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.
Highlights
The goal is to describe the present state of forecasting power output from solar and wind farms
Narrowing the topic from forecasting the resource arises from the present needs of the Australian National Electricity Market (NEM) and we suggest the near future needs of markets throughout the world
In this article we have put forward various methods to forecast wind speed and solar irradiation, added to a small number of papers that focus on forecasting output from installations as well
Summary
The goal is to describe the present state of forecasting power output from solar and wind farms. We will document some of this activity, the focus has been on forecasting the resource, solar radiation or wind speed. In [1], there is an explicit representation of what forecasting tools operate at which time and spatial scales. The depiction does not include artificial intelligence tools apart from artificial neural network (ANN) models. Our work in this review will focus mainly on the forecast horizon that is relevant to the NEM. Renewable energy generators with capacity between 30 and 100 MW are termed semi-scheduled generators. They do not submit bids, but can be curtailed
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