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

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Thank you

Questions?thahirah.jalal@unison.co.nz