The document discusses implementing a big data analytics solution for renewable energy companies. It analyzes two strategic alternatives: creating an in-house big data unit or outsourcing to startups. A hybrid approach is recommended, initially outsourcing work while training staff and gradually bringing responsibilities in-house. An overview of data types, tools, architecture, costs and risks is provided. Implementing big data analytics could help optimize operations and maintenance costs, improve production forecasting, and enable predictive maintenance to realize estimated annual cost savings of over 50 crores Indian rupees.

1. Case: Data, Energy & Analytics
Team Incognito | MDI Gurgaon
Arush Sharma | Niteesh Kumar Singh | Rahul Agarwal

2. Analyze
Structured, Semi
structured and
Unstructured Data
and gather
valuable Insights
High
Technology
Systems
Mathematical
Computations
Huge Amount
of Data
Big Data
Overview
Strategic
Alternatives
Approach
High-Level
Architecture
Requirement
Analysis
Impact &
Risks
Proof of ConceptBig Data Overview
What is Big Data?
Diagnostic
Why has it happened?
Descriptive
What has happened?
Predictive
What’s likely to happen?
Prescriptive
How best to act in the future?
Company Area of Analytics Impact
Vestas
Met data from 35,000 weather stations
combined with historical operational
data from 50,000 wind turbines for a
more precise wind map
• Reduce its wind data grids
from 27x27km to 10x10m
• Predict the amount of energy
that will be generated
General
Electric
• Turbine analysis to optimize the pitch
of the blades and electricity storage
• Periodic turbine data to deliver
predictable power from the farms
• Farm controller analyses cluster data
to evaluate turbine performance and
health
• Managements, including
predictive maintenance
• Efficient power production
and forecasting
• Tune turbine parameters like
speed, torque, etc. to
increase AEP
Geostellar
• Automates solar electricity
generation model
• Financial Calculation conducted
using utility rates, energy usage
profiles, and energy initiatives
Accurate data obtained on
electricity generation as
verified by the National
Renewable Energy
Laboratory

3. Alternative #1
Top-Down Big Data Unit
Alternative #2
Open up data to startups
Pros
✓ Flexible & Customizable
✓ Empowering – after all, the
staff will become experts
✓ Accessibility – Yield
valuable insights for both
day-to-day decision making
& long-term planning
✓ Avoid ongoing cost of
retaining an analytics team
long-term
✓ Comparatively safe &
secure due to greater
amount of control over data
Cons
✓ Not a core competence,
can lead to slow
implementation as need to
build expertise first
✓ Recruitment of expert
data professionals is a
challenge
✓ Unexpected tech glitches
can be difficult to
troubleshoot
✓ Scalability & performance
challenges
Pros
✓ Cost Advantage
✓ Expertise from startups will
result in good data mining,
crunching & analysis
✓ Allow management team to
focus on core
competencies
✓ Seamless compliance
with evolving regulatory
standards
✓ Future Business
Opportunities
Cons
✓ There is always a risk of
exposing sensitive
company data and losing
confidentiality
✓ Legal & other issues can
arise if the contract is not
‘tight’
Recommendation:
Hybrid Sourcing
✓ Split responsibilities with Analytics Partner; retain responsibilities for database infrastructure etc.
✓ Initially outsource work to partner, have them train & gradually hand over analytics responsibility
✓ Allows us to maximize use of own staff resources & minimize outsourcing costs
Skills Required:
✓ Analyzing existing capabilities
✓ Building a culture around data
✓ Conducive environment for operations
✓ Establishing a center of excellence
✓ Data Security
Big Data
Overview
Strategic
Alternatives
Approach
High-Level
Architecture
Requirement
Analysis
Impact &
Risks

4. • Project Finance
• Revenue Forecast
• Wind
• Solar
• O&M
Problem Statement Our Approach
Key Issue Areas Affecting Growth and Efficiency
Complexity vs Time Required
COMPLEXITY
1. Supply Curve
Modelling
2. Generator
Performance
Modelling
1. Simple Mapping
2. Data
Visualization
1. Production Cost
Modelling
2. Capacity
Expansion
Modelling
1. Economic Potential
Analysis
2. Technical Potential
Analysis
High
Low
HighLow TIME REQUIRED
Need To
Solve The
Problem
Smart Grids
Energy providers
need to adapt
(SNMP)
Cost
Optimization
Production
Forecasting &
Reducing
Penalties
Target Setting
Investment in
Future
Projects
Predictive
Maintenance
Asset
Performance
Management
Production
Forecasting
Regulatory
Compliance
Site Selection
Expected Return
On Investments
Reliability of
Assets
Optimization of
Operating &
Maintenance
Costs
Downtime
Optimization
Component
Replacement
Planning
Potential Business Units
How will Data Analytics Help?
Operations Finance
Potential Impact Areas
Operational
Cost
Optimization
Production
Variability
Reduction
Predictive
Maintenance
Accurate
Forecasting
Ability
Big Data
Overview
Strategic
Alternatives
Approach
High-Level
Architecture
Requirement
Analysis
Impact &
Risks

5. Renewable
Energy
Potential
Model
(reV)
Lawrence
Berkeley
National
Lab MapRE
RED-E
platform
Resource
Planning
Model
(RPM)
Regional
Energy
Deployme
nt System
(ReEDS)
PLEXOS-LT
PLEXOS
GridView
IRENA Global
Energy Atlas
Lawrence
Berkeley
National
Laboratory
MapRE
Renewable
Energy Data
Explorer
World Bank
Group Global
Solar Atlas
World Bank
Group
Global Wind
Atlas.
Mapping &
Visualisation
Systems
Advisor
Model (SAM)
PVSyst
HOMER
Windograp
her
Generator
Performance
Modelling
Renewable
Energy Data
Explorer
Renewable
Energy
Potential
(reV) model
Lawrence
Berkley
National
Laboratory
MapRE
(ArcGIS
Tools).
Technical
Potential
Analysis
Supply
Curve
Modelling
Economic
Potential
Analysis
Capacity
Expansion
Modelling
Production
Cost
Modelling
Data Type
Geospatial
✓ Sample interval - 1 min, 15 min
etc.
✓ Instantaneous or Integrated
✓ Intervals - sample clock time rep.
✓ Temporal Range - Years of
coverage
Temporal
Data Storage
✓ Reduced Capex
✓ Low Maintenance
✓ Highly Scalable
Cloud based
SAP/AWS
Tools Required At Different Stages Of Implementation
Sample Architectural backbone of Big Data setup
✓ Terrain details
✓ Elevation
✓ Forest Cover
✓ Humidity
✓ Average Incident Irradiance
✓ Wind Speed
Data Granularity
Big Data
Overview
Strategic
Alternatives
Approach
High-Level
Architecture
Requirement
Analysis
Impact &
Risks
Point-Specific spatial
resolution with grid size
2Km*2Km
(30 terabytes data)
✓ Renewable energy resource
data vary spatially
✓ Spatial resolution can have a
significant effect on the validity
of a data set in an analysis
✓ Change in granularity can
widely change data being
captured. E.g. 1km*1km can
result in 4x increase
Data
Analytics
Real-time
monitoring
Forecasting
Data
Preparation
Data Sets
Data
Warehousing
Data
Integration
Storage
Backup
Data Pre-
processing
Cleaning
Validity
Metadata
Data
Gathering
Sensors
Logs
Smart
Meters

6. Tools And Resources Required
S.No. Particular
Capacity/
Block
Quantity
1 Storage in Blocks 10 TB 2
2
Compute Hours in
Blocks
5000
Hours
2
3 File in Blocks 1 million 1
4 Data Transfer Server 1
5 Workbench 1
6 VPN Connection 1
S. No. Particular Number
1 Lead Data Scientist 1
2 Data Analysts 3
Data Analysts, Network Administrators, DevOps
Engineers, IT personnel might be required depending on
the existing Data Maintenance Resources in the
company
Estimated Costs
S.No. Particular Capacity/Block Quantity
Rate
(Euros)
Amount
(Euros)
1 Storage in Blocks 10 TB 3 1400 4200
2
Compute Hours in
Blocks
5000 Hours 3 1400 4200
3 File in Blocks 1 million 1 2245 2245
4
Data Transfer
Server
1 1120 1120
5 Workbench 1 2430 2430
6 VPN Connection 1 3085 3085
Total Cost/Month
17,280
Amount in INR @ 1 Euro = 79 INR 79 13,65,120
S.No. Total Costs Rate Quantity
Amount
(INR)
Comments
1 Technical Costs 13,65,120
12 1,63,81,440
Rate/month
2 Human Resource 15,00,000 4
60,00,000
Annual
Rate/head
Total Annual Costs 2,23,81,440
SAP Cloud Platform Big Data Requirements
* Based on SAP Cloud Services Pricing Plans
Human Resources Requirement
Big Data
Overview
Strategic
Alternatives
Approach
High-Level
Architecture
Requirement
Analysis
Impact &
Risks

7. Potential Risks
Unorganized
data
Data storage
& retention
Cost
management
Data
protection
Financial Implications O&M costs → 15-20%
S. No. Particular Year 1 Year 2 Year 3 Comments
1 O&M Costs 450 495 544.5
Assuming 1% of Capex,
Capex Rate = Rs. 8cr/1 MW capacity,
O&M Costs increasing by 10% Y-o-Y
2 O&M Savings 22.5 34.65 54.45
Assuming 5% O&M Savings in Year 1,
7% in Year 2, 10% in Year 3
3 Data Analytics Cost 2.24 2.69 3.22 Assuming 20% increment Y-o-Y
Total Savings 20.26 31.96 51.23 Amount in INR crores
Assumptions
• O&M Savings - 5% in Year 1, which will grow to around 10% by Year 3
• O&M costs - around 1% of the Capex in the respective plants
• Estimate Capex ~Rs. 8 crores for every 1 MW of plant capacity
• Total Capex of Rs. 45,000Cr translates to ~ Rs. 450 crores annual O&M expenses
Big Data
Overview
Strategic
Alternatives
Approach
High-Level
Architecture
Requirement
Analysis
Impact &
Risks

8. Thank You!
Team Incognito | MDI Gurgaon
Arush Sharma | Niteesh Kumar Singh | Rahul Agarwal