Search Results (190)

Electromobility is central to urban decarbonisation, but its charging infrastructure must be sized under substantial uncertainty about user behaviour that varies across stations, time of day and user type. This study couples a hierarchical Bayesian model with an agent-based, discrete-event simulation of a charging network. It is fitted by Markov chain Monte Carlo to the public ACN-Data dataset (13,694 sessions across 52 stations; 16,468 user requests), with partial pooling across stations. Posterior parameters drive a 24 h simulation of 500 vehicles across nine configurations and 30 to 180 slots. Service success rises from 19% to 81% and mean waiting falls from 110 to 62 min; long workplace dwell times limit turnover, so capacity rather than energy binds. TOPSIS with a paired bootstrap selects 160 slots under balanced weighting, but that optimum holds for only a tenth of the weight simplex, and the recommendation spans 140–180 slots. Spreading the arrival peak at fixed hardware raises service from 67% to 83%, matching a 29% expansion. Spatially explicit assignment costs three percentage points when stations are evenly sited, and six when clustered. The framework makes the cost of over-provisioning explicit and preference-conditional rather than naming a single optimum, giving a reproducible basis for sustainable capacity planning. Full article

This research concentrates on the electric vehicle (EV) industry in China and Japan. China is the largest and Japan the third-largest auto production country after the U.S. This study explores the current and future transition to battery electric vehicles and hybrid electric vehicles in Japan and China. Three Japanese auto companies, Toyota, Honda, and Nissan, and BYD from China are studied in this research. Toyota, Honda, and Nissan are actively pursuing the development of hybrid electric vehicles in Japan. Meanwhile, the research examines the Chinese EV company BYD, which is a major global competitor in the EV industry. This study compares the companies in terms of their strategies, strengths, weaknesses, and export destinations and delineates their competitive strategies and outlooks. In addition, the study examines the elements of the supply chain needed for building EVs, such as lithium, nickel, and cobalt. Furthermore, this research discusses some of the issues with EVs, such as the challenges related to the production and recycling of batteries and the implications as far as green and sustainable practices regarding EVs in the selected countries are concerned. The final part of this research explores how the production of EVs can affect the global reduction of carbon emissions. The findings of this study indicate that the transition to EVs depends on the structural position as far as the supply chain, the manufacturing of electric batteries, charging stations, and the size of the operations are concerned. The results indicate that BYD is in a stronger position in terms of the infrastructure necessary for the production of EVs. Meanwhile, Japanese auto companies are focused on hybrid EVs due to infrastructure related to EV batteries, supply sources, and charging stations. In addition, this research provides informative insights into the future of electric vehicles in the global market. The study offers recommendations for a comprehensive approach that integrates national policies, technological innovation, and the environmental impact of the transition to electric vehicles on a global scale. Full article

In order to improve the electrical grid flexibility and efficiency, distributed energy resources (DERs), capacitor banks (CBs), and electric vehicle charging stations (EVCSs) are being integrated into active power distribution networks. However, radial distribution networks have large electrical losses from unidirectional power flow and growing EV penetration, as well as voltage drops and power flow exceeding the thermal capacity limit of some distribution branches. This study is based on the Classification Global Optimization (CGO) approach to include EVCS’s under different hosting factors (30%, 40%, and 50%). The applied CGO framework uses a deterministic global function that includes minimization of electrical losses, reducing the voltage deviation index, increasing the cost of saved energy due to losses and decreasing the annual amount of CO₂ emission, this done for the simultaneous placement and sizing of EVCSs, DGs, and CBs after classifying the network buses according to voltage sensitivity and power consumption. Active power loss reductions of up to 94.75% and 98.061% with combined integration are demonstrated by validation on IEEE 33-bus and 69-bus systems, while computational efficiency (simulation times < 5 s) is maintained. This enhanced technique offers a scalable solution for contemporary active distribution networks and directly helps Sustainable Development Goals (SDGs) 7, 9, 11, and 13 by improving grid performance with high EV adoption. Full article

Electric vehicle (EV) usage has increased significantly in the past few years, which may create challenges for distribution system operators due to EV charging needs. In this paper, we propose an approach for planning and operating EV charging stations, considering the challenges that distribution networks may face. A two-step framework is proposed in this paper. First, the optimal size and location of a charging station is determined using a multi-objective optimization problem considering minimizing power losses and voltage drop while maximizing load placements. Then, an optimal scheduling scheme is employed to charge and discharge the vehicles on the selected buses. Simulation studies were conducted using IEEE 33- and 123-bus systems; the results show that the proposed framework significantly enhances the buses’ voltages and line power flows. In order to plan for charging stations, several factors need to be considered, such as optimal size and location, the daily load curve for the given system, the time of use (TOU), and the charging patterns of EV owners. Without careful planning and operation, the system may suffer vulnerability and line overloading, which may lead, eventually, to cascading outages and interruptions. By improving grid utilization, reducing losses, and enabling coordinated EV charging and discharging, the proposed framework supports more sustainable energy use and facilitates the integration of electric mobility into future low-carbon power systems. Full article

The large-scale integration of electric vehicles (EVs) has made the siting and sizing of electric vehicle-charging stations (EVCSs) a critical challenge in distribution systems, as inadequate deployment may compromise secure network operation due to voltage and thermal limit violations. This problem is formulated as a mixed-integer nonlinear programming (MINLP) model, where discrete variables define EVCS locations and charging capacities expressed in terms of the number of EVs served. To address this problem, this paper proposes a unified parallel AC-feasible optimization framework that maximizes EV hosting capacity while explicitly enforcing all operational constraints of the distribution system. Particle Swarm Optimization (PSO), a Population-based Continuous Genetic Algorithm (PGA), and Monte Carlo (MC) optimization are evaluated under a common decision-variable encoding, objective function, AC power-flow evaluator, and constraint-handling strategy, enabling a fair comparison among methodologies under identical operating conditions. The proposed framework is assessed on a modified 33-bus distribution system considering a representative weekly operating scenario and 100 independent runs. Results show that PSO achieves the highest hosting capacity, integrating up to 1246 EVs and an average of 1213.3 EVs, compared with 1225 and 1196.1 EVs for PGA and 1077 and 1049.4 EVs for MC, respectively. All methodologies exhibit standard deviations below 3%, confirming robust and repeatable performance, while requiring less than 1428 s on average to identify feasible planning solutions. In addition, the parallel implementation reduces computational times by 42.22%. These results demonstrate the effectiveness of the proposed framework for identifying high-capacity EVCS planning solutions while preserving secure network operation. 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

Institutional shuttle fleets with fixed routes and predictable terminal parking are well-suited to charging photovoltaic–battery energy storage system (PV–BESS) charging for sustainable campus mobility. However, siting and sizing are often solved numerically without identifying the physical constraints that determine the optimum. This study develops a sustainability-oriented framework for converting a 10-van diesel shuttle fleet at Rajamangala University of Technology Lanna into an electric fleet supported by distributed PV–BESS charging stations. A centralized one-station layout is compared with a distributed two-station layout, and a closed-form active-constraint sizing rule is derived using Karush–Kuhn–Tucker (KKT) analysis. Results show that the distributed configuration eliminates dead-run travel and provides higher lifecycle value than the centralized case. KKT analysis identifies two binding constraints: the PV rooftop-area limit and the BESS one-day autonomy requirement. Under base-case assumptions, the transition achieves positive lifecycle value and substantial CO₂ reduction relative to the diesel baseline. Monte Carlo analysis confirms financial robustness within the uncertainty ranges, while deterministic stress tests show sensitivity to diesel prices, PV electricity credit values, discount rate, and fleet utilization. The framework provides an interpretable decision-support method for institutional fleet electrification in solar-rich campus settings, contributing to SDGs 7, 11, and 13 through clean-energy adoption, sustainable transportation, and CO₂-emission reduction. 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

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

This study analyzes the economic viability of electric vehicle charging stations (EVCSs) in medium-sized cities with low electric vehicle (EV) adoption. Based on EVCS usage patterns from both Europe and the USA, and validating EV energy consumption with a microscopic model of roads and traffic through the Eclipse SUMO simulator, the analysis provides a comprehensive assessment. Also, level 2 and level 3 (DC fast) charging stations are considered with installation and operation costs. Finally, a photovoltaic (PV) system and governmental subsidies are considered as support. The Pasto city, Colombia, is the case study due to its medium-sized city characteristics in an emerging economy country, where EV penetration is concentrated in the capital and large cities, with a national EV penetration rate of less than 1%. Scenarios are developed with varying annual EV penetration rates and different financial discount rates. The results suggest that, without significant increases in EV adoption, government subsidies, and PV generation, EVCSs are not economically viable in most of the analyzed scenarios. The study concludes that the financial sustainability of these projects is heavily reliant on supportive public policies that incentivize infrastructure deployment, particularly in medium-sized cities. Full article

The accelerating adoption of electric vehicles (EVs) alongside renewable distributed generators (RE-DGs), particularly solar photovoltaic (PV) and wind-based systems, is reshaping the operational and planning paradigms of modern power distribution networks. In this study, an optimal allocation framework is developed for the simultaneous integration of EV charging stations (EVCSs) and RE-DGs within a looped configuration of the IEEE 33-bus distribution system. Two advanced metaheuristic techniques—Improved Grey Wolf Optimizer (IGWO) and Metaheuristic COOT-Based Optimization (MCBO)—are employed to determine the optimal siting and sizing of these resources. The optimization objectives focus on minimizing active power losses while enhancing voltage stability and reducing overall voltage deviation across the network. Simulation results reveal that the MCBO algorithm demonstrates superior performance, yielding a maximum reduction of 82.49% in active power losses with the integration of standalone PV, and 78.14% when PV is deployed in conjunction with EVCSs. Similarly, wind turbine generator (WTG) integration resulted in a loss reduction of 85.74% without EVCSs and 81.57% with EVCS integration using the same approach. The findings further indicate that looped network configurations consistently outperform traditional radial systems in both loss reduction and voltage profile enhancement, underscoring their suitability for accommodating future EV and renewable energy penetrations in smart distribution grids. Full article

In this paper, the effects of gravity-gradient potential on a spacecraft of arbitrary shape are outlined. The potential expressing the planet’s gravity-gradient torque on a triaxial spacecraft is formed. The planet’s shape is considered oblate spheroidal, and the dimensions of the spacecraft are assumed small compared to its distance from the center of the planet. The radial, transverse and normal components of the Lorentz force, in terms of orbital elements, are constructed. The variations in the orbital elements due to both gravity-gradient potential and Lorentz force are derived. The charges per unit mass needed to balance such perturbation are obtained. The symmetrical results in mathematical equations are obvious. The International Space Station (ISS) is used as an example to test our model. A three-dimensional diagram was plotted to illustrate the charge per unit mass with the shape and size of the orbits. Full article

In the Latin American Pacific region, rivers are the primary transportation routes for isolated and non-interconnected areas; however, river transport relies heavily on fossil fuels, resulting in high operating costs, CO₂ emissions, and energy dependence. To address this challenge, this study proposes a methodology for the optimal sizing of renewable-based charging stations specifically adapted to the environmental and operational conditions of the Colombian Pacific coast. This research fills a critical gap in the literature by moving beyond urban-centric charging models and simplified theoretical assumptions, instead integrating real river navigation data with technical modeling of electric boat energy consumption. The methodology evaluates the technical, economic, and operational performance of photovoltaic and hybrid photovoltaic–hydrokinetic microgrids designed to ensure reliability under the region’s extreme resource seasonality and bimodal pluvial regime. Results indicate that while purely photovoltaic systems offer lower initial investment costs, hybrid configurations significantly enhance energy resilience by leveraging complementary renewable sources during periods of low solar irradiation. Crucially, the transition to electric propulsion reduces annual CO₂ emissions by more than 98%, mitigating approximately 3421 kg per vessel compared to conventional 20 HP gasoline engines. A comparative analysis shows that the 1.1 kW electric boat is a cost-effective solution, with a 1.76-year return on investment. In contrast, the 4 kW model offers operational performance comparable to conventional gasoline boats, with a 4.95-year payback. This study provides a foundational framework for sustainable mobility in high-vulnerability territories by adapting technological solutions to site-specific environmental realities. Full article

This study introduces a multi-stage, multi-objective optimization framework based on the Gravitational Search Algorithm (GSA) for determining the optimal sizing and placement of distributed energy resources (DERs) and associated infrastructure. The proposed approach considers solar distributed generation (DG) units with battery storage systems (BSSs), wind DGs, shunt capacitors (SCs) and electric vehicle charging stations (EVCSs). With the rapid adoption of electric vehicles as part of global decarbonization efforts, integrating EVCSs into already stressed distribution networks poses significant operational challenges, often requiring system reinforcement supported by renewable-based DGs. The uncoordinated deployment of EVCSs and DGs can exacerbate power losses and deteriorate voltage profiles. To address these issues, the first stage of the methodology employs GSA to optimally allocate solar DGs with BSSs, wind DGs and SCs, targeting objectives such as minimizing power losses, enhancing voltage stability and alleviating substation loading. The second stage identifies optimal locations and maximum feasible capacities for EVCS integration. Finally, the third stage upgrades the network to mitigate the impacts of EVCS integration. The effectiveness of the proposed approach is validated through simulations on a practical 52-bus, 11 kV distribution network under hourly varying load, solar irradiance and wind velocity conditions for all seasons. The simulation results show an 85% reduction in power losses during peak hours, with nodal voltages maintained above 0.95 p.u. under all scenarios. Additionally, net-zero grid power exchange during peak periods confirms the full islanded operation. Full article

This paper investigates the joint scheduling problem of battery electric bus fleets and plug-in charging infrastructure in an urban transit system. The operation of an electric bus network is inherently a multi-component system, where vehicle assignment, battery energy management, and charger capacity decisions interact and jointly determine system performance and cost efficiency. To capture these interdependencies, we propose a system-level integrated scheduling framework that simultaneously determines bus trip assignments, charging event timing and duration, and charger utilization plans. The problem is formulated as a continuous-time mixed-integer linear programming model that minimizes the total system cost, subject to operational feasibility, battery state-of-charge dynamics, and charger capacity constraints. To enhance computational tractability, a Lagrangian relaxation-based decomposition approach is developed, coupled with a linear programming-based diving heuristic. Computational experiments on benchmark instances demonstrate that the proposed framework produces high-quality system-level schedules with substantially reduced solution time compared with directly using a commercial solver. A real-world case study based on a large charging station in Beijing shows that the optimized joint schedules reduce the required fleet size from 22 to 13 buses and the number of chargers from five to two, leading to a 38.3% reduction in total system cost. These results highlight the effectiveness and practical value of the proposed approach for the planning and operation of urban electric bus transit systems. Full article