Solar and Wind Forecasting
As solar and wind power become more common, forecasting that is integrated into energy management systems is increasingly valuable to electric power system operators.
- Machine learning capability and data analytics for generating short-term forecasts
- Simulation using PLEXOS, a mathematical optimization tool for simulating transmission systems and better understanding the value of accurate forecasting
- Wind power visualization to direct questions and feedback during industry meetings
In this three-year project, NREL researchers are developing an innovative, integrated, and transformative approach to mitigate the impact of wind ramping. The project will reduce wind integration costs by making wind power dispatchable and allowing the efficient management of wind ramping characteristics. NREL is collaborating with industry to test and validate this methodology, taking into account economic and reliability goals, by integrating it into the operations of two independent system operators (MISO and ERCOT).
Enhancing Power System Operational Flexibility with Flexible Ramping Products: A Review, IEEE Transactions on Industrial Informatics (2016)
WindView is an open-source situational awareness and decision support platform that provides grid operators with knowledge of the state and performance of their power system. Its emphasis is on wind energy and enabling the reliable and efficient integration of larger amounts of wind energy. This three-year project involves close collaboration with Western Area Power Authority's Electric Power Training Center to develop a production-level version of WindView, including feedback and training sessions, for the Western Area Power Authority network. The focus will be on advanced visualization to display pertinent information, extracted through computational techniques, from wind power forecasts for high-wind-penetration systems. WindView will be publicly available for industry and academic researchers and will incorporate forecasting tools designed by NREL and Argonne National Laboratory.
Development of a Neural Network-Based Renewable Energy Forecasting Framework for Process Industries, 26th European Symposium on Computer Aided Process Engineering (2016)
Value of Improved Short-Term Wind Power Forecasting, NREL Technical Paper (2015)
The Value of Day-Ahead Solar Power Forecasting Improvement, Solar Energy (2016)
Quantifying the Economic and Grid Reliability Impacts of Improved Wind Power Forecasting, IEEE Transactions on Sustainable Energy (2016)
Ramp Forecasting Performance from Improved Short-Term Wind Power Forecasting: Preprint, ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (2014)
View all NREL publications about forecasting.