A Post-Processing Approach for Solar Power Combined Forecasts of Ramp Events
1 online resource (134 pages) : PDF
University of North Carolina at Charlotte
The growing integration level of wind and solar energy resources introduces new regulating and operating challenges in the electric grid. Ramp-rate limits of conventional power plants in the generation mix impose an operating constraint on renewable energy sources to the point that, at high integration levels, the ramp-rates of wind and solar resources must be managed by situational awareness tools that are based on forecasts, especially the ramp event forecasts. To leverage such tools, an adjusting post-processing approach is proposed in this dissertation for improving the predictive capability of the combined forecasts of solar power to capture ramp events. The performance evaluation is conducted with several evaluation metrics that consider the accuracy of forecasts in terms of ramp events. Results of case studies demonstrate the efficacy of the adjusting approach. Probabilistic forecasts are also generated to quantify the uncertainty associated with the solar power ramp event forecasts and an uncertainty analysis is carried out.
ENSEMBLEFORECASTMACHINE LEARNINGPOST-PROCESSINGRAMP EVENTSOLAR ENERGY
Hong, TaoCecchi, ValentinaKumar, Ram
Thesis (Ph.D.)--University of North Carolina at Charlotte, 2018.
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