Today, many appliances and loads are becoming more energy-efficient, but is power-intensive, hence drawing a high amount of power from the grid. In certain periods, this can strain the capacity of the grid in some areas. The focus in ENERGYTICS will be on electric vehicles, as their power use is very high when charging. Non-intrusive detection of such elements in the electricity grid have not been possible to any large degree before the appearance of smart meters, because consumption data in Norway earlier have been stipulated by monthly manual readings done by the customers. When moving from monthly manual readings to hourly automated readings by the AMS-meters, the electricity consumption pattern of customers can be known to a more accurate degree. In ENERGYTICS, the ability to non-intrusively detect such power-intensive appliances will be investigated. Alternatively, such appliances can be identified using surveys.

If electric vehicles and other high-power appliances can be detected in the electricity grid, there are several benefits:

  • Better assessment of the need for and value of coordinating customer consumption: the network operators will be able to know where the capacity of the network should be increased, and where there is a possibility for consumer flexibility; that customers can respond to signals, i.e. prize signals, given from their network operator, and move their consumption to other times of the day when the network is less strained. This will solve capacity problems that can occur in power peak periods.
  • Enabling mobile batteries for network operation application: the electric vehicles can be used as energy storage when connected to the charger, again increasing the flexibility in the network to reduce the consumption when the network capacity is strained.
  • Better management of voltage quality: high-power appliances can affect the voltage quality, and the AMS-meters enable monitoring of the voltage quality.

If this demonstrator succeeds in detecting consumption from electric vehicles, an exciting next step can be to analyse the relationship between measured voltage and consumption in areas with varying network strength (the ability to withstand short-circuiting, where high strength implies a low voltage loss in the lines), and/or density of electric cars, and other factors.

News: Big Data Symposium and corresponding publication