9 research outputs found
The role of hydropower in decarbonisation scenarios
An increased penetration of renewable energy sources is essential for the energy transition. A major role will be played by wind and solar, as they are widely available. Hydropower is another crucial resource, currently covering large shares of power generation (e.g., Norway, Italy, Brazil). Despite little expected growth, in a context of increasing electrification, improved integration of hydropower can play a critical role thanks to programmable operation. This work addresses the modelling of hydropower flexibility in energy system models and analyses the impact of hydropower operation on CO2 emission-constrained scenarios. To implement the study, a detailed dataset of the Italian programmable hydroelectric plants is created, using open-source information, covering location, rated power, and storage capacity. Inflow timeseries are derived from historical operational data. These new sets of data are employed in OMNI-ES (a multi-node, multi-sector, and multi-vector energy system model) to study optimal configurations and operation of the Italian energy system in decarbonisation scenarios, such as net-zero-CO2 and Fit-for-55 targets. Considering different operational strategies and multiple historical reference years (impacting the inflow), results demonstrate significant changes in hydropower behaviour and highlight its relevance as zero-carbon resource in terms of both power and energy output, influencing the installation of other technologies
Design and Optimization of a Multi-Mode Hydrogen Delivery Infrastructure for Clean Mobility
This work addresses the infrastructural needs arising from the widespread deployment of hydrogen for clean mobility, by developing a model to optimize the design and operation of a hydrogen distribution infrastructure. The developed tool combines the use of detailed spatial data through a Geographic Information System to define the candidate networks’ topologies and the resolution of a multi-modal transport optimization model to determine the cost-optimal infrastructure, considering a year-long time horizon with daily resolution.
The analysis looks at a 2050 scenario with a 25% share of fuel cell electric vehicles among passenger cars, considering the Italian region of Lombardy as case study. Results show the advantages of infrastructural integration in terms of modalities and delivery areas. The resulting optimal infrastructure relies on the parallel use, with a specific mix, of all transport modalities (pipelines, compressed hydrogen trucks, and liquid hydrogen trucks), achieving an average cost of hydrogen production and delivery between 5 €/kg and 8 €/kg
A comprehensive multi-node multi-vector multi-sector modelling framework to investigate integrated energy systems and assess decarbonisation needs
The pathway towards decarbonised energy systems involves massive changes in adopted energy vectors, installed technologies, networks roles, and interaction capabilities. To investigate the combination of these effects, this work presents the OMNI-ES modelling framework (Optimisation Model for Network-Integrated Energy Systems), which offers a comprehensive approach to analyse multi-node, multi-vector, multi-sector energy systems. It adopts a detailed temporal and spatial resolution and implements multiple conversion options between energy vectors (electricity, hydrogen, natural gas, biomethane, biofuels, e-fuels, …). The formulation solves the energy vector balances at each time step, taking into account sources, sinks, conversion processes, and storage systems. CO2 flows are also tracked, allowing the introduction of CO2 emission constraints that account for all contributions (fossil and biogenic, direct and indirect) and mitigation measures (capture, re-use, sequestration). In the article, OMNI-ES is applied to investigate an Italian scenario for 2050, adopting a regional (NUTS-2) resolution. The model output yields the cost-optimal energy system configuration that is capable to support the demand with net-zero CO2 emissions. Results show that the need for CO2 balance closure calls in several technologies, including massive renewable power generation (up to 20 times today’s capacities), storage systems (batteries, hydrogen, pumped hydro), biogenic sources (residual biomass and biomethane), and CO2 capture (both on fossil and biogenic sources). Networks emerge as critical elements, as the need to transport energy vectors saturates the expected capacities of grid infrastructures, especially in the case of hydrogen
Impact of Detailed Hydropower Representation in National Energy System Modelling
Renewables are becoming more and more important due to the ambitious decarbonization targets. In this scenario, the improved integration of hydropower can play a crucial role thanks to its programmable operation, which is a valuable feature. In some countries it is a primary alternative to fossil resources, for example Italy, where hydro currently covers roughly half of the renewable power generation. Hydropower flexibility poses considerable modelling challenges due to the scarce availability of data. This work aims at addressing this research gap, by analysing the impact of hydropower details on energy system models. Using open-source information, a detailed dataset of Italian hydroelectric programmable plants (pumped hydro and reservoirs) is created. For each plant, storage capacity, geographical location, and nominal power are available. The multiannual historical operational data are exploited to derive a precipitation inflow timeseries for each electricity market bidding zone, which is then distributed on power plants aggregated by administrative region. This new set of data is applied to a multi-node, multi-sector, and multi-vector energy system model, which optimises the design and operation of a carbon-neutral Italian energy system, looking at a 2050 framework with assigned energy vectors demand. Results are compared to those of a fixed-hydropower operation case, thus being able to assess how the modelled flexibility impacts the optimal solution. The analysis favours an improved understanding of future energy systems, helping to shape properly integrated systems with a great amount of non-programmable sources
The hydrogen role in the Italian energy system for Net-Zero CO2
Hydrogen allows a 'sector-coupled' evolution of the energy infrastructures towards a net zero-CO2 emission target, acting as clean energy vector with multiple roles ranging from energy storage to decarbonization of hard-to-abate applications. The role of hydrogen in the national energy system can be evaluated through detailed integrated energy system models. This work shows the main results of ongoing simulation efforts, evidencing the impact of hydrogen in the different energy fields and the effect of different key assumptions on the results
Evaluation of cyst fluid analysis in the diagnosis of pancreatic cysts.
The pre-operative differential diagnosis of pancreatic cystic lesions is often difficult because of the lack of reliable clinical or radiological criteria. In order to improve the pre-operative recognition of these lesions, we performed cyst fluid analysis for enzymes (amylase and lipase), tumour markers (CEA, CA 19-9, CA 125, CA 72-4), and cytology in 52 pancreatic cysts. The cases included 21 pseudocysts, 12 mucinous cystic neoplasms, 7 ductal carcinomas, 7 benign lesions, and 5 rare malignancies observed from 1989 to 1994. Cyst fluid amylase, lipase, CEA, and CA 19-9 were variable and not discriminant between the groups. CA 125 fluid levels were high in 63% of malignant cysts. CA 72-4 fluid levels were significantly higher in mucinous cystic tumours than in pseudocysts (p < 0.0001), showing 95% specificity in detecting mucinous or malignant cysts. Cytology showed a sensitivity of 61% and a specificity of 100%. CA 72-4 determination raised the sensitivity of cytology to 92% in detecting mucinous or malignant cysts. This study confirms the low sensitivity of cytologic examination and low amylase specificity in distinguishing cystic neoplasms from pseudocysts. Cyst tumour markers assay is useful to improve the sensitivity of cytology, and CA 72-4 shows the best specificity in detecting (pre)malignant neoplasms