Day-ahead probabilistic forecasting at a co-located wind and solar power park in Sweden: Trading and forecast verification
This paper presents a first step in the field of probabilistic forecasting of co-located wind and photovoltaic (PV) parks. The effect of aggregation is analyzed with respect to forecast accuracy and value at a co-located park in Sweden using roughly three years of data. We use a fixed modelling fram...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | Advances in Applied Energy |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666792422000385 |
| _version_ | 1828004346064797696 |
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| author | O. Lindberg D. Lingfors J. Arnqvist D. van der Meer J. Munkhammar |
| author_facet | O. Lindberg D. Lingfors J. Arnqvist D. van der Meer J. Munkhammar |
| author_sort | O. Lindberg |
| collection | DOAJ |
| description | This paper presents a first step in the field of probabilistic forecasting of co-located wind and photovoltaic (PV) parks. The effect of aggregation is analyzed with respect to forecast accuracy and value at a co-located park in Sweden using roughly three years of data. We use a fixed modelling framework where we post-process numerical weather predictions to calibrated probabilistic production forecasts, which is a prerequisite when placing optimal bids in the day-ahead market. The results show that aggregation improves forecast accuracy in terms of continuous ranked probability score, interval score and quantile score when compared to wind or PV power forecasts alone. The optimal aggregation ratio is found to be 50%–60% wind power and the remainder PV power. This is explained by the aggregated time series being smoother, which improves the calibration and produces sharper predictive distributions, especially during periods of high variability in both resources, i.e., most prominently in the summer, spring and fall. Furthermore, the daily variability of wind and PV power generation was found to be anti-correlated which proved to be beneficial when forecasting the aggregated time series. Finally, we show that probabilistic forecasts of co-located production improve trading in the day-ahead market, where the more accurate and sharper forecasts reduce balancing costs. In conclusion, the study indicates that co-locating wind and PV power parks can improve probabilistic forecasts which, furthermore, carry over to electricity market trading. The results from the study should be generally applicable to other co-located parks in similar climates. |
| first_indexed | 2024-04-10T07:13:18Z |
| format | Article |
| id | doaj.art-bfdb9c17329f43408e67489b96b1f376 |
| institution | Directory Open Access Journal |
| issn | 2666-7924 |
| language | English |
| last_indexed | 2024-04-10T07:13:18Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Advances in Applied Energy |
| spelling | doaj.art-bfdb9c17329f43408e67489b96b1f3762023-02-26T04:28:26ZengElsevierAdvances in Applied Energy2666-79242023-02-019100120Day-ahead probabilistic forecasting at a co-located wind and solar power park in Sweden: Trading and forecast verificationO. Lindberg0D. Lingfors1J. Arnqvist2D. van der Meer3J. Munkhammar4Corresponding author.; Department of Civil and Industrial Engineering, Uppsala University, Lägerhyddsvägen 1, Uppsala 752 37, SwedenDepartment of Civil and Industrial Engineering, Uppsala University, Lägerhyddsvägen 1, Uppsala 752 37, SwedenDepartment of Earth Sciences, Uppsala University, Villavägen Uppsala 16 752 36, SwedenMines Paris, PSL University, Centre for processes, renewable energy and energy systems (PERSEE), Sophia Antipolis 06904, FranceDepartment of Civil and Industrial Engineering, Uppsala University, Lägerhyddsvägen 1, Uppsala 752 37, SwedenThis paper presents a first step in the field of probabilistic forecasting of co-located wind and photovoltaic (PV) parks. The effect of aggregation is analyzed with respect to forecast accuracy and value at a co-located park in Sweden using roughly three years of data. We use a fixed modelling framework where we post-process numerical weather predictions to calibrated probabilistic production forecasts, which is a prerequisite when placing optimal bids in the day-ahead market. The results show that aggregation improves forecast accuracy in terms of continuous ranked probability score, interval score and quantile score when compared to wind or PV power forecasts alone. The optimal aggregation ratio is found to be 50%–60% wind power and the remainder PV power. This is explained by the aggregated time series being smoother, which improves the calibration and produces sharper predictive distributions, especially during periods of high variability in both resources, i.e., most prominently in the summer, spring and fall. Furthermore, the daily variability of wind and PV power generation was found to be anti-correlated which proved to be beneficial when forecasting the aggregated time series. Finally, we show that probabilistic forecasts of co-located production improve trading in the day-ahead market, where the more accurate and sharper forecasts reduce balancing costs. In conclusion, the study indicates that co-locating wind and PV power parks can improve probabilistic forecasts which, furthermore, carry over to electricity market trading. The results from the study should be generally applicable to other co-located parks in similar climates.http://www.sciencedirect.com/science/article/pii/S2666792422000385Forecast valueQuantile forecastsPV powerWind powerHybrid power parkProbabilistic forecasting |
| spellingShingle | O. Lindberg D. Lingfors J. Arnqvist D. van der Meer J. Munkhammar Day-ahead probabilistic forecasting at a co-located wind and solar power park in Sweden: Trading and forecast verification Advances in Applied Energy Forecast value Quantile forecasts PV power Wind power Hybrid power park Probabilistic forecasting |
| title | Day-ahead probabilistic forecasting at a co-located wind and solar power park in Sweden: Trading and forecast verification |
| title_full | Day-ahead probabilistic forecasting at a co-located wind and solar power park in Sweden: Trading and forecast verification |
| title_fullStr | Day-ahead probabilistic forecasting at a co-located wind and solar power park in Sweden: Trading and forecast verification |
| title_full_unstemmed | Day-ahead probabilistic forecasting at a co-located wind and solar power park in Sweden: Trading and forecast verification |
| title_short | Day-ahead probabilistic forecasting at a co-located wind and solar power park in Sweden: Trading and forecast verification |
| title_sort | day ahead probabilistic forecasting at a co located wind and solar power park in sweden trading and forecast verification |
| topic | Forecast value Quantile forecasts PV power Wind power Hybrid power park Probabilistic forecasting |
| url | http://www.sciencedirect.com/science/article/pii/S2666792422000385 |
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