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Title: Probabilistic Swinging Door Algorithm as Applied to Photovoltaic Power Ramping Event Detection

Photovoltaic (PV) power generation experiences power ramping events due to cloud interference. Depending on the extent of PV aggregation and local grid features, such power variability can be constructive or destructive to measures of uncertainty regarding renewable power generation; however, it directly influences contingency planning, production costs, and the overall reliable operation of power systems. For enhanced power system flexibility, and to help mitigate the negative impacts of power ramping, it is desirable to analyze events in a probabilistic fashion so degrees of beliefs concerning system states and forecastability are better captured and uncertainty is explicitly quantified. A probabilistic swinging door algorithm is developed and presented in this paper. It is then applied to a solar data set of PV power generation. The probabilistic swinging door algorithm builds on results from the original swinging door algorithm, first used for data compression in trend logging, and it is described by two uncertain parameters: (i) e, the threshold sensitivity to a given ramp, and (ii) s, the residual of the piecewise linear ramps. These two parameters determine the distribution of ramps and capture the uncertainty in PV power generation.
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Conference: Presented at the 5th Solar Integration Workshop: International Workshop on Integration of Solar Power into Power Systems, 19-20 October 2015, Brussels, Belgium
Darmstadt, Germany: Energynautics GmbH
Research Org:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
Country of Publication:
United States
14 SOLAR ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION; solar forecasting; uncertainty; Bayesian