Search Results (7,403)

Electric vehicles (EVs) are experiencing rapid global adoption driven by environmental concerns and fuel security. This article presents a new dual-source converter based on a hybrid modular multilevel configuration (DCHMMC) designed for optimal cell utilisation in EV battery systems. Contrary to conventional converters that can either charge or discharge the cells using a single source, thereby leaving several cells/modules (Ms) idle during each time step, the proposed converter enables the integration of two sources that can utilise the cells simultaneously. This dual source feature minimises idle cells/Ms, enhances energy efficiency, and supports flexible bidirectional power flow. The proposed converter operates in three distinct modes. The first involves dual-source charging for fast charging and improved vehicle availability. The second involves one source charging while the other discharges for dynamic operation. Finally, the last involves dual-source discharging for maximum power delivery and support vehicle-to-grid (V2G) operation. The simulation results demonstrated smooth multilevel sinusoidal output voltages (Vout_a and Vout_b), each with a peak of 350 V, generated simultaneously using 132 cells (six cells per M, 22 Ms). The total harmonic distortion (THD) values for Vout_a and Vout_b were 0.42% and 2.25%, respectively, confirming the high-quality performance. Furthermore, only 0–36 cells and 0–6 Ms were idle during operation, showing improved cell utilisation. Full article

This study explores the relationship between electricity consumption and financial development in 20 emerging market economies (EMEs) from 2000 to 2020. Employing the panel ARDL–PMG estimator and a two-step system GMM to address endogeneity, we identify a significant positive long-run cointegrating relationship, where electricity consumption fosters financial development. The estimated error correction term suggests a stable equilibrium, with deviations corrected at a 29% annual rate, in the short-run adjustment. These results underscore the significance of targeted energy investments in driving financial market growth. Policies promoting grid action, renewable integration, and innovative financing tools, such as green bonds, can align electricity expansion with financial stability objectives. By incorporating recent global disruptions and applying advanced econometric methods, this study provides updated empirical evidence and actionable policy insights on the electricity–finance nexus in EMEs. Full article

This paper explores how the deployment of “High-Performance Heat-Powered Heat Pumps” (HP³s)—a novel heating technology—could help meet the domestic heating demand in the UK and reduce how much grid-scale energy storage is needed in comparison to a scenario where electrical heat pumps fully supply the heating demand. HP³ systems can produce electricity, which can partially alleviate the stress caused by electrical heat pumps. A parametric analysis focusing on two variables, the penetration of HP³ systems (H) and the amount of electricity exported (Ɛ), is presented. For every combination of H and Ɛ, the electricity system is optimized to minimize the cost of electricity. Three parameters define the electricity system: the generation mix, the energy storage mix and the amount of over-generation. The cost of electricity is at its highest when electrical heat pumps supply all demand. This reduces as the penetration of HP³ systems increases due to a reduction in the need for energy storage. When HP³ systems supply 100% of the heating demand, the total cost of electricity and the storage capacity needed are 6% and 50% lower, respectively, compared to a scenario where electrical heat pumps are in 100% of residences. Full article

The rapid use of renewables like wind power (WP) and photovoltaic (PV) power is essential for a sustainable energy future, yet their volatility poses a threat to grid stability. Electric vehicles (EVs) contribute to the solution by providing storage, while biomass energy (BE) ensures a reliable and sustainable power supply, solidifying its critical role in the stable operation and sustainable development of the power system. Therefore, a dynamic economic emission dispatch (DEED) model based on WP–PV–BE–EVs (DEED_WPBEV) is proposed. The DEED_WPBEV model is designed to simultaneously minimize operating costs and environmental emissions. The model formulation incorporates several practical constraints, such as those related to power balance, the travel needs of EV owners, and spinning reserve. To obtain a satisfactory dispatch solution, an adaptive improved multi-objective evolutionary algorithm based on decomposition with differential evolution (IMOEA/D-DE) is further proposed. In IMOEA/D-DE, the initialization of the population is achieved through an iterative chaotic map with infinite collapses, and the differential evolution mutation operator is adaptively adjusted. Finally, the feasibility and effectiveness of the proposed model and algorithm are verified on the ten-units system. The experimental results show that the proposed model and algorithm can effectively mitigate renewable energy uncertainty, reduce system costs, and lessen environmental impact. Full article

The growing market for pre-sliced and packaged bread, driven by convenience and extended shelf life, raises environmental concerns due to its reliance on single-use polyethylene (PE) bags. To evaluate this trade-off, a cradle-to-distribution-center Life Cycle Assessment (LCA) of white sliced bread in 4-slice modified atmosphere PE bags was conducted, following ISO 14040/14044 guidelines and using 2021–2022 factory data from Southern Italy. The initial carbon footprint (CF) of the packaged bread was estimated at 2.77 kg CO_2e/kg when using 100% Grid Electricity. The transformation phase was the largest contributor (41.5%), with electricity accounting for over 90% of this impact, followed by packaging (22.3%) and ingredients (19.4%). Allocation of by-products reduced the CF to around 2.68 kg CO_2e/kg, while the adoption of on-site renewable electricity significantly lowered impacts by up to 30% (to 1.95 kg CO_2e/kg). A key finding is the environmental trade-off between the product and its packaging: a wasted bread slice embodies approximately 70 g CO₂, whereas the production of the corresponding portion of the PE bag emits only about 5 g CO₂. This finding, which is confirmed to be statistically significant, demonstrates that the packaging’s footprint is substantially smaller than the potential impact of even a single wasted slice, proving its crucial role in preventing a larger environmental burden from food waste. Full article

The increasing integration of decentralized technologies such as photovoltaic (PV) systems and electric vehicles (EVs) poses significant challenges to the reliable operation of radial distribution grids. In this paper, we study Active Power Curtailment (APC), which is a cost-effective method that maintains grid safety by temporarily reducing power injections. However, APC can place disproportional curtailment burden on grid buses that may in fact undermine the continuous adoption of PVs and EVs. In this work, we propose different novel APC methods that incorporate fairness properties for radial grids with PVs, EVs, and battery energy storage systems (BESSs). In addition, we integrate BESSs and show their benefits in lowering APC levels and achieving better PV and EV utilization while enhancing fairness. The proposed APC designs allow for fast decision making and can be generalized to unseen grids. To do so, a two-step solution is adopted, where in the first step, a reinforcement learning (RL)-based agent determines uniform per-feeder APC and BESS actions, and in the second step, heuristic controllers disaggregate these actions into tailored per-bus decisions while incorporating fairness features. Through simulations, the controllers are shown to mitigate over $99\%$ of constraint violations and significantly enhance fairness in curtailment distribution. BESSs are shown to improve the violations count and APC trade-off, leaning towards reduced APC percentages. Finally, we exemplify how the solution generalizes effectively to unseen grid configurations. Full article

Joint overheating in low-voltage distribution cabinets presents a major safety risk, often leading to insulation failure, accelerated aging, and even fires. Conventional threshold-based inspection methods are limited in detecting early temperature evolution and lack predictive capabilities. To address this, a short-term temperature prediction method for electrical joints based on deep learning is proposed. Using a self-developed sensing device and Raspberry Pi edge nodes, multi-source data—including voltage, current, power, and temperature—were collected and preprocessed. Comparative experiments with ARIMA, GRU, and LSTM models demonstrate that the LSTM achieves the highest prediction accuracy, with an RMSE, MAE, and MAPE of 0.26 °C, 0.21 °C, and 0.54%, respectively. Furthermore, a lightweight version of the model was optimized for edge deployment, achieving a comparable accuracy (RMSE = 0.27 °C, MAE = 0.21 °C, MAPE = 0.67%) while reducing the inference latency and memory cost. The model effectively captures temperature fluctuations during 6 h prediction tasks and maintains stability under different cabinet scenarios. These results confirm that the proposed edge-enabled lightweight LSTM model achieves a balanced trade-off between accuracy, real-time performance, and efficiency, providing a feasible technical solution for intelligent temperature monitoring and predictive maintenance in low-voltage distribution systems. Full article

Sand-based thermal energy storage systems represent a paradigm shift in sustainable energy solutions, leveraging Earth’s most abundant mineral resource through advanced nanocomposite engineering. This review examines sand-based phase change materials (PCM) systems with emphasis on integration with human-powered energy generation (HPEG). Silicon-based hierarchical pore structures provide multiscale thermal conduction pathways while achieving PCM loading capacities exceeding 90%. Carbon-based nanomaterial doping enhances thermal conductivity by up to 269%, reaching 3.1 W/m·K while maintaining phase change enthalpies above 130 J/g. This demonstrated cycling stability exceeds 1000 thermal cycles with <8% capacity degradation. Thermal energy storage costs reach ~$20 kWh⁻¹—60% lower than lithium-ion systems when normalized by usable heat capacity. Integration with triboelectric nanogenerators achieves 55% peak mechanical-to-electrical conversion efficiency for direct pathways, while thermal-buffered systems provide 8–12% end-to-end efficiency with temporal decoupling between intermittent human power input and stable electrical output. Miniaturized systems target off-grid communities, offering 5–10× cost advantages over conventional batteries for resource-constrained deployments. Levelized storage costs remain competitive despite efficiency penalties versus lithium-ion alternatives. Critical challenges, including thermal cycling degradation, energy-power density trade-offs, and environmental adaptability, are systematically analyzed. Future directions explore biomimetic multi-level pore designs, intelligent responsive systems, and distributed microgrid implementations. Full article

With growing global demand for sustainable decarbonization, hydrogen energy systems have emerged as a key pillar in achieving carbon neutrality. This study assesses the greenhouse gas (GHG) reduction efficiency of Republic of Korea’s hydrogen ecosystem from a life-cycle perspective, focusing on production and utilization stages. Using empirical data—including the national hydrogen supply structure, fuel cell electric vehicle (FCEV) deployment, and hydrogen power generation records, the analysis compares hydrogen-based systems with conventional fossil fuel systems. Results show that current hydrogen production methods, mainly by-product and reforming-based hydrogen, emit an average of 6.31 kg CO₂-eq per kg H₂, providing modest GHG benefits over low-carbon fossil fuels but enabling up to a 77% reduction when replacing high-emission sources like anthracite. In the utilization phase, grey hydrogen-fueled stationary fuel cells emit more GHGs than the national grid. By contrast, FCEVs demonstrate a 58.2% GHG reduction compared to internal combustion vehicles, with regional variability. Importantly, this study omits the distribution phase (storage and transport) due to data heterogeneity and a lack of reliable datasets, which limits the comprehensiveness of the LCA. Future research should incorporate sensitivity or scenario-based analyses such as comparisons between pipeline transport and liquefied hydrogen transport to better capture distribution-phase impacts. The study concludes that the environmental benefit of hydrogen systems is highly dependent on production pathways, end-use sectors, and regional conditions. Strategic deployment of green hydrogen, regional optimization, and the explicit integration of distribution and storage in future assessments are essential to enhancing hydrogen’s contribution to national carbon neutrality goals. Full article

The rapid growth of electric vehicle markets is producing large volumes of retired lithium-ion batteries retaining 70–80% of their original capacity, suitable for stationary energy storage. This study assesses the techno-economic and environmental viability of second-life battery energy storage systems (SLBESS) in a California commercial building, using one year of operational data. SLBESS performance is compared with equivalent new battery systems under identical dispatch strategies, building load profiles, and time-of-use tariff structures. A dispatch-aware framework integrates multi-year battery simulations, degradation modeling, electricity cost analysis, and life cycle assessment based on marginal grid emissions. The economic analysis quantifies the net present value (NPV), internal rate of return (IRR), and operational levelized cost of storage (LCOS_op). Results show that SLBESS achieve 49.2% higher NPV, 41.9% higher IRR, and 13.8% lower LCOS_op than new batteries, despite their lower round-trip efficiency. SLBESS reduce embodied emissions by 41% and achieve 8% lower carbon intensity than new batteries. Sensitivity analysis identifies that economic outcomes are driven primarily by financial parameters (incentives, acquisition cost) rather than technical factors (degradation, initial health), providing a clear rationale for policies that reduce upfront costs. Environmentally, grid emission factors are the dominant driver. Battery degradation rate and initial state of health have minimal impact, suggesting that technical concerns may be overstated. These findings provide actionable insights for deploying cost-effective, low-carbon storage in commercial buildings. Full article

This review analyzes how recent electric-vehicle LCAs have been carried out, emphasizing goals and scope, functional units, system boundaries (cradle-to-grave and well-to-wheel), and attributional versus consequential modeling rather than reporting outcomes. Using a systematic search of studies mainly from 2018–2025, it maps common tools and data sources (Ecoinvent, GREET, GaBi, and regional inventories) and summarizes LCIA practices, underscoring the need to report versions, regionalization, and assumptions transparently for comparability. Uncertainty studies are uneven: sensitivity and scenario analyses are common, while probabilistic approaches (e.g., Monte Carlo) are less used, indicating room for more consistent, multi-parameter uncertainty analysis. The results show that outcomes are context-dependent: BEVs deliver the largest life-cycle GHG cuts on low-carbon grids with improved battery production and end-of-life management; PHEVs and HEVs act as transitional options shaped by real-world use; and FCEV benefits depend on low-carbon hydrogen. Vehicle-integrated photovoltaics and solar-powered vehicles are promising yet under-studied, with performance tied to local irradiance, design, and grid evolution. Future research suggests harmonized reporting, more regionalized and time-aware modeling, broader probabilistic uncertainty, and comprehensive LCAs of VIPV/SPV and circular pathways to support policy-ready, comparable results. Full article

Effective integration of photovoltaic (PV) systems into electric power grids presents significant challenges due to the inherent variability in solar energy. Therefore, accurate PV power forecasting in various timescales is critical for the reliable operation of modern electric power systems. For short-term horizons, the primary source of solar power stochasticity is cloud movement and deformation, which are typically captured at high spatiotemporal resolutions using ground-based sky images. In this paper, we propose a novel multi-step sky image prediction framework for improved cloud tracking, which can be deployed for short-term PV power forecasting. The proposed method is based on deep learning, but instead of being purely data-driven, we propose a hybrid approach where we combine Auto-Encoder-like Convolutional Neural Networks (AE-like CNNs) with physics-informed sky image clustering to enhance robustness towards fast-varying sky conditions and effectively model non-linearities without adding to the computational overhead. The proposed method is compared against several state-of-the-art approaches using a real-world case study comprising minutely sky images. The experimental results show improvements of up to 17.97% on structural similarity and 62.14% on mean squared error, compared to persistence. These findings demonstrate that by combining effective physics-informed preprocessing with deep learning, multi-step ahead sky image forecasting can be reliably achieved even at low temporal resolutions. Full article

Electric vehicle charging stations (EVCSs) are critical interfaces between urban mobility and distribution grids and are increasingly exposed to false data that can mislead operations and degrade voltage quality. This study proposes a defense-planning framework that models how cyber manipulation propagates to physical impacts in a coupled transport–power system. The interaction is modeled as a tri-level defender–attacker–operator problem in which a defender hardens a subset of charging stations, an attacker forges measurements and demand, and an operator redispatches resources to keep the system secure. We solve this problem with a method that embeds corrective operation into the evaluation and uses improved implicit enumeration (IIE) with pruning to identify a small set of high-value stations to protect with far fewer trials than an exhaustive search. On a benchmark feeder coupled to a road network, protecting a few traffic-critical stations restores compliance with voltage limits under tested attack levels while requiring roughly an order of magnitude fewer evaluations than complete enumeration. Sensitivity analysis shows that the loss of reactive power from PV inverters (PV VARs) harms voltage profiles more than an equivalent reduction in distributed storage, indicating that maintaining local reactive capability reduces the number of stations that must be hardened to meet a given voltage target. These results guide utilities and city planners to prioritize protection at traffic-critical EVCSs and co-plan local Volt/VAR capability, achieving code-compliant voltage quality under adversarial conditions with markedly lower planning effort. Full article

In this article, an approach to balance the State of Charge (SoC) of two batteries connected to the DC bus of a fuel cell (FC) electric aircraft by Droop-controlled converters is described. The proposed algorithm is based on shifting the Droop reference voltages and prevents the simultaneous charging and discharging of the batteries. This approach is not only practical but also highly versatile, as it is compatible with all converters as long as the Droop voltage can be changed remotely, and a current measurement is provided to a central controller. No further programming access to the DC/DCs is necessary. There is no need for nonlinear or different-valued Droop resistances for charging and discharging. The balancing approach is validated via simulation in MATLAB/Simulink 2024a.The results show that the proposed approach achieves SoC balancing without degrading the dynamic performance of the grid. The delays added by the slower communication with the central controller have a minimal impact on performance. Full article

Algeria’s transition toward sustainable energy requires the exploitation of its abundant solar and wind resources for green hydrogen production. This study assesses the techno-economic feasibility of an off-grid PV/wind hybrid system integrated with a hydrogen subsystem (electrolyzer, fuel cell, and hydrogen storage) to supply both electricity and hydrogen to decentralized sites in Algeria. Using HOMER Pro, five representative Algerian regions were analyzed, accounting for variations in solar irradiation, wind speed, and groundwater availability. A deferrable water-extraction and treatment load was incorporated to model the water requirements of the electrolyzer. In addition, a comprehensive sensitivity analysis was conducted on solar irradiation, wind speed, and the capital costs of PV panels and wind turbines to capture the effects of renewable resource and investment cost fluctuations. The results indicate significant regional variation, with the levelized cost of energy (LCOE) ranging from 0.514 to 0.868 $/kWh, the levelized cost of hydrogen (LCOH) between 8.31 and 12.4 $/kg, and the net present cost (NPC) between 10.28 M$ and 17.7 M$, demonstrating that all cost metrics are highly sensitive to these variations. Full article