Abstract This study performs a techno-economic assessment of a photovoltaic (PV)–hydrogen system across nine representative sites in Vietnam. A MATLAB/Simulink-based framework was developed to integrate solar radiation data, PV generation models, and electrolyzer performance for evaluating energy yield and the Levelized Cost of Hydrogen (LCOH). System performance was analyzed under varying performance ratios (PR = 0.75–0.85). For a 1 MW PV system, the Net Present Value (NPV) reached approximately USD 1.01 million, with annual operation and maintenance costs of USD 1,587. Southern provinces such as Tay Ninh and Binh Thuan achieved the lowest LCOH (2.81–2.85 USD/kg at PR = 0.85) due to high annual irradiance (1,868–2,002 kWh/m²/year), while northern sites like Hanoi and Cao Bang exhibited higher costs (3.76–3.88 USD/kg). A strong inverse correlation (R² = 0.99) between PR and LCOH demonstrates that small gains in PV efficiency substantially reduce hydrogen production costs. The results identify Vietnam’s southern and Central Highlands as priority regions for green hydrogen deployment. This study contributes a reproducible MATLAB/Simulink framework that supports pre-feasibility evaluation and data-driven planning for renewable-based hydrogen production in emerging markets.

Abstract Biomass waste contains considerable amounts of ash-forming elements that adversely affect boiler operation and consequently reduce economic efficiency. Moreover, such waste may contain heavy metals, requiring thorough assessment prior to use. In this study, the mode of association of ash-forming matter in various categories of biomass waste was determined using a chemical fractionation method. The results obtained were subsequently verified through experimental testing in a bubbling fluidized bed pilot plant. Laboratory results showed that K and Cl exhibit similar modes of association across the different biomass types examined, whereas notable differences were identified for major ash-forming elements such as Na, Ca, S, and P. Based on the differing modes of occurrence of these elements, wheat straw presented the highest risk of hydroxide or carbonate release into the flue-gas phase during combustion, followed by eucalyptus wood. This indicates a greater risk of fouling and corrosion compared with the other biomasses studied. Analyses of ash samples from fluidized-bed combustion experiments were consistent with the chemical fractionation results, confirming the reliability of the method as a useful tool for predicting the ash-related behavior of new fuels. In the final part of the study, heavy metals in biomass combustion tests were assessed. A significant content of certain commercially valuable metals was found in fly ash from different biomass wastes, indicating the potential for recovery and contributing to the promotion of a circular economy.

Abstract This study aimed to evaluate the thermal performance of solar water heater system with the trickle flow method using a combination of PMMA (polymethyl-methacrylate) as cover and galvalume as an absorber plate. The flat plate type collector was designed with a closed flow system, where water trickled directly on the surface of the absorber and was recirculated through the storage tank. The experiment was conducted in tropical area in Indonesia with four variations of collector orientation, namely south, north, east, and east tilted at 30°. Furthermore, the parameters measured included solar radiation intensity, wind speed, and water temperature at the inlet, outlet, and in the tank. Collector efficiency was also calculated based on the useful thermal energy absorbed by the plate, while system efficiency was reviewed from the heat energy transferred to the water. The results showed that collector direction had a significant effect on system performance. Collector with east orientation produced the highest total efficiency of 32.7%, with a maximum water temperature of 45.8°C. These results indicate that east orientation is more effective for trickle flow-based solar water heater system in tropical areas. Additionally, PMMA–galvalume material shows potential as a lightweight and efficient alternative for household applications.

Abstract Accurate prediction of the Remaining Useful Life (RUL) of lithium–ion batteries is a critical enabler for the safety, reliability, and energy efficiency of modern electric vehicles (EVs). However, the nonlinear, multi-scale, and condition-dependent nature of battery degradation presents formidable challenges for conventional prognostic models. This work proposes a high-performance hybrid prognostic architecture that synergistically integrates (i) multi-resolution feature extraction via the Discrete Wavelet Transform (DWT), (ii) long-range temporal dependency modeling through an encoder–decoder Transformer network with multi-head self-attention, and (iii) nonlinear residual correction using XGBoost. To ensure globally optimal hyperparameter configuration and robust convergence, the full pipeline is optimized using the Chaotic Billiards Optimizer (CBO), complemented by local refinement with the Adam optimizer. Experimental evaluations conducted on benchmark battery aging datasets from the National Aeronautics and Space Administration (NASA) and the Center for Advanced Life Cycle Engineering (CALCE) demonstrate that the proposed framework substantially outperforms state-of-the-art deep learning and ensemble baselines, including recurrent neural networks, convolutionalrecurrent hybrids, transformer-based models, and gradient-boosted decision trees. The proposed approach achieves performance improvements exceeding 15% in both mean absolute error and root mean square error, with an average prediction accuracy characterized by a mean absolute error below 0.020, a root mean square error below 0.032, and a coefficient of determination exceeding 0.98. Ablation analyses further confirm the complementary contributions of multi-scale signal decomposition, attention-based temporal modeling, residual learning, and chaotic meta-heuristic optimization. Despite its hybrid structure, the framework remains computationally efficient, converging within a limited number of training epochs and enabling real-time inference (approximately 0.038 seconds per prediction window) with a lightweight model size of 2.14 million parameters, highlighting its suitability for embedded battery management systems. Overall, the proposed framework establishes a robust and interpretable foundation for next-generation battery prognostics, enabling intelligent predictive maintenance, enhanced safety, and energy-aware management in electric mobility systems.

Abstract This study presents a detailed computational fluid dynamics analysis of the needle -nozzle interaction in a Pelton turbine and its influence on jet formation and energy transfer efficiency. Using operational data from the Cañon del Pato hydroelectric plant in Peru, the research addresses key aspects of impulse turbine fluid dynamics, including jet velocity profiles, and torque generation in buckets. High resolution meshing and advanced turbulence modelling are employed to accurately the interaction between water jets and turbine buckets. The numerical results show strong agreement with measured data, achieving a torque deviation of 7.46%. The analysis also demonstrates that needle and nozzle alignment play a critical role in jet quality, producing a non-uniform velocity profile in which boundary layer effects reduce the jet-core velocity by approximately 12% near the bucket impact region. This work contributes to advancing computational fluid dynamic methodologies for hydropower applications by integrating experimental validation in order to optimise turbine design in future works. The findings have broader implications for improving efficiency and reliability in aging Pelton turbines globally, addressing challenges such as cavitation, erosion, and fatigue. By bridging computational and experimental approaches, this study offers a robust framework for analysing fluid dynamics and supports future developments in hydropower technology.

Abstract This study explores a process to prepare porous carbon - based electrode from hemp (Cannabis Sativa L.), a biomass source, without using binder. Hemp-bast fibers were obtained from three retting processes: water, bacteria, or chemicals. These retted fibers were processed into uniform composite fiberboard with a thickness of less than 0.5 mm, and then carbonized and activated to obtain the carbon boards with surface area greater than 500 m2/g. This bio-based carbon board is used directly as the carbon host material without using binder for lithium-sulfur (Li-S) batteries and is called binder-free bio-based carbon electrode. The certain flexibility with minimum mechanical strength enables the binder-free bio-based carbon electrode to be handled without breaking during electrode processing. Li-S batteries fabricated from the binder-free bio-based carbon electrode show a stable cycling performance with a specific capacity of 833 mAh g-1 after 80 cycles. Compared with the electrodes made from carbon powders with polyvinylidene fluoride binder, the binder-free bio-based carbon electrode showed a 60% increase in capacitance. This binder-free bio-based carbon electrode is made solely from biomass without using polymer binders. It not only enhances the performance of the Li-S batteries but also reduces their environmental impact due to the use of bio-based carbon with no polymer binder.

Abstract Tilted solar stills are a promising option for sustainable water desalination, but their productivity is limited by low evaporation and condensation efficiencies. This study presents a dual-functional strategy for improving the performance of a modified tilted solar still using a mesh cotton wick to enhance water distribution and continuous surface wetting and a thermoelectric cooling tube to lower the condensing surface temperature and increase the condensation rate, thus increasing the overall system productivity. Tests were conducted in Yekaterinburg, Russia, under identical environmental conditions for both the modified tilted solar still and the conventional still. The results demonstrated a significant improvement in the modified system, with a 40% increase in freshwater production. The maximum daily production was 1.290 liters/m² from the cooling channel and 0.615 liters/m² from the glass cover, compared to 0.805 and 0.555 liters/m² for the conventional system. Thermal efficiency reached 13% versus 9% for the conventional system. Economic analysis confirmed the system's viability, with the cost of distilled water reduced to $0.0296/liter, 13.7% lower than the conventional system, while maintaining competitive costs at varying utility rates. These results demonstrate the effectiveness of the wick-thermoelectric cooling integrated approach in providing a low-cost, energy-efficient, scalable solar desalination solution suitable for arid and energy-limited regions.

Abstract The Chinese government has set long-term carbon neutrality and variable renewable energy development goals for the power sector. The clean energy transition requires a co-evolution of innovation, investment, and deployment strategies for emerging energy storage technologies. Hydrogen could play a pivotal role as long-duration energy storage in future low-carbon electricity systems, balancing inflexible or intermittent supply with demand. Cost projections are important for understanding this role, but data are scarce and uncertain. Here, we provide a techno-economic evaluation and uncertainty analysis of hydrogen as a long-duration energy storage, using a learning rate approach to estimate the long-term cost. We find that the levelized cost of hydrogen energy storage based on a modeled 25 MW system with Proton Exchange Membranes technology is 3.981 yuan/kWh in 2025. Capital expenditure and equipment replacement costs are the top two contributors, accounting for 32.7% and 33.6%, respectively. Technological advancements and deployment scaling of electrolyzers and fuel cells are projected to drive the levelized cost of hydrogen energy storage down to 1.848 yuan/kWh by 2050 under an 18% learning rate trajectory. Although projections indicate that hydrogen energy storage remains less cost-competitive compared to mature technologies through 2050, like electrochemical storage and pumped hydro storage, its unique value proposition for long-duration storage and grid resilience sustains development potential in high variable renewable energy penetration scenarios.

Abstract Global offshore wind faces rising threats from frequent extreme typhoons and prolonged low wind spells that erode power system resilience. To counter these challenges, we present an offshore wind hydrogen hybrid that integrates alkaline water electrolyzers and proton exchange membrane electrolyzers, hydrogen tanks and fuel cells, and we develop a mixed integer linear programming life cycle cost model driven by hourly wind data for a continuous typhoon to calm extreme scenario. Compared with the traditional alkaline water electrolyzers plus battery scenario, the proposed hybrid electrolyzers plus hydrogen storage cuts extreme weather loss of load probability from 40.4% to 17.5%, a reduction of 60%, halves annual expected energy not served to 943 MWh, reduces levelized cost of hydrogen by 22% from 4.64$ to 3.59$/kg, adds 27 million dollars in life cycle revenue and shortens payback by 3.6 years, delivering an economical, resilient and readily replicable hydrogen solution for high penetration offshore wind grids under extreme weather. These findings underscore the system's potential to improve both economic profitability and risk resistance, offering a promising solution for enhancing the resilience and economic performance of offshore wind-hydrogen systems.

Abstract Alluvial soils contain natural biological, ionic, and thermal gradients that offer an underutilized opportunity for sustainable energy harvesting. By integrating sediment microbial fuel cells, salinity-gradient energy, and thermoelectric modules, riverbeds can be transformed into distributed micro-scale power sources capable of supporting autonomous sensors and off-grid monitoring networks. This work proposes a unified conceptual framework that links microbial electron transfer, ion-selective transport, and low-grade thermal conversion within a single sediment-embedded architecture. Recent advances in electrogenic biofilms, nanofluidic membranes, and flexible thermoelectric materials reveal the feasibility of hybrid systems that operate continuously with minimal ecological disturbance. While challenges such as low power density, environmental variability, fouling, and material degradation remain significant, emerging strategies—ranging from advanced electrode materials to AI-aided control—are expanding the performance limits of these technologies. The envisioned hybrid microgrids hold promise for climate-resilient energy access in rural, riverine, and estuarine settings, offering a pathway toward decentralized, low-carbon power infrastructures rooted in the natural gradients of alluvial soils.