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.

Impact of Detailed Hydropower Representation in National Energy System Modelling

Fattori F.;
2023-01-01

Abstract

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.
2023
ECOS 2023
978-1-7138-7492-8
978-1-7138-7481-2
36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2023
Las Palmas de Gran Canaria
25-30 June 2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2164433
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