Beykirch, Mario ; Janke, Tim ; Steinke, Florian (2022)
Bidding and Scheduling in Energy Markets: Which Probabilistic Forecast Do We Need?
17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS 2022). virtual Conference (12.06.2022-15.06.2022)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Probabilistic forecasting in combination with stochastic programming is a key tool for handling the growing uncertainties in future energy systems. Derived from a general stochastic programming formulation for the optimal scheduling and bidding in energy markets we examine several common special instances containing uncertain loads, energy prices, and variable renewable energies. We analyze for each setup whether only an expected value forecast, marginal or bivariate predictive distributions, or the full joint predictive distribution is required. For market schedule optimization, we find that expected price forecasts are sufficient in almost all cases, while the marginal distributions of renewable energy production and demand are often required. For bidding curve optimization, pairwise or full joint distributions are necessary except for specific cases. This work helps practitioners choose the simplest type of forecast that can still achieve the best theoretically possible result for their problem and researchers to focus on the most relevant instances.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Beykirch, Mario ; Janke, Tim ; Steinke, Florian |
| Art des Eintrags: | Bibliographie |
| Titel: | Bidding and Scheduling in Energy Markets: Which Probabilistic Forecast Do We Need? |
| Sprache: | Englisch |
| Publikationsjahr: | 25 März 2022 |
| Veranstaltungstitel: | 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS 2022) |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 12.06.2022-15.06.2022 |
| URL / URN: | https://arxiv.org/abs/2203.13159 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Probabilistic forecasting in combination with stochastic programming is a key tool for handling the growing uncertainties in future energy systems. Derived from a general stochastic programming formulation for the optimal scheduling and bidding in energy markets we examine several common special instances containing uncertain loads, energy prices, and variable renewable energies. We analyze for each setup whether only an expected value forecast, marginal or bivariate predictive distributions, or the full joint predictive distribution is required. For market schedule optimization, we find that expected price forecasts are sufficient in almost all cases, while the marginal distributions of renewable energy production and demand are often required. For bidding curve optimization, pairwise or full joint distributions are necessary except for specific cases. This work helps practitioners choose the simplest type of forecast that can still achieve the best theoretically possible result for their problem and researchers to focus on the most relevant instances. |
| Freie Schlagworte: | EnEff Campus LW |
| 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: | 19 Mai 2022 09:50 |
| Letzte Änderung: | 15 Nov 2022 10:05 |
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