Currently electricity losses are a significant issue for utilities totalling an estimated annual costs of over $200 billion and electricity theft is a worldwide problem but which varies depending on regions. These losses also have environmental impacts causing 1.200 M tonnes of CO2 emissions and wasting up to 8% of generation capacity annually.

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Reduce losses, improve efficiency and protect
revenues with analytics
True Grid Intelligence (TGI) using In-Grid Analytics
April 28th, 2015
Jean-Yves Blanc, Schneider electric
Mischa Steiner-Jovic, Awesense

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Schneider Electric protects grid revenue
by helping distribution utilities easily
locate energy losses and improve grid
operations & efficiency.
Schneider Electric has partnered with
Awesense Inc. to deliver a best in class
grid data analytics solution.

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Over $200B of energy wasted yearly
Annual value of global Non Technical
electricity losses (annual increase +2,5%)
Source: World Bank 2011-2014

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Technical Losses
MV Theft
&
LV Theft
Wiring errors
TotalLosses
Metering errors
Line Losses
Transformer
core loss
Transformer copper
loss - overload
Transformer copper loss
phase imbalance
Billing errors
Capital costs ARE NOT needed to
reduce these losses
Capital costs ARE needed
to reduce these losses
Non-Technical Losses
Technical vs. non-technical losses?
Savings
Total
Losses

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What do you think your non-technical
losses currently are?
a - < 2%
b - 2% to 5%
c - 5% to 10%
d - >10%
e - Don’t know

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Smart meters find some theft
Meter
tampering
Low voltage
diversion
Losses identified
using Smart Meters
and Meter Data
Analytics
✔
✔

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In-Grid Data finds MORE losses
Losses identified
using Meter Data
Analytics alone
LV diversion
at transformer
Unmetered loads & illegal
MV connections
Low efficiency from
phase imbalance
Low efficiency from
heavy transformer
loading
Losses identified using
In-Grid Data combined
with Meter Analytics
Data
Meter setting &
wiring errors
✔
✔
✔✔
✔✔

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The Challenges
Many distribution companies
struggle to:
• recover their investment in
smart metering
• interpret the meaning of the
trends and alerts generated
from analyzing big data
• relate the customer and
consumption data to the grid
operating condition
• determine the Next Best
Action to reduce losses In many cases smart meter investments are motivated by a
desire to reduce grid losses
Total
Losses
Start recovering losses sooner
Recovermorelosses
20152014 20172016 20192018 20212020 20232022Losses

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Immediate & Long Term Benefits
Losses
50%
Time
~3 years
Start of
Program
In-grid data
collection &
analysis
Losses identified
and reduced
Persistent in-grid
data collectors
deployed

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But where to start looking?
With the TGI platform, Schneider
Electric helps distribution utilities
determine the highest risk segments of
the grid – and the best places to start
investigating.

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Revenue
Protection
Manager
Investigations
Manager and
Analyst
Field
Investigator
Data analytics approach
Possible
Theft Cases
(~10 per day)
Monetize
results
Special
Investigations
Unit
Investigate?
No Yes
Data Analytics
Triage Meter Data Alerts
(>100 per day)
Feedbackfalsepositive
Metering Data (>1000 per day)
Insurance, finance and IT industries have long used a
systematic approach to reduce loss due to fraud & abuse.
The TGI platform brings this systematic approach to
distribution utilities.
Ricardo

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Data analytics approach – and beyond
Most analytics vendors stop here.
The utility is assumed to ingest the
results into their business processes
to find the cause of theft.
CUSTOMER SYSTEMS
BUILDING DATA
RATES & MEASURES
ENTERPRISE SYSTEMS
METER DATA
OPERATIONAL SYSTEMS
List of possible
theft locations
3rd Party Analytics
Many distribution
companies don’t have
smart meters – and
don’t have the data they
provide.

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TGI segments the grid
List of possible
theft locations
TGI uses conventional meter data
analytics as an input to the Risk Advisor
to determine the highest risk Grid
Segments, making field investigations
more effective.
TGI RISK
ADVISOR
Ranked list of high-
risk grid segments
with all types of losses
(not just theft)
CUSTOMER SYSTEMS
BUILDING DATA
RATES & MEASURES
ENTERPRISE SYSTEMS
METER DATA
OPERATIONAL SYSTEMS
CASE MANAGEMENT
GIS
RISK FACTORS
FIELD INVESTIGATIONS
BILLING SYSTEM
1
2
3
3rd Party Analytics

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TGI applies risk algorithms
List of possible
theft locations
TGI uses conventional meter data analytics
as an input to the Risk Advisor to determine
the highest risk grid segments, making field
investigations more effective.
TGI RISK
ADVISOR
CUSTOMER SYSTEMS
BUILDING DATA
RATES & MEASURES
ENTERPRISE SYSTEMS
METER DATA
OPERATIONAL SYSTEMS
CASE MANAGEMENT
GIS
RISK FACTORS
FIELD INVESTIGATIONS
BILLING SYSTEM
TGI CASE
MANAGER
TGI
REPORTING
TOOLS
TGI
INVESTIGATION
MANAGER
TGI FIELD
INVESTIGATION
TOOLS
TGI
DASHBOARD
TGI IN-GRID
DATA
ANALYTICS
3rd Party Analytics

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TGI RISK ADVISOR
Identifies and ranks
high-risk grid segments
TGI Dashboard: Assess top priorities
and recommend Next Best Actions
TGI Placement Advisor: plan optimal
investigations of target segment.
TGI Repository, TGI Reporter:
Chain of evidence and secure documentation of Energy
Balance, Phase Balance, Transformer Load Study, etc.
…with tools for each step
Recommend
Plan
Investigate
Analyze
TGI Sensor Management: sample load
data on live distribution lines

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… and a methodology for each step
Recommend
TGI Risk Advisor
• High loads
• Transformers
• Tamper flags
• Demographics
• Customer type
• other
1
2
3
Plan
TGI Placement Advisor
Data sampling plan:
• Total kVA
• Customer load
• Customer count
• other
Investigate
TGI Investigation Tools
• Detailed sensor location
info
• Verify GIS data
• Annotate each
placement (photos, etc.)
TGI Reporter
In-Grid data analytics:
• Losses & theft
• Billing/wiring errors
• Phase imbalance
• Energy balance
• Transformer overload
• Phase association
using Smartscan
Σ
Σ
Analyze

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In-Grid Data Analytics
TGI Dashboards provide
recommendations for Next Best Actions:
• Verify billing
• Balance phases
• Upgrade high-risk transformers
TGI retains full audit trail of all
investigations:
• People involved
• Locations identified and reasons
• Time-stamped snapshots of grid
• Process followed with photo evidence
• Full reporting

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Does NTL´s analysis require a Big Data
Architecture?
a - Yes, always
b - Not at all
c - Most of the times
d - It depends of data volumes to be
managed

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Key feature comparison
Meter Vendors IT Software TGI
Requirements
Analytics engine ✔ ✔ ✔ ✔
Ability to segment the grid ✔
Prioritization of cases by risk ✔ ✔ ✔
Roving in-grid sampling ✔
“Next best action” recommendations ✔ ✔ ✔
Case management ✔ ✔
Litigation-ready evidence trail ✔
Role-based dashboards ✔ ✔ ✔

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Progressive and timely approach
Pre project Proof of Concept Pilot Deployment Continuous
Improvement
•Define stake
•Commitment from
power sponsor
•Assess
feasibility
4-6 weeks 3-6 months 3-5 years As long as relevant
•Demonstrate
methodology
works and
operational
compliance
•Demonstrate NTL
identification in a
small scale
•Identify NTL •Sustain results
•Dashboard
•Project follow-up
•Recommendations
•NTL
identification
•Dashboard
•Project
follow-up
•Recommendations
•Dashboard NTL
•(quantify & locations)
•Project follow-up
•Integration of legacy
systems (GIS, MDM, CRM)
•Ranking of feeder
segment
•Report booklet
•Recommendations
Timeline
Objective
Deliverables
4-6 weeks
•Data source
inventory

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Conclusion
As the global specialist in Energy
Management™, Schneider Electric helps
Electric Distribution Utilities to identify,
measure and locate Non Technical
Losses:
• Minimize the losses
• Improving grid operations
• Improve grid efficiency

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Make the most of your energySM