Artificial Learning Dispatch Planning with Probabilistic Forecasts: Using Uncertainties as an Asset
Weather forecast uncertainty is a key element for energy market volatility. By intelligently considering uncertainties on the schedule development, renewable energy systems with storage could improve dispatching accuracy, and therefore, effectively participate in electricity wholesale markets. Deter...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/13/3/616 |
| _version_ | 1798035378868846592 |
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| author | Ana Carolina do Amaral Burghi Tobias Hirsch Robert Pitz-Paal |
| author_facet | Ana Carolina do Amaral Burghi Tobias Hirsch Robert Pitz-Paal |
| author_sort | Ana Carolina do Amaral Burghi |
| collection | DOAJ |
| description | Weather forecast uncertainty is a key element for energy market volatility. By intelligently considering uncertainties on the schedule development, renewable energy systems with storage could improve dispatching accuracy, and therefore, effectively participate in electricity wholesale markets. Deterministic forecasts have been traditionally used to support dispatch planning, representing reduced or no uncertainty information about the future weather. Aiming at better representing the uncertainties involved, probabilistic forecasts have been developed to increase forecasting accuracy. For the dispatch planning, this can highly influence the development of a more precise schedule. This work extends a dispatch planning method to the use of probabilistic weather forecasts. The underlying method used a schedule optimizer coupled to a post-processing machine learning algorithm. This machine learning algorithm was adapted to include probabilistic forecasts, considering their additional information on uncertainties. This post-processing applied a calibration of the planned schedule considering the knowledge about uncertainties obtained from similar past situations. Simulations performed with a concentrated solar power plant model following the proposed strategy demonstrated promising financial improvement and relevant potential in dealing with uncertainties. Results especially show that information included in probabilistic forecasts can increase financial revenues up to 15% (in comparison to a persistence solar driven approach) if processed in a suitable way. |
| first_indexed | 2024-04-11T20:56:25Z |
| format | Article |
| id | doaj.art-4713ffcecf6046c6af05e4bdd557b6ff |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T20:56:25Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-4713ffcecf6046c6af05e4bdd557b6ff2022-12-22T04:03:39ZengMDPI AGEnergies1996-10732020-02-0113361610.3390/en13030616en13030616Artificial Learning Dispatch Planning with Probabilistic Forecasts: Using Uncertainties as an AssetAna Carolina do Amaral Burghi0Tobias Hirsch1Robert Pitz-Paal2Institute of Solar Research, German Aerospace Center (DLR), Wankelstrasse 5, 70563 Stuttgart, GermanyInstitute of Solar Research, German Aerospace Center (DLR), Wankelstrasse 5, 70563 Stuttgart, GermanyInstitute of Solar Research, German Aerospace Center (DLR), Linder Höhe, 51147 Cologne, GermanyWeather forecast uncertainty is a key element for energy market volatility. By intelligently considering uncertainties on the schedule development, renewable energy systems with storage could improve dispatching accuracy, and therefore, effectively participate in electricity wholesale markets. Deterministic forecasts have been traditionally used to support dispatch planning, representing reduced or no uncertainty information about the future weather. Aiming at better representing the uncertainties involved, probabilistic forecasts have been developed to increase forecasting accuracy. For the dispatch planning, this can highly influence the development of a more precise schedule. This work extends a dispatch planning method to the use of probabilistic weather forecasts. The underlying method used a schedule optimizer coupled to a post-processing machine learning algorithm. This machine learning algorithm was adapted to include probabilistic forecasts, considering their additional information on uncertainties. This post-processing applied a calibration of the planned schedule considering the knowledge about uncertainties obtained from similar past situations. Simulations performed with a concentrated solar power plant model following the proposed strategy demonstrated promising financial improvement and relevant potential in dealing with uncertainties. Results especially show that information included in probabilistic forecasts can increase financial revenues up to 15% (in comparison to a persistence solar driven approach) if processed in a suitable way.https://www.mdpi.com/1996-1073/13/3/616renewable systemsstoragedispatchoptimizationmachine learningprobabilistic forecasts |
| spellingShingle | Ana Carolina do Amaral Burghi Tobias Hirsch Robert Pitz-Paal Artificial Learning Dispatch Planning with Probabilistic Forecasts: Using Uncertainties as an Asset Energies renewable systems storage dispatch optimization machine learning probabilistic forecasts |
| title | Artificial Learning Dispatch Planning with Probabilistic Forecasts: Using Uncertainties as an Asset |
| title_full | Artificial Learning Dispatch Planning with Probabilistic Forecasts: Using Uncertainties as an Asset |
| title_fullStr | Artificial Learning Dispatch Planning with Probabilistic Forecasts: Using Uncertainties as an Asset |
| title_full_unstemmed | Artificial Learning Dispatch Planning with Probabilistic Forecasts: Using Uncertainties as an Asset |
| title_short | Artificial Learning Dispatch Planning with Probabilistic Forecasts: Using Uncertainties as an Asset |
| title_sort | artificial learning dispatch planning with probabilistic forecasts using uncertainties as an asset |
| topic | renewable systems storage dispatch optimization machine learning probabilistic forecasts |
| url | https://www.mdpi.com/1996-1073/13/3/616 |
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