Data Economy in the energy landscape - II

The Sacramento Municipal Utility District uses data analytics for more effective operation. Credit: Space Time Insight

Power companies managing the electrical infrastructure have been used to set up their "networks", normally called "grids", with self protection and self balancing systems and plan the overall networks, and the production, using statistical analyses.

Basically the self balancing (and the architecture supporting it) provides real time adjustment whilst long term adjustment is the result of planning and rightsizing production and distribution.

Data on user consumption only matter for creating the bill.

More recently, in Italy it started at the turn of the century, digital meters have been deployed and these potentially allows the gathering of punctual consumption data that in turns can generate huge amount of information.

So far digital meters have been used to create more flexible tariffing schemes to induce desirable usage behaviours helping a better use of the grid and the production plant. As an example, by lowering the price of energy during night-time the hope is to stimulate usage at night when normally very few demand exist. People can plan their laundry at night time by programming their washing machines thus decreasing demand during the day and increasing it during the night.

Big production plan depending of classic fuel (fossil, geo, hydro, nuclear) work most efficiently if they can maintain constant production level, whilst other plants, like solar, tidal, wind depend on the source of power for production and hence it would be ideal to stimulate demand when offer exists.

Digital meters provide an amazing tool to analyse the demand, although so far they have been scarcely exploited for this.

 

A few companies, like Space Time Insight whose data analytics software is being used by Sacramento Municipality to understand usage patterns, have started to develop tools that let power utilities to analyse patterns at the user level and this can provide better forecasting tools that in turns increase the efficiency of the grid and make upgrade plans more effective.

Author - Roberto Saracco

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