Bott, Andreas ; Friedrich, Pascal ; Rehlich, Lea ; Steinke, Florian (2021)
Model Reduction for Heat Grid State Estimation.
2021 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe). virtual Conference (18.10.2021-21.10.2021)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Traditional district heating networks have been built around few central heat production units. Now, many more decentral heat sources based on renewable energies or waste heat are being included to reduce carbon emissions. Given the resulting complex flow patterns in the network reliable and fast heat grid state estimation becomes mandatory for efficient grid control. In this paper we first show an approach to reduce computational efforts for various grid computations by summarising pipe segments of the network. We then develop a probabilistic state estimator based on the reduced model by locally linearising the non-linear grid equations around the best state estimate. We show that the linearisation approach with a significantly lower computational burden achieves a prediction quality comparable to those of a sampling-based Monte-Carlo approach that uses the full model. This allows state estimation to become an online routine even in complex heating networks.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Bott, Andreas ; Friedrich, Pascal ; Rehlich, Lea ; Steinke, Florian |
| Art des Eintrags: | Bibliographie |
| Titel: | Model Reduction for Heat Grid State Estimation |
| Sprache: | Englisch |
| Publikationsjahr: | 2021 |
| Veranstaltungstitel: | 2021 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe) |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 18.10.2021-21.10.2021 |
| Kurzbeschreibung (Abstract): | Traditional district heating networks have been built around few central heat production units. Now, many more decentral heat sources based on renewable energies or waste heat are being included to reduce carbon emissions. Given the resulting complex flow patterns in the network reliable and fast heat grid state estimation becomes mandatory for efficient grid control. In this paper we first show an approach to reduce computational efforts for various grid computations by summarising pipe segments of the network. We then develop a probabilistic state estimator based on the reduced model by locally linearising the non-linear grid equations around the best state estimate. We show that the linearisation approach with a significantly lower computational burden achieves a prediction quality comparable to those of a sampling-based Monte-Carlo approach that uses the full model. This allows state estimation to become an online routine even in complex heating networks. |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Datentechnik > Energieinformationsnetze und Systeme (EINS) 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Datentechnik Forschungsfelder Forschungsfelder > Energy and Environment Forschungsfelder > Energy and Environment > Integrated Energy Systems |
| Hinterlegungsdatum: | 26 Jul 2021 10:54 |
| Letzte Änderung: | 12 Nov 2021 12:05 |
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