Smart Grids

Thomas Andritsch1/11/19

Workshop on Smart InfrastructureCentre for IoT and Pervasive Systems

Power Grids

Supergrids

Source: https://spectrum.ieee.org/energy/the-smarter-grid /chinas-ambitious-plan-to-build-the-worlds-biggest-supergrid

China is building 89,000 km new HV lines(55,3k miles)

Currently system operators have incomplete picture of the state of the network (state estimator)

Smart Grids (IoT connected devices) promise to give unprecedented data collection and connectivity

There is broad agreement that future electricity networks will strive to incorporate certain smart grid concepts, such as…

Smart Grids

• Higher reliability through automated fault location and restoration (self-healing),

• Flexible topology and bidirectional flows, making distribution networks more like transmission networks,

• Radically changed load profiles, due to demand side management, embedded generation, and electrification of transport,

• Increased generation from intermittent renewable sources, such as wind and solar,

• Novel markets and services, e.g. power quality and frequency response,

• More communications, leading to more data and an increased need for cyber and physical security.V.Catterson et al “The impact of smart grid technology on dielectrics and electrical insulation”

https://ieeexplore.ieee.org/document/7367548

Improved Asset ManagementHow are assets affected by

changes?

How do we get the most out of them?

Can we reliably predict their remaining life from online measurements?

Minimise operational expenditure

Deploy sensors to provide sufficient information of asset health

How do we best use all the data (from sensors above)?

Interests of the EPE Group in Smart Grids

https://smartgrid.ieee.org/newsletters/july-2019/insulation-systems-and-the-smart-grid

Improved Asset DesignBetter materials

Do our standards need to change?

Optimise the design of new assets

What new capabilities are required future equipment?

Loading patterns much more variable

Generation sources more decentralised

Decades of service experience lead to conservative design practices

Circuits may need to be more heavily loaded during peak periods, but thermal headroomsreduced

Dynamic circuit ratings that use live sensor data with dynamic calculations to determine allowable loading for any circuit at any time.

Predictive Circuit Ratings (James Pilgrim)

Challenges:

• Improving weather prediction

• Developing better algorithms to capture diversity of loading scenarios seen on the system

• More cost-effective thermal monitoring techniques

https://smartgrid.ieee.org/newsletters/july-2019/predictive-circuit-ratings

• Many assets in the power grid have insufficient monitoring data (e.g. for HVDC cable temperature only known near the ends)

• Diagnostics and online monitoring of cable systems

• How to make sense of the wealth of data? (distributed temperature sensing, distributed acoustic sensing, space charge, PD detection,etc.)

Cable Operation in the Smart Grid (TA)

Smart accessories (cable joints)

https://smartgrid.ieee.org/newsletters/july-2019/the-prospects-and-challenges-for-hvdc-cable-technology-in-a-smart-grid-world

Example: Smart Transformer

G. Bajracharya: Multi-Agent Model-Based Optimizationfor Future Electrical Grids, 2014, ISBN 978-94-6259-066-3ABB Smart Transformers: https://www.youtube.com/watch?v=j2DL_DVZ3-E https://new.abb.com/news/detail/4425/abb-launches-the-worlds-first-digitally-integrated-power-transformer