Schmitt, Thomas ; Rodemann, Tobias ; Adamy, Jürgen (2021)
The Cost of Photovoltaic Forecasting Errors in Microgrid Control with Peak Pricing.
In: Energies, 2021, 14 (9)
doi: 10.26083/tuprints-00019312
Artikel, Zweitveröffentlichung, Verlagsversion
Kurzbeschreibung (Abstract)
Model predictive control (MPC) is widely used for microgrids or unit commitment due to its ability to respect the forecasts of loads and generation of renewable energies. However, while there are lots of approaches to accounting for uncertainties in these forecasts, their impact is rarely analyzed systematically. Here, we use a simplified linear state space model of a commercial building including a photovoltaic (PV) plant and real-world data from a 30 day period in 2020. PV predictions are derived from weather forecasts and industry peak pricing is assumed. The effect of prediction accuracy on the resulting cost is evaluated by multiple simulations with different prediction errors and initial conditions. Analysis shows a mainly linear correlation, while the exact shape depends on the treatment of predictions at the current time step. Furthermore, despite a time horizon of 24h, only the prediction accuracy of the first 75min was relevant for the presented setting.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Schmitt, Thomas ; Rodemann, Tobias ; Adamy, Jürgen |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | The Cost of Photovoltaic Forecasting Errors in Microgrid Control with Peak Pricing |
| Sprache: | Englisch |
| Publikationsjahr: | 2021 |
| Publikationsdatum der Erstveröffentlichung: | 2021 |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Energies |
| Jahrgang/Volume einer Zeitschrift: | 14 |
| (Heft-)Nummer: | 9 |
| Kollation: | 13 Seiten |
| DOI: | 10.26083/tuprints-00019312 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/19312 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (Abstract): | Model predictive control (MPC) is widely used for microgrids or unit commitment due to its ability to respect the forecasts of loads and generation of renewable energies. However, while there are lots of approaches to accounting for uncertainties in these forecasts, their impact is rarely analyzed systematically. Here, we use a simplified linear state space model of a commercial building including a photovoltaic (PV) plant and real-world data from a 30 day period in 2020. PV predictions are derived from weather forecasts and industry peak pricing is assumed. The effect of prediction accuracy on the resulting cost is evaluated by multiple simulations with different prediction errors and initial conditions. Analysis shows a mainly linear correlation, while the exact shape depends on the treatment of predictions at the current time step. Furthermore, despite a time horizon of 24h, only the prediction accuracy of the first 75min was relevant for the presented setting. |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-193128 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik > Regelungsmethoden und Robotik (ab 01.08.2022 umbenannt in Regelungsmethoden und Intelligente Systeme) |
| Hinterlegungsdatum: | 20 Aug 2021 12:03 |
| Letzte Änderung: | 23 Aug 2021 07:51 |
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