Identifying critical demand periods in capacity planning for networks including storage
| dc.date.accessioned | 2023-11-15T08:27:37Z | |
| dc.date.issued | 2023 | |
| dc.description | Erschienen in: Grothe, O., Nickel, S., Rebennack, S., Stein, O. (eds) Operations Research Proceedings 2022. OR 2022. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-031-24907-5_27 | ger |
| dc.identifier | doi:10.17170/kobra-202311108996 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/15179 | |
| dc.language.iso | eng | |
| dc.rights | Urheberrechtlich geschützt | |
| dc.rights.uri | https://rightsstatements.org/page/InC/1.0/ | |
| dc.subject | time series analysis | eng |
| dc.subject | capacity planning | eng |
| dc.subject | model reduction | eng |
| dc.subject.ddc | 510 | |
| dc.subject.swd | Zeitreihenanalyse | ger |
| dc.subject.swd | Kapazitätsplanung | ger |
| dc.subject.swd | Ordnungsreduktion | ger |
| dc.subject.swd | Hauptkomponentenanalyse | ger |
| dc.subject.swd | Speicherung | ger |
| dc.title | Identifying critical demand periods in capacity planning for networks including storage | eng |
| dc.type | Konferenzveröffentlichung | |
| dc.type.version | submittedVersion | |
| dcterms.abstract | We consider a capacity planning problem for networks including storage. Given a graph and a time series of demands and supplies, we seek for integer link and storage capacities that permit a single commodity flow with valid storage in- and outtakes over all time steps. This problem arises, for example, in power systems planning, where storage can be used to buffer peaks of varying supplies and demands. For typical time series spanning a full year at hourly resolution, this leads to huge optimization models. To reduce the model size, time series aggregation is commonly used. The time horizon is sliced into fixed size periods, e.g. days or weeks, a small set of representative periods is chosen via clustering methods, and a much smaller model involving only the chosen periods is solved. Representative periods, however, typically do not contain the situations with the most extreme demands and supplies and the strongest effects on storage. In this paper, we show how to identify such critical periods using principal component analysis (PCA) and convex hull computations and we compare the quality and solution time of the reduced models to the original ones for benchmark instances derived from power systems planning. | eng |
| dcterms.accessRights | open access | |
| dcterms.creator | Bley, Andreas | |
| dcterms.creator | Hahn, Philipp | |
| dcterms.event | Operations Research Society of Germany | eng |
| dcterms.event.date | 2022-09-06 - 2022-09-09 | |
| dcterms.event.place | Karlsruhe | ger |
| dcterms.extent | 7 Seiten | |
| kup.iskup | false |