Presentation wind & solar forecasting & schedulong in india

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

www.del2infinity.xyz || contact@del2infinity.xyz

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What is Forecast?

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

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

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• What • Why • When • Where • How 3

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

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

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Variability in Solar 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 www.del2infinity.xyz || contact@del2infinity.xyz Š del2infinity Energy Consulting Private Limited

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Variability in Wind There are days-long lulls in wind power

Fig: Variation in wind power output on four different days in 2011 for Karnataka www.del2infinity.xyz || contact@del2infinity.xyz Š del2infinity Energy Consulting Private Limited

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Why to Forecast? Grid Stability

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

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Smart Power Grid = Complex network Breaking Network Stability?

Need some regulation 9

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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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 GenerationActual Generation)/Available Capacity.

 The penalties for deviation have been computed as per Power Purchase Agreements and shall be levied for deviation beyond +/-15%

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• What • Why • When • Where • How 12

When to Forecast…….

Uses Yearly

Monthly

• Resource Planning

Weekly

• Contingency Analysis

Day Ahead

• Scheduling

• Trading 1-6 hour ahead

• Load following

• Commitment for next operating hour 1-2 hour ahead

• Real time despatch decision • Regulation

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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 Forecast Horizon 1 Hour Day Ahead Weekly Monthly

N 4 4 X 24 = 96 96 X 7 = 672 96X30 = 2880 15

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

An approximate Tree structure. Forecast is possible at any Node 17

Aggregation of forecast and Aggregate level forecast are different

Aggregated Forecast Aggregation of Forecast

If Forecast: 18

Aggregated Forecast is better than Aggregation of Forecast i.e.

• Forecast Function is Non-Linear • Above relation is Stable to Linear Transformation of Error

• Propagation of Uncertainty can create false precision 19

• What • Why • When • Where • How 20

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 www.del2infinity.xyz || contact@del2infinity.xyz © del2infinity Energy Consulting Private Limited

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Some simple relation

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Forecast Accuracy = • Using MAE • Using RMSE • Using point error

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Forecast Accuracy vs Penalty for New Regulation

Average Penalty per kw-Hr of Installed Capacity

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An example for Wind Forecast 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

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An example for Solar Forecast 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

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Energy Accuracy

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What is the acceptable value of m Forecast Process:

Forecast Error:

No Deviation Charge if:

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Wind Power Forecast with revision in Wind

Regulation CERC FOR

m 0.15 0.10 31

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 33

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

• What • Why • When • Where • How 35

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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About del2infinity  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

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del2infinity Solution for Wind & Solar Energy

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Automatically delivers the wind and solar power forecasts via a customizable web-based / FTP-based / Email-based platform

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Proprietary algorithm based on statistical machine learning and pattern recognition

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Parallel architecture to integrate other forecast solutions to reduce computation time & delay effects

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Secured data storage & data transmission protocols (SSL encrypted) www.del2infinity.xyz || contact@del2infinity.xyz © del2infinity Energy Consulting Private Limited

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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)

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Forecasting Performance Analysis

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

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

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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)

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Forecast (R0) on 05-July in a Solar Plant at Gujrat 25

Error 30

20

25 15

20 Actual Forecast

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Error

10 10 5

5 0

0

1 4 7 1013161922252831343740434649525558616467707376798285889194 1 4 7 1013161922252831343740434649525558616467707376798285889194

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

Normalized RMSE

15-04-2016 16-04-2016 17-04-2016 18-04-2016 19-04-2016 20-04-2016 21-04-2016 22-04-2016 23-04-2016 24-04-2016 25-04-2016 26-04-2016 27-04-2016 28-04-2016 29-04-2016 30-04-2016

4.54 4.32 4.47 3.55 5.53 2.22 1.86 2.50 2.63 1.21 4.45 1.08 1.06 2.53 2.08 2.08

%PPA TNERC 0.45 0.38 0.40 0.21 0.67 0.03 0.03 0.05 0.05 0.00 0.42 0.00 0.00 0.04 0.06 0.06

No-Penalty Probability(%) 91.67 78.13 80.21 87.50 85.42 95.83 96.88 91.67 90.63 98.96 84.38 98.96 100.00 95.83 91.67 91.67

%PPA FOR 0.24 0.04 0.10 0.08 0.36 0.00 0.00 0.00 0.00 0.00 0.12 0.00 0.00 0.01 0.00 0.00

No-Penalty Probability(%) 95.83 93.75 93.75 98.96 94.79 100.00 100.00 100.00 100.00 100.00 93.75 100.00 100.00 98.96 100.00 100.00

%PPA CERC 0.11 0.00 0.02 0.04 0.19 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00

www.del2infinity.xyz || contact@del2infinity.xyz © del2infinity Energy Consulting Private Limited

No-Penalty Probability (%) 95.83 100.00 98.96 98.96 95.83 100.00 100.00 100.00 100.00 100.00 98.96 100.00 100.00 100.00 100.00 100.00

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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 || contact@del2infinity.xyz June 2016

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Not only Power forecast Analyse Variability

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1-Ramp Analysis Approach •Gather deeper insights into power variability

Karnataka wind

Probability of power ramping up from 2040 MW in the time interval of interest?

Similar approach applied for solar PV power (kWscale variability) Abhik Kumar Das et al., “A Statistical Model for Wind Power on the Basis of Ramp Analysis,” International Journal of Green Energy, 2013

Abhik Kumar Das et al., “An Empirical Model for Ramp Analysis of Utility-Scale Solar PV Power” Solar Energy, Elsevier, vol. 107, September 2014

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2-Ratio Based Approach Ensuring Grid Reliability: Renewable plant operators have to comply with grid code Dimensionless “Ratio Based” Model

µ is related to Ramp Limit

AK Das, “An Analytical Model for Ratio Based Analysis of Wind Power Ramp Events,” Sustainable Energy Technology and Assessments, Elsevier vol.

49 9, pp.49-54, March 2015

Variability Representation: Simplified  System operators want simple, yet robust, insights  Enables decision in fast paced environment 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: www.del2infinity.xyz || contact@del2infinity.xyz © del2infinity Energy Consulting Private Limited

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• 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

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

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