Search Results (2,667)

With the widespread adoption of electric vehicles, commercial charging stations (CCS) have grown rapidly as a core component of charging infrastructure. Due to the concentrated and high-power charging load characteristics of CCS, a ‘peak on peak’ phenomenon can occur in the power distribution network. Demand response (DR) serves as an important and flexible regulation tool for power systems, offering a new approach to addressing this issue. However, when CCS participates in DR, it faces a dual dilemma between operational revenue and user satisfaction. To address this, this paper proposes a bi-level, multi-objective framework that co-optimizes station profit and nonlinear user satisfaction. An asymmetric sigmoid mapping is used to capture threshold effects and diminishing marginal utility. Uncertainty in users’ charging behaviors is evaluated using a Monte Carlo scenario simulation together with chance constraints enforced at a 0.95 confidence level. The model is solved using the fast non-dominated sorting genetic algorithm, NSGA-II, and the compromise optimal solution is identified via the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Case studies show robust peak shaving with a 6.6 percent reduction in the daily maximum load, high satisfaction with a mean of around 0.96, and higher revenue with an improvement of about 12.4 percent over the baseline. Full article

To improve the accuracy and robustness of lithium-ion battery state of charge (SOC) estimation, this paper proposes a generalized mixture maximum correlation-entropy criterion-based adaptive extended Kalman filter (GMMCC-AEKF) algorithm, addressing the performance degradation of the traditional extended Kalman filter (EKF) under non-Gaussian noise and inaccurate initial conditions. Based on the GMMCC theory, the proposed algorithm introduces an adaptive mechanism and employs two generalized Gaussian kernels to construct a mixed kernel function, thereby formulating the generalized mixture correlation-entropy criterion. This enhances the algorithm’s adaptability to complex non-Gaussian noise. Simultaneously, by incorporating adaptive filtering concepts, the state and measurement covariance matrices are dynamically adjusted to improve stability under varying noise intensities and environmental conditions. Furthermore, the use of statistical linearization and fixed-point iteration techniques effectively improves both the convergence behavior and the accuracy of nonlinear system estimation. To investigate the effectiveness of the suggested method, experiments for SOC estimation were implemented using two lithium-ion cells featuring distinct rated capacities. These tests employed both dynamic stress test (DST) and federal test procedure (FTP) profiles under three representative temperature settings: 40 °C, 25 °C, and 10 °C. The experimental findings prove that when exposed to non-Gaussian noise, the GMMCC-AEKF algorithm consistently outperforms both the traditional EKF and the generalized mixture maximum correlation-entropy-based extended Kalman filter (GMMCC-EKF) under various test conditions. Specifically, under the 25 °C DST profile, GMMCC-AEKF improves estimation accuracy by 86.54% and 10.47% over EKF and GMMCC-EKF, respectively, for the No. 1 battery. Under the FTP profile for the No. 2 battery, it achieves improvements of 55.89% and 28.61%, respectively. Even under extreme temperatures (10 °C, 40 °C), GMMCC-AEKF maintains high accuracy and stable convergence, and the algorithm demonstrates rapid convergence to the true SOC value. In summary, the GMMCC-AEKF confirms excellent estimation accuracy under various temperatures and non-Gaussian noise conditions, contributing a practical approach for accurate SOC estimation in power battery systems. Full article

Wireless power transfer (WPT) has emerged as a key enabling technology for the large-scale adoption of electric vehicles (EVs), offering enhanced charging flexibility, improved safety, and seamless integration with intelligent transportation and renewable energy infrastructures. This paper presents a comprehensive review and technical synthesis of WPT technologies spanning both near-field and far-field domains, including inductive power transfer (IPT), magnetically coupled resonant WPT (MCR-WPT), capacitive power transfer (CPT), microwave power transfer (MPT), and laser wireless charging (LPT). Particular emphasis is placed on MCR-WPT, the most widely adopted approach for EV wireless charging, for which the coupler structures, resonant compensation networks, power converter architectures, and control strategies are systematically analyzed. The review further identifies that hybrid WPT architectures, adaptive compensation design and wide-coverage coupling mechanisms will be central to enabling high-power, long-distance, and misalignment-resilient wireless charging solutions for next-generation electric transportation systems. Full article

The electrification of transport is rapidly reshaping power distribution networks, introducing new technical, regulatory, and operational challenges for Distribution System Operators (DSOs). This article presents an international review of electromobility integration strategies, analyzing experiences from Europe, Canada, Australia, and Greece. It examines how DSOs address grid impacts through smart charging, vehicle-to-grid (V2G) services, and demand flexibility mechanisms, alongside evolving regulatory and market frameworks. European initiatives—such as Germany’s Energiewende and the UK’s Demand Flexibility Service—demonstrate how coordinated planning and interoperability standards can transform electric vehicles (EVs) into valuable distributed energy resources. Case studies from Canada and Greece highlight region-specific challenges, such as limited access in remote communities or island grid constraints, while Australia’s high PV penetration offers unique opportunities for PV–EV synergies. The findings emphasize that DSOs must evolve into active system operators supported by digitalization, flexible market design, and user engagement. The study concludes by outlining implementation barriers, policy implications, and a roadmap for DSOs. Full article

Under China’s “dual-carbon” strategic goals and the advancement of smart city development, the rapid adoption of electric vehicles (EVs) has deepened the spatiotemporal coupling between transportation networks and distribution grids, posing new challenges for integrated energy systems. To address this, we propose a collaborative optimization framework for power–transportation coupled networks that integrates multi-modal data with physical priors. The framework constructs a joint feature space from traffic flow, pedestrian density, charging behavior, and grid operating states, and employs hypergraph modeling—guided by power flow balance and traffic flow conservation principles—to capture high-order cross-domain coupling. For prediction, spatiotemporal graph convolution combined with physics-informed attention significantly improves the accuracy of EV charging load forecasting. For optimization, a hierarchical multi-agent strategy integrating federated learning and the Alternating Direction Method of Multipliers (ADMM) enables privacy-preserving, distributed charging load scheduling. Case studies conducted on a 69-node distribution network using real traffic and charging data demonstrate that the proposed method reduces the grid’s peak–valley difference by 20.16%, reduces system operating costs by approximately 25%, and outperforms mainstream baseline models in prediction accuracy, algorithm convergence speed, and long-term operational stability. This work provides a practical and scalable technical pathway for the deep integration of energy and transportation systems in future smart cities. Full article

Against the backdrop of China’s dual-carbon goals, traditional road transportation has relatively high carbon emissions and is in urgent need of a low-carbon transition. The intermittency of photovoltaic (PV) power generation and the stochastic nature of electric vehicle (EV) charging demand introduce significant uncertainty for PV-energy storage-charging systems in highway service areas. Existing approaches often struggle to balance economic efficiency and reliability. This study develops a min-max-min robust optimization model for a full-route PV-energy storage-charging system. A box uncertainty set is used to characterize uncertainties in PV output and EV load, and a tunable uncertainty parameter is introduced to regulate risk. The model is solved using a column-and-constraint generation (C&CG) algorithm that decomposes the problem into a master problem and a subproblem. Strong duality, combined with a big-M formulation, enables an alternating iterative solution between the master problem and the subproblem. Simulation results demonstrate that the proposed algorithm attains the optimal solution and, relative to deterministic optimization, achieves a desirable trade-off between economic performance and robustness. Full article

Silicon carbide (SiC) MOSFETs, as one of the representative power electronic devices, have faced reliability challenges due to threshold voltage (V_th) instability under dynamic gate stress. To explore the underlying mechanisms, this work investigates 4H-SiC MOS structures (P-MOS and N-MOS) under AC bias temperature instability (AC BTI) stress, utilizing a laser to generate minority carriers and simulate realistic switching conditions. Through combined capacitance–voltage (C-V) and gate current–voltage (J_g-V_g) characterizations on P-MOS and N-MOS devices before and after degradation at different temperatures, we reveal a critical temperature dependence in defect interactions. At room temperature, degradation is dominated by electron trapping in shallow interface states and near-interface traps (NITs). In contrast, high-temperature stress activates charge exchange with deep-level, slow states. Notably, a positive V_FB shift is consistently observed in both N-MOS and P-MOS devices under AC stress, confirming that electron trapping is the dominant cause of the commonly observed positive V_th shift in SiC MOSFETs. These findings clarify the distinct defect-mediated mechanisms governing dynamic V_th instability in SiC devices, providing fundamental insights for interface engineering and reliability assessment. Full article

This work presents the fabrication and characterization of a bow-tie antenna integrated uni-traveling carrier photodiode (UTC-PD) on a silicon carbide (SiC) substrate for efficient terahertz (THz) wave generation. The proposed device exploits the superior thermal conductivity and mechanical robustness of SiC to overcome the self-heating limitations associated with conventional indium phosphide (InP)-based photodiodes. An epitaxial layer transfer technique was utilized to bond InP/InGaAs UTC-PD structures onto SiC. The study systematically examines the influence of critical geometric parameters, specifically the mesa diameter and length between the antenna arms, on the emitted THz intensity in the 300 GHz frequency band. Experimental results show that the THz radiation efficiency is primarily governed by the mesa diameter, reflecting the trade-off between light absorption, device capacitance, and bandwidth, while the length between the antenna arms exhibits only a weak influence within the investigated parameter range. The fabricated device demonstrates strong linearity between photocurrent and THz output power up to 7.5 mA, after which saturation occurs due to space-charge effects. This work provides crucial insights for optimizing SiC-based bow-tie antenna integrated UTC-PD devices to realize robust, high-power THz sources vital for future high-data-rate wireless communication systems such as beyond 5G and 6G networks. Full article

To enhance the power supply resilience of distribution networks with high-penetration distributed photovoltaic (PV) integration during extreme disasters, deploying Mobile Energy Storage Systems (MESSs) proves to be an effective countermeasure. This paper proposes an optimized operational strategy for distribution networks, integrating coordinated clustering of distributed PV systems and MESS operation to ensure power supply during both pre-disaster prevention and post-disaster restoration phases. In the pre-disaster prevention phase, an improved Louvain algorithm is first applied for PV clustering to improve source-load matching efficiency within each cluster, thereby enhancing intra-cluster power supply security. Subsequently, under the worst-case scenarios of PV output fluctuations, a robust optimization algorithm is utilized to optimize the pre-deployment scheme of MESS. In the post-disaster restoration phase, cluster re-partitioning is performed with the goal of minimizing load shedding to ensure power supply, followed by reoptimizing the scheduling of MESS deployment and its charging/discharging power to maximize the improvement of load power supply security. Simulations on a modified IEEE 123-bus distribution network, which includes two MESS units and twenty-four PV systems, demonstrate that the proposed strategy improved the overall restoration rate from 68.98% to 86.89% and increased the PV utilization rate from 47.05% to 86.25% over the baseline case, confirming its significant effectiveness. Full article

The development of integrated renewable energy and high-density thermal energy storage systems has been fueled by the need for environmentally friendly heating and cooling in buildings. In this paper, MiniStor, a hybrid thermochemical and phase-change material storage system, is presented. It is equipped with a heat pump, advanced electronics-enabled control, photovoltaic–thermal panels, and flat-plate solar collectors. To optimize energy flows, regulate charging and discharging cycles, and maintain operational stability under fluctuating solar irradiance and building loads, the system utilizes state-of-the-art power electronics, variable-frequency drives and modular multi-level converters. The hybrid storage is safely, reliably, and efficiently integrated with building HVAC requirements owing to a multi-layer control architecture that is implemented via Internet of Things and SCADA platforms that allow for real-time monitoring, predictive operation, and fault detection. Data from the MiniStor prototype demonstrate effective thermal–electrical coordination, controlled energy consumption, and high responsiveness to dynamic environmental and demand conditions. The findings highlight the vital role that digital control, modern electronics, and Internet of Things-enabled supervision play in connecting small, high-density thermal storage and renewable energy generation. This strategy demonstrates the promise of electronics-driven integration for next-generation renewable energy solutions and provides a scalable route toward intelligent, robust, and effective building energy systems. Full article

Alternative energies are essential for meeting the global demand for environmentally friendly energy, especially as the use of fossil fuels is being reduced. In recent years, largely due to diminishing fossil fuel reserves, Portugal has been actively promoting investment in renewable energies to reduce its reliance on energy imports and fossil fuels. However, despite the country’s high daily sunshine hours and utilization of wind and hydropower, energy production remains unstable due to climate variability. Climate instability leads to fluctuations in the energy supplied to the grid and can even partially withstand blackouts such as the one that occurred on 28 April 2025 on the Iberian Peninsula. To address this problem, energy storage systems are crucial to guarantee the stability of the supply during periods of low production or in situations such as the one mentioned above. This paper analyzes the feasibility of implementing an energy storage system to increase the profitability of a wind farm located in Alto Douro, Portugal. The study begins with a demand analysis, followed by simulations of the system’s performance in terms of profitability based on efficiency and power. Based on these assumptions, a modular lithium battery storage system with high efficiency and rapid charge/discharge capabilities was selected. This battery, with less autonomy but high capacity, is more profitable, since a 5% increase in efficiency results in high profits (€84,838) and curtailment (€70,962) using batteries with lower autonomy, i.e., 2 h (power rating of 5 MW combined with 10 MWh energy storage). Therefore, two scenarios (A and B) were considered, with one more optimistic (A) in which the priority is to discharge the batteries whenever possible. In the more realistic scenario (B), it is assumed that the batteries are fully charged before discharge. On the other hand, in the event of a blackout, it enables faster commissioning of the surrounding water installations, because solar and battery energy have no inertia, which facilitates the back start protocol. Full article

This paper quantifies the steady-state voltage-compliance impact of ultra-fast electric vehicle (EV) charging on the IEEE 33-bus radial distribution feeder. Four practical scenarios are examined by combining two penetration levels (6 and 12 charging points, i.e., ≈20% and ≈40% of PQ buses) with two charger ratings (1 MW and 350 kW per point). Candidate buses for EV station integration are selected through a nodal voltage–reactive sensitivity ranking ($\partial V/\partial Q$), prioritizing electrically robust locations. To capture realistic operating uncertainty, a 24-hour quasi-static time-series power-flow assessment is performed using Monte Carlo sampling ($N=100$), jointly modeling residential-demand variability and stochastic EV charging activation. Across the four cases, the worst-hour minimum voltage (uncompensated) ranges from 0.803 to 0.902 p.u., indicating a persistent under-voltage risk under dense and/or high-power charging. When the expected minimum-hourly voltage violates the 0.95 p.u. limit, a closed-form, sensitivity-guided reactive compensation is computed at the critical bus, and the power flow is re-solved. The proposed mitigation increases the minimum-voltage trajectory by approximately 0.03–0.12 p.u. (about 3.0–12.0% relative to 1 p.u.), substantially reducing the depth and duration of violations. The maximum required reactive support reaches 6.35 Mvar in the most stressed case (12 chargers at 1 MW), whereas limiting the unit charger power to 350 kW lowers both the severity of under-voltage and the compensation requirement. Overall, the Monte Carlo V–Q sensitivity framework provides a lightweight and reproducible tool for probabilistic voltage-compliance assessment and targeted steady-state mitigation in EV-rich radial distribution networks. Full article

Precisely assessing electric vehicle hosting capacity (EVHC) is critical for ensuring the secure integration of EVs and optimizing the use of distribution network resources. Although optimization-based methods such as Particle Swarm Optimization (PSO) can identify a high theoretical HC under steady-state voltage constraints, these static formulations fail to capture short-term dynamics such as photovoltaic (PV) intermittency and uncoordinated EV arrivals. As a result, the hosting capacity that can actually be used in practice is often reduced to a much lower capacity to keep the system operating safely. This study compares optimization-based and simulation-based HC assessments and introduces a Weighted Average Power Estimator (WAPE)-based dynamic control framework to preserve the higher HC identified by optimization under real-world conditions. Case studies on a modified IEEE 13-bus system show PV drops of 90% during a 4-s cloud event. Studies also demonstrate that a sudden clustering of multiple EVs would significantly lower effective HC. With WAPE control, the system maintains stable operation at full HC, holding the bus voltage within an acceptable range (400–430 V) during the two events, representing a 2–3% voltage improvement. In addition, WAPE allows the EV to continue charging at a lower rate during disturbances, reducing the total charging time by almost 10% compared with completely stopping the charging process. Overall, the proposed WAPE substantially improves the usable and sustainable HC of distribution networks, ensuring reliable EV integration under dynamic and uncertain operating conditions. Full article

In situ preparation routes have become central to advancing lead sulfide (PbS) quantum dots (QDs) for solar-energy conversion, owing to their ability to create strongly coupled QD/oxide interfaces that are difficult to achieve with ex situ colloidal methods, along with their simplicity and potential for low-cost, scalable processing. This review systematically examines the fundamental mechanisms, processing levers, and device implications of the dominant in situ approaches successive ionic layer adsorption and reaction (SILAR), voltage-assisted SILAR (V-SILAR), and chemical bath deposition (CBD). These methods enable conformal QD nucleation within mesoporous scaffolds, improved electronic coupling, and scalable low-temperature fabrication, forming the materials foundation for high-performance PbS-based architectures. We further discuss how these in situ strategies translate into enhanced solar-energy applications, including quantum-dot-sensitized solar cells (QDSSCs) and photoelectrochemical (PEC) hydrogen production, highlighting recent advances in interfacial passivation, scaffold optimization, and bias-assisted growth that collectively suppress recombination and boost photocurrent utilization. Representative device metrics reported in recent studies indicate that in-situ-grown PbS quantum dots can deliver photocurrent densities on the order of ~5 mA cm⁻² at applied potentials around 1.23 V versus RHE in photoelectrochemical systems, while PbS-based quantum-dot-sensitized solar cells typically achieve power conversion efficiencies in the range of ~4–10%, depending on interface engineering and device architecture. These performances are commonly associated with conformal PbS loading within mesoporous scaffolds and quantum-dot sizes in the few-nanometer regime, underscoring the critical role of morphology and interfacial control in charge transport and recombination. Recent studies indicate that performance improvements in PbS-based solar-energy devices are primarily governed by interfacial charge-transfer kinetics and recombination suppression rather than QD loading alone, with hybrid heterostructures and inorganic passivation layers playing a key role in modifying band offsets and surface trap densities at the PbS/oxide interface. Remaining challenges are associated with defect-mediated recombination, transport limitations in densely loaded porous scaffolds, and long-term chemical stability, which must be addressed to enable scalable and durable PbS-based photovoltaic and photoelectrochemical technologies. Full article

Hybrid trolleybuses (HTBs) with in-motion charging (IMC) can extend zero-emission service using existing catenary, but high on-wire charging powers may concentrate loads and accelerate battery aging. We present a data-driven simulation that replays recorded high-resolution Controller Area Network (CAN) logs through a per-vehicle electrical model with (Constant-Current/Constant-Voltage) (CC/CV) charging and a stress-map aging estimator, a configurable partial catenary overlay, and fleet aggregation by simple summation and an iterative node-voltage analysis of a resistor-network catenary model. A parameter sweep across battery sizes, upper state of charge (SoC) bounds, and charging power caps compares a minimal “charge-whenever-possible” policy with a per-vehicle lookahead (“oracle”) policy that spreads charging over available catenary time. Results show that lowering maximum charging power and/or the upper SoC bound reduces capacity fade, while energy-demand differences are small. Fleet load profiles are dominated by timetable-driven concurrency using 40 recorded days overlaid into one synthetic day: varying per-vehicle power or target SoC has little effect on peak demand; per-vehicle lookahead does not flatten the peak. The node-voltage analysis indicates catenary efficiency around 97% and fewer undervoltage events at lower charging powers. We conclude that per-vehicle policies can reduce battery stress, whereas peak shaving requires cooperative, fleet-level scheduling. Full article