Wasserstein-distance-based temporal clustering for capacity-expansion planning in power systems

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

8 Downloads (Pure)

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.

Original languageEnglish
Title of host publicationSEST 2020 - 3rd International Conference on Smart Energy Systems and Technologies
PublisherIEEE
ISBN (Electronic)9781728147017
DOIs
Publication statusPublished - Sep 2020
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Smart Energy Systems and Technologies - Virtual, Istanbul, Turkey
Duration: 7 Sep 20209 Sep 2020
Conference number: 3

Conference

ConferenceInternational Conference on Smart Energy Systems and Technologies
Abbreviated titleSEST
CountryTurkey
CityIstanbul
Period07/09/202009/09/2020

Fingerprint Dive into the research topics of 'Wasserstein-distance-based temporal clustering for capacity-expansion planning in power systems'. Together they form a unique fingerprint.

Cite this