Identifying Wind and Solar Ramping Events

Florita, Anthony; Hodge, Bri-Mathias; Orwig, K.

doi:10.1109/greentech.2013.30

Cited by 70 publications

(61 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Accurate forecast of irradiance ramps is essential to solar power plants for inverter control, plant management and real-time dispatch operations [62,63]. The Hybrid PIs provide possible ranges for the DNI ramps, quantify the uncertainty in the point predictions, and therefore provide useful information for plants or grid operators to make informed decisions to mitigate the weatherdependent variance of solar power production.…”

Section: Real Time Forecastingmentioning

confidence: 99%

Real-time prediction intervals for intra-hour DNI forecasts

Chu¹,

Li²,

Pedro³

et al. 2015

Renewable Energy

View full text Add to dashboard Cite

a b s t r a c tWe develop a hybrid, real-time solar forecasting computational model to construct prediction intervals (PIs) of one-minute averaged direct normal irradiance for four intra-hour forecasting horizons: five, ten, fifteen, and 20 min. This hybrid model, which integrates sky imaging techniques, support vector machine and artificial neural network sub-models, is developed using one year of co-located, high-quality irradiance and sky image recording in Folsom, California. We validate the proposed model using six-month of measured irradiance and sky image data, and apply it to construct operational PI forecasts in real-time at the same observatory. In the real-time scenario, the hybrid model significantly outperforms the reference persistence model and provides high performance PIs regardless of forecast horizon and weather condition.

show abstract

Section: Real Time Forecastingmentioning

confidence: 99%

Real-time prediction intervals for intra-hour DNI forecasts

Chu¹,

Li²,

Pedro³

et al. 2015

Renewable Energy

View full text Add to dashboard Cite

show abstract

“…In the first place, we need to establish the definition of a ramp and the criteria to identify a ramp. There are two common definitions of ramps: (1) the irradiance difference between the start and end points of a time interval, and (2) the difference between minimum and maximum irradiance within a time interval (Zheng and Kusiak, 2009;Kamath, 2010;Florita et al, 2013;. In this case, the two definitions are equivalent since in the interval ½t; t þ FH there are only two available data points: the measured valued at time t and the measured at time t þ FH for the measured ramps, and the measured valued at time t and the forecasted value at time t þ FH for the forecasted ramps.…”

Section: Testing Processmentioning

confidence: 99%

“…Accurate forecast of irradiance ramps is essential for inverter control, plant management and real-time dispatch operations for solar power plants (Zhang et al, 2013;Florita et al, 2013). However, common statistics metrics, such as root mean square error (RMSE), are unable to quantitatively evaluate the performance of a forecast model in estimating irradiance ramps.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al (2013Zhang et al ( , 2015 evaluate a suite of metrics which are commonly used to assess solar forecasts and suggest that skewness, kurtosis, and Renyi entropy are qualitatively sensitive to the performance of ramp forecasts. Florita et al (2013) propose a swinging door algorithm to extract solar ramps by identifying the start and end points of ramps. However, these available metrics do not explicitly quantify the fourth central moment performance of ramp forecasts.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Real-time forecasting of solar irradiance ramps with smart image processing

Chu¹,

Pedro²,

Li³

et al. 2015

Solar Energy

123

View full text Add to dashboard Cite

“…Key metrics for uncertainty quantification include the standard deviation and the Rényi entropy [41]. Heat maps and the swinging-door algorithm [44] will be used to quantify ramps at a variety of scales. Finally, economic metrics that also account for load forecast error will be used to assess the need and cost for flexibility reserves.…”

Section: E Forecast Uncertainty and Performance Metricsmentioning

confidence: 99%

Recent Trends in Variable Generation Forecasting and Its Value to the Power System

Orwig

Ahlstrom²,

Banunarayanan

et al. 2015

IEEE Trans. Sustain. Energy

Self Cite

View full text Add to dashboard Cite

The rapid deployment of wind and solar energy generation systems has resulted in a need to better understand, predict, and manage variable generation. The uncertainty around wind and solar power forecasts is still viewed by the power industry as being quite high, and many barriers to forecast adoption by power system operators still remain. In response, the U.S. Department of Energy has sponsored, in partnership with the National Oceanic and Atmospheric Administration, public, private, and academic organizations, two projects to advance wind and solar power forecasts. Additionally, several utilities and grid operators have recognized the value of adopting variable generation forecasting and have taken great strides to enhance their usage of forecasting. In parallel, power system markets and operations are evolving to integrate greater amounts of variable generation. This paper will discuss the recent trends in wind and solar power forecasting technologies in the U.S., the role of forecasting in an evolving power system framework, and the benefits to intended forecast users.

show abstract

Identifying Wind and Solar Ramping Events

Cited by 70 publications

References 2 publications

Real-time prediction intervals for intra-hour DNI forecasts

Real-time prediction intervals for intra-hour DNI forecasts

Real-time forecasting of solar irradiance ramps with smart image processing

Recent Trends in Variable Generation Forecasting and Its Value to the Power System

Contact Info

Product

Resources

About