A Post-Processing Approach for Solar Power Combined Forecasts of Ramp Events

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Abuella, M. (2018). A Post-Processing Approach for Solar Power Combined Forecasts of Ramp Events. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

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.

Details
Author

Abuella, Mohamed

Title

A Post-Processing Approach for Solar Power Combined Forecasts of Ramp Events

Physical Description

1 online resource (134 pages) : PDF

Date

2018

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

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.

Genre

doctoral dissertations

Subjects--Topics

Electrical engineering

Degree

Ph.D.

Keywords

Ensemble
Forecast
Machine Learning
Post-Processing
Ramp Event
Solar Energy

Subject Area

Electrical Engineering

Advisor(s)

Chowdhury, Badruld

Committee Members

Hong, Tao
Cecchi, Valentina
Kumar, Ram

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2018.

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Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Abuella_uncc_0694D_11881

Permalink

http://hdl.handle.net/20.500.13093/etd:1107