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
W D
EVIA
TIO
N
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