Using Grid data analytics to protect revenue, reduce network losses and improve efficiency

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Reduce losses, improve efficiency and protect revenues with analyticsTrue Grid Intelligence (TGI) using In-Grid Analytics

April 28th, 2015

Jean-Yves Blanc, Schneider electricMischa 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.

Presenter

Presentation Notes

This presentation is about revenue protection in electric distribution networks. After a general overview of what is at stake and the various kinds of losses we'll be presenting how to tackle non technical energy losses in electrical distribution networks; we have understood that this is a major area of concerns for utilities and grid operators. We will show how we can help you to mitigate these losses with or without smartmeters.

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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

Presenter

Presentation Notes

From different sources, the assessment is Distribution non technical losses average 3-4% in the European Union (up to 30% in India) -This number (4%) includes losses in the medium and low voltage lines and in primary and secondary substations We are discussing huge amounts of money on a worldwide basis Utilities and Analysts acknoledge this is unfortunately on a growing trend… �Smartgrid technologies can help to reduce this. We’ll see how we can understand the problem and contribute to fix it.

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Technical Losses

MV Theft&LV Theft

Wiring errors

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Metering errors

Line LossesTransformer 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?

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ngs

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Presenter

Presentation Notes

Technical losses are generally a result of inefficiencies in the lines and transformers. These include: Line losses Transformer core losses Transformer copper losses Copper losses can be caused by heavy transformer loading, and by phase imbalances. You can reduce losses in some cases by re-assigning loads to less heavily loaded phases or transformers, when possible. Otherwise a transformer upgrade and the associated capital cost is the only way to reduce these technical losses. Non-technical losses can be more easily reduced without investing large amounts of capital. These include Theft Wiring errors Metering errors Billing errors When understanding the problem the same tool and methodology allow to decrease copper losses at the same time because, from measurements in the field, at MV/LV substations, we will visualize overloads and imbalances. So we can build recommendations to reduce them. Let’s have a look on how to proceed

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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 diversionLosses 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

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Start recovering losses sooner

Rec

over

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20152014 20172016 20192018 20212020 20232022Lo

sses

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Immediate & Long Term Benefits

Loss

es50

%

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.

Presenter

Presentation Notes

This presentation is about energy efficiency in electric distribution networks. After reviewing the various kinds of losses and the regulatory environment pressure for energy efficiency, with the example of Europe, I'll be presenting 5 practical strategies to tackle energy losses in electrical distribution networks; strategies that allow distribution network operators to comply with the new regulatory constraints and that at the same time can save cost.

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Revenue Protection Manager

Investigations Manager and

Analyst

Field Investigator

Data analytics approach

PossibleTheft Cases

(~10 per day)

Monetize results

Special InvestigationsUnit

Investigate?No Yes

Data Analytics

Triage Meter Data Alerts (>100 per day)

Feed

back

fals

e po

sitiv

e

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 SYSTEMSList 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 RISKADVISOR

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

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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 RISKADVISOR

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

Presenter

Presentation Notes

TGI then identifies the highest risk grid segments that need permanent in-grid data collectors (sensors) to eliminate any chance of resurgence of losses. For utilities that have not deployed smart meters, TGI compares in-grid data to billing data.

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TGI RISK ADVISORIdentifies and rankshigh-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

InvestigateAn

alyz

e

TGI Sensor Management: sample load data on live distribution lines

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… and a methodology for each stepRecommendTGI Risk Advisor• High loads• Transformers• Tamper flags• Demographics• Customer type• other

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PlanTGI Placement AdvisorData sampling plan:• Total kVA• Customer load• Customer count• other

InvestigateTGI Investigation Tools

• Detailed sensor location info

• Verify GIS data• Annotate each

placement (photos, etc.)

TGI ReporterIn-Grid data analytics:• Losses & theft• Billing/wiring errors• Phase imbalance• Energy balance• Transformer overload• Phase association

using Smartscan

Σ

Σ

Analyze

Presenter

Presentation Notes

Energy balance can been done against meter data (for those with smart meter) Schneider Electric’s TGI-ready Smartscan software app can determine the phase association of LV networks.

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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, alwaysb - Not at allc - Most of the timesd - 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 ✔ ✔ ✔

Presenter

Presentation Notes

The simplest way actually is to implement a smartmeter infrastructure, to get the database and analyse th database. By detection of unnormal patterns we can find some losses. Then, IT companies and specialized software companies have built a set of tools that go a step further. With TGI, we add a great component to the equation: the knowledge of the grid, of the MV/LV substations and the documentation. So that we go broader and deeper in the analysis and we can provide utilities with a trail of document that show evidence of non technical losses and facilitate the revovery process, up to legal actions if necessary…

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Progressive and timely approach

Pre project Proof of Concept Pilot Deployment Continuous Improvement

•Define stake•Commitment from power sponsor

•Assessfeasibility

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

•NTLidentification

•Dashboard•Projectfollow-up

•Recommendations

•Dashboard NTL • (quantify & locations)•Project follow-up• Integration of legacysystems (GIS, MDM, CRM)

•Ranking of feeder segment

•Report booklet•Recommendations

Timeline

Objective

Deliverables

4-6 weeks

•Data source inventory

Presenter

Presentation Notes

The approach is in several steps. First is to understand what is at stake. What are the overall level of losses, what is the level of commitment at the executive level and to show on paper how we can help. Then we propose a proof of concept phase where we show how it works, what is the process and what would be the deployment process. It is then time to demonstrate through a pilot our ability to identify losses. �Finally we can deploy full scale in the utility territory. Afterwards, it is possible to continue some surveillance of critical areas to maintain the results overtime. All along the process we provide integration with the actual tools of the utilities like GIS, MDM… and dashboard to monitor the process, show achieved results and progresses to the different stakeholders. This dashboard can be customized to address the key questions of the utility.

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Conclusion

As the global specialist in EnergyManagement™, Schneider Electric helpsElectric Distribution Utilities to identify,measure and locate Non TechnicalLosses:

• Minimize the losses• Improving grid operations• Improve grid efficiency

Presenter

Presentation Notes

To conclude our presentation, I’d now like to sum up … We have seen distribution losses is a big problem. Regulation pushes more and more DSO and retailers to address and reduce the issue. Many distribution networks are poorly or partially instrumented. It is clear that smartmeters implementation will contribute to reduce non technical losses but it is not sufficient. This is why more accurate and highly networked sensors, and management systems will be required to achieve important savings. The end results is lost revenue is recovered and the grid efficiency improves. Thank you for attention

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Make the most of your energySM

Technology

Using Grid data analytics to protect revenue, reduce network losses and improve efficiency