Search Results (2,768)

Flywheel energy storage systems (FESSs) offer millisecond-level response speed, making them highly suitable for providing system inertia/frequency support in emergency grid scenarios. However, the FESSs often have limited energy capacity due to their high capacity cost, which necessitates a comprehensive consideration between remaining stored energy and sustained support capability. Thus, this paper proposes a virtual synchronous generator (VSG) control strategy based on a multi-time-step model predictive control (MPC) that considering flywheel’s state of charge (SOC), which provides both emergency frequency support and autonomous flywheel energy recovery within a single integrated framework. First, a multi-time-step MPC with the objective function aiming for both fast frequency response and smooth power output is introduced to compensate the reference power generated by the VSG strategy. Second, an SOC-adaptive frequency weight function is designed and incorporated into the objective function to balance the frequency deviation and the inertia/frequency support duration. Furthermore, an SOC self-recovery strategy is developed, allowing the flywheel to autonomously adjust its SOC to the desired range when the FESS is not participating in frequency regulation. Finally, the proposed strategy is verified through comprehensive simulations on various scenarios, demonstrating that it can efficiently and rapidly meet the frequency regulation demands when the SOC is sufficient, as well as achieve the balances between the frequency regulation performance and the support continuity when the SOC is insufficient. Full article

Dynamic Wireless Power Transfer (DWPT) is attracting increasing interest as a promising solution to extend the operating range of battery electric vehicles while reducing stationary charging needs. In this study, a DWPT system for Electric Vehicle charging is investigated through a comparative simulation-based case study focused on the Italian A4 highway, a strategic transport corridor characterized by high traffic intensity and long-distance mobility demand. The proposed system is based on a segmented magnetic coupling architecture with planar circular coils installed along the roadway and a vehicle-side pickup coil. Under common roadway, vehicle, and magnetic coupling assumptions, a benchmark Tesla Model 3 Long Range traveling at a constant speed of 90 km/h and characterized by an estimated energy consumption of 0.129 kWh/km is considered. Two compensation solutions are comparatively assessed, namely the Series–Series (SS) topology and the Inductor-Capacitor-Capacitor (LCC) topology. The methodology evaluates the two topologies under the same benchmark conditions in terms of peak power, average transferred power, transferred energy per kilometer, and effect on vehicle State Of Charge (SOC). The SS topology provides a peak power of 22.52 kW, an average power of 12.30 kW, and an energy transfer of 0.14 kWh/km, whereas the LCC topology reaches a peak power of 20.44 kW, an average power of 13.47 kW, and an energy transfer of 0.15 kWh/km. Starting from an initial SOC of 30%, the final SOC after traveling through the usable electrified highway section reaches 37.48% with SS compensation and 44.28% with LCC compensation. The results show that both topologies enable effective dynamic charging, with the LCC solution exhibiting better energy transfer capability and higher operational stability, while the SS topology delivers higher instantaneous power peaks. From a comparative simulation perspective, the study supports the technical feasibility of DWPT deployment in highway environments and provides useful design insights for selecting compensation topologies in dynamic electric vehicle charging applications. Full article

The electrification of long-haul freight transport requires a comprehensive public charging infrastructure along motorways. This study presents a framework combining multi-agent transport simulation (MATSim) with evolutionary bi-objective optimization (NSGA-II) to determine the number and spatial distribution of high-power charging (HPC) points for battery-electric trucks (BETs) on the German motorway network. Beyond infrastructure sizing, the approach also quantifies the impact of BET charging on the duration and distance of long-haul truck trips. The optimization simultaneously addresses the perspectives of two key stakeholders: charge point operators (CPOs), who seek to maximize charger utilization, and logistics operators, who aim to minimize waiting times. The results yield a range of Pareto-optimal configurations balancing the two objectives. A multi-iteration replanning step further lets trucks adapt their routes to experienced waiting times for a more realistic performance assessment, reducing mean waiting times by up to 92%. We evaluate five electrification levels from 1% to 20% across two charging network scenarios with 347 and 779 potential locations, respectively. For the balanced solutions—the knee-point configurations that best reconcile both objectives—at a 10% electrification level, the optimized network reaches a temporal charger utilization of 23% to 32% at mean waiting times of about 1.4 to 1.9 min per charging process. Compared with an internal combustion engine truck (ICET) reference, BET trip durations increase by only 0.9% to 1.3% due to charging detours. Overall, the fast-charging network planned by the German federal government appears sufficient for the HPC demand at electrification levels up to 10% to 15%, whereas additional low-power charging (LPC) infrastructure beyond the planned locations will be needed to cover overnight charging requirements. Full article

In modern power systems such as new energy generation and smart grids, inverters serve as core equipment for electrical energy conversion and transmission. Their operational reliability directly impacts system power supply quality and safety stability. Currently, research on inverter fault diagnosis technology primarily focuses on linear load conditions, with diagnostic method design and validation based on linear load characteristics. However, with the rapid advancement of power electronics technology, power electronic loads such as variable frequency drives, charging stations, and distributed power sources are increasingly prevalent in power systems. These loads exhibit nonlinear and time-varying characteristics under complex operating conditions, leading to a growing variety of inverter faults with significantly diversified and complex fault signatures. Traditional diagnostic methods fail to adapt to the unique characteristics of power electronic loads, making it difficult to accurately identify various faults. Consequently, they no longer meet the diagnostic demands of practical engineering scenarios. In addition, current diagnostic methods for open-circuit power transistors, intermittent faults, and sensor faults often employ different approaches, which consume significant controller resources and are prone to mutual interference, leading to false triggers. This paper takes a three-phase four-wire inverter as the research subject. Targeting the challenge of fault diagnosis under power electronic load conditions, it proposes a comprehensive diagnostic method capable of simultaneously diagnosing power switch open circuits, intermittent faults, and current sensor faults. First, the characteristics of various faults are analyzed. Subsequently, fault diagnosis variables are constructed using the actual arm voltage of the inverter and the ideal arm voltage. Logical rules for each type of fault are established, and diagnosis is performed through fuzzy logic inference. Finally, experiments validated the effectiveness of this fault diagnosis scheme, with open-circuit faults detected in less than 2 ms, intermittent faults in less than 0.5 ms, and sensor faults in less than 3 ms. Full article

Electric Vehicle (EV) charging infrastructure plays a critical role in modern energy systems, affecting energy load distribution, demand-response programs, and grid stability. As EV adoption accelerates globally, the varied charging habits and concurrent interactions among users, stations, and shared infrastructure create operational inefficiencies that existing machine learning and optimization approaches cannot fully diagnose, because these methods rely on aggregated or single-entity representations that discard cross-object process dependencies. To address this gap, we propose EnOptiMine (Energy Optimization Framework for Electric Vehicles through Object-Centric Process Mining), a novel four-phase analytical framework that applies Object-Centric Process Mining (OCPM) to EV charging infrastructure. EnOptiMine operates by transforming raw EV charging data into an Object-Centric Event Log (OCEL 2.0), discovering the complete charging lifecycle as a structured multi-object process through Object-Centric Directly-Follows Graphs (OC-DFGs), performing conformance analysis to detect and quantify process deviations across object-type lifecycles, and proposing process improvement interventions. Applied to the EV charging dataset, EnOptiMine identifies sessions that exhibit post-charge station idle-blocking, departure mismatch, and carry lifecycle ordering violations. In the present work, the real-world simulation confirms that a graduated idle fee policy recovers 22.9% of wasted station-hours, and a departure reconfirmation protocol reduces mismatch sessions by 54.0%. These results demonstrate that OCPM provides process-transparent diagnostic capabilities for EV charging infrastructure that are inaccessible to existing prediction- and optimization-based methods. Full article

Electric Vehicle Charging Stations (EVCSs) have become increasingly important due to the growing penetration of electric vehicles (EVs) and renewable-based power generation. However, challenges such as fluctuating renewable energy availability, increasing charging demand, power losses, operational cost, and charging delays continue to affect overall grid performance and stability. To address these issues, this study proposes a hybrid Goat Optimization Algorithm–Hierarchical Multi-Granularity Interaction Graph Convolutional Network (GOA–HMGIGCN) framework for intelligent smart grid energy management and EV charging coordination. The proposed framework combines the Goat Optimization Algorithm (GOA) for optimal EVCS placement and charging scheduling with the Hierarchical Multi-Granularity Interaction Graph Convolutional Network (HMGIGCN) for forecasting renewable generation, charging demand, and load variations. The framework was implemented and evaluated in MATLAB/Simulink R2024a using the IEEE 14-bus smart grid test system under varying operating conditions. Simulation results demonstrated that the proposed framework achieved superior performance compared with the Coot Optimization Algorithm–Fractional Backpropagation Physics-Informed Neural Network (COA-FBPINN), Dingo Optimization Algorithm–Convolutional Hypergraph Graph Neural Network (DOA-CHGNN), Self-Feedback Feedforward Artificial Neural Network (SFFANN), Deep Neural Network (DNN), and Golden Jackal Optimization–Attention-Based Probabilistic Convolutional Neural Network (GJO-APCNN) techniques by attaining the lowest operational cost of USD 1561, the highest efficiency of 99.2%, the minimum power loss of 10.6 kW, and the shortest charging time of 32 min. In addition, the proposed framework and overall grid reliability, confirming its effectiveness for intelligent renewable-integrated smart grid applications. Full article

The worldwide shift to electric mobility has intensified in recent years owing to heightened apprehensions over greenhouse gas emissions, energy security, and the necessity for sustainable transportation systems. Electric vehicles (EVs) are acknowledged as a viable alternative for diminishing reliance on fossil fuels and enhancing energy efficiency in the transportation sector. While affluent nations have achieved considerable advancements in electric vehicle adoption and charging infrastructure, numerous developing countries still encounter significant technical and infrastructural obstacles that hinder extensive EV integration. In South Africa, these difficulties are exacerbated by ongoing electrical supply limitations, deteriorating transmission and distribution facilities, and recurrent load shedding, which heighten worries about the dependability and stability of the national power grid. The rising adoption of electric vehicles adds extra electrical demands to power systems, especially at the distribution network level, where most of the charging takes place. Disorganized EV charging can substantially modify current load patterns, leading to heightened peak demand, voltage variations, transformer overload, and network congestion. The technical consequences are especially significant in South Africa, where the power grid functions with constricted generation capacity and minimal reserve margins. Various mitigating measures have been suggested to tackle these difficulties, including intelligent charging, demand-side management, time-of-use pricing, and vehicle-to-grid technologies. This paper establishes a basic theoretical framework through an extensive literature review to investigate the technological problems related to electric vehicle adoption in South Africa, while assessing the environmental and economic ramifications for sustainable urban transportation systems. Full article

Photovoltaic (PV) generation and electric vehicle (EV) charging infrastructure are changing the dynamic behavior of current power systems, especially in terms of voltage stability and LVRT capabilities. In this work, 50% PV penetration on a modified Kundur two-area power system was tested to mitigate transient instability under severe fault circumstances. With PV units running at unity power factors under steady-state conditions, 50% PV penetration was defined relative to the system’s total active load demand. A steady-state power-flow study ensured generation–load balance before MATLAB/Simulink dynamic simulations. Controllable reactive power compensation was used as an EV aggregator on Bus 7. We constructed and evaluated a genetic algorithm (GA)-optimized fractional-order proportional–integral–derivative (FOPID) controller with a traditional PID controller utilizing identical optimization conditions. An inter-area tie-line critical three-phase fault was applied and removed after 100 ms to evaluate system performance. While the GA-PID controller increased transient performance, it did not restore system stability. Instead, the GA-FOPID controller provided superior dynamic support by restoring Bus 7 voltage to 0.9–1.1 pu within 250 ms after fault clearance and maintaining about 95% LVRT compliance. The suggested controller also reduced rotor angle oscillations and enhanced inter-area damping. Fractional-order control increased EV aggregators’ reactive power response during transient shocks. Thus, in renewable-energy-dominated power systems, the GA-FOPID-controlled EV support technique may improve voltage stability and LVRT compliance. Full article

Real-time scheduling in large-scale electric vehicle charging stations is challenged by stochastic vehicle arrivals, dynamic departures, limited charging resources, and station-level power constraints. To address this problem, this paper proposes a two-stage Supervised Service Matching and Reinforcement Power Dispatch (SMPD) framework, termed SMPD, which decomposes the original coupled scheduling problem into supervised service matching and reinforcement learning-based power dispatch. In the first stage, a supervised matching network learns EV-charger service suitability from historical charging-session records and determines service access decisions for feasible EV–charger pairs. In the second stage, a Soft Actor-Critic-based controller allocates continuous charging power to connected EVs under EV-side charging limits, charger capacity constraints, and the station-level total power constraint. The proposed framework is evaluated using public charging-session data from the ElaadNL dataset. Experimental results show that SMPD achieves lower average waiting time, higher average revenue, lower composite penalty, and comparable demand satisfaction compared with rule-based, single-stage reinforcement learning, and multi-agent baselines. Sensitivity and robustness analyses further indicate that SMPD maintains favorable scheduling performance and acceptable online decision time under the tested charger-scale settings and operational disturbance scenarios. These results suggest that the proposed two-stage design provides an effective and computationally tractable approach for real-time scheduling in large-scale EV charging stations. Full article

With increasing penetration of distributed energy resources, peer-to-peer (P2P) electricity trading has attracted attention for locally utilizing surplus renewable energy. This paper proposes a distributed consensus-based P2P electricity trading method that explicitly considers prosumer equipment operation constraints. Each prosumer autonomously solves a daily scheduling problem considering electricity demand, PV generation, battery operation, grid purchase and sale, and P2P trades with neighboring prosumers. P2P prices and desired trading quantities are iteratively adjusted through local information exchange. After convergence, bidirectional trades are converted into net one-way trades, and the final feasible daily schedule is obtained by re-optimizing with fixed trading quantities. Numerical simulations were conducted for six low-voltage prosumers using annual residential demand data and a representative daily PV generation profile. In the base case, the proposed method reduced annual electricity cost by 13.7% compared with the no-P2P case, while its total cost was only 2.3% higher than that of the centralized benchmark. Unlike the centralized benchmark, which increased costs for some prosumers, the proposed method reduced costs for all prosumers. Wheeling-charge sensitivity analysis showed that the charge affects P2P trading volume and benefit allocation. Future work will address tariff design, PV uncertainty, scalability, and distribution-network constraints. Full article

Emerging economies face mounting pressure to adopt sustainable and cost-efficient methods for delivering products and services in urban areas. This study examines the Electric Vehicle Routing Problem with Time Windows (EVRPTW) within a pragmatic urban context. We concentrate on the short-haul delivery network in Marrakesh, Morocco, whose operational viability is influenced by climatic, infrastructural, and regulatory limitations. We present a simulated annealing (SA) metaheuristic, augmented with repair heuristics and a penalty-based cost function, to concurrently reduce routing costs and lateness fines, subject to time-window and battery capacity restrictions. The technique undergoes evaluation through extensive computer tests utilizing realistic instance sets that replicate local demand patterns and charging infrastructure. The penalty-calibrated model demonstrates delivery completion rates of up to 100%, significantly reducing route costs and the number of unserved clients relative to baseline setups. We thoroughly analyze the tuning parameters among several runs. This study intends to provide a useful tool for real-world decision support by fusing extensive literature synthesis with local context validation and by integrating a simulation module that evaluates time-window settings and charging patterns under realistic traffic. Full article

The rapid adoption of electric vehicles (EVs) creates a planning challenge for the Kampala-Wakiso metropolitan region in Uganda, where the electricity grid already faces local network constraints. This study applies the EVPV-Simulator, an open-source geospatial modelling framework that links mobility demand, charging demand, and EV-PV complementarity, to assess projected charging demand and solar integration potential in the Kampala-Wakiso metropolitan region. By simulating the charging requirements of a projected fleet of 60,000 EVs, the study identifies a pronounced evening charging peak concentrated in residential areas and weakly aligned with daytime solar availability. Under the base-case charging pattern, increasing PV capacity raises the self-sufficiency potential, but has limited influence on the evening peak. In the base-case with 40 MW of installed PV capacity, the self-sufficiency ratio reaches 39.6%, while peak demand falls by only 0.20%. A charging location sensitivity analysis then shows that temporal alignment improves substantially when charging shifts from home towards workplaces and Points of Interest (POI). In a selected daytime oriented scenario with 40% workplace charging and 60% POI charging, the self-sufficiency potential reaches 68.97% and the mean daily maximum net load falls to about 18 MW at 40 MW of installed PV capacity. These results show that the value of solar integration depends strongly on where charging occurs, and that daytime charging access should be treated as a central variable in EV infrastructure planning. The study provides a planning oriented basis for future work incorporating feeder level validation, explicit PV siting constraints, and storage. Full article

Hybrid tractors are a promising solution for reducing fuel consumption and emissions in agricultural machinery. However, their low-speed, high-torque operation with frequent load fluctuations demands an energy management strategy (EMS) that is both real-time capable and highly adaptive. This study focuses on a coaxial parallel hybrid electric tractor, developing a forward simulation model that integrates longitudinal vehicle dynamics, engine, motor, battery, and transmission systems. An improved equivalent fuel consumption minimization strategy (ECMS) with state-of-charge feedback correction, termed F-ECMS, is proposed. It dynamically adjusts the equivalence factor based on real-time battery SOC to approach optimal fuel economy while sustaining charge. Dynamic programming (DP) is used to establish a global benchmark. Simulations under a typical plowing cycle show that over 14,400 s, the F-ECMS maintains SOC (0.5964) close to the DP reference (0.6000), while achieving a 1.51% reduction in equivalent fuel consumption compared to a rule-based strategy. The results demonstrate that the proposed F-ECMS offers an effective balance between real-time performance and fuel economy, showing strong potential for practical implementation in hybrid agricultural vehicles. Full article

Hydrogen is a promising clean-energy carrier, but its low ignition energy, high diffusivity, and wide flammability range demand reliable leak detection. Chemiresistive sensors based on n-type metal oxide semiconductors are attractive owing to their simple architecture, low cost, large resistance modulation, thermal robustness, and compatibility with miniaturized devices. This review focuses on n-type metal oxide semiconductor nanomaterials for hydrogen sensing, particularly ZnO, SnO₂, In₂O₃, WO₃, TiO₂, and related mixed oxides. The fundamental sensing mechanisms are examined, including oxygen chemisorption, electron-depletion-layer modulation, grain-boundary barrier control, catalytic hydrogen spillover, and hydrogen-induced surface reduction or metallization, together with the way these mechanisms compete and cooperate under different operating conditions. Recent performance-enhancement strategies are organized around morphology and porosity control, noble-metal sensitization, defect and dopant engineering, n–n heterojunctions, molecular sieving, and low-temperature activation. Density functional theory is discussed as a design tool for evaluating adsorption energetics, vacancy formation, work-function shifts, band alignment, and interfacial charge transfer, along with its current limitations for modeling humid surfaces. Finally, key challenges and future directions, including humidity tolerance, standardized reporting, device integration, and emerging materials, are summarized to guide the development of high-performance hydrogen sensors. Full article

Decarbonizing heavy-duty road transport requires comparing zero-emission options to guide infrastructure investments along strategic corridors. This study develops a scenario-based techno-economic model to evaluate hydrogen fuel cell trucks (HFCTs) and battery electric trucks supported by dynamic wireless power transfer (DWPT) on a 100 km segment of Italy’s A4 motorway in 2030 and 2050 scenarios. The framework integrates traffic flows, vehicle archetypes, infrastructure sizing, and end-to-end energy chains (power-to-hydrogen-to-wheel for hydrogen and grid-to-wheel for WPT) to estimate capital and operating costs, efficiencies, and energy demand. Results show that hydrogen refueling infrastructure requires lower initial investment (approximately €60 million CAPEX and €20 million annual OPEX) than wireless charging systems (€80 million CAPEX and €15 million OPEX). However, WPT achieves significantly higher grid-to-wheel efficiency (96% vs. 62%) and lower per-vehicle energy demand (18 MWh/year vs. 25 MWh/year). These findings highlight a fundamental trade-off: hydrogen solutions offer operational flexibility and are better suited to long-haul or low-density contexts, while WPT systems are more efficient and become increasingly competitive in high-traffic corridors with high infrastructure utilization. Overall, the results suggest that no single technology universally dominates and that optimal deployment depends on traffic density, infrastructure usage, and system integration. A combined implementation of hydrogen and wireless charging technologies may provide the most effective pathway to balance efficiency, flexibility, and cost in future heavy-duty transport systems. Full article