The Fulcrum3D CloudCAM solar forecasting project aims to provide high accuracy short-term power forecasts utilising all-sky cameras and state-of-the-art software. The project will forecast power generated at Genex Power’s 50 MW Kidston Solar Farm in Queensland (including reduction in output from cloud cover) to reduce uncertainty in solar power generation.
Fulcrum3D CloudCAM Solar Forecasting project aimsto reduce causer pays charges which can be enacted when a semi-scheduled generator is unable to meet the generation target set by the Australian Energy Market Operator (AEMO) dispatch system. AEMO is now allowing semi-scheduled generators to submit their own five-minute forecasts. If these are more accurate than AEMO’s current forecasts then this should lower the causer pays penalties payable by the solar farm.
Short term forecasts are also expected to increase robustness of electricity supply and reduce costs. The project will:
- Optimise storage dispatch, demand response and other grid support technologies,
- Spin reserve management,
- Provide Frequency Control Ancillary Service (FCAS) support,
- Assist with grid connection requirements.
Fulcrum3D installed 9 CloudCAMs (cameras) with their associated data loggers and NBN connection at Kidston Solar Farm in Queensland. These were incorporated with the existing Kidston SCADA system in conjunction with a new, highly detailed power model. The model uses data inputs from power station equipment (including inverters, dataloggers, and met stations) and models the current unconstrained generation capability. These systems work together to produce five-minute forecasts of Kidston Solar Farm output and submit them to AEMO’s MP5F API. This forecast is now being used in dispatch and is consistently outperforming ASEFS and reducing Causer Pays charges. The project has met its contracted outcomes. The project’s core achievement was demonstrating that it is possible to satisfy AEMO’s accuracy requirements and achieve zero Causer Pays charges at solar farms via MP5F with generators operating under normal conditions.
How the project works
The Fulcrum3D CloudCAM solar forecasting project will:
- Install nine Fulcrum3D CloudCAMs (with logging and local processing equipment) throughout the Kidston Solar Farm,
- Integrate the CloudCAMs and other solar farm datasets to develop a detailed power model and provide five-minute forecasts into the AEMO system,
- Assess the accuracy of these forecasts and providing a cost-benefit analysis to evaluate value to the solar farm.
Area of innovation
This will be the first time multiple CloudCAMs will be used and integrated on a single site to allow forecasting over a large solar farm.
The intended long-term outcomes of the project are to:
- Reduce the cost of renewable energy by reducing market charges for solar farm owners / operators,
- Improve economics and reduce uncertainty for planned or proposed solar farms by reducing market charges and removing grid connection barriers,
- Increase skill, capacity and knowledge and further commercialisation of the Australian-developed CloudCAM system.
Fulcrum3D is an Australian company working to enable higher and more efficient penetration of renewables into the global energy system. The company develops, manufactures and supports world class high-tech monitoring, data science and forecasting technology for the wind and solar energy industry.
Fulcrum3D’s R&D, software development, design, assembly and testing happens in Australia and it is proud to be creating quality, high-tech Australian jobs. More than 100 GW of solar PV is now being installed annually (ref. Bloomberg New Energy Finance) so the global potential for solar generation forecasting and the leadership opportunity for Australian companies is significant.
This project will demonstrate Fulcrum3D CloudCAM, further develop its applicability and assist in global awareness of Australian technology.
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.