Search Results (3,433)

This study aims to develop a decision-making framework for equalizing urban electric vehicle (EV) charging services, which is applied to improve Wuhan’s charging infrastructure. Using grid units as the basic analytical units, the study constructs measurement models for two scenarios—daily commuting and weekend travel—including a spatial demand index based on classified population distribution prediction, a spatial supply index derived from regional charging station statistics, and a supply–demand balance index. Grading systems are established for each scenario’s demand, layout thresholds, and supply, together with an integrated classification combining both scenarios. According to the suitability of grid units for service improvement, three optimization strategies are proposed: adding charging stations, expanding existing stations, and retrofitting parking lots. Evaluation methods are designed to assess spatial equilibrium pre- and post-optimization for residential quarters and commercial POIs. An empirical case study of Wuhan’s main urban area shows that service satisfaction reaches 88.68% for residential quarters and 75.93% for commercial POIs under the current conditions. The proposed scheme recommends the addition of 6 new stations, expansion of 23 stations, and retrofit of 52 parking lots, increasing satisfaction to 99.16% and 89.66%, respectively. The model provides a feasible technical framework for urban EV charging station planning. Full article

The growing penetration of renewable energy has intensified building load fluctuations, substantially increasing balancing costs. Electric vehicles (EVs) in building clusters often have considerable idle parking time beyond essential charging needs, enabling them to provide significant flexibility while meeting scheduled demands. This EV flexibility can balance intra-day load deviations and enable arbitrage in day-ahead electricity markets. However, conventional model-based approaches are fundamentally limited by their dependence on forecasting accuracy under high uncertainty from renewable generation and EV behavior. To address this, we propose a novel bi-level online optimization framework. The upper level employs a Lyapunov optimization-based algorithm that operates without predictions, making real-time decisions on total EV charging power to balance supply-demand mismatches. The lower level introduces novel flexibility metrics for individual EVs—encompassing temporal, volumetric, and cross-day dimensions—and optimizes power allocation by minimizing flexibility loss. Furthermore, we model EV flexibility as virtual queues and rigorously derive mathematical bounds on their limits, providing theoretical support for managing flexibility reserves. Rigorous analysis validates the framework’s feasibility, and comprehensive simulations demonstrate its superiority over benchmark algorithms, achieving significant cost reductions under various uncertainty scenarios. Full article

The location and capacity of electric vehicle charging stations (EVCSs) directly determine the capital invested and construction costs while also affecting the travelling convenience and economy of electric vehicle (EV) users. Furthermore, the siting and sizing of EVCSs has an impact on distribution network flexibility. Therefore, a method for the siting and sizing of EVCSs that takes into account distribution network flexibility is proposed. Firstly, based on the definition of distribution network flexibility, the flexibility deficit is analyzed, and five flexibility assessment indicators are established. Secondly, the travel characteristics of EVs are simulated based on urban road topology and a trip probability matrix, and a model incorporating users’ bounded rationality is adopted to predict the temporal and spatial distribution of EV charging requirements. Furthermore, based on charging requirements and distribution network flexibility deficit, this paper establishes a model for the siting and sizing of EVCSs considering distribution network flexibility. Finally, case studies are conducted with a 29-node transportation network and a 33-node distribution network. The results show that the proposed method can formulate a more reasonable siting and sizing scheme for EVCSs, decrease the flexibility deficit of the distribution network, and reduce the annual comprehensive cost by 11.96%. Full article

The growing penetration of electric vehicles (EVs) and distributed photovoltaic (PV) generation is increasing operational uncertainty in distribution networks and intensifying long-standing challenges such as higher power losses, rising peak demand, and voltage instability. To address these issues, this paper proposes a multi-objective optimization framework for the strategic placement of smart meters equipped with demand response (DR) capability in radial distribution systems. Unlike conventional placement approaches that mainly focus on monitoring or reducing non-technical losses, the proposed method integrates active load control into the planning stage and explicitly considers the stochastic behavior of loads, PV generation, and electric vehicle charging stations (EVCSs). The problem is formulated with four objectives: minimizing total power losses, substation peak demand, voltage deviation penalty, and installation cost. A scenario-based stochastic model is employed to represent operational variability across the network. The resulting nonlinear mixed discrete optimization problem is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), an evolutionary multi-objective optimization technique that generates a set of Pareto-optimal solutions representing trade-offs among conflicting objectives. Smart meters are allowed to curtail a portion of controllable demand during critical loading conditions, which helps reduce feeder loading and improve voltage profiles. The proposed approach is evaluated on the IEEE 33-bus and IEEE 69-bus distribution systems. Simulation results demonstrate significant reductions in power losses and peak demand, with the IEEE 33-bus system achieving up to a 26.2% reduction in power losses and 52.5% reduction in substation peak demand compared with existing metaheuristic approaches. The results also indicate improved voltage stability and effective performance in the IEEE 69-bus system, confirming the importance of topology-aware DR-enabled planning. Overall, the findings show that embedding demand response capability within smart meter allocation can significantly enhance the resilience and operational efficiency of modern distribution networks with high EV and PV penetration. Full article

Cities preparing Sustainable Urban Mobility Plans (SUMPs) increasingly require practical tools capable of merging diverse mobility datasets and transforming them into planning-relevant indicators. This article introduces PRIORITY (Platform for the tRansition to sustaInable zerO-caRbon mobilITY), a prototype platform designed to support mobility analysis and decision-making in urban contexts. The platform integrates Floating Car Data, GTFS feeds describing public transport supply, and detailed land-use and zoning information. By relying on these heterogeneous data streams, PRIORITY generates indicators such as travel and stop times, trip distances, trip volumes, energy consumption, pollutant emissions, external costs, and electric-vehicle charging behavior. The platform is organized into two main components: a back end and a front end. The back end, which constitutes the operational core, manages all collected data and ensures their structured storage in a shared database capable of handling large volumes of information on urban form, individual mobility patterns, public transport services, and modeling outcomes. The front end provides an intuitive and versatile interface that dynamically presents the outputs generated by the platform’s analytical and modeling processes. A case application for the Metropolitan City of Rome (Italy) illustrates the operational use of the prototype and shows how PRIORITY can support transparent and reproducible evaluations during the preparation and monitoring of SUMPs. The demonstrated workflow highlights the prototype’s value for public authorities and planners seeking data-informed approaches to urban mobility assessment and decarbonization strategies. Full article

Electric vehicle (EV) fleets are expanding rapidly and will place substantial demand on distribution grids. Day-ahead scheduling of city-scale EV charging constitutes a constrained multi-objective optimization problem that must balance peak load, load variation, and valley utilization simultaneously. This paper proposes a structured warm-start strategy that embeds a load-conservation valley-filling (LCVF) heuristic into the NSGA-III metaheuristic, seeding the entire initial population with grid-compliant, valley-filling schedules before the first generation runs. This search-space shaping approach restricts the evolutionary search to a feasible subspace defined by LCVF, enabling convergence that random initialization cannot achieve within the same computational budget. On four seasonal city-level instances derived from real electricity consumption data from Campo Grande, MS, Brazil ($N=50,336$ vehicles), VF–NSGA-III reduces peak load by $0.54$–$2.52\%$ (mean $1.31\%$) relative to standalone LCVF while requiring only $\approx 1.5\%$ of its runtime. The warm-start provides a structural advantage that population scaling alone cannot overcome: LCVF-initialized NSGA-III with $N_{\mathrm{pop}}=10$ achieves a hypervolume $35\%$ above the randomly initialized variant with $N_{\mathrm{pop}}=100$. A 32-day generalization study (June 2022–May 2023) confirms a mean peak-load reduction of $4.91\%$ over standalone LCVF and $4.93\%$ over randomly initialized NSGA-III across all seasons, demonstrating consistent performance over a full annual demand cycle. Full article

This study investigates the charging behaviors, routines, and perceptions of Swedish electric vehicle (EV) users who lack access to home charging, a group that remains underrepresented in the EV adoption literature. Based on an online survey of 250 EV users—primarily located in Gothenburg—respondents were divided into two groups: those with and those without home charging availability. Nearly half of the sample (47.6%) reported not having access to charging at home. Comparative analyses, including linear regression models, were conducted to examine differences in sociodemographic characteristics, charging patterns, and perceptions of public charging. While the two groups were similar in terms of age, gender, vehicle type, charging frequency, and minimum state of charge preferences, significant differences emerged in perceived convenience, distance, and freedom to charge. Users without home charging availability reported lower access to workplace charging and evaluated public charging as less convenient and less accessible. Charging behavior in both groups was primarily goal-oriented and triggered by minimum state of charge rather than spontaneous opportunities. The findings highlight the structural disadvantages faced by users without home charging and underline the importance of adapting public charging infrastructure and policy strategies to support a broader and more equitable transition to electric mobility. Full article

The increasing global demand for renewable energy has intensified the search for high-efficiency and cost-effective solar cell technologies. Quantum dot-sensitized solar cells (QDSSCs) have emerged as promising candidates due to their tunable optoelectronic properties and enhanced light absorption. In this study, SnS quantum dots were synthesized from dithiocarbamate complexes using different ligands, namely m-toluidine (SnS1), aniline (SnS2), and p-toluidine (SnS3), to investigate the influence of precursor chemistry on material properties and device performance. Structural analysis confirmed the formation of an orthorhombic phase for all samples, while morphological studies revealed well-dispersed nanocrystals for SnS1 (5.93 nm), increased aggregation for SnS2 (8.57 nm), and partially fused domains with an intermediate size for SnS3 (6.67 nm). Optical measurements showed bandgap energies of 2.8, 2.2, and 2.7 eV for SnS1, SnS2, and SnS3, respectively, with SnS3 exhibiting reduced charge-recombination behaviour. Photovoltaic devices fabricated using these materials yielded power conversion efficiencies of 3.40, 2.03, and 7.63% for SnS1, SnS2, and SnS3, respectively, with no significant improvement observed for bifacial configurations. The superior performance of SnS3 is attributed to an optimal balance between light absorption, morphology, and charge transport properties, highlighting the critical role of precursor ligand selection in tuning quantum dot characteristics for improved QDSSC performance. Full article

Electric vehicle (EV) charging behaviors exhibit significant heterogeneity in terms of price sensitivity, time-of-day preference, and weekend charging habits, creating challenges for charging demand prediction and service management. To address this issue, this paper proposes a three-variable charging response framework that jointly considers electricity price, time-of-day preference, and weekend preference. Using real charging-order data from a public charging platform, four behavioral parameters, namely baseline charging demand (Q₀), price sensitivity (α), time preference (β), and weekend preference (γ), are estimated through nonlinear least squares (NLS). Based on the extracted parameter vectors, K-means clustering is employed to identify five representative user groups: Commuting-Dominant, elastic energy-saving, Weekend-Switching, Night-Preferential, and discount-sensitive users. The results reveal substantial behavioral heterogeneity among users. To validate the proposed framework, both parameter interpretability analysis and benchmark comparisons are conducted. Compared with the best baseline model, the proposed method reduces the test RMSE from 11.5 kWh to 8.3 kWh (27.8%), decreases the test MAPE from 25.3% to 18.7% (26.1%), and improves the test R² from 0.70 to 0.80. The proposed framework provides an interpretable approach for EV charging behavior modeling and user segmentation, offering practical support for differentiated pricing, charging demand management, and intelligent charging service operation. Full article

The operational reliability of wind-powered electric vehicle charging systems (WPECS) depends not only on average wind resources but also on their temporal variability and continuity. This paper proposes a reliability engineering approach for assessing WPECS performance using long-term meteorological data and translating wind resource variability into practical engineering indicators. The proposed methodology adapts classical reliability concepts, including operational availability, deficit frequency, and redundancy sizing, to systems where unavailability is driven mainly by energy source variability rather than component failures. Four indicators are introduced: the Operational Availability Index (OAI), Deficit Event Frequency (DEF), Seasonal Load Factor (SLF), and Operational Continuity Index (OCI). The minimum required energy storage capacity (E_red) is also estimated. The method was applied to 15 meteorological stations in Poland using data from 2001 to 2024. The results revealed substantial spatial differences in WPECS reliability. Four locations achieved high operational availability (OAI_L2 ≥ 0.83) with low storage requirements (<25 MWh), whereas other locations required large or practically infeasible storage capacities. A negative trend in wind resource availability was observed at most stations, indicating a gradual decline in reliability. The results indicate that temporal continuity of wind availability, rather than average energy level alone, is the dominant factor governing operational feasibility and storage requirements of WPECS. The proposed approach supports site selection, storage sizing, and operational planning of WPECS. Full article

With the large-scale integration of electric vehicles (EVs), charging demand exhibits significant spatiotemporal variability, further intensified by climatic factors, which makes it difficult for existing uncertainty models to capture underlying dependency structures. To address this issue, this paper proposes a Copula–Wasserstein-based distributionally robust optimization (C-WDRO) framework for the coordinated operation of distribution networks and charging stations. A climate-sensitive physical mapping model of electric vehicle energy consumption is first developed to establish a coupled climate–energy–load mechanism. Copula functions are then used to characterize dependencies among temperature, precipitation, and charging demand, and are incorporated into a bi-level optimization formulation. The model is solved using Karush–Kuhn–Tucker (KKT) conditions and a column-and-constraint generation (C&CG) algorithm. Case studies on the IEEE 33-bus system show that the proposed method reduces total operating cost by 4.26% compared with robust optimization (RO), while maintaining economic efficiency, and reduces the load shedding rate by 0.14 percentage points compared with Wasserstein distributionally robust optimization (WDRO), while keeping voltage security. These results demonstrate that explicitly modeling dependency structures can enhance operational efficiency and support more sustainable and reliable power–transportation system operation under uncertainty. Full article

This paper presents a primary side model predictive control (MPC) strategy for wireless power transfer (WPT) based charging of light electric vehicle (LEVs). A battery simulator develops a model to accurately reproduce constant-current (CC) charging profile from Open Ciruit Voltage (OCV) and State of Charge (SoC) parameters of the battery. This model forms the foundation of the predictive control design, allowing accurate prediction of the charging trajectory while avoiding reliance on secondary-side feedback signals. The WPT system employs a phase-shifted full-bridge (PSFB) inverter with S-S compensation, where the primary-side controller regulates the secondary-side charging current using only primary-side current measurements. In contrast to conventional secondary side control, which is tuned around nominal coupling, requires explicit feedback, and degrades under coil misalignment and parameter variations, the proposed MPC leverages integrated system and battery models to predict future states and optimally adjust the phase shift for robust charging operation. Simulation and experimental validation on a real-time LEV charging prototype under aligned, lateral, and angular misalignment conditions demonstrate significant reduction in current-settling time compared to fixed-gain proportional-integral (PI) and known adaptive feedback controllers for same system, with lower RMS current and reduced current spikes at the battery. On the embedded controller, the proposed MPC executes within approximately 1 µs per 85 kHz PWM cycle, corresponding to less than 10% CPU utilization, confirming its practical real-time feasibility. Full article

Fast growth in electric vehicle (EV) charging stations is changing the way regional distribution networks are loaded. The difficulty is not only the size of the added demand, but also the fact that charging appears at different places, at different times, and under different voltage constraints. This paper considers the common planning situation in which station-level charging records are incomplete and only transformer-side aggregate measurements are available. A data-driven hosting capacity (HC) assessment method is developed for this setting. The method first constructs zone-specific daily load profiles and then separates EV charging components from mixed transformer curves through an improved ISODATA clustering method and an improved genetic algorithm (IGA). For planned electric vehicle charging stations (EVCSs) without historical measurements, Ordinary Kriging (OK) is used to infer charging profiles from nearby observed stations in the same functional zone. The calculated HC is then checked successively at the 10 kV, 35 kV, and 110 kV levels. When an upstream constraint is violated, an improved Entropy-weight TOPSIS (EW-TOPSIS) model reallocates the available capacity according to both network constraints and zone priority. The case study indicates that the method can identify upstream bottlenecks that are hidden in local assessments, preserve residential charging demand, and provide zone-specific guidance for EVCS expansion. Full article

With the increasing penetration of electric vehicles (EVs), multi-virtual power plant (multi-VPP) systems face growing challenges in coordinating heterogeneous flexible resources, managing stochastic EV charging and discharging behaviors, and maintaining distribution network security. This paper develops an integrated collaborative scheduling strategy for multi-VPPs with EV cluster participation. In the proposed framework, EV clusters, energy storage systems, and distributed generation units are coordinated under distribution-network operational constraints. The regulation capability of EV clusters is characterized by considering state of charge (SOC) dynamics, charging/discharging power limits, arrival and departure times, vehicle availability, and user travel requirements and is further embedded into the scheduling decision space of each VPP. To coordinate operational economy and nodal voltage security, a voltage-security-aware optimization objective is formulated and transformed into a Markov game. A multi-agent deep reinforcement learning (MADRL) method is then adopted to learn coordinated scheduling policies among multiple VPP agents. Case studies show that the proposed method achieves stable convergence after approximately 3500 training episodes, with a normalized reward exceeding 0.92, and outperforms TD3, DDPG, and PPO in terms of convergence speed and training stability. The scheduling results further indicate that the proposed strategy effectively coordinates EV clusters and energy storage systems, maintains nodal voltages within safe limits, and improves the operational performance of multi-VPP systems. These results demonstrate the applicability of the proposed framework for secure and economic collaborative scheduling in distribution networks. Full article

This work targets efficient visible-light-driven hydrogen evolution by construction of a dendritic CdS-based photocatalytic system decorated with Pt-Ni bimetallic cocatalysts (CdS@PtNi). The dendritic CdS was synthesized via a hydrothermal method, followed by in situ deposition of Pt and Ni using NaBH₄ chemical reduction, with cocatalyst loading tuned between 2 and 5 wt%. Among them, C@PN4 (4 wt% total metal loading) demonstrated the best performance, with a bandgap of ~2.15 eV. XRD results show that the samples retain the hexagonal CdS phase without significant impurities. SEM/TEM and elemental mapping confirm uniform dispersion of Pt and Ni, forming intimate interfaces with CdS. XPS results reveal positive shifts in S 2p and Cd 3d binding energies, indicating that the bimetallic cocatalyst promotes electron transfer from CdS to the metals and enhances interfacial coupling. Photoelectrochemical analysis shows C@PN4 features enhanced absorption above 500 nm, significantly reduced PL, extended carrier lifetime, higher transient photocurrent, and lower charge-transfer resistance, suggesting greater efficiency in charge separation and transport. Band structure analysis reveals a negative shift of the conduction band to a more reductive potential. In photocatalytic tests, C@PN4 achieves an H₂ yield of 15.6 mmol g⁻¹ over 4 h (3.9 mmol g⁻¹ h⁻¹), with <5% activity loss after four cycles. AQY reaches 0.0483% at 420 nm, with a monochromatic photon-to-hydrogen conversion efficiency (MPH) of up to 2.01%. Mechanistically, the Pt/CdS Schottky junction drives directional electron extraction, while Ni likely synergistically optimizes interfacial electronic distribution and facilitates water activation/dissociation; together, they accelerate surface reaction kinetics and suppress photocorrosion, achieving efficient and stable hydrogen evolution with low noble metal loading. Full article