1,720,969 research outputs found
Modeling and Statistical Analysis for Low-Carbon Transition Pathways - Hydrogen Storage Optimization and Data-Driven Methane Leak Mitigation Strategies
Full text linkL'abstract è presente nell'allegato / the abstract is in the attachmen
Simulation and validation of a Power-to-Power system with hydrogen storage in microporous adsorbent materials
No full textTechno-Economic Assessment of Deep Biogas Cleaning For Solid Oxide Fuel Cell Application
Full text linkThe use of biogas in fuel cells could be an attractive alternative to combustion technologies. However, biogas contains sulfur-based compounds that can be detrimental to fuel cells. A techno-economic analysis was conducted to assess the impact of the H2S cleaning system on the plant investment and operational costs
Modeling hydrogen storage at room temperature: Adsorbent materials for boosting pressure reduction in compressed H2 tanks
Full text linkEnergy storage systems are required for an efficient integration of renewables into the grid to achieve a net zero energy system. Hydrogen compressed at 700 bar, is one of the key energy storage technologies. This study evaluates the effectiveness of solid-state hydrogen storage, particularly physisorption in porous materials, to enhance storage performance and safety at room temperature by reducing the operating tank pressure. We model dynamically the entire storage system, comparing adsorbent materials to traditional compression in terms of maximum tank pressure and round-trip storage efficiency. Different energy system applications with varied cycle frequencies and discharge durations were examined. Results indicate that porous material-based systems exhibit higher efficiency for long-duration energy storage services than the compressed hydrogen. Notably, bulk density plays a pivotal role in storage performance. For instance, IRMOF-1 with a bulk density of 500 kg/m3, shows a 70 % pressure reduction compared to compressed hydrogen systems. In contrast, when its bulk density is reduced to 130 kg/m3, the maximum tank pressure is even 30 % higher than the compressed tank. We emphasize the need for comprehensive material characterization, highlighting the significance of parameters like bulk density for determining the most performing hydrogen adsorbent material in terms of maximum tank pressure and efficiency. As general outcome, the best performing material depends on the specific target or system requirements, such as pressure, volume, cost, or weight
Experimental analysis of commercial and innovative adsorbent materials for deep biogas cleaning for fuel cell applications
No full textAdsorption model for biogas purification: A design tool for solid oxide fuel cells applications
No full textWith biogas production projected to exceed 35-45 billion cubic meters by 2030, addressing the critical challenge of biogas purification to allow its exploitation through the best-in-class technology available, the solid oxide fuel cell (SOFC), while preserving their durability, is essential. This study investigates the decontamination aspects related to the energy option of SOFCs fueled by biogas, a highly efficient and sustainable solution for renewable energy generation. We have developed a flexible and cost-effective biogas cleaning unit capable of removing harmful sulfur-based impurities. A novel adsorption model was created to predict contaminant concentration profiles, supporting the design of scalable cleaning units. Our comprehensive techno-economic analysis reveals that in 3-kW systems, capital and operational expenditures for SOFCs account for 56–70 % of the levelized cost of electricity (LCOE), with biogas cleaning systems constituting 30–37 % in single-vessel configurations and 35-44 % in lead-and-lag setups. In 100-kW systems, economies of scale reduce SOFC investment costs, while the impact of the biogas cleaning system becomes more pronounced. Sensitivity analysis indicates that variations in sorbent costs significantly affect LCOE, with lead-and-lag configurations offering advantages in sorbent utilization and operational efficiency. Overall, our findings indicate that biogas-SOFC systems obtain a competitive LCOE, below 0.32 €/kWh for 3-kW systems and below 0.12 €/kWh for 100-kW systems. This highlights their viability as a cleaner, high-efficiency alternative to conventional combustion technologies for decentralized energy production
Model complexity and optimization trade-offs in the design and scheduling of hybrid hydrogen-battery systems
Full text linkThe production of hydrogen from renewable sources could play a significant role in supporting the transition toward a decarbonized energy system. This study has involved investigating optimization strategies - mixed-integer linear programming (MILP), a hybrid particle swarm optimization (PSO)-MILP framework, and PSO combined with a rule-based energy management strategy (EMS) - applied to a power-to-hydrogen system for industrial applications. The analysis evaluates the levelized cost of hydrogen production (LCOH), carbon emissions, and the impact of key factors, such as battery degradation, electrolyzer efficiency, real-time pricing, and hydrogen load management. The obtained results indicated that the MILP-based models achieved moderate LCOH values (10.1-10.7 €/kg) but incurred higher CO2 emissions (20.2-24.6 kt/y). Instead, the PSO model, combined with the rule-based EMS, lowered emissions to 14.3 kt/y (a 27-45% reduction), albeit with a higher LCOH (11.6 €/kg). The hybrid PSO-MILP models struck a balance, achieving LCOH values of between 9.2 and 9.7 €/kg, with CO2 emissions of 19.7-20.3 kt/y, as they benefited from the integration of piecewise affine linearization for modeling electrolyzer efficiency and battery degradation. In terms of computational efforts, the MILP-based models required more than 48 h to converge, while the PSO-MILP models completed within 27-35 h, and the PSO model with rule-based EMS achieved results in 1.5 h. These findings offer guidance that can be used to select the most suitable optimization method on the basis of the desired performance targets, resource constraints, and computational complexity, thereby contributing to the design of more sustainable energy systems
Life cycle inventory dataset for energy production and storage technologies: Standardized metrics for environmental modeling
Full text linkThe presented dataset provides the results of a comprehensive inventory of Life Cycle Assessments (LCA) for multiple energy production and storage technologies. Unlike conventional LCA studies, which often provide case-specific data that are difficult to apply in the analysis and design of energy systems, this work delivers standardized values, expressed per unit of installed capacity (kW), and, where relevant, per unit of operational energy output (kWh). These normalized metrics are essential for the integration of environmental considerations into energy system modeling and optimization. The dataset is the result of a comprehensive review of peer-reviewed studies, institutional reports, industrial data, and existing LCA databases. Robust average values were derived by consolidating information from multiple sources, thereby addressing a key gap in the literature whereby heterogeneous and fragmented data can make their practical application problematic. Minimum and maximum estimates are also reported to characterize the variability across technologies and datasets, providing a more comprehensive understanding of the underlying uncertainty. Unlike existing LCA databases, which are often paywalls, and can have highly detailed but less accessible data, this method provides aggregated and user-friendly parameters that are ready for direct use. The inventory covers such technologies as photovoltaic systems, energy storage solutions, hydrogen production, internal combustion engines, boilers, heat pumps, and organic Rankine cycles, as well as energy carriers, including natural gas, electricity, hydrogen, and biomass. Environmental impacts are reported across multiple midpoint categories, such as the global warming potential, mineral and metal depletion, land and water use, particulate matter, acidification, eutrophication, ecotoxicity, ionizing radiation, human toxicity, and the ozone depletion potential. The dataset, which was drawn up according to International Reference Life Cycle Data System (ILCD), ReCiPe Life Cycle Impact Assessment Method (ReCiPe), and Intergovernmental Panel on Climate Change Global Warming Potential (IPCC GWP) guidelines, ensures consistency and comparability across technologies. Moreover, the dataset, which is provided as an open-access Excel Workbook, is readily applicable to environmental assessments, optimization studies, and energy planning in energy communities and smart grids, to support informed decisions for sustainable energy transitions
Techno-economic dataset for hydrogen storage-based microgrids
Full text linkThe challenge of energy storage is a pivotal consideration in renewable energy-based power systems. Hydrogen emerges as a highly promising alternative or complementary solution to electric batteries, showcasing its potential for long-term and high-capacity storage. In this context, energy system modeling and optimization has gained prominence as an indispensable research tool, aiding in the processes of designing, sizing, and managing the day-to-day operations of renewable energy systems integrated with a hydrogen storage unit. However, the gathering of reliable and accurate techno-economic data emerges as time-consuming tasks, and the lack of standardized reference data introduces variability in model results. This variability arises from inconsistent input parameters rather than the physics or complexity of energy systems, leading to potentially erroneous results and misguided policy recommendations. Recognizing the need for comprehensive and transparent datasets, we introduce this open data techno-economic repository. The dataset is meticulously designed to encompass key technologies essential for hydrogen production, compression, storage, and utilization within a power-to-power system. Specifically, techno-economic data are reported for electrolysers, fuel cells, battery energy storage systems, hydrogen compression units, and hydrogen storage vessels. The learning curves and cost functions embedded in this paper, delineating investment costs as a function of production scale up and size, are derived directly from the raw data, providing a nuanced understanding of the economic landscape
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