  Citation: John Boland; Sleiman Farah; Lei Bai. Review of Forecasting of Wind and Solar Farm Output in the Australian National Electricity Market. Energies 2022, 15, 370. https://doi.org/10.3390/en15010370 Academic Editors: Tek Tjing Lie, Adrian Ilinca and George Xydis Received: 11 November 2021 Accepted: 4 January 2022 Published: 5 January 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). energies Review Forecasting of Wind and Solar Farm Output in the Australian National Electricity Market: A Review John Boland * ,† , Sleiman Farah † and Lei Bai † Industrial AI Research Centre, UniSA STEM, University of South Australia, Adelaide 5000, Australia; sleiman.farah@unisa.edu.au (S.F.); lei.bai@mymail.unisa.edu.au (L.B.) * Correspondence: john.boland@unisa.edu.au † These authors contributed equally to this work. 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: solar farms; wind farms; probabilistic forecasting; ARMA models; ramping; ARCH effect 1. Introduction 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 the past, and 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. The way the NEM works is that there are three types of generators. Scheduled generators submit a bid stack every five minutes throughout the year detailing how much electricity they can supply in the subsequent five minutes at each of ten price bands from - AUD1000 to AUD15,000 per MWh. They are termed price makers. Renewable energy generators with capacity between 30 and 100 MW are termed semi-scheduled generators. They do not submit bids, but can be curtailed. Generators of any type under 30 MW are non scheduled and cannot be curtailed. The latter two categories are termed price takers, and they cannot affect the market. After the scheduled generators submit their bid stacks, the Australian Energy Market Operator (AEMO) runs a linear program to determine how far up the stack they have to go to meet their forecasted electricity demand. This is then the spot price and all generation in that five-minute period is paid that price. Energies 2022, 15, 370. https://doi.org/10.3390/en15010370 https://www.mdpi.com/journal/energies