How to Make Data Analytics Successful in Energy Transition?

Dutch DSO
Industry: Electricity & Gas Distribution

One of the peer companies shared their initiatives in leveraging predictive analytics to manage risk, reduce costs and optimize grid performance in the energy transition.

They are aiming to understand the potential impact of electric vehicles, renewable energy, and distributed generation on network development and investment requirements by identifying internal and external data to predict future investment levels and minimize risk.

The company has built a modular model to understand the future impact of these developments, quickly process new scenarios and data to visualize results on LV cable and MV/LV transformers.

They calculate future scenarios using data such as: prognosis PV feed-in per housing type, development of the energy demand, current installed DER, prognosis electrical vehicles, MV/LV transformers, etc.

Lessons learned:

  • Ability to accurately model the current grid
  • Ability to incorporate local circumstances and agent behavior (e.g. per connection)
  • Modular model to quickly incorporate new insights (e.g. heat pumps)