VG Forecasting – Centre of Excellence

Dr.K.BALARAMAN DIRECTOR GENERAL, National Institute of Wind Energy Chennai

Disclaimer: The presentation, views and opinions of the presenter is his personal and does not convey the position he holds. It is intended for professional discussion & interaction only

NIWE NIWE is an autonomous body under MNRE focusing on Wind sector along with wind & solar resource assessment

NIWE is the custodian of largest ground based measured wind and solar data bank with 1881 wind monitoring stations and 125 solar monitoring stations across the country

4 Advanced Measurement Stations for R & D purpose under MNRE project and are under prestigious Base Line Surface Radiation Network(BSRN), of WMO for climate change studies

Developed Indian Wind and Solar Radiation Atlas using Meso scale coupled with measured wind data and SRRA data

Developed SWURJA mobile app for Wind & Solar applications in ios and Android platform

Indian Grid Power: A Glance (As at the end of March 2019)

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RE Installed Capacity – 77.64 GW

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Total Installed Capacity – 356.1 GW

Renewable Energy is growing at an exponential rate since the last decade

In some of the countries like Denmark, the share of renewable generation is higher than the demand during some part of the time.

Power System Operators face challenges in operating the system with large renewable energy due its intermittency and variability.

Curtailment of generation from wind and solar is a growing concern across the world

Introduction

India as one of the Global leaders in RE

India has recently emerged as a global leader in RE

• 4th in the world in Wind Energy capacity • 5th in the world in Solar Energy capacity • 5th in the world in Total Renewable Energy Capacity

Tremendous progress seen in wind sector during last 10 years

• Wind energy capacity increased 3 times i.e., from 11.8 GW in 2009-10 to 35.135 GW in 2018-19 • Solar Generation increased from 0.16MW in 2010 to 28.18 GW in 2019

New initiatives taken - Solar parks, Transparent reverse auction, Free ISTS, Wind Solar hybrid, Floating Solar, Offshore and storage

India targets to have an Installed RE capacity 175GW by 2022 and 500 GW by 2030

Challenges in System Operations

Conventional System Only Demand is varying -> Demand Forecasting -> Generation follows the load Addition of RE Generation Both Demand and RE Generation are varying -> Demand + RE Power Forecasting

Forecasting • Uses: Regulation, real-time dispatch

decisions Near Term

(5 - 60 minutes)

• Uses: Load-following, near term unit commitment (Hydro units & short start up generator commitment)

Very Short term (1-6 hours ahead)

• Uses: Unit commitment and scheduling, market trading

Short Term (Day-ahead/Multi-

Day)

• Uses: Resource planning, contingency analysis

Medium Term (Seasonal – Year or couple years)

• Uses: Project Siting, Long Term Planning

Long Term (10 to 20 years

ahead)

Phenomena: Large eddies, turbulent mixing transitions Methods: Largely statistical, driven by recent measurements

Phenomena: Fronts, sea breezes, mountain-valley circulations Methods: Blend of statistical, NWP models

Phenomena: “Lows” and “Highs,” storm systems Methods: Mainly NWP with corrections for systematic biases

Phenomena: Climate oscillations, global warming Methods: Based largely on analysis of cyclical patterns

Phenomena: Long term averages Methods: Meso scale modelling coupled with long term measurements

Phenomena: Density of Cloud Methods: Cloud imaging camera and driven by recent measurements

Phenomena: Cloud cover Methods: Cloud motion vector and Satellite Imagery

Phenomena: Cloud Cover, rainfall.. Methods: Mainly NWP with corrections for systematic biases

Phenomena: Dust & Aerosols, Climate oscillations, global warming Methods: Based largely on analysis of climate change

Phenomena: Long term averages Methods: Meso scale modelling coupled with long term measurements

Methods of VG Forecasting Physical Approach

◦ Using Numerical Weather Prediction ◦ Power Curve to convert power

◦ Using Wind Flow Modeling ◦ Computational Fluid Dynamics ◦ Linear wind flow modeling

Statistical Approach ◦ Using Statistical Models ◦ Using Machine Learning / Artificial Neural Networks

Mixed Physical – Statistical Approach

Numerical Weather Prediction

◦ NWP Model components ◦ Initial Conditions:

◦ Input data and Initialization ◦ Governing Equations ◦ Numerical Procedures

◦ Grid Point Models ◦ Spectral Models

NWP Sources Weather Measurement Stations

Source of Weather Measurement Gridding the World

Conservation of Momentum 3 equations for

accelerations of 3-d wind (F=ma)

Conservation of mass 1 Eqn for conservation of

air 1 Eqn for conservation of

water Conservation of energy

1 Eqn for the first law of thermodynamics

Relationship among p, V and T 1 Eqn of state (Ideal gas

law)

NWP Model components Physical Process

o Modeling Local Effects

o Parameterization Model Output

o File with Model forecast

o Post processing

NIWE’s Forecasting Services NIWE has largest measured wind and solar data bank with 1881 wind monitoring stations and 125 solar

monitoring stations data available with NIWE across the country

NIWE is utilizing Indian NWP model data to predict the wind power

NIWE has developed In-house Data management system, Indigenous Wind Power Forecasting model, Monitoring System and Forecast simulation tools

The NIWE’s forecast is single largest regional forecast with 22 GW (64%) of Wind power across India. NIWE also signed MoU with various SLDCs to provide 13 GW of additional forecasting services in upcoming months this would cover about 90% of entire wind installation in the country.

Centre for Excellence in VG forecasting has been established in NIWE. A dedicated VG (Variable Generation) Forecasting lab has been set up to provide Forecasting service to all wind-rich states of India.

NIWE already signed MoU with Tamil Nadu, Gujarat, Karnataka, Andhra Pradesh, Maharashtra , SRLDC and Rajasthan SLDC to establish operational wind power forecasting system. NIWE proposed to sign MoU with other RE rich states in couple of months.

Met. Data Analysis Data Analysis & Modelling

Module Purpose

Data Analysis & Modelling

To Monitor, Clean, Analyse, Process and Model the data for generating forecast.

Met. Data Analysis To analyse the meteorological data and visualize the meteorological parameters for modeling

GIS, Data Management & Reporting

To carry out Spatial analysis and storing / archiving the Generation / Meterological data

Web based dashboards To deliver the Forecast results to stakeholders

6 Technologies

Web based dashboards

7 Technologies

GIS, Data Management & Reporting

7 Technologies 7 Technologies

NIWE using 27 Emerging Technologies to carry out wind power forecasting services

Emerging Technology used

Data Management

Typical Data receiving Structure of one Substation

Statistical data cleaning Process

Generation data

Storing in Database

Data Receiving from Secured FTP / Web server

Total Substations : 318 Total no of receiving data feed : 1290 Data receiving frequency : 1/3/15 Minutes No. of data process cycles in a day: 5,38,752 State of Art Data Management tools is being used to speed up the overall process

Data Management

Meteorological forecast received from ISRO_SAC, IITM and NCMRWF High resolution : 8,10,000 (Grid points) Global resolution: 15,625 (Grid Points)

Spatial data analysis and extraction

Meteorological forecast data

Storing in Database

In a day Forecast system would process about 3,870 meteorological data stream Da

ta R

ecei

ving

from

Sec

ured

FTP

Data Monitoring

Actual generation data monitored every 3 minutes : No. of data process cycles in a day: 3,45,120

Meteorological data monitored every 3 hours No. of data process cycles in a day: 2,544

Innovation (Indigenous model)

9 forecast output 36 forecast output

Innovation (Indigenous model) ✓NIWE Indigenous forecast model uses Mixed Physical statistical

approach ✓Day ahead Model use Meteorological and real time generation

data ✓45 different statistically analysed forecast output would be

generated @ every updation of NWP ✓DMS system would intelligently select the best output ✓Day ahead Model will runs 2 times in a day ✓ forecast system will carry out statistical analysis of about

28,636 set of calculations ✓ Intraday Model uses real time generation data to refine the

forecast ✓ Intraday model will runs 16 times in a day ✓The forecast system will carry out statistical analysis of

about 5,088 set of calculations ✓State of Art Statistical analysis tools / technologies used to carry

out calculations in real time

Data Communication and Security ✓Communication Technology used

✓ SLDC is receiving data from Substations using MODBUS technology

✓ NIWE is receiving data from TANGEDCO through Secured Webserver

✓ Meteorological data is receiving through secured FTP connection

✓ NIWE is sharing the forecast result through Secured FTP

✓Data Security Measures ✓ NIWE uses IP-tables and UFW tool to secure the server access ✓ White listing of Public / private IP ✓ RSA 2048 bits encrypted secure shell connection established ✓ Logging system created to record complete data usage of the

server and stored on a daily basis ✓ Regular verification of security arrangement ✓ Back up of data will be carried out on a daily basis

Operational forecast system

State-wise Overall Performance Analysis

*Period of analysis – Oct’18-Jan’19. Due to some technical issues, NIWE

is not receiving real time generation data since January 2019.

State

Dayahead (Within

600MW)

Intraday (within

600MW) Analysis Period

Tamil Nadu 88% 91% Sep 2015-May 2019

Gujarat 83% 92% April 2018- May 2019

Karnataka 95% 97%* Oct 2018-May 2019

Maharashtra 81% May 2019 70

75

80

85

90

95

100

TamilNadu Gujarat Karnataka Maharashtra

% O

F BL

OC

KS W

ITH

IN 6

00 M

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STATE LEVEL FORECAST

Performance Analysis Dayahead Intraday

Day Ahead Solar Forecasting

Gujarat has more than 2 GW of installed Capacity of Solar. Load dispatch centers need to know solar power forecast to ensure power scheduling with almost zero deviations, • We selected 12 reference plant with total installed capacity of 336.23 MW • Forecast is given for 12 selected plants and the aggregated values are finally up scaled to total installed capacity.

Forecast Reference plant 1

Forecast Reference plant 2

Forecast Reference plant 12

AGGREGATION OF FORECAST UPSCALING FINAL

FORECAST

Satellite Based Irradiance Forecasting: INTRADAY FORECASTS

Cloud Index Animations, ISRO-INSAT3D Images

Ground reflectance Image, ISRO-INSAT3D

Satellite Based Irradiance Forecasting: INTRADAY FORECASTS

Skycam Based Irradiance Forecasting: INTRAHOUR FORECASTS

Way Forward

NIWE is actively working on to Data Analytics in wind and solar generation and prediction models for variable generation forecasting.

Ministry of New and Renewable is in process on signing a MoU with Ministry of Earth Science to collaborate on the indigenous weather prediction model.

NIWE focuses on improving the existing model of wind and solar generation to include the Machine learning and Artificial Intelligence.

NIWE is in process of creating Centre of Excellence in Resource data analytics.

Thank you