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Contract No. DE-AC36-08GO28308

Identifying Wind and Solar 

Ramping Events

Preprint

A. Florita, B.-M. Hodge, and K. Orwig

To be presented at the IEEE Green Technologies Conference

Denver, Colorado

April 4–5, 2013

Conference Paper

NREL/CP-5500-57447

January 2013
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1 

Identifying Wind and Solar Ramping Events 

Anthony Florita, Bri-Mathias Hodge, and Kirsten Orwig

Transmission and Grid Integration Group

National Renewable Energy Laboratory

Golden, CO USA

Abstract—Wind and solar power are playing an increasing role 

in the electrical grid, but their inherent power variability can 

augment uncertainties in the operation of power systems. One 

solution to help mitigate the impacts and provide more flexibility

is enhanced wind and solar power forecasting; however, its 

relative utility is also uncertain. Within the variability of solar 

and wind power, repercussions from large ramping events are of 

primary concern. At the same time, there is no clear definition of 

what constitutes a ramping event, with various criteria used in 

different operational areas. Here, the swinging door algorithm, 

originally used for data compression in trend logging, is applied 

to identify variable generation ramping events from historic 

operational data. The identification of ramps in a simple and 

automated fashion is a critical task that feeds into a larger work 

of 1) defining novel metrics for wind and solar power forecasting 

that attempt to capture the true impact of forecast errors on 

system operations and economics, and 2) informing various 

power system models in a data-driven manner for superior 

exploratory simulation research. Both allow inference on 

sensitivities and meaningful correlations, as well as quantify the 

value of probabilistic approaches for future use in practice.

Index Terms—wind energy, solar energy, forecasting, time 

series analysis

I. INTRODUCTION

The increasing amounts of wind and solar power capacity 

being installed in the electrical system are causing more 

concern from system operators about the variable and uncertain 

nature of these generators. To an extent, power system

operations are already able to handle variability and

uncertainty, e.g., power demand. Existing techniques include

regulation reserves, load-following reserves, and sub-hourly 

economic dispatch. However, in simplistic terms, the 

uncertainty in load, now coupled with increasing levels of 

uncertainty in generation, can lead to wider distributions of 

uncertainty for all variables and parameters of interest; 

responding to variability under increased uncertainty is all the 

more difficult. Enhanced wind and solar power forecasting can 

help address some of these concerns through the reduction of

uncertainty faced by the system of interest. Because there are 

mechanisms in place to handle small amounts of uncertainty 

and variability, power system operators place primary 

emphasis on better understanding the impact of extreme events 

(e.g., large ramps), which can have significant influence on 

system economics and reliability. Secondary concern is for 

uniform power forecasting improvements for enhanced 

planning applications. 

Wind and solar ramps can occur at different timescales, 

geographic scales, and in both the positive and negative 

directions. Variable generation forecasting can help remove 

some of the uncertainty involved with the power supply, but 

may have trouble forecasting large ramping events. The 

numerical weather prediction models often used for forecasting 

are generally good at predicting roughly when a ramping event 

may occur; however, there are two main ways in which 

inaccurate forecasting of ramp events can lead to large errors: 

ramp magnitude and timing errors. In ramp magnitude errors, a

ramp is forecast, but the actual value changes significantly 

more/less than was forecast. In ramp timing errors, the actual 

ramp in power significantly leads/lags the forecast time. Of 

course, both errors can occur simultaneously, which indicates a 

poor forecast. It is the hope that offline ramp analyses, coupled 

with extensive unit commitment and dispatch simulation 

studies, will allow the synthesis of knowledge for enhanced 

dispatch in cases of large variable generation power ramps. 

The automated identification of ramping events must be

computationally inexpensive to justify online applications, but 

can also help facilitate the improvement of forecasting 

algorithms by providing metrics on how well ramping events

are captured. Kamath analyzed wind ramping events in the 

Bonneville Power Administration area using two definitions of 

ramping [1]. The first definition was simply the slope of 

change between two points; the second considered the 

minimum and maximum values of generation between two 

points. Zheng and Kusiak [2] focused on forecasting wind 

power ramping events. They employed the rate of change of 

wind plant power over a 10-minute interval to define ramps.

Hodge et al. [3] used similar fixed-point definitions to identify 

and characterize the number of ramping events that occurred

for solar power at different timescales. Hansen et al. [4] used 

the swinging door algorithm to characterize irradiance time 

series data in the Southwest United States. Because of the 

flexibility and simplicity of the algorithm, both wind and solar 

power ramps over varying time frames can be identified. 

II. SWINGING DOOR ALGORITHM

In this work, we propose the application of an algorithm 

from the area of data compression, known as the swinging door 

algorithm [5], to identify wind and solar power ramping events. 

Its computational and structural simplicity, requiring only one 

parameter in its definition, are favorable attributes considering 

its robustness in the face of noisy data.
2 

Ramps are typically extracted through a linear piecewise 

approximation to the original time series of data. If extracting 

ramps from measured data, the approximation can be thought 

of as a disregard for the noise inherent to the measurement 

process and/or insignificant changes. If extracting ramps from 

simulated data, the approximation can be thought of solely as 

disregard for the insignificant changes. In either case, the focus 

of ramp extraction is placed on the significant linear ramps (in 

terms of magnitude and duration) present in the dataset.

Mathematically speaking, a ramp is quantified by its 

instantaneous rate of change, its derivative, dG

dt , and is 

approximated initially by a local ratio of differences: dG

dt =

G(k)−G(k−1)

k−(k−1) . The discrete-time nature of either the measured or 

simulated data easily allows such a calculation. However, the 

point of ramp extraction is to determine a trend in a sequence 

of local derivatives and the magnitude and duration of such a 

trend. For example, when considering a time series of power, 

the local derivative (ramp) from the first two points may be 

3.0

2−1 = 3 MW and from the second and third points 3.23−2 =

3.2 MW. The trend is apparent and the average ramp is 3.1 MW

over the three discrete-time samples. The question of interest is 

when a particular ramp has started and/or when the local 

derivative has changed to the point it can no longer be 

considered part of a particular ramp.

Figure 1 illustrates a simplified example of a signal and the

ramps that may be extracted. Of course, a realistic time series

of wind or solar power is much more complicated, but the same 

strategies and goals for the extraction of ramps apply as 

described here. The measurement points are discrete-time

samples, and the spline fit is included in the figure to show 

what the continuous process may resemble. The identified 

ramps are nearly of equal magnitude, but in general this will 

not be the case. It is somewhat easy to visually discern the 

ramps (trends) even though it is apparent the sign of the slope 

can change within a particular ramp. Although noise is inherent 

to any real measured data, here there are no assumptions about 

the probability density of a realization and the piecewise linear 

approximation to the time series is anchored to dominant 

points. Considering a threshold for the ramp trend and 

anchoring the piecewise linear approximation to measurement 

points allows for reduced sensitivity to inflection points and 

other insignificant fluctuations.

The swinging door algorithm allows the extraction of ramps 

in a signal, in a piecewise linear fashion, while allowing for

consideration of a threshold parameter influencing its 

sensitivity to ramp variations. The only tunable parameter is ε, 

the width of one “door” in the algorithm (as shown in Figure 2) 

that directly allows the (threshold) sensitivity to noise and/or 

insignificant fluctuations to be specified. If the tolerance is very 

low (a small ε value), the ramp extraction algorithm will 

identify many small ramps as it basically traces the original 

signal, violates the threshold, and starts over. If the tolerance is 

very high (a large ε value), the algorithm will identify a few

large ramps as it is under constrained and a large fluctuation is 

required for the threshold to be violated. In the figure, it should 

be noted the scale is arbitrary for the purposes of explanation, 

and in general the signal magnitude is much larger than the 

scale of the threshold bounds.

From Figure 2, the swinging door algorithm is briefly 

described: 1) the initial (dominant) point, or new (ramp 

segment) iteration of the algorithm, is on the y-axis and 

threshold doors of width ε are placed above and below it; 2) a 

new point A is acquired and the doors “swing open,” as 

indicated, to include the point – i.e., lines are drawn from the 

doors’ hinges to the point; 3) a new point B is acquired and 

lines are again drawn (updated) to intersect at B; 4) a new point 

C is acquired, but there has been an inflection in the signal, and 

the swinging doors open only to accommodate new points in a 

ramp segment iteration, so the top door (extended line) remains 

in its angle position above C and the lower door line is drawn 

to point C – further extension of the lines would result in an 

intersection at some point in the future; 5) a new point D is 

acquired; again the top door (line) angle position is not 

updated, and the lower door line is drawn to point D. The lines 

Figure 1. Example of the piecewise linear approximation 

to a time series for ramp extraction and analysis; the 

scale is arbitrary for explanation purposes.

Figure 2. The swinging door algorithm for the extraction 

of ramps in power from the time series; the scale is 

arbitrary for explanation purposes.
3 

are now parallel (or do not intersect in the future), which starts 

a new iteration of the algorithm – i.e. the threshold has been 

exceeded when the line angle from the hinges to their most 

open position is greater than or equal to parallel. The threshold 

could be violated somewhere between C and D, but because of

the discrete-time nature of the approximation, a new iteration 

would still start at D. The piecewise linear approximation (the 

ramp, shown in red) starts at the end of the previous iteration 

(dominant point) and ends when the threshold is exceeded 

(next dominant point).

Ramp sign changes are an indicator of fluctuation, but it is 

not obvious what an insignificant fluctuation is when 

considering noisy measured data and/or actual (but slight) 

power variations. There are two applications that are noted for 

defining the threshold and thus what is considered an 

insignificant fluctuation: 1) according to the accuracy of the

measurement device as defined by its distribution of 

measurement uncertainty, or 2) according to the utility of the 

measure as defined by power system economics and its relative 

importance in driving operations. In this work, neither 

application is explicitly employed, but the ε value varied to 

explore the sensitivity of ramp events extracted according to its 

value. Specifically, the ε value is set to a percentage of the

maximum capacity observed in the time frame of interest.

Figure 3 shows a typical example in the extraction of ramps 

from a large wind farm over a two-day period. The power 

profile, composed of hourly data, is variable but somewhat 

smooth because of the diversity in power from individual 

turbines aggregating to cancel high-frequency variability, 

combined with time-averaged power output over the hour. 

Therefore, a rather high tolerance, ε value of 10% of maximum 

capacity, was used and provided an accurate piecewise linear 

approximation to the wind power profile.

Figures 4 and 5 show typical examples in the extraction of 

ramps from a solar plant over a two-day period, both using data 

sampled on a one-minute basis; first, the clear day of Figure 4, 

followed by the somewhat cloudy day of Figure 5. The power

profile is smooth in Figure 4 and shows high-frequency 

variability in Figure 5. A rather low tolerance, ε value of 1% of 

maximum capacity, was used and provided an accurate 

piecewise linear approximation to the solar power profile. 

However, it is apparent the ε choice introduces tradeoffs 

between the count of ramps and their approximation accuracy. 

That is, a clear day may be adequately described by fewer 

piecewise segments, whereas a cloudy day may require more 

for an adequate description. Economics of the system under 

consideration will likely determine the choice of ε. 

III. WIND AND SOLAR DATA

To showcase the use of the swinging door algorithm for 

wind and solar power ramp detection, it was applied to various 

datasets. Wind data came from a wind plant in the Xcel 

Colorado territory with an approximate capacity of 300 MW; 

the discrete-time sample was 1 minute. The solar data came 

from Oahu and Maui, Hawaii, in association with the Hawaiian 

Solar Integration Study; the discrete-time sample was 1-second.

Figure 3. Typical example of ramp extraction from two 

days of power at a large wind farm, showing up and 

down ramps of large, medium, and insignificant nature.

Figure 4. Typical example of ramp extraction from the 

first of two days of power at a PV solar plant, showing a 

clear day leading to a smooth profile.

Figure 5. Typical example of ramp extraction from the 

second of two days of power at a PV solar plant, 

showing up and down ramps because of clouds.
4 

Figure 6. Bivariate distribution of wind power, ramp rise 

versus run, as a function of the ε value; top subplot is ε = 

1% maximum capacity in December, followed by ε = 2, 

3, and 5%, respectively.

Figure 7. Bivariate distribution of solar power, ramp rise 

versus run, as a function of the ε value; top subplot is ε = 

1% maximum capacity in December, followed by ε = 2, 

3, and 5%, respectively.

IV. RESULTS

The resolution of the extracted ramp events is a function of

ε, which is informed by application specifics. The utility of a 

given magnitude of ramp event (as part of power system 

economics) was not considered here, but it is the subject of 

ongoing research toward understanding the probabilistic 

relationships of various systems. Typical wind and solar power 

examples were provided in this section, but the time resolution, 

geographic diversity, and extent of smoothing from plant 

aggregation were limited to the data available. 

Ramp extractions were visualized by rise-run distributions.

Figures 6 and 7 give the bivariate distributions of wind and 

solar power, respectively, as a function of various ε values; in

both figures, the ε value was, from top-to-bottom subplots, 

equal to 1, 2, 3, and 5% of the maximum capacity observed in 

the month of December.

From the wind power ramp extraction of Figure 6 (i.e., rise 

[MW] versus run [min]), it is noted that with lower tolerance, 

more ramps of longer duration were extracted. This would be 

expected, but it is also interesting to note how the distribution 

spreads within the more immediate (quick) ramp region. In the 

solar power ramp extraction of Figure 7 (rise [kW] versus run 

[s]), the same trends as the wind example were noted; however, 

there appeared to be a correlated “what goes up, must come 

down” pattern to the ramps because of the diurnal nature of 

solar irradiance. That is, there was an approximate balance of 

up and down ramps of similar magnitude and duration.

Furthermore, the dispersion of ramps was driven by the plant 

(area) size and the December cloud cover.
5 

Figure 8. Bivariate distribution of wind power, ramp rise 

versus run, as a function of the aggregation level of wind 

turbines; top subplot is ε = 25% total wind farm, followed 

by ε = 50, 75, and 100%, respectively. An ε = 3% of the 

maximum capacity was used for the month of December.

Figure 9. Bivariate distribution of solar power, ramp rise 

versus run, as a function of the aggregation level of PV 

modules; top subplot is ε = 25% total plant, followed by 

ε = 50, 75, and 100%, respectively. An ε = 3% of the 

maximum capacity was used for the month of December.

As might be expected, smoothing from aggregation was 

observed in both power datasets and varied according to the

size (area) of the total plant. In wind power, the downstream 

turbines generally experienced slower and more turbulent 

wind, and spatial correlations in power variability diminished

with distance. In solar power, cloud cover seemed to have only 

influenced a portion of the array, and spatial correlations in 

power variability diminished with distance. In either case, and 

as is frequently observed, variability was smoothed with 

increasing plant (area) size.

It was of interest to determine the extent of smoothing 

observed in the extracted ramp events. Figures 8 and 9 give the 

bivariate distributions of wind and solar power, respectively, as

a function of levels of aggregation in either percentage of wind 

turbines or PV modules; in both figures, the percentage was, 

from top-to-bottom subplots, equal to 25, 50, 75, and 100% of 

the total fleet in the month of December. As shown by the wind 

power ramp extraction of Figure 8 (i.e. rise [MW] versus run 

[min]), there was a slight reduction in the dispersion of ramps 

as the aggregation level increased. In the solar power ramp 

extraction of Figure 9 (rise [kW] versus run [s]), the same 

trends as the wind example were noted; however, the correlated 

nature caused by diurnal behavior became more pronounced 

with increasing levels of aggregation. In addition, the 

frequency of more immediate (quick) ramps seemed to level

off around one-half the total capacity of the PV solar plant.
6 

V. CONCLUSIONS

The forecasting of solar and wind power ramps is a major 

area of concern in the field of variable generation forecasting.

In this work, the application of a data compression technique to 

the identification of solar and wind power ramps was shown.

Because these ramping events are one of the most pressing 

concerns of system operators in balancing areas with large 

penetrations of variable generation, this automated 

identification process is helpful toward creating algorithms and 

assessment metrics that can better forecast variable generation 

ramps and their economic impact.

One of the critical issues in wind and solar power 

forecasting is that the metrics used to assess forecasting 

techniques are simple statistical measures that do not take into 

account the factors that are most critical for power system 

operations. For example, because power systems have means 

by which they can compensate for small forecast errors, and 

large forecasting errors are both expensive and can present 

reliability concerns, it would be better to improve the 

forecasting for these extreme events, even at the cost of slightly 

decreased performance during the rest of the times. This is 

something that is very difficult to capture with the currently 

used statistical techniques in which the impact of a large 

number of small error events can overwhelm the impact of a 

small number of large error events.

Because ramping events comprise a large percentage of 

these large error events, their automated identification is an 

important step toward developing metrics that can be used to 

tune forecasting algorithms to consider their importance. In 

addition, similar identification techniques could be used 

actively in system operations. One possible example of how 

this could be used to improve operations would be an increase 

in reserves being triggered by the signal when a down ramp in 

power output had begun. The automated identification would 

also be useful in assessing probabilistic forecasts. Some system 

operators currently request that downward ramps in wind 

power are forecast in a probabilistic manner, in a separate 

forecast product from the normal forecasts. These forecasts 

indicate degree-of-belief, giving the likelihood of a down ramp 

occurring in the specified time frame, and the automated 

identification techniques advocated here could lead to 

improvements in assessing system performance.

ACKNOWLEDGMENT

This work was supported by the U.S. Department of Energy 

under Contract No. DE-AC36-08-GO28308 with the National 

Renewable Energy Laboratory. 

The authors would also like to thank Keith Parks at Xcel for

the wind power data.

REFERENCES

[1] C. Kamath, “Understanding wind ramp events 

through analysis of historical data,” in 2010 IEEE 

PES Transmission and Distribution Conf. and Expo., 

New Orleans, LA, 2010.

[2] H. Zheng and A. Kusiak, “Prediction of wind farm 

power ramp rates: A data-mining approach,” J. of 

Solar Energy Eng., vol. 131, 2009.

[3] B.-M. Hodge, M. Hummon, and K. Orwig, “Solar 

ramping distributions over multiple timescales and 

weather patterns,” in The 1st Int. Workshop on 

Integration of Solar Power Into Power Systems, 

Aarhus, Denmark, 2011.

[4] C. Hansen, J. Stein, and A. Ellis, “Statistical criteria 

for characterizing irradiance time series,” Sandia 

National Laboratories, Albuquerque, NM, 2010.

[5] E. Bristol, “Swinging door trending: Adaptive trend 

recording?,” in International Standards for 

Automation, 1990.