Search Results (44)

The decarbonization of the transportation sector necessitates the adoption of practical measures that can be implemented within existing fleets. One such measure is the installation of solar panels on trucks, which has shown potential to reduce fuel consumption in heavy-duty vehicles (HDVs). This study presents lessons learned from a monitoring project involving 200 commercial trucks retrofitted with 300–500 W solar panels, aimed at supplementing battery charging and minimizing alternator operation. The system incorporated commercially available flexible photovoltaic (PV) modules, adhesive mounting techniques, a charge controller, and a data logger housed within a control box. Documentation covered installation procedures, wiring practices, and safety considerations across various truck models, with additional insights from electrical contractors regarding labor time and costs. Results indicate that adhesive-based mounting can be carried out safely and reliably without structural modifications, although wiring and control box placement constitute the most significant portions of the installation process. The project further identified variability in installation duration and economic viability, depending on vehicle configuration and technician expertise. Overall, the findings affirm that vehicle-integrated photovoltaic (VIPV) retrofits are both technically feasible and operationally robust. They also underscore the practical requirements, constraints, and workforce considerations essential for scaling deployment within commercial fleets. Full article

This paper investigates the multi-objective optimization of the peak–valley difference, operating cost, and carbon emissions for urban logistics battery-swapping stations (BSSs) under photovoltaic uncertainty and stochastic demand. Unlike conventional plug-in charging, battery swapping decouples energy replenishment from the vehicle dwell time, enabling rapid service, but introducing discrete swap arrivals and power–inventory coupling challenges that continuous-load models cannot capture. A Stackelberg game-based framework models grid–BSS interactions, where the grid acts as the leader by setting time-of-use prices and BSSs respond by optimizing charging/discharging schedules. Carbon emissions are quantified using real-time carbon intensity data obtained from the Electricity Maps platform. The battery-swapping demand is modeled as a Poisson process, and a unified power–inventory coupling model captures the bidirectional dependence among PV generation, grid purchases, energy storage operations, and battery inventory dynamics, where the inventory feasibility constrains the power decisions. For multi-station coordination, an adaptive ADMM decomposes the problem into parallelizable sub-problems. Case studies of a 49-vehicle fleet across three BSSs in Qingdao, China, show that, compared with a no-optimization baseline, the proposed method reduces the peak–valley difference by approximately 21.6%, the operating cost by approximately 10.2%, and carbon emissions by approximately 15.7%. Compared with the single-objective counterparts, the multi-objective formulation further improves the peak–valley difference by approximately 26.9% and increases emission reduction by approximately 16.9%; paired t-tests on repeated runs indicate statistical significance (p < 0.05). The framework provides a scalable methodology for low-carbon BSS scheduling with explicit power–inventory coupling. Full article

To enhance access to efficient and low-carbon public transportation, the city of Alexandria, Egypt, has introduced a fleet of electric buses. Additionally, an ongoing project aims to upgrade and electrify the existing urban railway system, which is expected to alleviate traffic congestion in this densely populated city. The implementation of electric vehicle (EV) parking facilities is also under consideration. This paper investigates the integration of photovoltaic (PV) systems and green hydrogen-powered gas turbines as components of the integrated energy system (IES). An optimal energy management strategy is proposed to maximize the benefits of incorporating renewable energy sources into the urban transportation system (UTS). The proposed energy management algorithm incorporates demand-side management (DSM) for UTS loads and EVs, increasing the complexity of the decision-making process due to the high uncertainty of decision variables. To address this challenge, a modified multi-agent reinforcement learning (MRL) approach is employed, in which uncertainty is incorporated through stochastic environment sampling. Simulation results demonstrate the economic potential of integrating renewable and sustainable energy resources into the IES of the electrified urban transportation system, achieving a 40.2% reduction in the average daily energy consumption cost. Full article

The increasing penetration of distributed renewable energy resources and the emergence of prosumers are reshaping the operational landscape of distribution grids. This work proposes a comprehensive prosumer-centric control and coordination framework integrated into the IEEE 33-bus radial distribution feeder. Selected buses are modeled as aggregated prosumer nodes equipped with photovoltaic (PV) generation, wind turbines, oncentrated solar power (CSP), a hybrid energy storage system (HESS) including redox flow batteries (RFBs), superconducting magnetic energy storage (SMES), and fuel cells (FCs), as well as electric vehicle (EV) fleets. A hierarchical power management strategy is developed, combining a decentralized fuzzy logic controller for real-time dispatch with a Particle Swarm Optimization (PSO) layer that tunes membership functions and rule weights to enhance system stability and renewable utilization. Time-series simulations are conducted to evaluate the impact of prosumer integration on network performance. The results show a significant improvement in the voltage profile across all buses, particularly at downstream nodes, highlighting the effectiveness of distributed renewable injections and coordinated storage management. The proposed framework illustrates the potential of clustered prosumers to support voltage stability, improve grid operation and enable high-renewable penetration in distribution networks. Full article

The ground impedance (insulation resistance R_isol) of photovoltaic (PV) modules is usually measured only in the dry state, even though arrays frequently operate under dew-wet or rain-wet conditions, when leakage paths can change. We measured dry insulation resistance R_dry and IEC 61215 MQT 15 wet leakage resistance R_wet for N = 37 field-aged crystalline-silicon modules from utility-scale plants and related the results to the IEC 40 MΩ·m² criterion (R_wet × A ≥ 40). The measurements used 1000 V DC and a 2 min dwell; R_wet was obtained in a salted bath with a solution resistivity < 3500 Ω·cm. The median R_dry was 42.4 GΩ, whereas the median R_wet was 462.5 MΩ, resulting in a median R_dry/R_wet ratio of ~110×. Three modules (8.1%) failed the 40 MΩ·m² limit already in the dry state, whereas eight modules (21.6%) failed under IEC-wet conditions; five were dry-pass/wet-fail cases that would have passed dry screening. For a representative area A = 1.8 m², a practical conservative dry triage threshold of approximately 55.5 GΩ identifies modules needing IEC-wet verification rather than serving as a stand-alone limit. Overall, combining dry and IEC-wet measurements improves safety and supports sustainability through resource-efficient repowering/revamping and end-of-life decisions in large PV fleets, particularly in hot climates. Full article

Rapid deployment of rooftop photovoltaics (PV), electric heating, and electric vehicles (EVs) is stressing low-voltage feeders in cold climates, where winter peaks push aging transformers to their limits. This paper quantifies how much stationary and mobile storage is required to keep feeder power nearly flat over a full year in such conditions. A mixed-integer linear programming (MILP) model co-optimizes stationary battery energy storage systems (BESSs) and EV flexibility, including lithium-ion degradation, under a flatness constraint on transformer loading, i.e., the magnitude of feeder power exchange (import or export) around a seasonal target. The framework is applied to a 48-dwelling neighborhood in Ardahan, northeastern Turkey (mean January ≈ −8 °C) with rooftop PV and an emerging EV fleet. Three configurations are compared: unmanaged EV charging, optimized smart charging, and bidirectional vehicle-to-grid (V2G). Relative to the unmanaged case, smart charging reduces optimal stationary BESS capacity from 4.10 to 2.95 MWh, while V2G further cuts it to 1.23 MWh (≈70% reduction) and increases flat-compliant hours within ±0.5 kW of the target transformer loading level from 92.4% to 96.1%. The levelized cost of demand equalization falls from 0.52 to 0.22 EUR/kWh, indicating that combining modest stationary BESSs with V2G can make feeder-level demand flattening technically and economically viable in cold-climate residential districts. Full article

The electrification of public transport with battery electric buses (BEBs) poses technical, regulatory, and environmental challenges. This paper analyzes the impact of BEB charging on a Brazilian urban medium-voltage (MV) feeder using a novel methodology to convert utility GIS data into OpenDSS simulation models. The study utilizes Geographic Database of the Distribution Company (BDGD) data from the Brazilian Electricity Regulatory Agency (ANEEL) and OpenDSS simulations. Motivated by Cuiabá’s proposal to electrify its public bus fleet, four realistic scenarios were simulated, incorporating distributed photovoltaic (PV) generation and vehicle-to-grid (V2G) operation. Results show that up to 118 BEBs can be charged simultaneously without voltage violations. However, thermal overload occurs beyond 56 units, requiring conductor upgrades or load redistribution. PV systems can supply up to 64% of the daily energy demand but introduce reverse power flows and overvoltages, indicating the need for dynamic control. V2G operation enables peak shaving but also leads to overvoltages when more than 33 buses inject power concurrently. The findings suggest that while the current infrastructure partially supports fleet electrification, future scalability depends on integrating smart grid features and reinforcing the system. Although focused on Cuiabá, the methodology offers a replicable approach for low-carbon urban mobility planning in similar developing regions. Full article

This study proposes a novel framework for analyzing Vehicle-Integrated Photovoltaic (VIPV) systems, integrating optical, thermal, and electrical models. The model modifies existing fixed PV methodologies for VIPV applications to assess received irradiance, PV module temperature, and energy production, and is available as an open-source MATLAB tool (VIPVLIB) enabling simulations via a smartphone. A key innovation is the integration of meteorological data and real-time driving, dynamically updating vehicle position and orientation every second. Different time resolutions were explored to balance accuracy and computational efficiency for optical model, while the thermal model, enhanced by vehicle speed, wind effects, and thermal inertia, improved temperature and power predictions. Validation on a minibus operating within the University of Palermo campus confirmed the applicability of the proposed framework. The roof received 45–47% of total annual irradiation, and the total yearly energy yield reached about 4.3 MWh/Year for crystalline-silicon, 3.7 MWh/Year for CdTe, and 3.1 MWh/Year for CIGS, with the roof alone producing up to 2.1 MWh/Year (c-Si). Under hourly operation, the generated solar energy was sufficient to fully meet daily demand from April to August, while during continuous operation it supplied up to 60% of total consumption. The corresponding CO₂-emission reduction ranged from about 3.5 ton/Year for internal-combustion vehicles to around 2 ton/Year for electric ones. The framework provides a structured, data-driven approach for VIPV analysis, capable of simulating dynamic optical, thermal, and electrical behaviors under actual driving conditions. Its modular architecture ensures both immediate applicability and long-term adaptability, serving as a solid foundation for advanced VIPV design, fleet-scale optimization, and sustainability-oriented policy assessment. Full article

The rapid growth of electric vehicles (EVs) and photovoltaic (PV) generation creates substantial power peaks that strain local electrical infrastructure. Coordinated bidirectional charging can mitigate these challenges while delivering benefits such as lower costs, improved PV utilization, and reduced emissions. This paper develops a framework for fleet charging that combines station assignment with a two-stage scheduling approach. A heuristic assignment method allocates EVs to uni- and bidirectional charging stations, ensuring efficient use of limited infrastructure. Building on these assignments, charging power is optimized in two stages: a Mixed-Integer Linear Program (MILP) generates day-ahead schedules from forecasts, while an intraday heuristic-based MILP adapts them to unplanned arrivals and forecast errors through lightweight re-optimization. A Python -based simulator is developed to evaluate the framework under stochastic PV, load, price, and EV conditions. Results show that the approach reduces costs and emissions compared to alternative methods, improves the utilization of bidirectional infrastructure, scales efficiently to large fleets, and remains robust under significant uncertainty, highlighting its potential for practical deployment. Full article

Driven by the growing availability of funding opportunities, electrolyzers have become increasingly accessible, unlocking significant potential for large-scale green hydrogen production. The goal of this investigation is to develop a techno-economic optimization framework for the design of a stand-alone photovoltaic (PV)-driven hydrogen production and refueling station, with the explicit objective of minimizing the levelized cost of hydrogen (LCOH). The system integrates PV generation, a proton-exchange-membrane electrolyzer, battery energy storage, compression, and high-pressure hydrogen storage to meet the daily demand of a fleet of fuel cell buses. Results show that the optimal configuration achieves an LCOH of 11 €/kg when only fleet demand is considered, whereas if surplus hydrogen sales are accounted for, the LCOH reduces to 7.98 €/kg. The analysis highlights that more than 75% of total investment costs are attributable to PV and electrolysis, underscoring the importance of capital incentives. Financial modeling indicates that a subsidy of about 58.4% of initial CAPEX is required to ensure a 10% internal rate of return under EU market conditions. The proposed methodology provides a reproducible decision-support tool for optimizing off-grid hydrogen refueling infrastructure and assessing policy instruments to accelerate hydrogen adoption in heavy-duty transport. Full article

The European Commission, through the REPowerEU plan and the “Fit for 55” package, aims to reduce fossil fuel dependence and greenhouse gas emissions by promoting electric and fuel cell hybrid electric vehicles (EV-FCHEVs). The transition to this mobility model requires energy systems that are able to provide both electricity and hydrogen while reducing the reliance of residential buildings on the national grid. This study analyses a poly-generative (PG) system composed of a Solid Oxide Fuel Cell (SOFC) fed by biomethane, a Photovoltaic (PV) system, and a Proton Exchange Membrane Electrolyser (PEME), with electric vehicles used as dynamic storage units. The assessment is based on simulation tools developed for the main components and applied to four representative seasonal days in Rende (Italy), considering different daily travel ranges of a 30-vehicle fleet. Results show that the PG system provides about 27 kW of electricity, 14.6 kW of heat, and 3.11 kg of hydrogen in winter, spring, and autumn, and about 26 kW, 14 kW, and 3.11 kg in summer; it fully covers the building’s electrical demand in summer and hot water demand in all seasons. The integration of EV batteries reduces grid dependence, improves renewable self-consumption, and allows for the continuous and efficient operation of both the SOFC and PEME, demonstrating the potential of the proposed system to support the green transition. Full article

This study evaluates a solar-powered electric mobility pilot implemented at the University of Cuenca (Ecuador), combining two electric vans with daytime charging from a 35 kWp PV microgrid. Real-world monitoring with SCADA covered one year of operation, including efficiency tests across urban, highway, and mountainous routes. Over the monitored period, the fleet completed 5256 km in 1384 trips with an average occupancy of approximately 87%. Energy use averaged 0.17 kWh/km, totaling 893.52 kWh, of which about 98.2% came directly from on-site PV generation; only 2.41% of the annual PV output was required for vehicle charging. This avoided 1310.52 kg of CO₂ emissions compared to conventional vehicles. Operating costs were reduced by institutional electricity tariffs (0.065 USD/kWh) and the absence of additional PV investment, with estimated savings of around USD 2432 per vehicle annually. Practical guidance from the pilot includes aligning fleet schedules with peak solar generation, ensuring access to slow daytime charging points, maintaining high occupancy through route management, and using basic monitoring to verify performance. These results confirm the technical feasibility, economic competitiveness, and replicability of solar-electric transport in institutional settings with suitable solar resources and infrastructure. Full article

The growing penetration of renewable energy sources (RES) has exacerbated operational flexibility deficiencies in modern power systems under time-varying conditions. To address the limitations of existing flexibility management approaches, which often exhibit excessive conservatism or risk exposure in managing supply–demand uncertainties, this study introduces a data-driven distributionally robust optimization (DRO) framework for power system scheduling. The methodology comprises three key phases: First, a meteorologically aware uncertainty characterization model is developed using Copula theory, explicitly capturing spatiotemporal correlations in wind and PV power outputs. System flexibility requirements are quantified through integrated scenario-interval analysis, augmented by flexibility adjustment factors (FAFs) that mathematically describe heterogeneous resource participation in multi-scale flexibility provision. These innovations facilitate the formulation of physics-informed flexibility equilibrium constraints. Second, a two-stage DRO model is established, incorporating demand-side resources such as electric vehicle fleets as flexibility providers. The optimization objective aims to minimize total operational costs, encompassing resource activation expenses and flexibility deficit penalties. To strike a balance between robustness and reduced conservatism, polyhedral ambiguity sets bounded by generalized moment constraints are employed, leveraging Wasserstein metric-based probability density regularization to diminish the probabilities of extreme scenarios. Third, the bilevel optimization structure is transformed into a solvable mixed-integer programming problem using a zero-sum game equivalence. This problem is subsequently solved using an enhanced column-and-constraint generation (C&CG) algorithm with adaptive cut generation. Finally, simulation results demonstrate that the proposed model positively impacts the flexibility margin and economy of the power system, compared to traditional uncertainty models. Full article

This paper investigates the potential use of Electric Vehicles (EVs) to enhance power grid stability through their energy storage and grid-support capabilities. By providing auxiliary services such as spinning reserves and voltage control, EVs can significantly impact power quality metrics. The increasing energy consumption and the global imperative to address climate change have positioned EVs as a viable solution for sustainable transportation. Despite the challenges posed by their variable energy demands and rising numbers, the integration of a smart grid environment with smart charging and discharging protocols presents a promising avenue. Such an environment could seamlessly integrate a large fleet of EVs into the national grid, thereby optimizing load profiles, balancing supply and demand, regulating voltage, and reducing energy generation costs. This study examines the large-scale adoption of EVs and its implications for the power grid, with a focus on State of Charge (SOC) estimation, charging times, station availability, and various charging methods. Through simulations of integrated EV–PV charging profiles, the paper presents a lookup-table-based data estimation approach to assess the impact on power demand and voltage profiles. The findings include multiple charging scenarios and the development of an optimal control unit designed to mitigate the potential adverse effects of widespread EV adoption. Full article

Electric vehicles (EVs) and photovoltaics (PVs) are expected to be broadly adopted in future power systems. However, the temporal variability of EV load and PV production presents challenges for integrating them into the power grid. This study evaluates and assesses the synergies between EVs and PV systems to maximize solar energy utilization for EV load coverage. The configurations studied include EV charging via the national grid as a reference case (Case 1) and two solar energy harvesting options: EVs powered directly by vehicle-mounted PVs (Case 2) and EV chargers connected to residential PV installations (Case 3). These cases are evaluated across different urban environments with large EV fleets and dissimilar weather conditions: Berlin and Los Angeles. A customized operation profile based on the worldwide harmonized light-duty test cycle (WLTC) and a charge-right-away (CRA) strategy is used. Energy performance analysis is conducted through dynamic simulations using the Modelica language, with environmental and economic indices derived. Key findings highlight the superior performance of residential PV systems in both cities compared to current solar EV technologies, with both solutions offering significant benefits over the reference case. Cases 2 and 3 result in a 44% and 59% reduction in annual energy consumption, greenhouse gas emissions, and charging costs in Berlin, while in Los Angeles, the reductions are 67% and 98%. The average daily solar driving range reaches 20.3% in Berlin and 30.4% in Los Angeles. Full article