AI, EVs and a green tech revolution

Powerline Technologies, Chronos and the European Space Agency join forces in the drive towards zero emissions and greener roads.

The UK power industry is undergoing a fundamental, once-in-a-lifetime change. With the UK Government legislating to produce net zero carbon emissions by 2050 [1], focusing on  reducing road emissions in particular, it has committed to expand the UK’s electric vehicle (EV) charging infrastructure [2] significantly. This move towards decarbonisation is welcome and necessary, however it raises significant challenges for low voltage (LV) distribution network operators (DNOs).

A powerful charge

EV charging units are significant power consumers. Rapid chargers, which can charge a typical mid-range EV to 80% of capacity in under an hour [3], are typically rated at 40-50 kW, with the most popular domestic chargers being rated at 7kW [4]. A 2017 study [5] recorded the average domestic EV charging event as delivering 9.1 kWh of charge, with over 400,000 events being recorded nationally each day.

With this number set to rise significantly, network operators need to be able to increase power provision in LV distribution networks to allow for local increases in demand. Given the significant regional variation in the level of public EV charging infrastructure [6], and the inevitable geographical variation in domestic charger installations due to socioeconomic factors, prediction of EV charging demand in local networks will be a hugely complex task.

An intelligent solution

The solution could come from artificial intelligence.  Powerline Technologies, part of the Fundamentals Group of companies, has teamed up with timing solutions developer Chronos Solutions, to create a leading-edge system that will provide DNOs with real-time estimates of the number of EVs charging on their network. Funded as part of the European Space Agency’s Enersyn project [7], to deliver a commercially-ready product, the system has already undergone the design and test stages for validation.

It includes:

  • High speed, time synchronised measurements of voltage and current waveforms
  • An integrated, rugged GNSS/LoRa timing engine
  • A sensor application platform with specific support for high profile distribution Smart Grid applications including Non-Intrusive Load Monitoring (NILM) for Electric Vehicles (EV)
  • Server-based application software to provide control and data analytics for the sensor network

According to Powerline Technolgies’ CEO, Brian Lasslett: “Non-Intrusive Load Monitoring uses innovative AI, advanced statistical signal processing and machine learning techniques, combined with accurate, time-stamped sensor events.  This will enable electricity operators to build up a multi-location picture of the number of EVs charging on their network, thus allowing them opportunity to react to EV related increases in load and develop short and long-term planning based on this observed usage.”

Real deep

To achieve this, a fast detection algorithm establishes whether changes in the observed current and voltage signals could potentially be due to a connected EV charger.

This is followed by use of the latest edge deep learning techniques to establish with high accuracy whether the event is indeed due to an EV charger.  In essence, deep learning algorithms can extract extremely complex relationships from large amounts of data.

The common structure for these algorithms is loosely based on the human brain’s neuron network, and ‘depth’ refers to their typical composition of many layers of neurons, allowing for highly complex non-linear representations of the data.

 

References

[1]

"Climate Change Act," 2008. [Online]. Available: https://www.legislation.gov.uk/ukpga/2008/27/contents. [Accessed June 2020].

[2]

"The Road to Zero," 2018. [Online]. Available: https://assets.publishing.service.gov.uk/government/uploads/system/uploa....

[3]

"EV Charging connectors - Electric car charging speeds," [Online]. Available: https://www.zap-map.com/charge-points/connectors-speeds/.

[4]

"Zap-Map EV Charging Survey," 2019. [Online]. Available: https://www.zap-map/.com/engine/wp-content/uploads/2019/11/Zap-Map-Surve....

[5]

"Electric Chargepoint Analysis 2017: Domestics," [Online]. Available: https://www.gov.uk/government/statistics/electric-chargepoint-analysis-2....

[6]

"Electric vehicle charging device statistics," 2019. [Online]. Available: https://www.gov.uk/government/statistics/electric-vehicle-charging-devic....

[7]

European Space Agency, "ESA's NAVISP Programmes," [Online]. Available: https://navisp.esa.int/project/details/26/show.