ABSTRACT Growing concerns over environmental degradation and fossil fuel depletion highlight Electric Vehicles (EVs) as a key solution for cutting emissions and improving urban air quality. This paper proposes a novel Vehicle-to-Grid (V2G) algorithm to minimize distribution grid power losses and enhance voltage profiles.By precisely controlling Point of Common Coupling (PCC) voltage, optimizing EV charging/discharging schedules within a time horizon, and incorporating battery State of Charge (SOC) constraints, the algorithm mitigates high EV penetration impacts and reduces peak-load losses.Simulations on the IEEE 33-bus system validate its efficacy: active power losses fall 5.9% (0.3954–0.3721 p.u.) and reactive losses drop 5.8% (1.3592–1.28 p.u.). MATLAB tests across scenarios show the approach outperforms conventional methods in loss reduction and voltage stability. This research underscores V2G’s potential to boost grid efficiency and cut energy costs under diverse conditions.

The nonlinear and intermittent nature of Photovoltaic (PV) systems introduces dynamic disturbances that negatively impact the stability of the DC bus voltage (Vdc) between PV sources and shunt active power filters (SAPFs). These fluctuations pose significant challenges to the performance of SAPFs, especially when the reference DC bus voltage (Vdc*) is constant and not adapted to the instantaneous operating conditions. In this study, a Perturb and Observe (P&O) algorithm is employed within the PV subsystem to perform Maximum Power Point Tracking (MPPT), further contributing to the time-varying behavior of Vdc. To address this problem, this paper proposes a real-time optimization strategy based on the Mantis Shrimp Optimization Algorithm (MShOA) for continuous Vdc* adjustment. This method relies on real-time Total Harmonic Distortion (THD) feedback to dynamically determine the optimal Vdc*, thereby improving harmonic mitigation and maintaining voltage stability. Simulation results demonstrate that the proposed MShOA-based approach effectively reduces THD from 3.59% to 2.85% obtained with conventional methods to 2.33% before PV injection, and maintains 4.19% after PV injection, remaining within the IEEE 519 - 92 standard limits. To confirm its superiority, a comparison with the Whale Optimization Algorithm (WOA) was performed, which achieved 2.65% before and 5.78% after PV injection. These findings validate the higher accuracy, faster convergence, and better adaptability of the proposed MShOA in ensuring robust voltage regulation and improved power quality under PV injection conditions.

Abstract This paper presents a novel sapphire-based hybrid gate p-GaN power high electron mobility transistor (SHyb-HEMT) which enhance the breakdown voltage of devices. Furthermore, a comparative study was conducted on the threshold voltage (Vth) drift under unclamped inductive switching (UIS) stress between the proposed SHyb-HEMT and a conventional sapphire-based Schottky-gate p-GaN HEMT (SSch-HEMT). After undergoing UIS stress, the Vth of the SSch-HEMT exhibited a drift of 43.6% at Vbus = 100V, while the SHyb-HEMT showed a drift of 29.5%. TCAD simulations were conducted on SHyb-HEMT incorporating a field plate, validating and elucidating the experimental phenomena. Additionally, based on repetitive stress tests and an E-model extrapolation, the device is projected to have a lifetime of 10 years under operational conditions of Vd= 645 V and Vg= 4.97 V.

Sodium-ion batteries are emerging as a promising alternative to lithium-ion technology due to the abundance and low cost of sodium. Among the cathode candidates, Na3V2(PO4)3 (NVP) with a NASICON framework and its analogues offer a high operating voltage and excellent structural stability. However, their practical use is limited by poor electronic conductivity, a low active material fraction, and trade-offs in terms of morphology and tap density. Here, we report a simple synthesis strategy that employs densely carboxylated graphene, graphene acid (GA), as a multifunctional additive. GA acts simultaneously as a chelating agent, pH regulator, and in situ-formed carbon shell prior to calcination. GA allows the efficient reduction of V5+ to electrochemically active V3+, phase-pure NVP formation, and the growth of a thin, conformal carbon shell strongly anchored to NVP particles. The resulting electrodes contain 85 wt % active material while maintaining outstanding charge-transfer kinetics. The optimized NVP@GA cathode delivers an excellent rate performance up to 15 A gEM-1 (151 C), retaining 65.4% of the theoretical capacity of NVP, and stable cycling. This approach provides a versatile route for tailoring NASICON cathodes and can be extended to other phosphate-based systems for high-power sodium-ion batteries.

With the advancement of the “dual carbon” goal, the power system is accelerating its transition towards a clean and low-carbon structure, with a continuous increase in the penetration rate of renewable energy generation (REG). However, the volatility and uncertainty of REG output pose severe challenges to power grid operation. Traditional distribution networks face immense pressure in terms of scheduling flexibility and power supply reliability. Active distribution networks (ADNs), by integrating energy storage systems (ESSs), soft open points (SOPs), and demand response (DR), have become key to enhancing the system’s adaptability to high-penetration renewable energy. This work proposes a DR-aware scheduling strategy for ESS-integrated flexible distribution networks, constructing a bi-level optimization model: the upper-level introduces a price-based DR mechanism, comprehensively considering net load fluctuation, user satisfaction with electricity purchase cost, and power consumption comfort; the lower-level coordinates SOP and ESS scheduling to achieve the dual goals of grid stability and economic efficiency. The non-dominated sorting genetic algorithm III (NSGA-III) is adopted to solve the model, and case verification is conducted on the standard 33-node system. The results show that the proposed method not only improves the economic efficiency of grid operation but also effectively reduces net load fluctuation (peak–valley difference decreases from 2.020 MW to 1.377 MW, a reduction of 31.8%) and enhances voltage stability (voltage deviation drops from 0.254 p.u. to 0.082 p.u., a reduction of 67.7%). This demonstrates the effectiveness of the scheduling strategy in scenarios with renewable energy integration, providing a theoretical basis for the optimal operation of ADNs.

Abstract Including renewable energy sources into contemporary power systems has greatly added unpredictability and presented problems including harmonic distortion, voltage instability, and degraded power quality. In response, we provide a new deep learning-enabled framework for the real-time adaptive control and optimal allocation of hybrid Flexible AC Transmission Systems (FACTS), specifically the Unified Power Flow Controller (UPFC) and Static VAR Compensator (SVC), in renewable-rich transmission networks. For multi-objective planning, the suggested method combines a Two-Point Estimation Method (2PEM)-based probabilistic load flow model with a hybrid Grey Wolf Optimization–Particle Swarm Optimization (GWO–PSO) algorithm. Trained offline on a scenario-rich dataset, a Long Short-Term Memory (LSTM) neural controller inferences control set-points in real time with sub-15 ms latency. Under operational contingency and changeable photovoltaic (PV) generation, this integration of offline probabilistic optimization with online artificial intelligence-driven control enables dynamic stabilization of voltage, harmonic mitigation, and power loss minimization. Application to the Southern Interconnected Grid of Cameroon shows compliance with IEEE-519 harmonic thresholds, a 47% increase in voltage stability index, and a 30.9% reduction in active power loss. These findings support the scalability and efficiency of the framework in improving power system resilience and quality by means of intelligent control, so fitting for both advanced and developing energy infrastructures. Graphical abstract

This paper presents a nine-level switched-capacitor (SC) based on ANPC inverter structure, where efficiency, compactness, and reliability are essential. Unlike conventional ANPC-based and previously reported SC topologies, the proposed structure suppresses the hard charging issue in SCs by inserting an inductor in the charging path, effectively reducing peak charging currents and associated switch stress. The inverter achieves natural self-starting and self-voltage-balancing of capacitors without additional circuitry, while requiring fewer active switches in the conduction path. These features lead to reduced component count, and minimized power losses. A peak efficiency of 96.9% is obtained at a rated power of 0.3kW. Experimental verification confirms the theoretical predictions, demonstrating stable capacitor voltage under varying load conditions and modulation indices. Comparative analysis highlights the superiority of the proposed topology over existing SCMLIs in terms of efficiency, charging current suppression, and reduced component requirements.

This paper introduces a novel control framework for a Free-Piston Stirling Linear Generator (FPSLG) system. The primary contribution is the first application of advanced sliding mode controllers specifically Super-Twisting (ST-SMC) and Third-Order Sliding Mode Control (TO-SMC) to regulate the DC-link voltage in a Space Vector Pulse Width Modulation (SVPWM) based rectifier. These controllers are implemented in the power converter to condition the FPSLG’s inherently variable electrical output, improving system performance under fluctuating conditions. The proposed ST-SMC and TO-SMC demonstrate superior performance by achieving reduced voltage/current ripple, improved robustness, and better dynamic response. Additionally, the system combines a bidirectional DC-DC converter with dual-loop PI control to maintain stable voltage for battery storage. MATLAB/Simulink simulations validate the effectiveness of this control strategy in significantly improving power quality and system stability for FPSLG-based renewable energy systems.

Purpose This paper aims to present a wind energy conversion system (WECS) integrated with a multi-cell boost chopper (MCBC) using a dandelion optimization (DO)-based maximum power point tracking (MPPT) method to enhance efficiency and enable reliable grid integration. Design/methodology/approach The proposed system comprises a permanent magnet synchronous generator (PMSG), an unregulated rectifier for AC–DC conversion and an MCBC for improved voltage gain. A meta-heuristic DO-based MPPT algorithm is applied to determine the optimal operating point. The model is developed in MATLAB®/Simulink® and evaluated under fixed and variable wind speed conditions (3 m/s, 4 m/s and 5 m/s). Performance is compared with the conventional perturb and observe (P&O) MPPT technique. Findings Simulation results confirm that the DO-based MPPT significantly outperforms the P&O method. The system achieved a voltage boost factor of 4.998, reduced ripple, faster settling time and a high accuracy of 99.98% in tracking the maximum power point of the PMSG-based WECS. Originality/value This study introduces the application of DO-based MPPT for a wind-powered MCBC, demonstrating improved voltage gain, stability and tracking accuracy. The proposed approach offers a robust and efficient solution for modern wind energy conversion and grid integration.

ABSTRACT The increasing presence of nonlinear devices in power systems introduces significant harmonic distortion in current and voltage waveforms, adversely affecting power quality and system efficiency. This paper presents a techno-economic analysis of harmonic mitigation using intelligent algorithms Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Recurrent Neural Network (RNN) implemented within a Shunt Hybrid Active Power Filter (SHAPF) framework. The study compares the performance of traditional pq0 theory with PI controllers, which resulted in a load current THD of 7.8%, against ANN and ANFIS-based controllers, which achieved THD reductions to 4.8% and 2.19%, respectively. These results underscore the superior harmonic suppression capability of intelligent controllers. Moreover, the proposed SHAPF system effectively eliminates neutral current and maintains stable DC-link voltage under various nonlinear load conditions. Simulation outcomes validate that the neural network-based controllers significantly improve power quality, adhering to IEEE-519 standards. Among all architectures tested, the RNN-based controller demonstrated the highest accuracy and computational efficiency, making it a robust solution for modern power distribution networks.

The sustained growth of electricity demand and the global transition toward low-carbon energy systems have intensified the need for efficient, flexible, and reliable operation of electrical distribution networks. In this context, the coordinated integration of distributed renewable energy resources and demand-side flexibility has emerged as a key strategy to improve technical performance and economic efficiency. This work proposes an integrated optimization framework for active power supply in a radial, distribution-like network through the optimal siting and sizing of photovoltaic (PV) units and wind turbines (WTs), combined with a real-time pricing (RTP)-based demand-side response (DSR) program. The problem is formulated using the branch-flow (DistFlow) model, which explicitly represents voltage drops, branch power flows, and thermal limits in radial feeders. A multiobjective function is defined to jointly minimize annual operating costs, active power losses, and voltage deviations, subject to network operating constraints and inverter capability limits. Uncertainty associated with solar irradiance, wind speed, ambient temperature, load demand, and electricity prices is captured through probabilistic modeling and scenario-based analysis. To solve the resulting nonlinear and constrained optimization problem, an Improved Whale Optimization Algorithm (I-WaOA) is employed. The proposed algorithm enhances the classical Whale Optimization Algorithm by incorporating diversification and feasibility-oriented mechanisms, including Cauchy mutation, Fitness–Distance Balance (FDB), quasi-oppositional-based learning (QOBL), and quadratic penalty functions for constraint handling. These features promote robust convergence toward admissible solutions under stochastic operating conditions. The methodology is validated on a large-scale radialized network derived from the IEEE 118-bus benchmark, enabling a DistFlow-consistent assessment of technical and economic performance under realistic operating scenarios. The results demonstrate that the coordinated integration of PV, WT, and RTP-driven demand response leads to a reduction in feeder losses, an improvement in voltage profiles, and an enhanced voltage stability margin, as quantified through standard voltage deviation and fast voltage stability indices. Overall, the proposed framework provides a practical and scalable tool for supporting planning and operational decisions in modern power distribution networks with high renewable penetration and demand flexibility.

ABSTRACT Solar-Powered Electric Vehicle (EV) charging systems are emerging as an efficient alternative to grid-dependent infrastructure. However, partial shading conditions (PSC) and converter-induced ripple significantly reduce the panel efficiency, resulting in longer charging time. Conventional maximum power point tracking (MPPT) techniques such as perturb and observe (P&O) and incremental conductance (InC) exhibit slow convergence and oscillation around MPP under PSC. Although numerous metaheuristic and hybrid MPPT strategies have been reported, they often suffer from high computational complexity, slower convergence rates, require large datasets, demand high processing power and limited real-time feasibility. Additionally, conventional boost converters suffer from high output ripple and limited voltage stability, making them unsuitable for high-power EV charging. To overcome these challenges, this study proposes a double integral sliding mode control (DISMC)-based KY converter integrated with the InC MPPT algorithm to enhance tracking accuracy, voltage stability and power conversion efficiency. A Lyapunov-based control law is developed for DISMC using the PV voltage and inductor current as state variables to ensure stable converter operation. Simulation studies on a 400 W PV system demonstrate improved and faster GMPP tracking, reduced oscillations around MPP, minimised chattering, higher power conversion efficiency and lower steady-state error, compared to conventional control methods. When applied to a 25-kW solar EV charging application, the simulation shows increased charging current, state-of-charge rate and power conversion efficiency, thereby reducing the battery charging time.Experimental validation using a 40 W prototype, confirms the robustness and scalability of the proposed DISMC–KY converter under practical operating conditions.

This study investigates a practical method to enhance the efficiency of a campus electrical distribution system by optimizing the control and power circuitry of a capacitor bank panel. The research addresses the persistent issue of low power factor and phase imbalance resulting from non-standard wiring configurations in existing installations. Unlike conventional maintenance procedures, the proposed rewiring strategy systematically redesigns the control and power connections to ensure accurate capacitor switching and reactive power compensation in accordance with operational load variations. A diagnostic improvement evaluation framework was employed, involving pre- and post-rewiring measurements of power factor, load current balance, and reactive power under both normal and full-load conditions. The rewiring intervention increased the power factor from 0.97 to 0.99 during normal operation and from 0.70 to 0.95 under full-load simulation (1100 kVA). These improvements corresponded to a measurable reduction in reactive power demand and overall system losses, indicating a substantial gain in energy efficiency and voltage stability. The findings confirm that targeted control circuit reconfiguration can significantly enhance the operational reliability of capacitor bank systems beyond conventional maintenance practices. This work contributes a replicable, technically validated approach for improving power quality in educational and industrial electrical installations.

Abstract Traditional multi-objective optimization in power transmission and transformation projects suffers from static reference point settings, inefficient constraint handling, and subjective evaluation weighting. To address these challenges, this study proposes a collaborative optimization–evaluation framework integrating an improved Non-dominated Sorting Genetic Algorithm III (INSGA-III) with a game-theoretic combined weighting approach. An adaptive reference point mechanism, guided by population distribution entropy, dynamically regulates reference density to enhance convergence and diversity across six conflicting objectives: lifecycle cost, reliability, short-circuit current, voltage stability, electromagnetic impact, and land occupation. A feasibility-first constraint strategy embeds power flow, capacity, and N−1 safety criteria directly into environmental selection, ensuring engineering validity. For solution evaluation, entropy, Criteria Importance Through Intercriteria Correlation (CRITIC), and analytic hierarchy process (AHP) weights are integrated via Shapley-value cooperative games, generating balanced, stable indicator weights. Validation on a 500 kV project shows the INSGA-III achieves 22.5% lower inverse generational distance (IGD), 14.0% higher hypervolume (HV), and 39.3% lower generational distance (GD) than the baseline, with 91% feasible solutions. The combined weighting maintains ranking stability under ±15% perturbations. The proposed framework effectively bridges theoretical optimization and engineering decision-making, offering a robust paradigm for scientific planning of complex power infrastructure in the context of new-type power systems.

Innovative hybrid GA-optimized ANN-based MPPT and hybrid super-twisting second-order sliding mode control for enhanced energy extraction and voltage stability in an isolated DC microgrid

A risk assessment method of multi-form transient voltage stabilities for high-proportion renewable energy sending-end power grids

ABSTRACT In urban public spaces, maintaining high power quality is very much needed for reliable and efficient energy consumption. This study aims to develop and validate an effective design method focused on improving urban power quality through the integration of renewable wind energy. This research proposes a novel and unique design method for enhancing and improving power quality in urban areas by connecting the wind energy through the utilization of vertical‐axis wind turbines (VAWTs). The whole concept of the proposed methods involves a structured methodology comprising system modeling, integration of VAWTs with a Unified Power Quality Conditioner (UPQC), and experimental validation to measure voltage stability, total harmonic distortion (THD) and reactive power performance. The UPQC, an advanced power electronic device, operates by combining series and shunt compensators to address a wide range of power quality disturbances simultaneously. The series compensator handles the whole voltage‐related problem and the shunt compensators fully manage and coordinate the current‐related issue. This dual compensation approach ensures synchronized mitigation of both voltage and current disturbances, thereby maintaining consistent grid performance. By utilizing wind energy harnessed from VAWTs, the recommended system provides an alternative and renewable source of power, minimizing dependencies on the conventional grids and improving the overall energy efficiencies. The vertical turbines are chosen due to their excellent adaptability and suitability for all urban environments, where space limitations and varying wind directions at all positions face significant challenges. The research contains a detailed analysis of the performance enhancement brought about by the UPQC in parallel with VAWTs, leading on key power quality metrics. Experimental results show a significant minimization in voltage sags and swells, with the normalized sag values improving by up to 75% and swell values by up to 65%. The method improves power stability and promotes sustainability by combining renewable energy with advanced power electronic solutions in urban areas.

The practical application of microbial fuel cells (MFCs) is often hindered by sluggish oxygen reduction reaction (ORR) kinetics and biofouling at the cathode. Herein, we developed a bifunctional MnS/Co co-anchored N-doped carbon catalyst (MnS/Co-SNC) derived from ZIF-67. This catalyst was designed to simultaneously tackle both problems by integrating enhanced ORR activity with intrinsic antibacterial functionality. The incorporation of MnS generates heterogeneous MnS/Co interfaces, inducing electron redistribution and optimizing oxygen adsorption, while carbon nanotubes (CNTs) grown in situ facilitate rapid electron transfer. Benefiting from these synergies, MnS/Co-SNC exhibits an onset potential of 0.92 V and a half-wave potential of 0.88 V in alkaline media, surpassing commercial Pt/C. More importantly, the sulfur species provide potent antibacterial activity, effectively suppressing biofilm formation and preserving catalytic sites. When applied as an air-cathode in single-chamber MFCs, MnS/Co-SNC delivers a maximum power density of 1400 mW m-2 and maintains a stable voltage output over 120 h, outperforming state-of-the-art non-precious metal catalysts. This work presents a rational strategy for designing multifunctional electrocatalysts that simultaneously address ORR kinetics and biofouling, advancing the practical deployment of MFCs for sustainable energy generation.

Reversible metal electrodeposition (RME)-based electrochromic devices (ECDs) offer intriguing prospects for energy-saving buildings, information displays, military camouflage, etc. As the most crucial component of RME-based devices, the electrolyte governs the efficiency of ion transport and the reversibility of deposition/dissolution, thereby determining the optical modulation capability and service life of the devices. Herein, by tailoring the solvation structure, we developed a deep eutectic solvent (DES) electrolyte with high ionic conductivity (6.8-8.0 mS cm-1 at 25 °C), stable voltage window (∼2.7 V) and wide temperature range (-20 to 80 °C). Moreover, the DES-based electrolyte enables highly reversible Zn deposition and dissolution. The assembled RME-based device exhibits three optical states (transparent, colored, and mirror), a large average optical modulation of up to 87% across a broad wavelength range (400-800 nm), robust cycling stability (87.3% retention after 2400 cycles) and stable performance over a wide temperature range (-20 to 80 °C). Our work provides a new direction for the development of environmentally friendly, high-performance DES electrolytes and establishes a foundation for their application in RME-based devices.

The optimization of power distribution networks is a critical challenge in the evolving energy sector, where increasing demand, aging infrastructure, and the integration of distributed energy resources necessitate smarter, more resilient systems. Smart grid technology offers a transformative framework by combining advanced sensing, two-way communication, and intelligent control mechanisms to enhance the efficiency, reliability, and flexibility of power distribution. This paper presents an optimization-oriented approach to modernizing distribution networks, focusing on adaptive reconfiguration, demand response, loss minimization, and voltage stability. Techniques such as real-time monitoring, predictive analytics, and automated feeder reconfiguration are explored to reduce technical losses and improve power quality. The proposed framework leverages optimization algorithms, including mixed-integer linear programming (MILP) and heuristic-based methods, to balance supply and demand, accommodate renewable integration, and ensure cost-effective operation. Simulation results demonstrate significant improvements in network performance, including reduced outage durations, enhanced load balancing, and operational cost savings. Additionally, the study highlights the importance of cybersecurity, interoperability, and scalability in enabling widespread adoption of optimized distribution systems. The findings underscore that the fusion of smart grid technology with optimization strategies can transform conventional distribution networks into adaptive, resilient, and sustainable energy infrastructures, capable of meeting future electricity challenges.