This document discusses wind and solar power forecasting and scheduling in India. It begins by defining forecasting as predicting a variable based on other variables or parameters. It then discusses why forecasting renewable energy generation from wind and solar is important for grid stability given the variability of these sources. The document outlines India's regulations for wind and solar power forecasting, scheduling, and penalties for deviations. It also discusses various forecasting concepts like horizon, accuracy metrics, and del2infinity's approach to mixed statistical and machine learning methods for high accuracy forecasting.

1. ACCURATE & TIMELY INSIGHTS INTO
VARIABLE RENEWABLE ENERGY – WIND &
SOLAR FORECASTING & SCHEDULING IN
INDIA
Presented by
Mr Abhik Kumar Das
Director, (abhik@del2infinity.xyz)
del2infinity Energy Consulting Private Limited
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2. What is Forecast?
2
Forecast is a Prediction of a variable (value, vector or matrix)
considering other similar or dissimilar variable (s) and/or parameters

3. • What
• Why
• When
• Where
• How
3

4. What to Forecast……
– Supply side
• Wind Power generation
• Solar Power generation
– Demand side
• Load Forecast
4

5. Why Solar, Wind ? Clean but Variable
5
20% to 40% Renewable energy is wasted due to variability

6. Variability in Solar
6
Sun does not shine at night, and there are cloudy days
Fig: Variation in solar PV output on two different days in 2011 at Yelesandra
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7. Variability in Wind
7
There are days-long lulls in wind power
Fig: Variation in wind power output on four different days in 2011 for Karnataka
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8. Why to Forecast? Grid Stability
8Picture: http://www.news.gatech.edu/features/building-power-grid-future

9. Breaking Network Stability?
9
Need some regulation
Smart Power Grid = Complex network

10. 10
Wind & Solar Power Forecasting Regulation
 The Central Electricity Regulatory Commission (CERC), India has
finalized the mechanism for Forecasting, Scheduling and Deviation
Settlement of wind & solar projects at Inter-State level.
 The CERC has issued the Indian Electricity Grid Code (Third
Amendment) Regulations, 2015 (IEGC) and Deviation Settlement
Mechanism and related matters (Second Amendment), Regulations 2015
(DSMR)respectively.
 The mechanism is applicable from November 1, 2015.
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11. Wind & Solar Power Forecasting Regulation
Key features of the mechanism :
 The mechanism shall be applicable to Wind and Solar Generators.
 Scheduling of Wind & Solar Generators have been made mandatory.
 The maximum number of revisions has been increased from 8 to 16.
 A new forecast error computation formula has been formulated, which is: =100*(Scheduled Generation-
Actual Generation)/Available Capacity.
 The penalties for deviation have been computed as per Power Purchase Agreements and shall be levied
for deviation beyond +/-15%
11

12. • What
• Why
• When
• Where
• How
12

13. When to Forecast…….
13
Uses
Yearly
• Resource Planning
• Contingency Analysis
Monthly
Weekly
Day Ahead • Scheduling
• Trading
1-6 hour ahead • Load following
• Commitment for next operating
hour
1-2 hour ahead • Real time despatch decision
• Regulation

14. Forecast Time Block
• 1 Data-point = 15 min Time-Block
• 1 Data = Energy Generation (kW-Hr) in 15 min
• 1 Data = Average Power X 0.25
14

15. • A is a vector of size N
15
Forecast Horizon N
1 Hour 4
Day Ahead 4 X 24 = 96
Weekly 96 X 7 = 672
Monthly 96X30 = 2880

16. Forecast Revision
T1 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0 R0
T2 A A A A R0 R0 R0 R0 R0 R0 R1 R1 R1 R1 R1 R1 R1 R1 R1 R1 R1 R1 R1 R1 R1 R1 R1 R1 R1 R1 R1
T3 A A A A A A A A A A A A R1 R1 R1 R1 R1 R1 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2
16

17. Where to Forecast…….
• Turbine level or PV module/array level
• Plant level (same IPP)
• Plant(s) level ( different IPPs)
• Aggregate level
17
An approximate Tree structure. Forecast is possible at any Node

18. 18
Aggregation of forecast and Aggregate level forecast are different
Aggregated Forecast
Aggregation of Forecast
If Forecast:

19. 19
• Forecast Function is Non-Linear
• Above relation is Stable to Linear Transformation of Error
• Propagation of Uncertainty can create false precision
Aggregated Forecast is better than Aggregation of Forecast
i.e.

20. • What
• Why
• When
• Where
• How
20

21. Forecast Error and Accuracy
21

22. Forecast Error
• Average Error
– MAE & Normalized MAE
– RMSE & Normalized RMSE
• Point Error (Time block wise error)
– Based on forecast value
– Based on available capacity
22
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23. Some simple relation
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24. Forecast Accuracy =
• Using MAE
• Using RMSE
• Using point error
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25. Forecast Accuracy vs Penalty for New Regulation
25
Average Penalty per kw-Hr of Installed Capacity

26. An example for Wind Forecast
26
For CERC Regulation considering PPA = INR 5.00/kW-Hr, the slab wise and total penalties are as follows
for 27.4 MW Wind Plant

27. An example for Solar Forecast
27
For CERC Regulation considering PPA = INR 5.00/kW-Hr, the slab wise and total penalties are as follows for
40.2 MW Solar Plant

28. Energy Accuracy
28

29. What is the acceptable value of m
29
Forecast Process:
Forecast Error:
No Deviation Charge if:

30. 30

31. 31
Regulation m
CERC 0.15
FOR 0.10
Wind Power Forecast with revision in Wind

32. What effects the accuracy?
• Model limitations
• Chaos
• Data & Data analysis uncertainties
32

33. Uncertainty of Forecast
• Requirement of more variables or less variables
• Is uncertainty grows with data complexity or data complexity
reduces uncertainty
• Uncertainty of data availability and uncertainty of forecast
33

34. Who is responsible for Low Accuracy & False Precision?
• Power generators if they do not share the value of Available
Capacity
• Power generators if they do not share their correct Schedule
• Forecast & Scheduling Service providers if their accuracy is low and
produce fake precision
Forecasting is computationally expensive, but if the Energy Accuracy
is below a certain level (say 85%-90%), Power Generators may charge
F&S Service providers for Low Accuracy and False Precision
34

35. • What
• Why
• When
• Where
• How
35

36. How to Forecast…..
• Persistence method : “What you see is what you get”
• Using Numerical Weather Prediction to predict meteorological variables
• Physical approach
• Statistical approach
• Del2infinity’s Mixed Approach
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37. 37
 del2infinity works in the domain of Energy Analytics
 del2infinity serves AAAS (Analytics As A Service) to its different clients
 An IT integrated and solution oriented approach for every energy analytics problems
 del2infinity’s Wind & Solar Power Forecasting product is capable of doing 24 hours day ahead wind
power forecast with maximum 16 revisions
 del2infinity’s forecast Integrator is capable of integrating maximum 7 parallel power forecast
About del2infinity

38. del2infinity Solution for Wind & Solar
Energy
 Automatically delivers the wind and solar power forecasts via a customizable web-based
/ FTP-based / Email-based platform
 Proprietary algorithm based on statistical machine learning and pattern recognition
 Parallel architecture to integrate other forecast solutions to reduce computation time &
delay effects
 Secured data storage & data transmission protocols (SSL encrypted)
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39. 39
• del2infinty believes : “Essentially, all models are wrong, but some are useful”
• del2infinity uses its proprietary useful F&S model(s) to forecast which
– Maximize the Energy Accuracy
– Minimize the Deviation Penalty
- Accepts fair percentage of Financial Responsibility (Client may charge Penalty on del2infinity’s
Service cost if forecast accuracy is not adequate)

40. Forecasting Performance Analysis
40
Fig.: a) Average wind speed vs forecast wind speed b)Error margin of Wind
speed vs Probability of error in forecast without revision (R0)

41. Forecasting Accuracy (aggregated Wind Power)
41
Accuracy: Normalized Mean Absolute Error (MAE) and Root Mean Square
Error (RMSE) for different forecast horizons (hours)
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42. Forecast (R0) on 12-July in a Wind Plant at KA
42

43. Actual & Forecast Power (R1) (40.2 MW Solar, 24 April, 2016)
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44. Forecast (R0) on 05-July in a Solar Plant at Gujrat
44
0
5
10
15
20
25
1 4 7 1013161922252831343740434649525558616467707376798285889194
Actual
Forecast
0
5
10
15
20
25
30
1 4 7 1013161922252831343740434649525558616467707376798285889194
Error
Error

45. Accuracy (40.2 MW Solar, 15 April 2016 – 30
April 2016)
45
Date Normalized RMSE %PPA No-Penalty %PPA No-Penalty %PPA No-Penalty
TNERC Probability(%) FOR Probability(%) CERC Probability (%)
15-04-2016 4.54 0.45 91.67 0.24 95.83 0.11 95.83
16-04-2016 4.32 0.38 78.13 0.04 93.75 0.00 100.00
17-04-2016 4.47 0.40 80.21 0.10 93.75 0.02 98.96
18-04-2016 3.55 0.21 87.50 0.08 98.96 0.04 98.96
19-04-2016 5.53 0.67 85.42 0.36 94.79 0.19 95.83
20-04-2016 2.22 0.03 95.83 0.00 100.00 0.00 100.00
21-04-2016 1.86 0.03 96.88 0.00 100.00 0.00 100.00
22-04-2016 2.50 0.05 91.67 0.00 100.00 0.00 100.00
23-04-2016 2.63 0.05 90.63 0.00 100.00 0.00 100.00
24-04-2016 1.21 0.00 98.96 0.00 100.00 0.00 100.00
25-04-2016 4.45 0.42 84.38 0.12 93.75 0.01 98.96
26-04-2016 1.08 0.00 98.96 0.00 100.00 0.00 100.00
27-04-2016 1.06 0.00 100.00 0.00 100.00 0.00 100.00
28-04-2016 2.53 0.04 95.83 0.01 98.96 0.00 100.00
29-04-2016 2.08 0.06 91.67 0.00 100.00 0.00 100.00
30-04-2016 2.08 0.06 91.67 0.00 100.00 0.00 100.00
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46. 46
Forecast Accuracy in Wind (R12) & Solar (R0)
Forecast (IPP) New Regulation
Absolute Error
Margin
Probability (%)
Wind
Probability (%)
Solar
< 15% 93.36 +/- 5 98.69 +/- 2.5
15%-25% 4.37 +/- 5 1.27+/-2.5
25%-35% 1.16 +/- 5 0.04+/- 2.5
>35% 1.11 +/- 5 0
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June 2016

47. Not only Power forecast
Analyse Variability
47

48. 1-Ramp Analysis Approach
48
Probability of power ramping up from 2040
MW in the time interval of interest?
Karnataka wind
Abhik Kumar Das et al., “An Empirical Model for
Ramp Analysis of Utility-Scale Solar PV Power”
Solar Energy, Elsevier, vol. 107, September 2014
•Gather deeper insights into power variability
Similar approach applied for solar PV power (kW-
scale variability)
Abhik Kumar Das et al., “A Statistical Model for
Wind Power on the Basis of Ramp Analysis,”
International Journal of Green Energy, 2013
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49. 2-Ratio Based Approach
49
Ensuring Grid Reliability:
Renewable plant operators have to comply with
grid code
µ is related to Ramp Limit
Dimensionless “Ratio Based” Model
AK Das, “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

50. Variability Representation: Simplified
50
PV output < 60% of maximum power for
80% of plant-operation time
Abhik Kumar Das, “Quantifying Photovoltaic Power Variability
using Lorenz Curve,” Journal of Renewable and Sustainable Energy,
Journal of Renewable and Sustainable Energy, AIP, vol.6 (3), June
2014
One step ahead for wind:
 System operators want simple, yet robust, insights
 Enables decision in fast paced environment
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51. • Massively ambitious targets for renewable
power across the globe
• Variability is our enemy
Do Forecast, Analyse Variability
“If you know the enemy and know yourself, you
need not fear the result of a hundred battles” – Sun
Tzu, The Art of War
51

52. Thank You
del2infinity Energy Consulting Private Limited
contact@del2infinity.xyz
www.del2infinity.xyz
52