Condeixa, L;
Oliveira, F;
Siddiqui, AS;
(2020)
Wasserstein-distance-based temporal clustering for capacity-expansion planning in power systems.
In:
Proceeding of the International Conference on Smart Energy Systems and Technologies (SEST) 2020.
(pp. pp. 1-6).
IEEE
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Abstract
As variable renewable energy sources are steadily incorporated in European power systems, the need for higher temporal resolution in capacity-expansion models also increases.Naturally, there exists a trade-off between the amount of temporal data used to plan power systems for decades ahead and time resolution needed to represent renewable energy variability accurately. We propose the use of the Wasserstein distance as a measure of cluster discrepancy using it to cluster demand, wind availability, and solar availability data. When compared to the Euclidean distance and the maximal distance, the hierarchical clustering performed using the Wasserstein distance leads to capacity-expansion planning that 1) more accurately estimates system costs and 2) more efficiently adopts storage resources. Numerical results indicate an improvement in cost estimation by up to 5% vis-à-vis the Euclidean distance and a reduction of storage investment that is equivalent to nearly 100% of the installed capacity under the benchmark full time resolution.
Type: | Proceedings paper |
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Title: | Wasserstein-distance-based temporal clustering for capacity-expansion planning in power systems |
Event: | 2020 International Conference on Smart Energy Systems and Technologies (SEST) |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/SEST48500.2020.9203449 |
Publisher version: | https://doi.org/10.1109/SEST48500.2020.9203449 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10127409 |
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