- Lead Organisation
Proa Analytics Pty LtdLocation
21 January 2019
12 July 2021
- Project PartnersSieltec Canarias, Oakey 1 Asset Company Pty Ltd, Genex Power LimitedThis solar project was completed on 12 July 2021.
The Innovative Optimally Combined Solar Forecasts project recognises that power output of a solar farm fluctuates due to cloud motion over the site.
These fluctuations impact the power system, so improved forecasts will help to integrate solar generation into the grid and reduce the cost of solar generation. This forecasting project will forecast the generation of three large scale solar farms using a new solar forecasting system developed by Australian company Proa Analytics. Every five minutes, the Proa Forecasting System (PFS) will produce an updated generation forecast for the next 5-minute period for each solar farm, as well as longer forecasts up to several days ahead. Proa’s forecasts use an innovative combination of four different technologies: skycams, satellites, live data and numerical simulations.
A combination of forecasting technologies is required to provide the best forecasting performance by harnessing the advantages of each technology across a variety of weather patterns. Skycams provide a significant benefit in reducing forecasting errors in high variability conditions, provided that the instruments are properly maintained and operated. -Self-forecasts present a solid Net Present Value (NPV), with benefits significantly outweighing their cost, which allows for their wide implementation across solar and wind farms in the NEM.
- Project reports
- ARENA media release 18/03/19
- Pro Analytics Final Report and Cost Benefit Analysis
- Proa Analytics Solar Farm Short Term Forecasting Project: Lessons Learnt 1
- Proa Analytics Solar Farm Short Term Forecasting Project: Lessons Learnt 2
- Proa Analytics Solar Farm Short Term Forecasting Project: Lessons Learnt 3
Report: Pro Analytics Final Report and Cost Benefit Analysis
This end of project report shares insights on ProaAnalytics’ project outcomes. In addition, the report provides a cost-benefit analysis to determine the most affordable and fit-for-purpose combination of forecasting technologies for solar PV farms.
How the project works
The Innovative Optimally Combined Solar Forecasts project consists of the Proa Forecasting System (PFS) used for short-term solar forecasting to track and predict the motion of clouds and the impact on solar generation in Australian conditions. It consists of four individual solar forecasting techniques that are combined to produce an optimal forecast. These include three advanced and proprietary forecasting techniques developed by Proa Analytics – our methods of geostationary satellite cloud motion vectoring (CMV) algorithms, skycam CMV, and live data techniques. While each technology performs best at different time scales and weather conditions, this project aims to demonstrate the benefits of optimally combining them to maximise forecast accuracy.
Area of innovation
The aims to demonstrate the improved techniques of combining the different forecast methods at short time scales. The project will also demonstrate new methods of identifying clouds at night using infrared satellite images to make accurate solar forecasts in the hours before dawn, and use an infrared skycam to provide additional information on cloud structure during the day.
The improved solar forecasts demonstrated by the project may help to integrate solar generation into the grid and to reduce the costs of existing and new solar generation. Improved solar forecasts are required to help AEMO operate the NEM with greater amounts of solar generation and help additional solar generation to enter the grid.
The project will also analyse the costs and benefits of different forecasting technologies and combinations of technologies and publish this information to the market, to help solar farms to determine the best choice of forecasting technologies that suit their technology type, size, and location.
A trial of forecasting technology has been launched to help predict the future output from wind and solar farms, which varies depending on the weather and time of day.