Probability based scenario analysis & ramping correction factor in wind power generation forecasting

1. 24 Indian Wind Power August - September 2017
1. Introduction
Day-ahead forecast of wind power generation is an essential
requirement for the proper grid management as the large
penetration of wind energy into the existing grid system can
create instability in the demand-supply ratio of power distribution
due to the variability and intermittency of wind generation
patterns. The variability and unpredictability inherent to wind
can create a threat to grid reliability due to balancing challenge
in load and generation as the unscheduled fluctuations of
wind power generation produce ramping events. Hence the
integration of significant wind into the existing supply system is
a challenge for large scale renewable energy penetration [1-6].
To accommodate the variability, the day-ahead and short-term
renewable energy forecasting is needed to effectively integrate
renewable energy to the existing grid and hence the forecasting
and scheduling of wind energy generation has become a widely
pursued area of research in Indian context. [7, 8]
Wind power generation forecasting can be done using different
models accommodating different observations like real-time and
historical data related to power generation, weather parameters,
topological space etc. One of the common ways to generate
wind power forcast is using NWP (Numerical Weather Prediction)
model in which different physical variable is simulated solving
few differential equations representing the physical phenomena
and derive the velocity tensor in the wind plant location which
then transformed into power generation using power curve
models [9]. Using CFD (Computational Fluid Dynamics) based
analysis and using pattern recognition technique considering
recently developed computational structure of DNN (Deep
Neural Network) the forecast models can be customized
for specific wind plants. But considering different parametric
uncertainties associated with forecasting and scheduling, the
perspective regarding forecasting methodology is to regard it
fundamentally as a statistical rather than deterministic solutions.
Thus from a computational point, forecasting of wind power
generation is best considered as the study of the temporal
evolution of probability distributions associated with parameters
in the power generation. Hence scenario based analysis using
probability distribution can play an important role in forecasting
the wind power generation.
2. Probabilistic Scenario Analysis
Scenario-based analysis using probability space can be
considered as a statistical technique of analyzing possible
wind forecast patterns assuming alternative possible outcomes.
Thus, scenario-based analysis does not try to predict one exact
deterministic solution of forecast. Instead, it predicts several
alternative forecast patterns with associated probabilities and
uncertainties leading to the outcomes. In contrast to prognoses
or likely outcome, the scenario-based analysis is not only based
on extrapolation of the past or the extension of past trends.
Depending on the different parametric approximation with
uncertainties, a forecast system can generate different plausible
scenarios, though the ensemble behavior of the forecast
patterns remains same considering the NWP models. The
localized solution and the distribution of different parameters
and the uncertainties associated with these parameters can
create different scenarios and the scenarios with maximum
overall probability can be considered as the best solution of the
day-ahead forecast.
For simplicity, consider k-th scenario of possible day-ahead
forecast of wind power generation at particular plant is
= (1), (2), (3), … . , (96) having overall probability
measure Pk. Here, N scenarios can create a matrix of size NX96
and ther associated probability can be represented as follows:
Since the forecast strategy is non-deterministic, the value of
Pk can be computed using different probability measures. For
N scenarios, a straightforward algorithm is to find the scenario
which has maximum Pi value.
3. Ramping Correction Factor
Unlike solar, wind power generation is much more affected
by its ramping behavior due to its variability. Though the
variability is uncertain, the ramping events in the wind power
generation follow some statistical distribution [1-3]. This
statistical distribution can be used as the correcting factor in
finding the best plausible scenario representing the day-ahead
Abhik Kumar Das, Del2infinity Energy Consulting, India
Email: contact@del2infinity.xyz
Probability-based Scenario Analysis and
Ramping Correction Factor in Wind Power
Generation Forecasting
(This paper was presented in Abstract Presentation at Windergy
India 2017 Conference organised by IWTMA and GWEC)
(1) … (96)
(1) … (96)
(1) … (96)
(1) … (96)
(1)

2. 26 Indian Wind Power August - September 2017
forecast values. The first order ramping in k-th scenario can be
represented as an event
If the ramping in an actual wind power generation follows
a particular distribution, without loss of much information
we can assume that the forecast generation can follow the
similar distribution. Hence we can state that follows the
cumulative distribution as G(m) [1],
Here AvC is the available capacity; α and β are two empirical
factors depending on the order of ramping and the plant
actual power generation characteristics and also have seasonal
variations. Hence, the correction factor of ( ) comes from
the distribution G(m) for some specific value m as,
The first order ramping correction factor can be used to update
the probability of the different scenarios as follows.
4. Experimentation
Due to simplicity in experimentation, we have considered only
6 possible scenarios in generating day-ahead forecast with a
data set of aggregated wind generation of Karnataka in 2014.
Considering different parametric behavior the different scenarios
are shown in Figures (a)-(f). The maximum probability scenario
is derived in Figure (g) and the scenario with ramping correction
is shown in Figure (h). It is interesting to see that the short-term
accuracy in Figure (h) has been in the acceptable region for 4
hours and also minimizing the effect of ramping events. The
forecast showing in Figure (h) also implies the need of revision.
Figure (d) is considered as a worst case scenario in this analysis.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
Figures (a)-(F): Different scenarios of forecasting events and
Figure (g) is initial forecast using probability-based scenario
analysis and Figure (h) is the forecast after ramping correction.
In the figures, black and blue lines represent the actual
generation of the required day and the last day, respectively.
The red line in each figure represents the plausible forecast
generation of the required day.
5. Conclusion
The probability-based scenario generation method in forecasting
is an effective tool in wind power generation forecasting
considering different parametric uncertainties in the forecast
process. The non-deterministic behavior of finding a stable
solution in the day-ahead forecast of wind generation needs
the generation of alternative outcomes to test different likely
(or unlikely) hypothesis in generation forecast. The ramping
correction factor plays an effective role in determining the
= (2) (1), (3) (2), … . , (96) (95) (2)
(5)
(4)
( ) = = ( )
(3)

3. 27Indian Wind PowerAugust - September 2017
best possible forecast pattern in high variability. The similar
theory using higher order ramping correction factors can be
applicable in aggregated wind forecasting model in determining
the weightages of different forecast pattern generating from
different models.
References:
1. Das Abhik Kumar, “An analytical model for ratio-based
analysis of wind power ramp events”, Sustainable Energy
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March 2015
2. Kamath, C. 2010. “Understanding Wind Ramp Events
through Analysis of Historical Data.” Transmission and
Distribution Conference and Exposition, 2010 IEEE PES in
New Orleans, LA, United States, April 2010
3. Das Abhik Kumar & Majumder Bishal Madhab, “Statistical
Model for Wind Power based on Ramp Analysis”,
International Journal of Green Energy, 2013
4. Gallego C., Costa A., Cuerva A., Landberg L., Greaves B.,
Collins J., “A wavelet-based approach for large wind power
ramp characterisation”, Wind Energy, vol. 16(2), pp. 257-
278, Mar. 2013
5. Bosavy A., Girad R., Kariniotakis G., “Forecasting ramps of
wind power production with numerical weather prediction
ensembles”, Wind Energy, vol. 16(1), pp. 51-63, Jan. 2013
6. Kirby B., Milligan M., “An exemption of capacity and
ramping impacts of wind energy on power systems”, The
Electricity Journal, vol.2(7), Sept. 2008, pp.30-42
7. Steffel, S.J., 2010. Distribution grid considerations for large
scale solar and wind installations. IEEE, 1–3, Transmission
and Distribution Conference and Exposition, 2010 IEEE
PES
8. Das Abhik Kumar, ‘Forecasting and Scheduling of Wind and
Solar Power generation in India’, NTPC’s Third International
technology Summit ‘Global Energy Technology Summit’
2016
9. Das Abhik Kumar, “An Empirical Model of Power Curve of
a Wind Turbine”, Energy Systems vol. 5(3), pp. 507-518,
March 2014
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