Description of project
Proa Analytics is proud to lead a 2 year ARENA funded project demonstrating its advanced, solar forecasting system at three Australian solar farms. The Proa Forecasting System was 100% developed in Australia for Australian conditions. It combines state-of-the-art ground based, digital imaging with geostationary satellite and other data. The three partnering solar farms will provide a valuable demonstration of Proa’s technology across Australia’s diverse climate:
- the Kidston Solar Farm with Genex Power in tropical, Far North Queensland;
- the Oakey 1 Solar Farm with Oakey 1 Asset Company in sub-tropical South East Queensland;
- the Bannerton Solar Farm with Foresight Group Australia in temperate, Northern Victoria.
The 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.
How the project works
Short-term solar forecasting aims to track and predict the motion of clouds and the impact on solar generation. The Proa Forecasting System (PFS) was developed in Australia for 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.
Proa Analytics will participate in the upcoming ARENA insights form 25 June 2019 in Sydney. We will discuss our solar forecasting as part of the panel discussion “Forecasting in a changing energy system” at 3:30-4:30pm, along with numerous other interesting presentations related to Large Scale Projects and Distributed Energy Resources.
More Knowledge Sharing activities will be added during the course of the project.
About Proa Analytics
Proa Analytics (proaanalytics.com) creates tools to understand energy systems. In response to the need to better manage the integration of solar technologies into the grid, Proa Analytics has developed advanced and innovative solutions to model and forecast solar PV generation in Australia.
For more information on:
Proa Analytics and this ARENA funded project, contact us using the form below: