Day-ahead probabilistic forecasting at a co-located wind and solar power park in Sweden: Trading and forecast verification

•We study the effect of aggregation at a co-located wind and solar power park.•We assess the performance of probabilistic forecasts in terms of quality and value.•Co-location improves the accuracy of forecasts the most in the spring, summer and fall.•A ratio of 50% - 60% wind power in the combined p...

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Bibliographic Details
Published in:Advances in applied energy Vol. 9; p. 100120
Main Authors: Lindberg, O., Lingfors, D., Arnqvist, J., van der Meer, D., Munkhammar, J.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.02.2023
Elsevier
Subjects:
Electric power
Engineering Science with specialization in Civil Engineering and Built Environment
Engineering Sciences
Forecast value
Hybrid power park
Probabilistic forecasting
PV power
Quantile forecasts
Teknisk fysik med inriktning mot byggteknik och byggd miljö
Wind power
Quantile forecasts
Probabilistic forecasting
Hybrid power park
PV power
Wind power
Forecast value
ISSN:2666-7924, 2666-7924
Online Access:Get full text
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Summary:•We study the effect of aggregation at a co-located wind and solar power park.•We assess the performance of probabilistic forecasts in terms of quality and value.•Co-location improves the accuracy of forecasts the most in the spring, summer and fall.•A ratio of 50% - 60% wind power in the combined park improves the accuracy the most.•The improved forecasts reduce the regulation costs in the day-ahead market. 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.
ISSN:2666-7924
2666-7924
DOI:10.1016/j.adapen.2022.100120