Exploring wind power prognosis data on Nord Pool: the case of Sweden and Denmark
| Autor: | Tadas Matusevicius, Jon Olauson, Lars Herre, Lennart Söder |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Other Electrical Engineering
Electronic Engineering Information Engineering Wind power Meteorology analys av vindkraftsprognoser Renewable Energy Sustainability and the Environment business.industry 020209 energy 020208 electrical & electronic engineering Weather forecasting Regression analysis 02 engineering and technology computer.software_genre vindkraftspognoser forecast anaylsis Production planning 0202 electrical engineering electronic engineering information engineering Environmental science Annan elektroteknik och elektronik wind power forecasts Energy Systems business computer Energisystem |
| Zdroj: | IET Renewable Power Generation. 13:690-702 |
| ISSN: | 1752-1424 1752-1416 |
| DOI: | 10.1049/iet-rpg.2018.5086 |
| Popis: | A good understanding of forecast errors is imperative for greater penetration of wind power, as it can facilitate planning and operation tasks. Oftentimes, public data is used for system studies without questioning or verifying its origin. In this paper, we propose a methodology to verify public data with the example of wind power prognosis published by Nord Pool. We focus on Swedish data and identify a significant bias that increases over the forecast horizon. In order to explore the origin of this bias, we first compare against Danish forecast and then describe the underlying structure behind the submission processes of this data. Based on the balance settlement structure, we reveal that Swedish "wind power prognoses" on Nord Pool are in fact rather wind production plans than technical forecasts. We conclude with the recommendation for improved communication and transparency with respect to terminology of public data on Nord Pool. We stress the importance for the research community to check publicly available input data before further use. Furthermore, the root-mean-square error and the spatio-temporal correlation between the errors in the bidding areas at different horizons is presented. Even with this compromised data, a stronger correlation is identified in neighbouring areas. QC 20190124 |
| Databáze: | OpenAIRE |
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