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Short-term solar an...
Short-term solar and wind variability in long-term energy system models - A European case study
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- Ringkjøb, Hans-Kristian (författare)
- University of Bergen, Norway; Institute for Energy Technology, Norway; IIASA International Institute for Applied Systems Analysis, Austria
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- Haugan, Peter (författare)
- University of Bergen, Norway
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- Seljom, Pernille (författare)
- Institute for Energy Technology, Norway
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- Lind, Arne (författare)
- Institute for Energy Technology, Norway
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- Wagner, Fabian (författare)
- IIASA International Institute for Applied Systems Analysis, Austria
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- Mesfun, Sennai (författare)
- RISE,Bioraffinaderi och energi,IIASA International Institute for Applied Systems Analysis, Austria
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(creator_code:org_t)
- Elsevier Ltd, 2020
- 2020
- Engelska.
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Ingår i: Energy. - : Elsevier Ltd. - 0360-5442 .- 1873-6785. ; 209
- Relaterad länk:
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https://doi.org/10.1...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Integration of variable renewables such as solar and wind has grown at an unprecedented pace in Europe over the past two decades. As the share of solar and wind rises, it becomes increasingly important for long-term energy system models to adequately represent their short-term variability. This paper uses a long-term TIMES model of the European power and district heat sectors towards 2050 to explore how stochastic modelling of short-term solar and wind variability as well as different temporal resolutions influence the model performance. Using a stochastic model with 48 time-slices as benchmark, the results show that deterministic models with low temporal resolution give a 15–20% underestimation of annual costs, an overestimation of the contribution of variable renewables (13–15% of total electricity generation) and a lack of system flexibility. The results of the deterministic models converge towards the stochastic solution when the temporal resolution is increased, but even with 2016 time-slices, the need for flexibility is underestimated. In addition, the deterministic model with 2016 time-slices takes 30 times longer to solve than the stochastic model with 48 time-slices. Based on these findings, a stochastic approach is recommended for long-term studies of energy systems with large shares of variable renewable energy sources. © 2020 The Authors
Nyckelord
- Energy modelling
- Stochastic modelling
- TIMES energy-Models
- Variable renewable energy
- Renewable energy resources
- Solar power generation
- Stochastic systems
- Deterministic modeling
- Deterministic models
- Electricity generation
- Energy system model
- Stochastic approach
- Stochastic solution
- Temporal resolution
- Variable renewable energies
- Stochastic models
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