 A statistical approach to modeling the variability between years in renewable infeed on energy system levelChristopher Jahns, Paul Osinski and Christoph Weber Energy, 2023, vol. 263, issue PA Abstract: Energy system models often rely on assumptions about the infeed of renewable energies. Despite their significance, the renewable time series are often based on single weather years, selected without applying clear criteria. For planning purposes of photovoltaic plants or heating and cooling systems, it is common practice to artificially create weather years composed of months from different years. However, there are only few models for the composition of artificial weather years that represent a well-defined high- or low-infeed-scenario. A new method is proposed to artificially construct infeed time series on system level. Under the assumption of a normal distribution, we compose an infeed time series which aims at meeting a certain quantile of annual infeed. Thus, it is possible to construct different infeed scenarios, to model the inter-year variability of the renewable infeed. The method at hand can be useful for everyone who uses exogenous infeed time series in energy modeling. Keywords: Renewable energy; Energy system model; Time series; Scenarios (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:263:y:2023:i:pa:s0360544222024963 DOI: 10.1016/j.energy.2022.125610 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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