How intelligence at the grid edge provides value for DSOs: Three use cases

19-Mar-2020 11:19:00

Intelligence at the grid edgeThe energy system is experiencing systemic change. Generation is becoming decentralized and decarbonized with increased amounts of volatile renewable energy in the mix. Loads are becoming more flexible and grid operations more digitalized. To adapt and transform, Distribution System Operators (DSOs) require new capabilities to maintain and improve resiliency, flexibility, transparency and security. New solutions that help meeting these challenges are available and the key to unlock them lies in the intelligence at the edge of smart metering infrastructures.

In the next decade smart meter rollouts happening across Europe will entail the generation of huge amounts of data every single day. These will be used for billing purposes, but they can do a lot more than that. Landis+Gyr has devised three exemplary use cases that show how leveraging ‘intelligence’ at the secondary substation, at the connection point of industrial customers and large solar panels and at the central system provides DSOs with actionable insights.

1. Ensure grid reliability with regular power quality reports

Power quality data identifies and rectifies grid operation problems with increased accuracy. This kind of data can be processed and reported in multiple ways, but not all of it is necessary in daily business. New intelligent endpoints have the capability to only process the relevant power quality measurements and produce alarms whenever power quality is violated.

An alarm initiates a process within the utility to access and read the whole database of the meter where the problem was detected. Now it is possible to identify whether there is a one-off problem or if the issue occurs regularly. This information is extremely useful for DSOs to better evaluate the need for grid investments and maintenance works. By accessing data also from neighboring meters, DSOs now can easily check whether the problem is on the grid side or on the customer side. With this knowledge, they can take the necessary action to guarantee grid stability without unnecessary conflicts with industrial customers.

2. Enable decentralized voltage control

When volatile decentralized energy resources are connected to the grid, voltage stability becomes a critical topic for DSOs. They need to find means to keep voltage under control and ensure a stable grid operation. The production of a newly installed PV system can increase the voltage levels in the low voltage grid and, especially, at the connection point. Here, the smart meter at that connection point needs to be extra smart to eliminate possible voltage violations: Based on the measured voltage a command is sent to the solar panel’s inverter instructing it to compensate with reactive power and, if necessary, decrease active power production.

Additionally, DSOs need the transparency to monitor such voltage control schemes remotely and they need to be able to react if necessary. Here, the capabilities of intelligent endpoints and the power of data processing at the grid edge can help. The meter can ‘talk’ to various systems, including the SCADA (Supervisory Control and Data Acquisition) system which gives DSOs the necessary visibility of what is going on. Commands can be triggered both on the edge through the local control scheme and remotely from SCADA.

3. Increase transparency in asset management and investment planning

Increasing dynamics in the distribution system caused by the influx of renewable energy, electric vehicles and storage, make it more and more difficult to forecast the need for grid investments in the low voltage system. There is, however, a way of using smart metering data to estimate the operational lifetime of grid components and, therefore, improve asset management and planning.

Data is generated by smart meters at every connection point to the low voltage network. These data sets provide accurate information not only for billing but also for asset management. They can be processed and combined with other information, e.g. electrical characteristics, to create more transparency towards understanding the service lifetime of grid assets. Knowing the technical age of transformers enables DSO’s to base their investment planning on tangible data rather than expensive or unreliable estimates.   

All three use cases open doors to further ideas and applications to leverage grid edge intelligence – either implemented locally, directly on the meter, or centrally, at system level.

If you like to know more or are interested in a pilot, contact our experts.


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