Search Results (3,937)

Renewable energy technologies are becoming more and more relevant in a variety of engineering fields as a result of the move toward low-carbon, sustainable energy systems. Although research has historically concentrated on power generation, it now covers a broad range of applications, including precision agriculture, smart grids, energy storage, healthcare devices, and sustainable buildings. However, existing review studies are often limited to single disciplines or specific technologies, lacking a unified cross-disciplinary perspective that captures the interconnected nature of modern renewable energy systems. This gap motivates the need for a comprehensive review that bridges multiple engineering domains. This review provides a comprehensive synthesis of literature on renewable energy applications in electrical and electronics, computer, environmental, biomedical, architectural, and agricultural engineering. In electrical and electronics engineering, the use of renewable energy sources is largely based on the efficient generation of electricity from natural resources such as solar, wind, and ocean energy. Computer engineering contributes through artificial intelligence (AI), Internet of Things (IoT) architectures, digital twins, and cybersecurity solutions, optimizing energy management. Environmental engineering emphasizes life cycle assessment, carbon footprint reduction, and circular economy strategies. In biomedical engineering, energy harvesting and self-powered devices illustrate micro-scale applications of renewable energy. Architectural engineering integrates renewable systems through building-integrated photovoltaics, net-zero energy designs, and smart building management, while agricultural engineering uses solar-powered irrigation, biomass utilization, agrivoltaic systems, and other sustainable practices. To support a low-carbon future with integrated and sustainable engineering solutions, this study not only highlights innovations within individual fields but also showcases how different disciplines can connect and work together. Overall, the review offers a novel cross-disciplinary framework that advances the understanding of renewable energy systems beyond isolated applications and provides direction for future integrative research. Full article

Distributed generation (DG) exhibits inherent volatility and intermittency, and its grid-integration expansion presents formidable challenges to microgrid regulation and control. Conventional control strategies often neglect the uncertainties associated with renewable energy generation and the coordinated management of flexible resources. This paper proposes a multi-time-scale optimal control strategy for microgrids that explicitly accounts for uncertainty. The strategy integrates a collaborative scheduling framework for assets, including electric vehicles (EVs) and energy storage systems, alongside a stochastic optimization model for microgrids that comprehensively incorporates uncertainties from wind and solar power generation, EV operations, and load forecasting errors. The improved Archimedean chaotic adaptive whale optimization algorithm is utilized to solve the optimal scheduling model, while the Latin hypercube sampling (LHS) technique is employed to address uncertainty-related problems in the optimization process. Case study results demonstrate that, in comparison with traditional optimal scheduling strategies, the proposed approach more effectively mitigates uncertainties in real-world operations, reduces microgrid operational risks, achieves a significant reduction in scheduling costs, and concurrently fulfills the dual objectives of microgrid economic efficiency and operational security. Full article

The Vehicle-to-Grid (V2G) technology is considered one of the best solutions for integrating renewable energy systems; however, most literature reports favorable economic results using synthetic data, without accounting for seasonal or market limitations. The current research presents the results of the MATLAB R2023b (Version 23.2, MathWorks, Natick, MA, USA) simulation of the 100-household energy community in Debrecen, Hungary, with 30 electric vehicles (EVs) using entirely simulation-based Lithium Iron Phosphate (LiFePO4) batteries, a simulation-based 150 kW solar photovoltaic (PV) system, and a simulation-based 200 kW wind power system, using real meteorological data for January 2024. The optimization of charging/discharging for electric vehicles was performed using a multi-objective genetic algorithm (GA) over 30 days at a 15 min time resolution, accounting for stochastic loads and temperature effects on battery degradation, with a sensitivity analysis of key parameters. The results of the optimized solution for the electric vehicle charging/discharging were unexpected: the total energy cost increased by 68.9% ($4337.65 to $7327.54), the peak demand increased by 266.2% (31.9 to 116.9 kW), the degradation cost was $479.63, the load factor was reduced from 0.847 to 0.722, and the SOC constraint was violated for 0.758% of measurements. The V2G is not economically viable under current Hungarian pricing and Central Europe winter conditions. Results are robust for varying parameters using sensitivity analysis and Pareto front tracing. The break-even point is achieved when ratios of peak-to-off-peak prices are above 3.5:1. Seasonal policies and market reforms are critical for V2G viability. Importantly, the influence of inherent design deficiencies in the optimization model on the reported results cannot be ruled out. Full article

Opuntia ficus-indica (L.) Mill., also named Cactus pear, is a crop widespread in many countries with Mediterranean and subtropical climates, where it represents a valuable source of food. However, in southern Europe, this fruit market is limited to a few months, from summer to autumn. The possibility to extend the ripening period of fruit is represented by the special pruning of the first bloom flush and consequent new development of late flowers and fruits. Extending the cultivation period would allow farmers to maximize the crop’s potential, thereby extending the Cactus pear market season throughout much of the year. In this study, conducted in southern Sardinia (Italy), progressive pruning was applied with the aim of evaluating the fruit characteristics in relation to this type of cultivation, also considering the weather conditions during the experimental period. Morphological traits and physicochemical compositions of fruit picked in four harvests during two sampling seasons from August 2022 to March 2023, and from August 2023 to March 2024 were compared. According to principal component analysis (PCA), most of the observed characters showed significant differences among harvest periods but also between the two seasons of cultivation (year of cultivation: r = 0.722 on PC1), suggesting that the meteorological trend strongly modulated fruit traits. Some fruit qualities were partially lost during the winter months, such as juice acidity and total soluble solids (TSS). October was the month with the highest TSS levels (13.5 ± 0.25), followed by August, January and March. On the other hand, juiciness and fresh weight remained unchanged or even improved in fruit harvested out-of-season. As observed in the redundancy analysis (RDA) a contribution of 54% due to weather variability emerged. In Particular, TSS levels, pH and juice dry matter were associated with high temperatures, solar radiation, and wind intensity. Wind speed was also moderately linked with betalain content. Moreover, high relative humidity was associated with lower pH values, higher water content, and higher fruit fresh weight. A significant difference was found between the two years in betalains content (80.0 ± 3.7 µg·mL⁻¹ in 2022–2023 and 28.2 ± 2.5 µg·mL⁻¹ in 2023–2024). The breakdown in the 2023–2024 season was likely due to the strong heat wave of July 2023 (up to 47 °C), which caused their partial degradation. In light of seasonal variability, this work provides some useful insights for future management of Cactus pear, also considering the possibility of usefully extending the period of cultivation and harvesting. Full article

To improve the economic efficiency and reliability of off-grid renewable energy hydrogen production systems, this paper proposes an integrated optimal variable temperature operation strategy for multi-electrolyzer systems. This paper develops a unified optimization model that deeply integrates the electro-thermal characteristics and dynamic operational states of multiple alkaline water electrolyzers. By actively regulating the operating temperature and optimizing power allocation, the strategy significantly improves economic efficiency under fluctuating power inputs. Furthermore, a collaborative dispatch principle is introduced to ensure balanced aging across the electrolyzer cluster. Simulation results based on real-world wind and solar data demonstrate that compared to traditional rule-based methods, the proposed strategy increases the monthly net profit by up to 14.6% and significantly reduces the frequency of cold and hot starts by 51.21% and 89.41%, respectively. This research provides an efficient and reliable technical framework for the collaborative management of large-scale green hydrogen infrastructure. Full article

Urbanization and climate change intensify urban heat islands and air pollution; therefore, street canyon building planning that accounts for road orientation, shading, thermal environment, and ventilation is crucial. This study uses numerical simulations to investigate how non-uniform wall and road heating affects airflow and pollutant dispersion in street canyons under varying Richardson numbers (Ri) and heating scenarios (windward wall, leeward wall, road surface). The results indicate that large wall–atmosphere temperature differences combined with low incoming wind speed (high Ri) make thermal buoyancy a dominant control on canyon flow and pollutant transport. Heating of the leeward wall and road surface enhances ventilation and pollutant removal (prominently when the Ri ≥ 0.49), whereas heating of the windward wall suppresses dispersion and increases concentrations (prominently when the Ri ≥ 0.12). For a north–south street, diurnal solar heating produces strong micro-environmental contrasts. With easterly winds, morning heating of the windward wall elevates pollutant levels, while afternoon heating of the leeward wall promotes dispersion and lowers concentrations. Specifically, compared with the isothermal condition, the turbulent exchange rate at the top of the street canyon is enhanced to 1.71~6.86 times, while the convective exchange rate is suppressed to 58%~83% in the morning and enhanced to 1.21~1.92 times. These findings suggest that urban planning should limit windward wall temperature rises via shading and greening; thus, single-sided sidewalk and greening layouts on the windward side are recommended. Full article

Harmonising heterogeneous met-ocean time series to a common temporal resolution is a prerequisite for integrated marine renewable energy assessments. Such datasets often differ in their sampling frequency, statistical distribution, and non-stationarity, complicating joint analysis. This study presents a practical multi-criteria framework for selecting temporal interpolation strategies for met-ocean datasets, explicitly balancing prediction accuracy and computational efficiency. Six environmental variables relevant to offshore renewable energy—wind speed, significant wave height, energy period, peak period, global horizontal irradiance, and upper-ocean thermal gradients—are analysed using ten-year reanalysis datasets for the Madeira Archipelago. Six commonly used deterministic time-domain interpolation methods are evaluated within a unified validation framework combining training–test splits, k-fold cross-validation, and Monte Carlo resampling. Their performances are quantified using the relative root mean square error and computational time, integrated through a composite performance score. The results show that makima interpolation provides the most consistent compromise between accuracy and efficiency for most variables in dense, regularly sampled met-ocean datasets, while spline-based approaches perform better for highly skewed solar irradiance. Preprocessing steps, such as detrending and distribution normalisation, yield only marginal improvements for dense, regularly sampled datasets, and method rankings remain stable under moderate changes in accuracy–speed weightings. Rather than proposing a universal interpolator, this work delivers a reproducible decision-support workflow for temporal resampling of multi-variable met-ocean datasets, supporting early-stage marine renewable energy assessments. Full article

This study develops a high-resolution techno-economic optimization framework to assess the feasibility of green hydrogen production in 100% renewable, off-grid systems. Utilizing 5-minute interval meteorological data aggregated to hourly resolution spanning 5 years across seven geographically diverse sites, this study co-optimizes the integration of hybrid wind–solar power generation, flexible electrolyzer operation, and a multi-timescale energy storage portfolio, incorporating short-duration, long-duration, and seasonal storage. On the generation side, a hybrid wind–solar configuration achieves the lowest levelized cost of hydrogen (LCOH). For energy storage, no single storage technology can economically address demand fluctuations across short-term, medium-term, long-term, and seasonal timescales. Instead, a coordinated multi-timescale storage strategy incorporating energy-to-energy mechanisms reduces the LCOH by up to 40%. Increasing hydrogen tank capacity and enabling flexible electrolyzer operation further lowers the LCOH. Significant regional resource variability leads to substantial cost disparities, with the most favorable region achieving a low LCOH of $2.45/kg. Several regions are projected to reach the $3/kg target by 2030, while areas with limited resources require large-scale hydrogen storage to ensure supply reliability. These results represent deterministic lower-bound estimates under perfect foresight; accounting for forecast uncertainty and real-world operational constraints would likely increase actual costs by approximately 5–15%. Full article

Accurate forecasting of wind and photovoltaic power remains challenging due to the strong nonlinearity, nonstationarity, and seasonal heterogeneity of renewable generation series. To address this issue, this study proposes a hybrid forecasting framework integrating time–frequency joint analysis (TFAA), temporal convolutional networks (TCN), long short-term memory (LSTM), and multi-head self-attention (MHSA). Wavelet transform is used to extract frequency-domain representations, which are jointly encoded with the original time-domain sequence through a dual-branch architecture and adaptively fused. The fused features are then processed by a TCN-LSTM backbone to capture both long-range dependencies and short-term dynamics, while MHSA is introduced to enhance global contextual modeling. Experiments on wind-farm and photovoltaic datasets from China, together with external validation on the NREL WIND Toolkit and the GEFCom2014 Solar benchmark, show that the proposed model achieves the best overall seasonal performance and maintains competitive improvements on public benchmarks. Additional ablation studies, repeated-run statistical validation, persistence-based skill-score analysis, prediction-interval evaluation, ramp-event assessment, meteorological-driver enrichment, permutation-based driver attribution, regime-conditioned error diagnostics, and transferability evidence analysis further confirm the effectiveness, robustness, physical consistency, and practical applicability of the proposed framework. The results indicate that the proposed model provides a reliable and operationally relevant solution for short-term wind and photovoltaic power forecasting. These findings further support sustainable renewable-energy integration, smart-grid dispatch, and low-carbon power-system operation. Full article

The Changbai Mountain–Liaodong region is a crucial component of the global black soil belt in Northeast China and a significant national grain production base. However, like many high-latitude agricultural regions worldwide, it faces persistent challenges during the spring sowing period, including low soil temperatures and excessive moisture. Therefore, developing region-specific, effective methods of reducing soil moisture and increasing temperature while improving soil fertility is essential for improving agricultural productivity. To this aim, a field experiment was conducted with two factors: a main plot subjected to ridge tillage (RT) and flat tillage (FT) and subplots with biochar (BC) and straw (ST) amendments. A subplot with no amendment (CK) was used as a control. During maize growth, the daily soil temperature and moisture were monitored, and the soil water evaporation rates and physical structure, as well as the maize yield performance, were evaluated. The results showed that biochar and straw application significantly decreased the soil monthly water content by 1.69–2.22% (p < 0.05) in the surface soil layer (0–15 cm) from May to June, with a more pronounced effect under RT. In contrast, biochar application increased soil moisture and water storage from July to September, indicating that the influence of biochar on soil moisture depends on time and field aging processes. Biochar amendment raised the soil maximum temperature by 0.32–0.79 °C in the top 0–15 cm layer, while straw incorporation decreased the minimum soil temperature by 0.11–0.52 °C. The increase in soil temperature was primarily due to the biochar’s darker color, which facilitated solar radiation absorption, while the decrease in soil temperature was caused by the “Wind Leakage Effect” induced by the large particle size of the straw. Biochar and straw incorporation effectively enhanced maize dry matter accumulation by an average of 15.8% and 8.2%, respectively, and grain yield by 13.0% and 7.8%, respectively. Correlation analysis indicates that these increments are primarily due to enhanced soil moisture and available N content during the middle to late stages of maize growth. Therefore, the integration of straw and biochar with high-ridge cultivation is an effective strategy for excessive moisture reduction and warming in spring soil and it also contributes positively to maize yield. Full article

The rapid growth of artificial intelligence (AI) workloads and data center infrastructure is driving a surge in electricity demand, underscoring the need for robust metrics to evaluate energy generation and storage strategies. This study introduces the Bring Your Own Battery (BYOBattery) metric, a region-specific, temporally resolved indicator designed to quantify the ideal energy storage capacity required to mitigate generation-demand mismatches. The BYOBattery metric is computed as the minimum ideal battery storage required to eliminate generation-demand imbalances over a given time window, and is extended to incorporate curtailment via a convex optimization formulation to better manage peak generation and storage requirements. We applied the BYOBattery metric to wind, solar, and nuclear generation technologies across three major U.S. grid regions: the California Independent System Operator (CAISO), the Electric Reliability Council of Texas (ERCOT), and the Pennsylvania–New Jersey–Maryland Interconnection (PJM), using operational data from 2021 to 2024. Key findings are: (1) nuclear consistently requires the least storage in order to meet demand (i.e., one equivalent load hour compared with 10–25 h for wind and solar); (2) wind storage requirements decrease with increased capacity, whereas solar necessitates consistent levels of storage; and (3) the 30-year non-discounted cost per kWh for nuclear ($0.10/kWh) is substantially lower than that of wind or solar by a factor of 1–4 across all studied region. The BYOBattery metric enables comparative benchmarking of generation technologies under dynamic demand conditions and supports cost-informed planning for energy systems. This work contributes a reproducible, interpretable, and computationally efficient tool for energy system analyses and broader performance evaluations. Full article

The decarbonization of hard-to-abate sectors remains a significant challenge in achieving net-zero emissions targets. These industries depend on energy-dense fuels, making direct electrification and the direct use of hydrogen technically and economically challenging. Electrofuels present a promising pathway to reducing emissions while leveraging surplus renewable energy. This review evaluates the feasibility of electrofuels for deep decarbonization, focusing on production processes, energy demands, and economic viability. Environmental performance is discussed in terms of lifecycle greenhouse gas (GHG) emissions, carbon circularity considerations, and energy conversion efficiencies, while techno-economic feasibility is evaluated using metrics such as levelized cost of hydrogen (LCOH), CO₂ capture costs, and projected fuel production costs. The review indicates that while electrofuels can achieve substantial lifecycle emission reductions up to 40–90%, depending on pathway and electricity source, their deployment remains constrained by high energy demand, conversion losses, and capital costs. Projected reductions in LCOH to below $2.1/kg by 2030 and declining renewable electricity costs could significantly improve competitiveness, particularly in regions with abundant solar and wind resources. However, substantial trade-offs exist between efficiency, infrastructure compatibility, scalability, and carbon neutrality across different electrofuel routes. The review identifies key technological bottlenecks, cost drivers, and research priorities necessary to position electrofuels as a strategic solution for deep decarbonization in sectors where direct electrification is not feasible. Full article

A three-body classical trajectory Monte Carlo method is used to investigate state-specific electron capture from H₂O by highly charged ions. The radial and momentum distributions of the target electron are modeled using a one-center molecular orbital wave function. Total single-electron capture cross sections, as well as cross sections for capture into specific $nl$-states, are calculated for the highly charged ion projectiles, C⁶⁺, N⁷⁺, Ne¹⁰⁺, and Ar¹⁸⁺, at relative collision energies ranging from 0.01 keV/amu to 50 keV/amu. Comparisons of relative $n$-state capture populations and total single-electron capture cross sections are made with experimental results. The results show a marked improvement in the prediction of relative $n$-states populated, with the overall single-electron single capture cross sections being slightly low compared with experimental values. Overall, this method of calculating $nl$-states of the captured electron appears to be a promising approach for those wishing to model X-ray and Extreme Ultraviolet (EUV) emissions from comets bombarded by solar wind ions, and fusion researchers trying to determine the effects of impurities in Tokomak reactors. Full article

The increasing integration of renewable energy sources increases the variability and uncertainty of power systems, requiring advanced prediction-based control strategies. This paper proposes an integrated AutoML–MPC framework for a hybrid renewable energy system (HRES) combining solar and wind generation, biomass, battery energy storage, and a hydrogen chain (electrolyzer and fuel cell). Short-term load and generation forecasts are made using H2O AutoML models, and the energy flow allocation is optimized using model-based control (MPC) formalized in the form of mixed-integer linear programming (MILP). The objective function minimizes electricity imports from the grid and the associated ${\mathrm{C}\mathrm{O}}_2$ emissions, subject to technological constraints. The results obtained showed a clear distribution of short-term (battery) and long-term (hydrogen) storage functions in time: during periods of excess generation, the electrolyzer operated close to nominal mode, and in the deficit phase, the fuel cell was activated, reducing the need for grid imports. The battery ensured fast short-term balancing, while the hydrogen system compensated for the longer-term energy shortage. The forecast models were characterized by high accuracy ($R^2>0.98$), which allowed for reliable planning of energy flows over the MPC horizon. The proposed methodology allows for effective coordination of storage technologies of different time scales, maximum use of renewable generation and reducing the system’s dependence on the external grid. Full article

Long-term cold exposure reduces livestock welfare and productivity in Inner Mongolia. This study assessed cold stress effects on 60 two-month-old female Dorper × Mongolia lambs allocated to four sheltering conditions (n = 15): indoor pens with enclosed housing (IP), outdoor pens (OP), house with playground pens (OPP), and polytunnel pens (PP). Compared with IP, OP exhibited significantly lower temperature, humidity, CO₂ concentration, NH₃ concentration, and WCI, and significantly higher wind speed and solar radiant heat (p < 0.001). Humidity, CO₂ concentration, and NH₃ concentration in PP was lower than in IP, but higher than in OP (p < 0.001); temperature, wind speed, and WCI did not differ significantly between PP and IP. ADG was significantly lower in OP and OPP than in IP (p < 0.001), whereas PP did not differ from IP. F:G was higher in OP than in IP and PP (p = 0.040). Feeding duration had significant effects on ACTH, leptin, T₃, T₄, TP, urea, TG, NEFA, LDL, and HDL concentrations. Rearing environment significantly affected GLU, ALB, LDH, and TG. Feeding duration × sheltering conditions interaction significantly influenced ACTH, TP, ALB, urea, LDH, TG, LDL, and HDL. OP induced cold stress and dysfunction, while IP and PP produced milder responses. PP raised indoor temperatures substantially, and is thus optimal for winter lamb production. Full article