Smart metering project and

new analytics in grid operations

Smart Baltic 2016

Heikki Kolk

Solution Architect in Digital Network Technology

Elektrilevi

Elektrilevi

Largest Network Operator in Estonia

Measurement points 676 190

Primary substations 271

MV/LV substations 24 009

Secondary (switching) substations 219

35 (110) kV transformers 194

6-35kV lines 29 786

0,4kV lines 36 175

Total of lines 65 961

ASSET BASE

• 90% of the marketshare

• Part of the Eesti Energia Group

• State-owned international energy company

• Operates in the Baltic countries and Poland, also in the international liquid fuels market

• Unique experience and technology in relation to processing oil shale and energy production

FINANCE 2015

Sales mln € 249,2

EBITDA mln € 106,8

Investments mln € 93,3

ROIC % 8,80 %

Sales GWh 6 521

Network losses GWh 326

Network losses % 4,8 %

Digital Network

Technology

in Elektrilevi

OPERATIONAL

TECHNOLOGY

• PS

• Locamation

• Elspec

SOLUTIONS

ARCHITECTURE

• Ericsson

• ABB

• Landys + Gyr

• GridMind

• Trimble

REMOTE

OPERATIONS

• Martem AS

• Eltech Service

• ABB

• Viola Systems

• TeliaSonera

• Tele2

CYBER

SECURITY RISK

MANAGEMENT

• European Network

for Cyber Security

• ISA

• ENISA

IT SERVICE

MANAGEMENT

• ABB

• Trimble

• Eliko

Two tracks in

extracting value

from data

• Improving existing business model by

redesigning business processes around

data-centric methods

• Unlocking new business models and

revenue streams in the emerging

home and industrial IoT space

GROWING THE

COMPANY’S

VALUE

MAINTAINING

EXISTING ASSET

EFFICIENCY

DATA ORIENTED

ASSET MANAGEMENT

Improving existing

business efficiency by

implementing new

technology

Electricity

distribution

services

INCREASING

CUSTOMER

SATISFACTION

CULTIVATING

NEW REVENUE

STREAMS

NEW BUSINESS

DOMAINS

• Electric mobility

• Demand side

management

• Production

management

New

business

growth

2010 Start

2011 Procurement

2012 5300 SM installed Contract with Ericsson

2013 162 000 SM Beginning of installment

2014 185 000 SM Beginning of development for the analytics platform

2015 173 000 SM

2016 98 000 SM SM installation finished

2017 Final stage and end of project

Metering Project • Ericsson delivered a complex challenging project on budget and schedule

• 623 672 smart meters in place

• 3800 smart meters installed weekly

625,253

594,674 597,327

623 672

400,000

450,000

500,000

550,000

600,000

650,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52

Operational Plan Agreement volume Installation actual

Installation prognosis Total meters

Grid operations

295

191 327 140

166 157 132 120 112 104

1567

943

1814

659

917

700 668 693 724 700

451 283 508

203 264 226 200 193 190 188

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

SA

IDI, m

in

SAIDI

Urban Rural SAIDI sum

GRID OPERATIONS PROCESS

Main process steps:

• Grid monitoring

• Fault location

• Grid state restoration

GOAL: Guarantee agreed upon

network operating conditions and

fulfil all customers’ energy

consumption needs

Main challenges:

• Extreme weather

• Legacy overhead lines

• Lots of forest and trees

The Blueprint

L+G

HES

Oracle MDM

Oracle

CIS Trimble

NIS

ABB

Asset Suite

Smart

Meter Mgr

(SMM)

Work

Order Mgmt

(WOM)

Field

Work Mgmt

(FWM)

DC450 E450 3ph E350 1ph E650 C&I

E450 1ph E450 3ph

E350 3ph IT

Field

Toolset

(FT)

LV grid visualization

Analytics platform

Performance metrics

Predictive maintenance

Elektrilevi

systems

ESB

Ericsson

support

systems

2G/3G cellular connectivity

PLC SFSK PLAN+

Predictive maintenance based on

smart meter data

Client SAIDI in 2016

Voltage quality

2016 operational testing results

Verified that grid needs

reinforcement

20%

Out of date information

10%

No apparent reason found

(yet)

17%

Tap changes

19%

Slowly failing transformer was

changed

15%

Loose cable connections

tightened

8%

Aging cables replaced

[PERCENTAGE]

Other

5%

Other

53%

NO FIELD ACTION TAKEN QUICK SOLUTION FOUND

Quality assurance for newly built substations

Further possibilities ……

5% preventable

4x shorter outages

–15% compensation claims

5,1 million Euros saved

known SAIDI: 218,7;

actual SAIDI from smart meters: 391

Planned outages instead of unplanned outages

Proactive approach in preventing customer equipment failure

Investing in areas, where an actual problem has been detected

Accurate SAIDI data for solving customer calls

How the systems approach to

handling data matters

THEORETICAL MODEL DRIVEN APPROACH OBSERVATION DRIVEN APPROACH

Emphasis on energy distribution specific knowledge and

know-how

Emphasis on IT/OT-specific knowledge and know-how

The resulting the application will be highly customized The resulting application will be mostly generic

Cost: Mostly for the Network operator alone. Costly

front-end customization to adapt all algorithms.

Cost: Companies can have shared costs (possible to

outsource and reuse the coded model)

Fundamental architecture will determine cost, time and

ultimately business value

Client

Clie

nt

Clie

nt

Asset, Business and Product

Development

Sales and Customer Service

Planning

Execution

Operations

Client Accounts

Service Quality Improvement

Strategic Management

Advanced

Analytics

Platform

Rethinking the

organization from a

data-centric

perspective

• New improved services

• Proactive approach and fault prevention

• Optimisation of low

voltage network investments

• Substation quality control

• Planned outages instead of unplanned ones

• More accurate SAIDI, transparency

• Fraud and loss • Meter tampering alarms

• Visual and accurate oversight of present situation

• True analytic grid monitoring has the

potential to revolutionize the way we work

• Our current approach is to organize around

the time-criticality of the insight

• Two teams of analysts – operational and

planning - responsible for generating

valuable insight and actionable data.

Conclusions

• Its possible to unlock value in “the data” without knowing too much about

what that value is on the frontend

• Using technology components that “just work”, in an flexible project framework

will deliver results much faster and in a cost effective manner than current

traditional approaches

• Developing open source algorithms for observation driven approach will

cut down the costs for implementing new models

Heikki Kolk

Solution Architect in Digital Network Technology

Elektrilevi