Search Results (39,593)

The intermittency of renewable-based power is a major barrier for long-term supply of clean energy, which necessitates the development of reliable solutions for clean energy storage and transition towards a carbon-neutral economy. Although hydrogen has emerged as a promising clean energy carrier to address this, its high compressibility requires safe, efficient and practical storage technologies for widespread deployment. Surface storage technologies for hydrogen have garnered attention due to their mobile and stationary applications, paving the way for a future hydrogen-based economy. This review provides a comprehensive review of surface hydrogen storage technologies, covering metal hydrides, metal-organic frameworks (MOFs), liquid organic hydrogen carriers (LOHCs), glass microspheres, capillary arrays, etc. Where previous reviews mostly address the chemistry behind these storage technologies, this study highlights practical integration and techno-economic assessment. Comparative analysis reveals that while LOHC and hydrides dominate in Technology Readiness Level, MOFs and carbohydrate-based systems offer high gravimetric potential, though they are currently quite costly. Other challenges like thermal management and large-scale regeneration remain critical for practical deployment. Moreover, recent advancements in Artificial Intelligence and Machine Learning offer unique insights, demonstrating their growing role in material screening, performance prediction, and the optimization of storage system designs. This review outlines the key challenges and research pathways required to support future deployment. Full article

In Canada and elsewhere, there is an ascendancy of a whole-of-society approach that centres shared responsibility for wildfire management. This article engages the debates on the rise of shared responsibility for wildfire management to argue that this context demands a renewed research focus on understanding how the public allocates responsibility for wildfire management. We illustrate this argument through a case study of public engagement with wildfire risk and shared responsibility in Whistler, British Columbia, western Canada. Our case study draws on evidence from a quantitative survey administered to 1311 participants in the spring and summer of 2024. The study reveals a near-universal concern about wildfires among the participants and a high level of risk perception. This is consistent with community climate and wildfire reports and plans. This level of concern is driving a high level of mitigation activity completion among participants, even though the level of preparedness is mixed. Our study found a marked pattern of responsibility allocation across the phases of wildfire management. Participants put the municipal government at the forefront of mitigation, preparedness, and response. The provincial government was ranked as most responsible for recovery. Homeowner responsibility declined as one moves from mitigation and preparedness through to response and recovery. Private actors, such as insurance, have greater responsibility in the recovery phase. Multivariate General Linear Models (GLMs) show that how respondents allocate responsibility for various aspects of wildfire management is influenced by home ownership, prior wildfire experience, perceived preparedness, and commitment to bearing the costs of FireSmart assessment. We conclude that a sustained research commitment is needed to further elucidate the dynamics of public expectations and attitudes in the context of shared responsibility for wildfire management. Full article

The integration of renewable energy sources (RESs) into electric power systems introduces new challenges for system operation, reliability, and emergency management. Causal analysis, as a powerful data analysis tool, can reveal the interactions and influences between components in the power system, thus supporting the design, operation and optimization of the system. This review examines causal analysis methods applied to electric power systems with high-RES penetration, highlighting their effectiveness in identifying interactions among system components, detecting potential risks, and supporting operational decision-making. Key system properties, including safety, efficiency, flexibility, survivability, and reliability, are discussed in the context of high renewable integration. The review also analyzes lessons from systemic accidents and explores strategies to mitigate risks associated with excessive RES penetration. Finally, directions for future research are outlined, emphasizing real-time monitoring, advanced causal modeling, and methods to enhance the resilience of modern power systems. Full article

Accurate modelling of hydrodynamic damping remains a critical challenge in the dynamic analysis of floating offshore wind turbines (FOWTs), particularly when motion coupling between degrees of freedom is significant. This study addresses the limitations of conventional single-degree-of-freedom damping identification techniques by proposing a novel multi-degree-of-freedom identification procedure capable of including off-diagonal coupling terms in the estimation of both linear and quadratic damping matrices. The aim is to assess whether viscous cross-coupling effects can be explicitly identified within a multi-degree-of-freedom lumped-parameter framework and to evaluate their impact on motion prediction. The methodology employs a hybrid optimisation approach, combining a genetic algorithm with a gradient-based solver. The procedure is applied to a taut-leg moored semi-submersible floating platform, focusing on surge–pitch coupling and using both experimental wave-basin data and high-fidelity CFD free-decay simulations. The results show that diagonal damping coefficients can be robustly identified even under coupled free-decay conditions, whereas the inclusion of off-diagonal viscous terms does not significantly improve the reconstruction of free-decay responses. Moreover, the simultaneous calibration of the added mass matrix enabled by the proposed procedure further improves agreement with the reference data. Although the findings highlight limited identifiability of viscous cross-coupling effects from free-decay tests, this paper provides a flexible tool for more advanced damping identification in operational and extreme conditions. Full article

Rural households in cold regions still rely heavily on coal for cooking and domestic hot water, while single renewable energy sources suffer from intermittency and limited system-level assessment. This study proposes a solar–biogas complementary energy supply system integrating evacuated-tube solar collectors, an above-ground anaerobic digester, thermal storage, and biogas utilization for rural residential applications in Minqin, Northwest China. A dynamic system-wide model was developed by coupling TRNSYS with nonlinear representations of anaerobic fermentation and biogas boilers, enabling hour-by-hour simulation of energy production, conversion, storage, and consumption. Field measurements were used for validation, and the root mean square deviation between simulated and measured temperatures and gas production remained below 10%. During the heating season, the solar subsystem supplied 10% of the digester heating demand and 90% of the domestic hot-water load, while the biogas subsystem contributed 9.29% and 90.71%, respectively. The system delivered 4728.96 MJ of heat against a seasonal demand of 4636.22 MJ, fully meeting user requirements. A comprehensive 3E (energy–environment–economic) assessment shows that, compared with traditional rural energy supply modes, the proposed system reduces CO₂ and NOx emissions by 65.85% and 98.13%, respectively, and demonstrates favorable economics with a benefit–cost ratio of 2.41 and a discounted payback period of 3.27 years. The proposed modeling and evaluation framework provides a replicable solution for clean energy substitution and circular waste utilization in rural areas. Full article

In this study, a techno-economic and carbon footprint (GHG, CO₂-equivalent) analysis was conducted on two alternative biofuels, green diesel and bio-jet fuel, produced from renewable lipids. The focus of the work is the comparison of various lipid feedstocks, including waste cooking oil, and four types of vegetable oils: cardoon, soybean, palm, and sunflower. Process optimization and design were performed to minimize production costs by using the process simulation software Aspen Plus^®. Green diesel and bio-jet fuel were obtained via hydrodeoxygenation and hydroisomerization/hydrocracking, respectively. Sensitivity analyses confirmed consistent results across the tested vegetable oils. Hydrodeoxygenation achieved triglyceride molar conversions exceeding 97%, with overall mass yields into the diesel fraction surpassing 79%. Conversely, hydroisomerization/hydrocracking of green diesel resulted in over 90% conversion of n-paraffins and more than 50% overall mass yield. The economic analysis showed that the primary cost factor influencing the payback selling price of the biofuels is the price of the lipid feedstocks. Biofuels are economically viable only when lipid prices are below 1000 €/ton and hydrogen prices are below 3000 €/ton. An important aspect is also represented by the combined-cycle energy recovery system, which strongly affects the overall capital cost and increases internal power generation efficiency. The carbon footprint calculated over a cradle-to-grave boundary showed shows net GHG reductions versus the fossil reference fuels for all scenarios. Net avoided emissions range from 1.74 to 3.63 kgCO₂-eq/kg green diesel and from 0.80 to 3.70 kgCO₂-eq/kg bio-jet fuel across the investigated feedstocks, approximately 40–84% and 20–95% of the respective savings relative to the fossil reference fuels under the stated background and logistics assumptions. Results are expressed per kg of produced fuel as a functional unit, using literature-derived upstream emission factors for oil supply and background inputs (hydrogen, Italian grid electricity and transport). For the bio-jet configuration, co-product burdens were partitioned by mass; the Discussion section highlights the sensitivity of the GD vs. BJF comparison to co-product handling and allocation choices. In this context, the choice of feedstock is essential in establishing the resulting GHG intensity of the two biofuels. From both economic and climate change perspectives, waste cooking oil emerges as the most promising option, particularly given its classification as waste-derived feedstock in the system boundary, unlike the virgin oil sources. Full article

This paper reviews artificial intelligence (AI) applications in the design and optimization of proton exchange membrane (PEM) materials for hydrogen fuel cells. Clean energy conversion is a substantial benefit of PEM fuel cells, which conventional membrane development struggles with due to time-consuming trial-and-error methods, which are not adequate in capturing the different interdependencies of the membrane structure, and environmental variables. The review establishes foundational design principles of PEMs and outlines their challenges and computational methodologies are constructed to address them. Various advanced AI methods have been highlighted which include graph neural networks, multitask frameworks, and physics-informed models that facilitate rapid prediction of polymer properties. Optimization methods have been reported with 10–30% performance improvements, for instance, NSGA-II frameworks achieving 13–27% gains in power density. Experimental requirements are reduced by 40–60%, as seen with Bayesian optimization, identifying optimal designs within as few as 40 iterations. Current challenges include data availability, generalizability, and scalability, which are closely assessed in this review. Full article

Cited2 is a transcriptional regulator essential for embryonic development and cellular homeostasis. Studies in vertebrate models highlight its critical roles in heart, placental, neural tube, and hematopoietic development. In humans, CITED2 variants are associated with congenital heart disease. Functionally, Cited2 interacts with the transcriptional co-regulators p300/CBP and modulates the activity of multiple transcription factors. In embryonic stem cells (ESC), Cited2 supports pluripotency, self-renewal, and differentiation potential. Here, we performed comparative transcriptomic analysis after acute Cited2 depletion in mouse ESC to define its role in maintaining self-renewal, lineage competence, and cell survival. Loss of Cited2 rapidly destabilized the pluripotency network and induced aberrant activation of developmental gene programs. Nodal/Activin pathway targets, including key regulators of mesoderm, cardiac, and neural development, were markedly downregulated, consistent with Cited2-null embryonic phenotypes. Cited2 depletion also altered the expression of genes involved in DNA damage response, immune signaling, and apoptosis, correlating with the increased γH2AX accumulation and decreased cell viability at least in part involving p53. Comparison with p300-, CBP-, and Cited2-depletion datasets revealed only partial overlap between affected gene sets. These results position Cited2 as a core regulator preserving ESC identity, genomic stability, and proper lineage engagement during early differentiation. Full article

The surface chemistry of graphene oxide (GO) and its nanocomposite with 12-tungstophosphoric acid (WPA) (up to 50 wt.% WPA) was studied both in aqueous suspension and in the solid state. The titrations revealed the formation of the composite already in the suspension and that WPA influences GO’s functionalities and their conversion (-COOR to -COOH). There is a loading of WPA (>20 wt.%) beyond which the WPA dominates the chemical character of the GO/WPA suspension. Part of the nanocomposite titrated with NaOH was processed into a powdered form and compared with an annealed sample (450 °C, Ar atmosphere). An FTIR analysis revealed the removal of functional groups in both titrated and thermally annealed samples. Annealing did not induce structural changes in WPA within the composite, whereas titration led to noticeable modifications of WPA-related bands. The TPD measurements revealed that the extent of functional group removal by titration was lower compared to annealing. The zeta-potential measurements demonstrated improved stability of the nanocomposite as the WPA content increased. Methylene blue adsorption experiments showed that the presence of oxygen functional groups and WPA on the GO enhances adsorption performance compared to pristine GO. Titration improved the adsorption capacity of the composites, whereas annealing completely suppressed their adsorption properties. Full article

Time delays are intrinsic to energy systems, arising from transport phenomena, communication latency, and control dynamics; however, their accurate modeling remains challenging, particularly under variable operating conditions. The most common delays are constant over time and are easy to model and simulate. However, simulation tools of time-varying delay systems rely on signal-delay representations that fail to enforce conservation laws, leading to unphysical results in applications involving mass or energy transport. This study develops a physically consistent mathematical framework for time-varying transfer delays that explicitly couples kinematic evolution with conservation principles through a dynamic gain term. A systematic classification is introduced, distinguishing between signal delays (information transfer) and transfer delays (physical transport), further categorized by the source of variability in time delay into Types R (variable extraction), W (variable supply), and M (variable medium). The proposed formulation was implemented in Simulink through newly developed functional blocks supporting all delay variants and validated against representative heat transport scenarios. Comparative analysis demonstrates that standard signal-delay models violate energy conservation by generating spurious energy, whereas the proposed transfer-delay formulation preserves physical consistency under variable-flow conditions. The framework provides a rigorous foundation for accurate modeling of district heating networks, renewable energy integration with power-to-gas systems, thermal storage, and smart grid communications, supporting the development of reliable control strategies essential for the ongoing energy transition. Full article

As a critical component of sustainable energy systems, enhancing the efficiency of Vertical Axis Wind Turbine (VAWT) is paramount. This study addresses the key Challenges of poor startup performance and low power output in VAWTs by investigating the aerodynamic performance of an optimized double Darrieus vertical axis wind turbine (DD-VAWT) via design of experiment (DOE) and response surface methodology (RSM). The numerical method was validated with experimental data and reported numerical work. Response surface statistical analysis was conducted to evaluate the effect of the designed variables on the objective function with 29 cases. The optimal parameters of four designed variables were determined after linear regression analysis to obtain the optimal DD-VAWT. The aerodynamic performance of the optimal DD-VAWT was numerically studied and compared with that of a one-blade VAWT and a pre-optimized DD-VAWT. The velocity contours of different azimuth angles reveal that the optimal blades significantly minimized flow disturbances at the interface of the primary and auxiliary blades, further enhancing their performance. The results demonstrate that the output power of the optimized double-layer blades increased by approximately 28.5% compared to the original ones. This study provides new insights for improving the aerodynamic performance of VAWT and has much potential beneficial to the application of the DD-VAWT technique, supporting the broader transition towards a sustainable energy future. Full article

Low-voltage ride-through (LVRT) capability is essential for grid-connected photovoltaic (PV) systems, especially as rising renewable integration challenges grid stability during voltage disturbances. Existing LVRT methods often target isolated control functions, leading to limited system resilience. This paper presents a unified control strategy integrating DC-link voltage regulation, reactive power injection, and overvoltage mitigation using a coordinated fuzzy logic framework. The proposed architecture employs a cascaded control structure comprising an outer voltage loop and an inner current loop with feed-forward decoupling, synchronized via a Synchronous Reference Frame Phase-Locked Loop (SRF-PLL). At its core is a dual-input, single-output Fuzzy Logic Controller (FLC), featuring optimized membership functions and dynamic rule-based logic to manage multiple control objectives during grid faults. The proposed FLC-based unified LVRT controller for grid-tied PV system was implemented and validated for both symmetrical and asymmetrical fault conditions in MATLAB/Simulink 2023b platform. The proposed FLC-based LVRT controller achieves voltage sag compensation of 97.02% and 98.4% for symmetrical and asymmetrical faults, respectively, outperforming conventional PI control, which achieves 94.02% and 96.5%. The system maintains a stable DC-link voltage of 800 V and delivers up to 78% reactive power support during faults. Fault detection and recovery are completed within 200 ms, complying with Bangladesh grid code requirements. This integrated fuzzy logic approach offers a significant advancement for enhancing grid stability in high-renewable environments and supports reliable renewable utilization, and more sustainable grid operation in developing regions. Full article

Synthetic natural gas (SNG) production from biomass residues represents a promising strategy to reduce greenhouse gas emissions and enhance energy security in regions with abundant agricultural waste. This study evaluates the thermodynamic performance of SNG synthesis from rice husk (RH) and empty fruit bunches (EFB) bio-oils, major residues in the department of Bolívar, Colombia. The process was simulated in Aspen Plus^®, integrating syngas data and methanation under equilibrium conditions at 320 °C and 30 bar, complemented by hydrogen injection via alkaline electrolysis to maintain an H₂/CO ratio above 3. Energy and exergy analyses were performed to quantify efficiencies and irreversibilities. Results indicate carbon conversion rates of 48.3% for EFB and 47.4% for RH, producing SNG with 96% CH₄ suitable for grid injection. Energy efficiencies reached 71.9% and 71.0%, while exergy efficiencies were 87.2% and 82.9%, respectively, aligning with or surpassing literature benchmarks. The main irreversibilities occurred in methanation and CO₂ removal, highlighting thermal integration and gas recycling as key improvement strategies. These findings demonstrate the potential of leveraging local biomass for clean energy production and support the development of Power-to-Gas systems in Colombia. Full article

This study develops an interpretable machine learning framework for type 1 motor insurance renewal classification using 70,290 real-world Thai policies, providing essential insights for pricing, customer retention, and operational decision making. The dataset was partitioned into a 70% training set, utilizing 5-fold cross-validation for hyperparameter tuning and model selection, and a 30% hold-out testing set to evaluate final model performance. Five machine learning models including Binary Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Random Forests, and XGB are systematically evaluated across multiple curated feature sets generated through statistical filtering, stepwise selection, and permutation-based importance. Non-parametric tests are employed to compare model performance across scenarios. Experimental results show that a reduced four-feature Random Forest model (car age, net premium, sum insured, and car group) achieves the highest predictive performance (AUC = 99.62%; F1 = 98.15%), outperforming full-feature models while maintaining superior computational efficiency. Consequently, H2OAutoML serves as an external validation tool to verify that this manually curated, interpretable pipeline remains highly competitive with fully automated systems. Integrating a SHAP-based explainability layer quantifies predictor influence, ensuring transparency and regulatory alignment. Prioritizing feature parsimony, this study provides integrable insights for dynamic pricing and risk-adjusted retention, enhancing decision support within evolving motor insurance markets through transparent systems. Full article

This study presents the use of a Battery Energy Storage System (BESS) and a thermal power plant to enhance Primary Frequency Regulation (PFR) in a power system. This integration seeks to mitigate operational challenges, such as the reduction in system inertia and frequency regulation, which are heightened when increasing renewable energy use in power grids with high hydroelectric generation. The proposed solution enables thermal generators to operate at optimal capacity, while the BESS provides a rapid frequency response, thereby enhancing operational efficiency and compliance with national standards. The process was structured in five stages: criteria definition, analysis, design, models, and evaluation. A comprehensive methodological approach was adopted, including dynamic system modeling and BESS sizing based on regulatory parameters. The method was tested with real data from a thermal plant under the conditions of the Colombian electricity market. The simulation results highlight the effectiveness of the proposed BESS, with a response time of approximately 0.6 s and regulation maintenance for over 30 s, reducing mechanical stress and preventing frequency overshoot. The control strategy was designed to maintain the energy neutrality of the BESS, thereby stabilizing its state of charge over the operational horizon. The results show that the BESS targets high-frequency transients and the generator focuses on low-frequency adjustments, managed by an Energy Management System (EMS) with a unified control approach. Full article