Carpe diem: A novel approach to select representative days for long-term power system modeling
Eva Schmid (),
Lion Hirth () and
Energy, 2016, vol. 112, issue C, 430-442
With an increasing share of wind and solar energy in power generation, properly accounting for their temporal and spatial variability becomes ever more important in power system modeling. To this end, a high temporal resolution is desirable but due to computational restrictions rarely feasible in long-term models that span several decades. Therefore many of these models only include a small number of representative ‘time slices’ that aggregate periods with similar load and renewable electricity generation levels. The deliberate selection of the time slices to consider in a model is vital, as an inadequate choice may significantly distort the model outcome. However, established selection methods are only based on demand variations and are not applicable to input data with a large number of fluctuating time series, which is a drawback for models with high shares of renewable energy. In this paper, we present and validate a novel and computational efficient time slice approach that is readily applicable to input data for all kinds of power system models. We illustratively determine representative days for the long-term model LIMES-EU and show that a small number of model days developed in this way is sufficient to reflect the characteristic fluctuations of the input data.
Keywords: Power system modeling; Variability; Renewable energy sources; Time slices (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (31) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:112:y:2016:i:c:p:430-442
Access Statistics for this article
Energy is currently edited by Henrik Lund and Mark J. Kaiser
More articles in Energy from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().