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The Application of MATLAB in Unison’s Smart GridDr Thahirah Jalal
Asset Intelligence Manager
1 May 2018
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Outline
• Unison’s Smart Grid
• Case study and benefits
• MATLAB application
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UNISON’S SMART GRID
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Smart Grid
Unison Networks:
“The application of real time
information, communication and
emerging trends in electricity delivery
to improve capacity utilisation,
optimise asset management practices
and improve services on the modern
network thereby optimising network
investment to the benefit of all
stakeholders.”
Unison Networks:
https://www.youtube.com/watch?v=7l4axYzt7c4
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Changing landscape
➢Reliability expectations
➢Adverse weather
➢Power quality expectations
➢New technologies – two way
power flow
➢Low cost
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How do we achieve that?
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Electricity automation history
1920s
• Supervisory Control and Data Acquisition (SCADA) to operate remote devices
1930s
• Interconnection between grids to balance generation (supply) and load (demand)
• Analogue computers
1950s
• Economic Dispatch and Automatic Generation Control to automatically dispatch lowest cost generators
1960s
• Digital computers and software for more computing power
21st
century
• Lower cost sensors, communication system, real time computation and automation through advanced software
Source: http://www.electricenergyonline.com/show_article.php?mag=&article=491
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Smart Grid Enablers
Communications
Technology
Data solutions
Systems
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Smart Grid Roadmap
Implementation Phase:
Expedited implementation of smart
network technology
Optimisation Phase:
Realise in full the potential of the smart
grid: Optimise the use of technology by
enhancing asset management practices
Integration Phase:
Achieve asset management excellence
with an integration of all elements of the
Smart Grid Vision, which will enable
Unison to deliver world-class network and
energy solutions to our customers.
Implementation
2011 - 2015
Optimisation
2016 - 2020
Integration
Beyond 2020
Excellence in
Asset
Management
• Maximising Performance• Minimising Cost• Minimising Risk
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International recognition
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Novel sensors
• Environmental monitoring
• Condition monitoring
• Power quality monitoring …
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Communication system
• Regional Fibre Backbone - Urban
Substations
• Mesh Radio Network -Distribution
Automation (DA)
• MimoMax - Rural Substations,
devices that require higher data
speeds e.g. Reclosers
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Advanced Distribution Management System (ADMS)
Integration of distribution management (DMS), outage management (OMS),
and supervisory control and data acquisition (SCADA) systems
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Data Solutions
o ASSET INTELLIGENCE :
▪ “a computational framework ensuring that well-chosen data streams
collected by smart network assets are optimally utilised”
CASE STUDY: PREDICTIVE ASSET RATING
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What is rating?Rating – upper limit of current or power we subject our
asset to
Determines if we can supply the demand and when we
need to upgrade our assets
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Conventional practice
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Reality
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Decisions decisions..
What current can I put on these assets
without overloading them?
When do I need to start upgrading these
assets?
Will the investments I approve still be
required in 10 years time?
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Rating options
A. Manufacturers rating - near worst-case scenarios
of weather and load conditions. E.g. transformer
rating is based on 30 degrees C of air
B. Dynamic Rating – adaptive to changing
environments conditions
C. Predictive Rating – forecast environmental factors
using AI to schedule loading accordingly
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Sensors
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Smart Grid and AI in action
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Case Study: Dynamic and Predictive Feeder Rating
• Implemented Dynamic and
Predictive Feeder Rating for 7 pilot
feeders
• Predictive: Forecast weather 6
hours ahead and calculate
corresponding rating
• Dynamic: Update rating calculation
every 30 minutes
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Dynamic Cable Rating
• Use soil thermal resistivity and
cable temperature to determine
rating
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Dynamic Lines Rating
• Use weather station
data, line temperature
and line clearance to
determine rating
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Control room view
• Increased operational flexibility and
resilience
• Helped during extreme event like
Cyclone Cook
• Less risk of lost load using real-time
and predictive data
• Greater confidence in line clearance
and asset condition for pilot feeders
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Planners’ dashboard
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Asset Management
• Dynamic and Predictive Rating provides data driven
decision support for network operations and planning
• Real time data reduces the risk of asset failures
• Additional ratings provide a significant (30-50%)
increase from manufacturer’s rating
• Non Network Solution to defer capital investments and
manage risk of stranded assets
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MATLAB APPLICATIONS
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MATLAB deployment path
2011 - Algorithm conversion to VB.net
2012 – MATLAB scheduling in on-
prem server
2017- MATLAB deployment in
Microsoft Azure via MATLAB Production
Server
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MATLAB scheduling on-premiseLoad
Ambient Temperature
Top Oil Temperature
Tap position
Cooling operation
Dynamic rating
Hot spot
temperature
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MATLAB in Microsoft Azure
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Conclusion
MATLAB provides us the ability to perform complex
computation for Smart Grid applications
