The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability
Open accessAbstract
Using a high-resolution planning model of the Great Britain power system and 25 years of simulated wind and PV generation data, this study compares different methods to reduce time resolution of energy models to increase their computational tractability: downsampling, clustering, and heuristics. By comparing model results in terms of costs and installed capacities across different methods, this study shows that the best method depends heavily on input data and the setup of model constraints. This implies that there is no one-size-fits-all approach to the problem of time step reduction, but heuristic approaches appear promising. In addition, the 25 years of time series demonstrate considerable inter-year variability in wind and PV power output. This further complicates the problem of time detail in energy models as it suggests long time series are necessary. Model results with high shares of PV and wind generation using a single or few years of data are likely unreliable. Better modeling and planning methods are required to determine robust scenarios with high shares of variable renewables. The methods are implemented in the freely available open-source modeling framework Calliope.
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Cites
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Self-citationUsing bias-corrected reanalysis to simulate current and future wind power output
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Cited by
Brown, T. W., Bischof-Niemz, T., Blok, K., Breyer, C., Lund, H., & Mathiesen, B. V. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.04.113 · Cited by 519
The increasing impact of weather on electricity supply and demand
Staffell, I., & Pfenninger, S. (2018) · Energy · doi:10.1016/j.energy.2017.12.051 · Cited by 415
Self-citationImpacts of Inter-annual Wind and Solar Variations on the European Power System
Collins, S., Deane, P., Ó Gallachóir, B., Pfenninger, S., & Staffell, I. (2018) · Joule · doi:10.1016/j.joule.2018.06.020 · Cited by 250
Self-citationDesigning a sector-coupled European energy system robust to 60 years of historical weather data
Gøtske, E. K., Andresen, G. B., Neumann, F., & Victoria, M. (2024) · Nature Communications · doi:10.1038/s41467-024-54853-3 · Cited by 92
Rüdisüli, M., Teske, S. L., & Elber, U. (2019) · Energies · doi:10.3390/en12122399 · Cited by 37
Metadata
- Authors
- Pfenninger, S.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2017-07
- Volume
- 197
- Pages
- 1-13
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2605167157
- Cited by
- 367
- References
- 29
- FWCI
- 12.75
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Pfenninger, S. (2017). Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability. Applied Energy. https://doi.org/10.1016/j.apenergy.2017.03.051
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
