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Impact of different levels of geographical disaggregation of wind and PV electricity generation in large energy system models: A case study for Austria

Simoes, S; Zeyringer, M; Mayr, D; Huld, T; Nijs, W; Schmidt, J; (2017) Impact of different levels of geographical disaggregation of wind and PV electricity generation in large energy system models: A case study for Austria. Renewable Energy , 105 pp. 183-198. 10.1016/j.renene.2016.12.020. Green open access

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Abstract

This paper assesses how different levels of geographical disaggregation of wind and photovoltaic energy resources could affect the outcomes of an energy system model by 2020 and 2050. Energy system models used for policy making typically have high technology detail but little spatial detail. However, the generation potential and integration costs of variable renewable energy sources and their time profile of production depend on geographic characteristics and infrastructure in place. For a case study for Austria we generate spatially highly resolved synthetic time series for potential production locations of wind power and PV. There are regional differences in the costs for wind turbines but not for PV. However, they are smaller than the cost reductions induced by technological learning from one modelled decade to the other. The wind availability shows significant regional differences where mainly the differences for summer days and winter nights are important. The solar availability for PV installations is more homogenous. We introduce these wind and PV data into the energy system model JRC-EU-TIMES with different levels of regional disaggregation. Results show that up to the point that the maximum potential is reached disaggregating wind regions significantly affects results causing lower electricity generation from wind and PV.

Type: Article
Title: Impact of different levels of geographical disaggregation of wind and PV electricity generation in large energy system models: A case study for Austria
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.renene.2016.12.020
Publisher version: http://dx.doi.org/10.1016/j.renene.2016.12.020
Language: English
Additional information: copyright: © 2016 Published by Elsevier Ltd. Published by Elsevier Inc. This is an open access article under the Creative Commons Attribution 4.0 International (CC BY 4.0) license (http://creativecommons.org/licenses/by/4.0/)
Keywords: Photovoltaic energy; Wind energy; Optimization energy system model; Spatially-explicit
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/1536069
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