested on one microgrid test system with different bus observability levels across static and gradual attack scenarios with multiple levels of attack sophistication. Across the scenarios, the proposed method achieves a 0.91–0.93 precision–recall area under the curve score (PR-AUC), demonstrating the method’s effectiveness in securing DER aggregators with partial system visibility. Full article

Grid unreliability can compromise electricity supply in developing regions, yet comparative evidence on residential-district hybrid systems under consistent outage assumptions remains limited. This study compares four grid-connected architectures for a residential district in Egypt: wind/battery/grid, PV/wind/battery/grid, PV/battery/grid, and PV/wind/grid, using a simulation–optimization framework with net present cost (NPC) as the sole optimization objective. Supply adequacy, renewable fraction, grid interaction, and grid-related operational CO₂ emissions were then assessed. Under baseline conditions, PV/wind/battery/grid was the minimum-NPC hybrid configuration, with an NPC of USD 481,851, LCOE of USD 0.0711/kWh, renewable fraction of 73.8%, unserved energy of 0.306 MWh/yr, and operational CO₂ emissions of 139,533 kg/yr. Relative to grid-only supply, it reduced unserved energy by 98.5% and emissions by 65.0%, although grid-only had the lower NPC of USD 369,414. To distinguish fixed-design deterioration from adaptive redesign, baseline-optimal capacities were evaluated unchanged under a common severe grid-availability profile and compared with re-optimized counterparts. For the three battery-containing architectures with complete adaptive results, re-optimization reduced unserved energy by 73.2–97.2%, while resizing differed markedly. Among these architectures, wind/battery/grid had the lowest severe-condition NPC, only 0.63% below PV/wind/battery/grid. Deterministic ±10% sensitivity analysis retained PV/wind/battery/grid as the minimum-NPC architecture. Overall, baseline cost optimality, fixed-design transferability, and adaptation requirements are distinct, architecture-dependent planning considerations. Full article

The DC–DC boost converter is a challenging control target because of its nonlinear dynamics, wide operating range, and non-minimum-phase behavior under continuous conduction mode. These control challenges are particularly pronounced under large-signal transients, parameter variations, constant-power-load effects, and hardware constraints. This review examines deep reinforcement learning-based control of DC–DC boost converters from an engineering-oriented perspective. It covers learning-assisted classical control, direct duty-cycle control, and hybrid architectures, with attention to action design, reward formulation, observation timing, safety constraints, and validation fidelity. A structured search of Scopus, Web of Science Core Collection, and IEEE Xplore was used to identify boost-specific studies and transferable adjacent-converter evidence. Rather than ranking algorithms alone, the review organizes the literature around converter-aware and hardware-oriented learning control. The review argues that recent progress should not be interpreted as a simple replacement of classical control by deep reinforcement learning. Accordingly, algorithm choice, physical knowledge, action and reward design, observation timing, safety constraints, and validation fidelity are treated jointly. The available evidence suggests that progress toward credible practical deployment requires integrating converter physics, bounded or hybrid control authority, explicit safety constraints, and hardware-oriented validation. Full article

Active distribution networks (ADNs) require battery energy storage system (BESS) scheduling strategies that reduce operating costs while preserving electrical feasibility and battery lifetime. This paper proposes a parallel adapted JAYA-based methodology for the day-ahead coordinated active and reactive power dispatch of BESS units in this type of grid. The novelty of this research lies in four key contributions: (i) the coordinated optimization of active and reactive power from BESS converters, exploiting their full capabilities for both energy management and voltage support; (ii) the integration of battery degradation costs within the optimization framework, preventing short-term economic strategies that accelerate aging; (iii) the implementation of a parallel adapted JAYA algorithm (AJAYA) with stagnation control and population reactivation mechanisms to enhance solution quality and convergence; and (iv) a comprehensive assessment under both deterministic and uncertainty-based operating conditions, providing a realistic validation of the proposed approach. Our model minimizes conventional generation, DER operation and maintenance, and BESS degradation costs while subject to power balance, distributed energy resource limits, voltage and current constraints, converter capacity, and state of charge (SoC) requirements. Each solution is encoded as BESS active/reactive power setpoints and evaluated through a multi-period AC power flow based on the successive approximations method, including SoC verification and a penalized fitness function. The methodology was validated in modified 33- and 69-node ADNs under deterministic and uncertainty scenarios (based on the conditions observed in Colombia), and it was benchmarked against the population-based genetic algorithm (PGA), the multiverse optimizer (MVO), the salp swarm algorithm (SALPS), the grey wolf optimizer (GWO), and the vortex search algorithm (VSA). According to the results, AJAYA outperformed the comparison methods, providing the best economic performance and exhibiting a robust behavior, with standard deviations below 0.06% and processing times below 0.05 h within a 24-h scheduling horizon. These findings demonstrate that the proposed framework constitutes an AC-feasible and degradation-aware academic contribution and a practical decision-support tool for operators and BESS owners, enabling a cost-effective and reliable BESS scheduling that preserves battery lifetime while improving network operation. Therefore, this research addresses the critical need for advanced energy management strategies that balance short-term economic benefits, technical feasibility, and long-term asset sustainability in modern distribution networks. Full article

Traditional Digital Twins (DTs) in energy sectors lack cyber-threat awareness, while cybersecurity DTs overlook downstream physical impacts. Loosely coupled co-simulations attempt to bridge this gap but introduce computational lags that mask critical cross-domain vulnerabilities. To address these limitations, this paper proposes a unified, tightly coupled Virtual Digital Twin (VDT) framework that integrates energy systems and cybersecurity domains into a single environment. The methodology models the precise mathematical, thermal, and electrical constraints of key assets to capture cross-domain feedback loops. Specifically, a power transformer and a microgrid-connected inverter serve as case studies to map cyberattack vectors directly onto physical definitions. Numerical simulation evaluates multiple threat scenarios, including supervisory, measurement, and physical-level (harmonic) attacks on the transformer, alongside short-circuit and hybrid phase-harmonic attacks on the inverter. Results show how subtle digital disruptions propagate past communication layers to induce physical degradation and operational stress. By explicitly detailing the governing equations and providing sensitivity analyses, this work delivers a transparent, high-fidelity methodology for protecting critical cyber–physical infrastructures from asset-destructive manipulations. Full article

The growing share of photovoltaic generation in power grids intensifies the need for converter architectures capable of combining efficient energy conversion, DC-bus stability, and ancillary service provision at the grid coupling point. This paper presents the modeling, implementation, and simulation-based evaluation of a 91 kWp three-phase photovoltaic (PV) system integrated with a battery energy storage system (BESS), developed in the PLECS environment. The proposed architecture comprises three interleaved Boost stages for maximum power point tracking (MPPT), a DC bus regulated at 600 V, three independent bidirectional buck–boost converters for LiFePO₄ bank management, and a two-level three-phase voltage source inverter (VSI) with an LC output filter. The control is organized in cascade voltage–current loops for the DC–DC stages and in vector control within the synchronous reference frame (SRF) for the inverter, with synchronization via SRF-PLL. A C-Script supervisory block integrates the Perturb and Observe (P&O) MPPT algorithm, independent state of charge (SOC) estimation per bank via coulomb counting, and Volt–VAR reactive power reference generation with a dead band of 0.90–1.10 pu. Five scenarios are analyzed for validation: DC-bus regulation under irradiance transients; reactive power support during undervoltage and overvoltage events (0.80–0.85 pu and 1.15–1.20 pu); BESS operation as an active DC-link support element; and PV curtailment with fully charged banks. All five scenarios were additionally corroborated on a Typhoon HIL402 Pro 2 hardware-in-the-loop platform, reproducing the PLECS waveforms within the amplitude and timing resolution of the oscilloscope captures. Across all scenarios, the DC bus is held within $\pm 15$ V (2.5%) of the 600 V reference, with the worst-case transient recovering in 80–100 ms; under a sustained 9 s bidirectional disturbance, redirecting PV surplus to BESS charging in both the undervoltage and overvoltage segments—with no externally imposed active-current limit—keeps the current-vector magnitude $\sqrt{i_d^2+i_q^2}$ below the 335 A rating throughout (≈271 A and ≈242 A, respectively), while the available reactive margin $Q_{disp}$ reaches ≈78–80 kVAr in both segments and the bank SOC advances by ≈$0.03$ pu; and supervisory curtailment under a sustained overvoltage ride-through with a saturated bank keeps the per-bank SOC dispersion within $4\times {10}^{-5}$ pu while expanding the available reactive margin $Q_{disp}$ from ≈50 to ≈90 kVAr. Full article

This paper presents an integrated day-ahead battery energy storage system (BESS) scheduling framework for radial active distribution networks with photovoltaic generation. Its main contribution is a reproducible evaluation chain combining non-ideal state-of-charge (SoC) feasibility correction, sequential AC power-flow verification, consistent metaheuristic benchmarking, and post-dispatch battery-aging analysis. The operating-cost objective coordinates hourly BESS active-power exchanges while enforcing storage and AC-network constraints. A parallel Coyote Optimization Algorithm (COA) is compared with parallel GWO, GA, PSO, and MVO implementations under a common formulation, correction procedure, evaluator, and computational environment. Validation uses modified 33-, 69-, and 136-bus radial feeders: 100 independent runs for the deterministic 33-bus benchmark, 100 independently optimized Monte Carlo scenarios for the 69-bus assessment, and seven representative daily profiles for the 136-bus weekly case. COA achieved an average cost reduction of 1.0084%, with the lowest dispersion of $\sigma =0.0070\%$, in the 33-bus system; a mean scenario-wise reduction of 1.8337% in the 69-bus system; and a weekly reduction of 0.4402% in the 136-bus system. It obtained the lowest operating costs among the evaluated calibrated configurations, and all pairwise comparisons remained significant after Holm’s step-down adjustment applied separately within each system, although COA required greater computational effort than PSO. The reported schedules satisfied the imposed BESS and AC-network limits. Battery aging was evaluated only after scheduling and was not included in the optimization objective. The resulting cost-oriented schedules produced equivalent full-cycle values near 0.8 day⁻¹ and projected 80% SoH lifetimes of approximately 6–8 years. These results provide an AC-feasible basis for comparing economic performance and post-dispatch battery-health implications under the evaluated conditions. Full article

This paper proposes a Quantum-Machine-Learning-enhanced Double Deep Q-Network (QML-DDQN) for the supervisory control of battery–supercapacitor hybrid energy storage systems in islanded microgrids. This method combines a variational quantum circuit as a nonlinear state encoder with a DDQN decision layer for safe discrete dispatch. Three representative islanded cases, Island 1, Island 2, and Island 3, were used to evaluate the robustness under different scales, renewable profiles, and reliability requirements. Compared with deterministic optimization, predictive control, metaheuristics, and classical reinforcement-learning baselines, the proposed controller delivers the best overall trade-off among operating cost, renewable utilization, diesel reduction, and loss-of-power-supply risk. On the three-case averages, QML-DDQN reduces daily cost and LPSP by 0.99% and 4.04% relative to DDQN, by 2.91% and 7.32% relative to DQN, and by 9.09% and 16.63% relative to MILP; it also lowers curtailment and diesel share by up to 13.02% and 9.09%, respectively, across the same benchmark sets. The largest gains appear under volatility-dominated and stress-scenario conditions, where the quantum encoder strengthens the state representation, and the DDQN backbone mitigates value overestimation. These results highlight the practical advantages of the QML-DDQN as a resilient and high-value supervisory strategy for islanded hybrid energy storage operations. Full article

Arc flash faults in industrial substations can release high levels of incident energy, particularly on the line side of incoming circuit breakers, where opening the local breaker alone does not eliminate the source contribution. This paper proposes and experimentally evaluates an IEC 61850-based methodology to mitigate this protection gap by transferring arc flash trip signals between substations using GOOSE messages. The methodology comprises laboratory validation of the complete GOOSE-based protection chain and communication network, followed by validation in an operating industrial substation. Laboratory tests demonstrated satisfactory performance for both homogeneous and multivendor IED configurations, although longer operating times were observed when the test current approached the overcurrent pickup setting. The communication network achieved a mean transfer time of 5.31 ms and a maximum of 6.00 ms. In the industrial case study, the maximum protection operating time was 90 ms. Considering a conservative total fault-clearing time of 107.5 ms, the incident energy was reduced from 7.34 cal/cm² to 2.04 cal/cm², corresponding to a reduction of approximately 72%. These results demonstrate the feasibility of IEC 61850 GOOSE communication for high-speed trip transfer, reducing fault-clearing time and mitigating incident energy under critical line-side fault conditions. Full article

Maintaining frequency stability in modern interconnected power systems (PSs) has become increasingly challenging due to the high penetration of renewable energy sources (RESs) and the dynamic nature of generation and demand. To address these issues, this paper proposes a novel load frequency control strategy that integrates a Dung Beetle Optimizer (DBO)-tuned fractional-order Proportional–Integral–Derivative (FOPID) controller with a newly developed multi-source interconnected power system. This model combines PV, thermal, hydro, nuclear, and advanced storage (HAE and fuel cells). Unlike existing methods, the proposed approach simultaneously leverages DBO’s balanced search mechanism and FOPID’s fractional dynamics to enhance frequency stability under high renewable penetration. The performance of the proposed controller is validated through a comparative analysis with Ant Lion Optimizer (ALO) and Particle Swarm Optimization (PSO) methods. Simulation results show that the DBO-based controller significantly improves dynamic response, achieving reductions in settling time of 9.5% and 4.7% compared to PSO and ALO, respectively. Furthermore, the proposed approach enhances frequency regulation and tie-line power stability over other optimization methods and controllers, demonstrating strong robustness and adaptability for future high-RES power systems. Full article

This study proposes a reproducible exploratory framework to link long-term territorial development with electricity demand in data-scarce contexts, and applies it to Ecuador’s Costa region. The pipeline combines three commonly available input streams: periodic census microdata, an official demand series, and macroeconomic aggregates. Socioeconomic heterogeneity across five non-uniform census rounds (1974, 1982, 1990, 2001, 2010) is summarized through Principal Component Analysis (PCA), and territorial indicators are projected to the demand horizon using a univariate linear trend. Eleven regression specifications are compared on a log-transformed demand variable, and a rolling-origin backtesting scheme plus a 2020–2024 holdout are used for validation. The selected Trend OLS log model attains $R^2=0.551$ and MAPE = 6.08%, and projects a regional demand of approximately 7055 MW by 2050, equivalent to a compound annual growth rate of 3.46%. Beyond the Ecuadorian case, the results show that transparent, low-data pipelines based on harmonized census information, macroeconomic drivers and simple regression models can provide defensible medium- and long-term demand signals for planners in other emerging economies with limited high-frequency data. Full article

The increasing penetration of distributed photovoltaic generation is changing voltage behavior in distribution feeders and creating operational challenges related to voltage violations, losses, curtailment, and hosting capacity. This paper proposes a topology-aware analytical and computational framework for assessing voltage regulation and continuous photovoltaic hosting capacity in distribution feeders with smart-inverter controls. The framework combines topology-dependent voltage sensitivities, balanced steady-state power-flow simulations, explicit inverter apparent-power constraints, and gain indices that quantify the contributions of feeder topology, inverter controls, and their interaction. The IEEE 33-bus and IEEE 69-bus feeders are evaluated under radial and meshed configurations, heavy- and light-load conditions, and four photovoltaic operation modes: no control, Volt–Var, Volt–Watt, and combined Volt–Var/Volt–Watt control. A discrete sweep from 20% to 150% PV penetration is used to characterize voltage, active and reactive losses, and curtailment trends. Hosting-capacity boundaries are subsequently determined through an interval-aware procedure consisting of a one-percentage-point scan followed by bisection refinement to 0.1 percentage point, without assuming a globally monotonic feasibility transition. The results show that the feeder topology strongly affects voltage sensitivity and photovoltaic hosting capacity. The meshed operation generally increases voltage margins, while the interval-aware assessment identifies nonzero feasible penetration ranges, even when the zero-PV operating point is constrained by undervoltage. Volt–Watt achieves the largest hosting-capacity gains at the expense of curtailment, whereas Volt–Var preserves photovoltaic injection but may increase feeder losses. Therefore, hosting capacity should be interpreted jointly with topology, inverter controls, injected power, curtailment, and losses. Full article

This paper presents a low-cost embedded monitoring system for real-time RMS voltage and RMS current acquisition in three-phase electrical networks. The proposed architecture is based on distributed Arduino Nano acquisition nodes equipped with ACS712 Hall-effect current sensors and isolated voltage transformers, while a Raspberry Pi 4 Model B is used as a centralized data acquisition and processing unit through the I²C communication protocol. The embedded acquisition nodes implement timer-controlled analog signal sampling using the internal 10-bit ADC of the ATmega328P microcontroller, allowing real-time acquisition of electrical waveforms for RMS computation. Unlike conventional low-cost IoT electrical monitoring systems focused mainly on basic parameter visualization and wireless communication, the proposed platform emphasizes synchronized three-phase RMS monitoring and experimental validation accuracy under real operating conditions. The proposed monitoring architecture is experimentally benchmarked against a FLUKE 435 professional power quality analyser used as a high-accuracy reference instrument. Experimental results demonstrate that the proposed low-cost embedded architecture can provide RMS voltage and RMS current measurements with acceptable accuracy for educational applications, experimental electrical platforms, and distributed IoT-based monitoring systems. The presented system does not aim to implement a fully IEC 61000-4-30-compliant power quality analyser but rather to validate the feasibility of low-cost embedded RMS monitoring architectures for real-time electrical applications. Full article

With the increase in voltage levels, higher requirements are imposed on the energy-dissipation capability of high-voltage direct current (HVDC) networks. Existing energy-dissipation schemes cannot satisfy the demands of future HVDC systems. In this paper, a composite energy-dissipation branch circuit based on liquid metal, zinc oxide varistors, and damping resistors is proposed for HVDC circuit breakers. First, the self-constricting arc initiation mechanism and energy-dissipation characteristics of gallium–indium–tin liquid metal are studied. The results show that the energy-dissipation process exhibits an obvious stage-wise characteristic. Subsequently, an energy-dissipation topology incorporating liquid metal elements is established. A simulation model for the liquid-metal module is developed using the Mayr arc theory, and the conductance evolution during arc initiation is simulated. The model is combined with a hybrid HVDC circuit breaker model for analysis. Finally, a composite energy-dissipation branch circuit is constructed. The energy allocation among different components and the corresponding power density are evaluated. In the case of connecting three liquid-metal components in series, the energy density reached 0.248 kJ/cm³, representing a 22.2% increase compared to the original. The results support the coordinated application of liquid-metal modules and conventional absorption units in HVDC circuit breakers. Full article

While multi-phase short circuits are reliably cleared by conventional overcurrent relays, single-phase-to-ground faults (SPGFs) in isolated, compensated, or resistance-grounded 6–10 kV distribution networks produce extremely low, highly distorted currents that frequently cause traditional zero-sequence protections to misoperate. Because SPGFs constitute the vast majority of network disturbances, resolving this specific low-current detection challenge remains a critical priority for grid resilience. This paper presents a preselective protection approach based on Vector Analysis of Reactive Asymmetry Current (VARAC), designed for implementation in digital relay protection and automation terminals. Instead of relying primarily on vulnerable zero-sequence quantities, the method derives diagnostic features from the reactive asymmetry structure of three-phase current phasors. A reactive asymmetry matrix is formed from pairwise imaginary cross-products, symmetrized to preserve real eigenvalues and stable modal interpretation. The dominant eigenvalue and eigenvector are then used to quantify fault intensity and directional skew through two decision features: a magnitude-based index and a normalized asymmetry ratio. This enables robust discrimination between normal and faulted operation, including low-current and compensated-fault conditions where conventional criteria lose sensitivity. Simulation and oscillographic evaluations show a clear separation between pre-fault and SPGF regimes, fast onset detection, and improved structural selectivity versus traditional zero-sequence indicators. The proposed algorithm is computationally lightweight and compatible with hierarchical distributed SCADA architectures, supporting coordinated monitoring, diagnostics, and adaptive protection functions in modern medium-voltage networks. Full article