India Smart Grid Forum - WEBINAR Presentation ON WIND & SOLAR FORECASTING & SCHEDULING IN INDIA!!!

ACCURATE & TIMELY INSIGHTS INTO VARIABLE RENEWABLE ENERGY – WIND & SOLAR FORECASTING & SCHEDULING IN

INDIA

Presented by

Mr Abhik Kumar Das Director, ([email protected])

del2infinity Energy Consulting Private Limited

1www.del2infinity.xyz || [email protected] © del2infinity Energy Consulting Private Limited

What is Forecast?

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Forecast is a Prediction of a variable (value, vector or matrix)

considering other similar or dissimilar variable (s) and/or parameters

•What•Why•When•Where•How

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What to Forecast……

– Supply side

• Wind Power generation

• Solar Power generation

–Demand side

• Load Forecast

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Why Solar, Wind ? Clean but Variable

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20% to 40% Renewable energy is wasted due to variability

Variability in Solar

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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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Variability in Wind

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There are days-long lulls in wind power

Fig: Variation in wind power output on four different days in 2011 for Karnataka


Why to Forecast? Grid Stability

8Picture: http://www.news.gatech.edu/features/building-power-grid-future

Breaking Network Stability?

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Need some regulation

Smart Power Grid = Complex network

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Wind & Solar Power Forecasting Regulation

The Central Electricity Regulatory Commission (CERC), India hasfinalized the mechanism for Forecasting, Scheduling and DeviationSettlement of wind & solar projects at Inter-State level.

The CERC has issued the Indian Electricity Grid Code (ThirdAmendment) Regulations, 2015 (IEGC) and Deviation SettlementMechanism and related matters (Second Amendment), Regulations 2015(DSMR)respectively.

The mechanism is applicable from November 1, 2015.

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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 leviedfor deviation beyond +/-15%

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When to Forecast…….

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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

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

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• A is a vector of size N

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Forecast Horizon N

1 Hour 4

Day Ahead 4 X 24 = 96

Weekly 96 X 7 = 672

Monthly 96X30 = 2880

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

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Where to Forecast…….

• Turbine level or PV module/array level

• Plant level (same IPP)

• Plant(s) level ( different IPPs)

• Aggregate level

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An approximate Tree structure. Forecast is possible at any Node

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Aggregation of forecast and Aggregate level forecast are different

Aggregated Forecast

Aggregation of Forecast

If Forecast:

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• 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.


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Forecast Error and Accuracy

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Forecast Error• Average Error

– MAE & Normalized MAE

– RMSE & Normalized RMSE

• Point Error (Time block wise error)

– Based on forecast value

– Based on available capacity


Some simple relation


Forecast Accuracy =

• Using MAE

• Using RMSE

• Using point error


Forecast Accuracy vs Penalty for New Regulation

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Average Penalty per kw-Hr of Installed Capacity

An example for Wind Forecast

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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

An example for Solar Forecast

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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

Energy Accuracy

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What is the acceptable value of m

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Forecast Process:

Forecast Error:

No Deviation Charge if:

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Regulation m

CERC 0.15

FOR 0.10

Wind Power Forecast with revision in Wind

What effects the accuracy?

• Model limitations

• Chaos

• Data & Data analysis uncertainties

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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

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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 Accuracyis below a certain level (say 85%-90%), Power Generators may chargeF&S Service providers for Low Accuracy and False Precision

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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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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

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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• 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)

Forecasting Performance Analysis

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

Forecasting Accuracy (aggregated Wind Power)

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Accuracy: Normalized Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for different forecast horizons (hours)


Forecast (R0) on 12-July in a Wind Plant at KA

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Actual & Forecast Power (R1) (40.2 MW Solar, 24 April, 2016)


Forecast (R0) on 05-July in a Solar Plant at Gujrat

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0

5

10

15

20

25

1 4 7 1013161922252831343740434649525558616467707376798285889194

Actual

Forecast

0

5

10

15

20

25

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1 4 7 1013161922252831343740434649525558616467707376798285889194

Error

Error

Accuracy (40.2 MW Solar, 15 April 2016 – 30 April 2016)

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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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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

www.del2infinity.xyz || [email protected] June 2016

Not only Power forecast

Analyse Variability

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1-Ramp Analysis Approach

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Probability of power ramping up from 2040 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


2-Ratio Based Approach

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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

Variability Representation: Simplified

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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


• 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

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Thank Youdel2infinity Energy Consulting Private Limited

[email protected]

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Data & Analytics

India Smart Grid Forum - WEBINAR Presentation ON WIND & SOLAR FORECASTING & SCHEDULING IN INDIA!!!