Search Results (1,452)

Decarbonizing the transportation sector requires quick adoption of low-carbon energy carriers, with green hydrogen becoming a promising option for zero/low-emission mobility. Hydrogen refueling stations powered by renewable energy sources present a practical way to cut down lifecycle greenhouse gases and ease grid congestion. Nonetheless, most existing photovoltaic (PV)-based hydrogen production systems focus solely on electrical aspects, overlooking thermal energy flows and temperature effects that greatly impact PV and Electrolyzer performance. This study provides a thorough techno-economic evaluation of a hybrid PV/photovoltaic-thermal (PVT) green hydrogen system for refueling stations. The simulation framework models the combined electrical, thermal, and hydrogen subsystems under realistic conditions, incorporating rooftop PV/PVT collectors, battery storage, a water Electrolyzer, and hydrogen storage. Thermal energy from the PVT is used to pre-heat Electrolyzer feedwater, lowering electricity demand for hydrogen production and boosting PV efficiency via active cooling. Hydrogen production follows a demand-driven control strategy based on randomly generated stochastic daily refueling events. Three configurations are compared: (i) grid-only electrolysis, (ii) PV-only assisted electrolysis, and (iii) fully integrated PV/PVT-assisted electrolysis. The results show that the integrated PV/PVT setup significantly increases self-consumption, autarky rate, and overall efficiency, while lowering reliance on grid electricity and hydrogen production costs. Developed case studies highlight the economic feasibility and real-world viability of PV/PVT-assisted (decentralized) hydrogen refueling infrastructure. 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

Onboard carbon capture and storage (OCCS) is promising, but downstream CO₂ conditioning and liquefaction dominate energy and operability constraints. An integrated OCCS onboard for CO₂ conditioning, deep cooling, phase separation and liquid CO₂ (LCO₂) storage for a dual-fuel marine engine was introduced and investigated. In addition, the proposed system has been scrutinized under Aspen HYSYS V12.1 steady state mode and a comprehensive sensitivity sweep on deep-cooler temperature and separation pressure. Sensitivity sweeps reveal a sharp liquefaction threshold governed by the deep-cooler outlet temperature. For the engine load range from 50% to 110% and exhaust gas from 1.288 to 2.863 kg/s with CO₂ from 3.65 to 6.67%, the model is validated at 90.3% capture. Near vent-free operation for T_E105 < −24.58 °C, and a P-T diagram indicates that near vent-free operation requires P_V105 > 190 kPa at −24.7 °C, while −22.45 °C is unattainable within 1600–2200 kPa. Increasing compressor discharge pressure from 1500 to 2500 kPa raises compression power from 34.8 to 80.23 kW at −21 °C without improving vent/yield under throttled control. By identifying threshold-based deep-cooling setpoints, creating a separator pressure-temperature feasibility envelope for near-vent-free operation, and clearly quantifying CO₂-rich vent slip as a system-level loss term, this study offers an operability-driven design layer for onboard CO₂ liquefaction. Full article

Photovoltaic (PV) arrays frequently operate under non-uniform environmental conditions, including partial shading, dust accumulation, and temperature differences across the array. These factors introduce an electrical mismatch among PV modules, considerably reducing overall power output. This study proposes a self-healing reconfiguration strategy that mitigates mismatch losses by dynamically redistributing PV modules across array strings based on irradiance levels. The main goal is to balance the current generation among strings and demonstrate performance improvements within scenarios characterised by highly uneven irradiance patterns under non-uniform operating conditions. The effectiveness of the proposed method is evaluated through simulations conducted using MATLAB R2025b (MathWorks, Natick, MA, USA) under several environmental scenarios. Deterministic shading patterns—including row shading, column shading, diagonal shading, and irregular dust distributions—are first analysed to investigate the behaviour of the PV array under regulated conditions. In addition, a statistical analysis of 100 randomly generated irradiance scenarios is carried out to assess the method’s robustness. Finally, realistic desert-dust patterns representative of environmental conditions in Saudi Arabia are used to evaluate the practical usefulness of the proposed approach. Simulation findings show that the self-healing reconfiguration strategy reduces mismatch effects and improves current balance within the PV array, enabling operation closer to the optimal power point under non-uniform irradiance conditions. These results indicate that the proposed method boosts current balance among PV strings and increases power extraction under strongly non-uniform irradiance scenarios. Full article

The increasing penetration of rooftop photovoltaic (RTPV) systems in low-voltage (LV) distribution networks introduces challenges such as voltage rises, reverse power flow, and reduced hosting capacity, thereby necessitating effective active power regulation (APR) in module-level micro-inverters. This paper proposes a dual-layer control framework for a 250 watt-peak (Wp) three-switch rooftop PV micro-inverter, integrating quantum-behaved particle swarm optimization with reinforcement learning (QPSO-RL) for accurate maximum power point tracking (MPPT) and a linear quadratic regulator (LQR) for reserve-aware APR. The QPSO-RL algorithm improves available-power estimation under varying irradiance, temperature, and partial-shading conditions, while the LQR-based controller ensures fast, well-damped, and grid-compliant power regulation. The proposed framework was developed and validated using MATLAB/Simulink 2024 for simulation studies and LabVIEW with NI myRIO 2022 for real-time hardware implementation. Both simulation and experimental results confirm that the proposed method achieves 99.5% MPPT accuracy, convergence within 20 ms, grid-injected current total harmonic distortion (THD) below 3%, and a near-unity power factor. In addition, the reserve-based regulation strategy improves feeder compliance and reduces converter stress, thereby supporting reliable rooftop PV integration. These results demonstrate that the proposed QPSO-RL + LQR framework offers a practical and intelligent solution for high-performance, grid-supportive rooftop PV micro-inverter applications. Full article

This paper develops and applies a techno-economic optimization framework for sizing photovoltaic (PV) and battery energy storage systems (BESSs) in grid-connected energy communities. An in-house developed modeling platform featuring custom MATLAB (R2025a) code implements a mixed-integer linear programming (MILP) model that minimizes differential net present value (NPV) over a 25-year lifetime, integrating capital expenditures, operating cash flows, and carbon taxation. The formulation captures temperature-dependent PV efficiency, battery round-trip efficiency, and time-varying electricity prices, and is validated on a real campus energy community with hourly demand, irradiance, and tariff data. Two design scenarios are examined: the optimal unconstrained case and a budget-constrained configuration (CAPEX ≤ 2.0 M€). Results show the unconstrained system installs 3.19 MW_p PV and 12.3 MWh storage, achieving 78.9% self-sufficiency and a 78.9% emissions reduction. The constrained case installs 0.99 MW_p and 1.68 MWh, achieves 32.0% self-sufficiency, and delivers a 4.46 M€ NPV with payback in 3.9 years. Under current costs and tariffs, PV-dominated configurations provide the highest value, with limited battery benefit except under generous budgets or higher carbon prices. A dedicated CAPEX sensitivity analysis explores PV and battery cost variability and its impact on optimal sizing and economic outcomes. The core methodological contribution is a master-planning formulation that solves design decision variables and optimal dispatch concurrently within a single MILP. The flexible platform enables future reassessment as technology, tariff, and policy landscapes evolve. Full article

Sunflower oil is particularly prone to thermo-oxidative degradation due to its high content of polyunsaturated fatty acids, especially under high-temperature conditions. This study investigated the oxidative stability of sunflower oil heated at 180 °C for 4 and 8 h, focusing on the protective effect of silibinin oleate (SIL-O), a lipophilic polyphenolic derivative, compared to the synthetic antioxidant butylated hydroxytoluene (BHT). Oxidative changes were evaluated through peroxide value (PV), p-anisidine value (p-AV), and total oxidation value (TOTOX), while structural alterations were monitored using FTIR spectroscopy. Additionally, fatty acid composition was analyzed by GC-MS to assess compositional changes associated with oxidation. Thermal treatment led to increases in PV, p-AV, and TOTOX, indicating progressive oxidation, alongside a decrease in unsaturated fatty acids. FTIR analysis revealed characteristic changes, including a reduction in the unsaturation band (~3008 cm⁻¹), modifications in the ester carbonyl region (~1743 cm⁻¹), and the emergence of bands associated with cis–trans isomerization (~968–970 cm⁻¹). Strong correlations were observed between fatty acid degradation, FTIR indices, and oxidation parameters. Compared to the control, SIL-O inhibited oxidation in a dose-dependent manner. At 300 ppm, it outperformed BHT, demonstrating its potential as a natural antioxidant for enhancing the stability of sunflower oil during high-temperature processing. Full article

The decarbonization of the residential sector is a critical component of the European Green Deal, particularly in transition economies like Poland. This study proposes a comprehensive techno-economic optimization of a deeply retrofitted single-family house aiming for net-zero energy building (NZEB) status. The research specifically focuses on the Polish coastal climate zone, characterized by distinct humidity, wind, and temperature profiles compared to inland regions, which significantly influence the efficiency of air-to-water heat pumps (ASHP). Based on a real-world energy audit, the study simulates the synergy between a deep thermal envelope upgrade and a hybrid system comprising an ASHP, photovoltaics (PV), and battery energy storage (BES). This paper presents a detailed economic analysis of such hybrid systems under the new Polish ‘net-billing’ prosumer mechanism. The study evaluates the impact of electricity tariff structures (flat-rate G11 vs. time-of-use G12w) on the investment’s profitability. By calculating key performance indicators—including the levelized cost of energy (LCOE), net present value (NPV), and self-sufficiency ratio (SSR)—the research assesses various system configurations. The initial evaluation indicates that while deep retrofitting significantly reduces heating demand, integrating battery storage plays a critical role in enhancing economic returns under the net-billing framework. The analysis demonstrates that the optimized hybrid system (9.0 kWp PV + 10 kWh BESS) achieves an average annual self-sufficiency ratio (SSR) of 49.8% and reduces the non-renewable primary energy (EP) indicator to 0.0 kWh/(m²·year). Economically, the investment yields a positive NPV of €3194, an IRR of 5.25%, and a LCOE of €0.184/kWh, which is 34% lower than projected grid prices. Furthermore, switching to a time-of-use tariff (G12w) generates an additional 11% (€139) in annual savings. These quantitative findings provide actionable guidelines for policymakers and investors, confirming the financial viability and environmental benefit (annual reduction of 6.12 MgCO₂) of NZEB standards in coastal areas. Full article

In the last two decades, the utilization of solar energy has been growing rapidly worldwide, mainly due to the increasing adoption of photovoltaic (PV) systems. Since solar energy is one of the most weather-dependent renewable energy sources, an increasing number of meteorological studies have focused on PV potential (PV_pot) and its projected changes under global warming. GCM outputs disseminated through the Coupled Model Intercomparison Project (CMIP) are often applied in energy-related urban climate studies, as they can be downscaled either statistically or dynamically. It is essential to evaluate raw (not bias-corrected) GCM data, which helps to determine the uncertainties in the GCM simulations before downscaling. Despite their coarse resolution, some studies even rely directly on the GCM grid cell time series to represent individual locations. Accordingly, this study evaluates 10 CMIP Phase 6 (CMIP6) GCMs with respect to some atmospheric variables (air temperature, solar radiation, and wind speed, which are the primary drivers of PV_pot) in four lowland grid cells representing four major urban areas in Central and Southeast Europe: Belgrade (Serbia), Budapest (Hungary), Vienna (Austria), and Prague (Czechia). The use of solar energy has increased significantly in most of these regions in recent years; however, it remains less studied than in Western Europe. ERA5 reanalysis is used as the reference dataset. We analyzed the boreal summer (JJA) days of three overlapping 30-year time periods: 1971–2000, 1981–2010, and 1991–2020. Our main findings are as follows: GCMs tend to overestimate solar radiation and underestimate maximum near-surface air temperature relative to ERA5 in all time periods and in all the four urban areas, which leads to a significant overestimation of the number of JJA days with high PV_pot (PV_pot,90). PV_pot,90 is increasing from 1971–2000 to 1991–2020 in the vast majority of GCMs, in all the four regions. EC-Earth3 and its different configurations (EC-Earth3-Veg, EC-Earth3-CC) are considered the most accurate GCMs relative to ERA5. Full article

This study investigates the long-term performance degradation and forecasting of three silicon-based photovoltaic technologies—polycrystalline (pc-Si), monocrystalline (mc-Si), and amorphous silicon (a-Si)—using a seven-year dataset (2015–2021) from a semi-arid climate. Degradation rates are quantified through seasonal-trend decomposition and Arrhenius analysis, revealing distinct mechanisms: pc-Si exhibits the lowest annual degradation (0.36%/year), followed by a-Si (0.57%/year), while mc-Si shows the highest (0.77%/year), with a notable thermal annealing effect partially compensating degradation in a-Si. For forecasting performance ratio, four models are compared, where long short-term memory networks achieve the highest accuracy by capturing nonlinear temporal dependencies, while SARIMA offers robust, interpretable results with lower complexity. Beyond predictive performance, the study establishes links between model behavior and underlying physical processes such as degradation and annealing, and analyzes prediction uncertainty in relation to temperature variability and dust accumulation. These findings highlight trade-offs between accuracy, interpretability, and deployment feasibility, providing a framework for PV performance forecasting under univariate, semi-arid conditions, with future work directed toward multivariate, physics-informed approaches across broader technologies and climates. Full article

In this study, the effect of temperature on thermal-assisted glass delamination was investigated using two treatment conditions differing in the set temperature of the process (100 °C vs. 140 °C). Thermogravimetric Analysis (TGA) confirmed that ethylene-vinyl acetate (EVA) remains thermally stable up to about 280 °C, with degradation onset near 300 °C, ensuring that both treatments operate below decomposition. Differential Scanning Calorimetry (DSC) analysis identified an endothermic transition attributable to the melting of crystalline regions in EVA within the thermal range of 35–65 °C, indicating enhanced polymer chain mobility at elevated temperatures. This endothermic transition corresponds to the melting of polyethylene crystallites within the EVA copolymer and should not be interpreted as a glass transition, since the Tg of EVA is typically located at approximately −30 to −35 °C. Fourier Transform Infrared (FTIR) analysis verified preservation of ester functional groups, confirming the absence of chemical degradation. The morphological analysis performed via Scanning Electron Microscopy (SEM) revealed a clear temperature-dependent morphology of EVA after thermal-assisted delamination. At 140 °C, enhanced polymer softening and viscous flow led to smoother surfaces and more uniform interfacial separation, whereas at 100 °C, limited mobility resulted in heterogeneous, fragmented residues and predominantly cohesive failure. These results highlight that optimizing temperature is key to balancing effective delamination with residue minimization, supporting more sustainable PV recycling. Full article

The dominant pores governing methane adsorption in coal are micropores (pore size < 2 nm). Their spatial heterogeneity can be quantitatively characterized using multifractal theory; however, the evolution patterns and mechanisms of microporous structures across different coalification degrees remain unclear. This research selected a series of coal samples from different ranks and identified the coalification degree using the maximum vitrinite reflectance (Rₒ_,max). By comprehensively employing low-temperature CO₂ adsorption experiments and multifractal analysis, the evolution patterns of the microporous structures and their multifractal spectral parameters were systematically revealed, and the underlying control mechanisms were explored. Results indicate that micropore volume (PV) and specific surface area (SSA) first exhibit a decrease and then increase as Rₒ_,max increases, with the trough occurring during the second coalification jump at Rₒ_,max = 1.2–1.4%. The pore sizes exhibit bimodal distributions, with the primary peak occurring in the range of 0.45–0.65 nm and the secondary peak occurring in the range of 0.8–0.9 nm. All microporous structures possess pronounced multifractal characteristics. The generalized dimension spectrum width (ΔD) and singularity spectrum width (Δα) exhibit an increasing–decreasing–increasing trend with Rₒ_,max, whereas the Hurst exponent (H) follows an inverted parabolic curve, first increases then decreases. This contrasts with the trends in PV and SSA, indicating that the evolution of pore-space heterogeneity and connectivity is independent of and lags the changes in micropore quantity. These patterns are governed by a structural phase transition within the coal macromolecular network. Marked by the second coalification jump, the microporous system shifts from a flexible degradation–polycondensation paradigm to a rigid ordering–construction paradigm. This transition drives the asynchronous, synergistic evolutions of pore quantity, spatial heterogeneity (ΔD and Δα), and topological connectivity (H). This research provides a theoretical basis for quantitatively evaluating pore heterogeneity in coal reservoirs. Full article

Utility-scale photovoltaic (PV) plants rely on supervisory control and data acquisition (SCADA) streams for performance verification, yet high-frequency measurements are routinely affected by sensor dropouts, intermittency, and operating-state transitions that bias regression-based capacity estimates. This study evaluates a reproducible SCADA processing workflow for capacity-style reporting and a complementary soiling–clean temperature prediction model using data from a documented October 2022 test window (5 s SCADA aggregated to 1 min). The following three filtering approaches are compared: (i) naïve thresholds (Baseline A), (ii) deterministic stability screening using ramp-rate and rolling-variability constraints (Baseline B), and (iii) an optional residual-based outlier trimming step (Method C). Capacity is estimated via a multivariate regression evaluated on a fixed-size reporting-condition subset (RC197) with day-coverage constraints. All methods achieved high fit quality on RC197 ($R^2\approx 0.99$), with Baseline B improving error and uncertainty over Baseline A (RMSE 2.05 vs. 2.18 MW; $U_{95}$ 0.97% vs. 1.03%) while preserving day coverage; Method C yielded the lowest in-sample RMSE (1.89 MW) but reduced day coverage. For temperature prediction, a baseline-plus-residual learning formulation substantially improved leave-one-day-out performance, reducing MAE/RMSE from 2.99/3.76 °C to 1.43/1.80 °C and increasing $R^2$ from 0.60 to 0.91. The results highlight trade-offs between fit tightness and representativeness in capacity-style filtering and demonstrate residual learning is an effective approach for SCADA-based thermal characterization. Full article

In this study, a thermally crosslinking clay-free weak gel water-based drilling fluid based on salt-responsive polymers and crosslinking agents was investigated as a promising and feasible strategy. Firstly, a salt-tolerant polymer was synthesized using N,N-dimethylacrylamide (DMAA), [2-(methacryloyloxy)ethyl]dimethyl-(3-sulfonopropyl)ammonium hydroxide (DMAPS), and acrylamide (AM). BPEI_10,000 was selected as the thermal crosslinking agent. The optimal crosslinking was achieved at 180 °C and 36% NaCl, with RMFL at 2.0% and BPEI_10,000 at 0.1%. Performance evaluation demonstrated that the crosslinking between RMFL and BPEI_10,000 could enhance the AV, PV, and YP of the RMFL(BPEI_10,000)/CF-WBDFs after aging at 180 °C for 16 h and reduce FL_API. The RMFL(BPEI_10,000)/CF-WBDFs exhibited appropriate shear-thinning behavior, viscoelasticity, thixotropy, and recoverable viscosity under high-temperature, high-salinity, and high-pressure conditions. Mechanism analysis revealed that RMFL and BPEI_10,000 could form a predominantly negatively charged, three-dimensional crosslinking weak gel at high temperatures. The crosslinking weak gel could form dense filter cakes, improving rheological properties and reducing filtration loss of CFWBDFs in high-temperature, high-salinity environments. This paper proposed a novel method to address the technical challenge of rheological performance failure of CFWBDFs, offering valuable insights for subsequent investigations. Full article

This study integrates phase change material (PCM) with semi-transparent photovoltaic (PV) glazing to develop a composite window providing thermal buffering and PV temperature regulation in summer. A PCM-PV double glazing window (PCM-PV-DGW) using paraffin PCM and CdTe semi-transparent PV glass was fabricated and evaluated through outdoor hot-box experiments and transient modeling in Qingdao, China. Four window types—DGW, PCM-DGW, PV-DGW, and PCM-PV-DGW—were tested under identical boundary conditions. The coupled system showed improved photothermal performance, achieving a daily average Solar Heat Gain Coefficient (SHGC) of 0.105, compared with 0.180 for PV-DGW without PCM filling, together with a temperature attenuation factor of 0.904 and a 35 min peak temperature delay. A two-dimensional transient heat transfer model incorporating radiative transfer through semi-transparent layers and an enthalpy-based phase change method was established and validated against measured inner-surface temperatures, showing good agreement (RMSE 1.54–1.80 °C). Parametric and sensitivity analyses indicate that PCM phase transition temperature is the dominant parameter (suggested 28–32 °C), while ~12 mm PCM thickness and 50% PV coverage offer a practical balance for the Qingdao summer scenario. The results provide preliminary guidance for PCM–PV window design under the investigated summer conditions. Full article