LAPSE:2023.34599v1
Published Article
LAPSE:2023.34599v1
Forecasting of Wind and Solar Farm Output in the Australian National Electricity Market: A Review
John Boland, Sleiman Farah, Lei Bai
April 27, 2023
Abstract
Accurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This is especially the case in Australia, where there has been a massive increase in solar and wind farms in the last 15 years, as well as in roof top solar, both domestic and commercial. For example, in 2020, 27% of the electricity in Australia was from renewable sources, and in South Australia almost 60% was from wind and solar. In the literature, there has been extensive research reported on solar and wind resource, entailing both point and interval forecasts, but there has been much less focus on the forecasting of output from wind and solar systems. In this review, we canvass both what has been reported and also what gaps remain. In the case of the latter topic, there are numerous aspects that are not well dealt with in the literature. We have added discussion on the value of forecasts, rather than just focusing on forecast skill. Further, we present a section on how to deal with conditionally changing variance, a topic that has little focus in the literature. One other topic may be particularly important in Australia at the moment, but may become more widespread. This is how to deal with the concept of a clear sky output from a solar farm when the field is oversized compared to the inverter capacity, resulting in a plateau for the output.
Keywords
ARCH effect, ARMA models, probabilistic forecasting, ramping, solar farms, wind farms
Suggested Citation
Boland J, Farah S, Bai L. Forecasting of Wind and Solar Farm Output in the Australian National Electricity Market: A Review. (2023). LAPSE:2023.34599v1
Author Affiliations
Boland J: Industrial AI Research Centre, UniSA STEM, University of South Australia, Adelaide 5000, Australia [ORCID]
Farah S: Industrial AI Research Centre, UniSA STEM, University of South Australia, Adelaide 5000, Australia
Bai L: Industrial AI Research Centre, UniSA STEM, University of South Australia, Adelaide 5000, Australia [ORCID]
Journal Name
Energies
Volume
15
Issue
1
First Page
370
Year
2022
Publication Date
2022-01-05
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en15010370, Publication Type: Review
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LAPSE:2023.34599v1
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https://doi.org/10.3390/en15010370
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