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)

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)

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 Technology and Assessments, Elsevier vol. 9, pp.49-54, 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

Sn

ipp

ets

on

Win

d P

ow

er

º PTC India Ties up Pacts for 1,050 MW Wind Power Supply

PTC India has signed agreements with seven states utilities (Uttar Pradesh (440MW), Bihar, Jharkhand, Assam, Odisha, Delhi and Noida) for sale of wind energy for a total 1049.9 mw. MNRE had formulated the scheme for tying up of 1,000 mw ISTS (Intra-state Transmission System) connected wind power in India. Under the scheme, the projects are to be set up in windy states for supply of power to non-windy ones and UTs.

º UP Government Pulls Plug on Costly Power Producers Worried

UP Government has decided to press the ‘undo’ button for power purchase agreements (PPAs) with a clutch of suppliers to reduce discoms’ costs but the move could open a Pandora’s box for lenders by adding to their NPAs (non-performing assets) as other states pick up the cue. The UPPCL notice says Kundarhki power cost an average of ` 7.11 per unit against an average ` 3.80 procurement tariff approved by the regulator. The notice says UPPCL cannot buy power at this rate since it has signed the Centre’s ‘Power for All’ document, binding it to reduce costs.

13th July 2017, Times of India, Chennai

º India will implement Paris Climate Pact in Letter and Spirit: PM

Prime Minister Shri Narendra Modi while speaking at the informal meeting of BRICS leaders on the sidelines of the G20 Summit in Hamburg, Germany told that India will implement the agreement in letter and spirit. France has already announced to become carbon neutral by 2050 and Germany is also expected to spell out its ambitious plan to join the league.

Source: TOI, 8.7.2017

º Developers Reel under Losses as Rajasthan Companies Shut Off Wind Power Supply

Wind power developers in Rajasthan face losses once again as state distribution companies unplug their supply from the grid every day, to the extent of 15-20%. Since the pre-monsoon and monsoon period, April-September is when winds blow the strongest and generate maximum power. WIPPA has already appealed to RERC in this regard.

Source: Economic Times, June 27, 2017

º Supreme Court upheld APTEL’s Order for Time Value of Money

Supreme Court has upheld the APTEL’s order against TNERC to rework the tariff taking into consideration the Time Value of Money. TNERC has to re-fix the tariff announced in 2006 and later years benefitting many generators.