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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