Analyzing and forecasting electricity distribution networks – part 2

By Jonas Jacobsson

In the first part of these two blogs, we looked at how short- and mid-term forecasting support electricity distributors when aiming for a sustainable energy production. Let us now continue and dive straight into the concept of long-term forecasting, and how our Network Information System dpPower, will support you.

Long-term forecasting

Long-term forecasting accounts for everything above mid-term forecasting (which is from one day to one month), that is from over one month to ten years. We all know that grid investments are needed in the years to come. But we also know that resources are scarce, not only from a financial perspective. There is a lack of skilled craftsmen to meet all needed investments. Using accurate short-term forecasting will help to get the most out of existing networks. Inevitably though, that might not be sufficient when looking ten years into the future. Clever investments must be done where and when needed the most.

When building new electricity distribution networks planning and investments for up to 50 years are required.

Just like when discussing short-term forecasting, long term forecasting needs to be done at least on the medium voltage levels. And we are back to the same challenge: data volumes. Another complexity arises: time. Model growth and decrease on energy on the existing customer base must be considered. Questions that must be answered include (but are not limited) to:

  • Where will new EV charging points be installed?
  • Who will invest in solar power?
  • Will new residential areas be developed?
  • Are new energy consuming industries being built?
  • Are there plans for large wind farms?

The planning model has to include these growth scenarios. Suffice to say, modelling this without ending up with a very complex structure, cranking out numbers which no one understands how they are derived, is hard.

In both existing and new residential areas, new EV charging points might be considered.

Forecasting and the Digital Twin

At Digpro, the way forward is to use dpPower, a Network Information System (NIS) for electricity distribution networks. It has the components to generate long term forecasting. The core of a proper NIS is to create a Digital Twin. But the Digital Twin can not only reflect what is happening today – it also needs to contain future projects. And in dpPower, a time dimension is available.

By adding a growth model on top of the existing network and future projects, you create reliable  long-term forecasting. What is needed now is a bit of practical user experience in order to fine tune the solution. And again – at Digpro, we develop solutions together with our customers.

The conclusion is that dpPower, which has been used by electricity distribution companies for decades, is the way forward. This advanced NIS is a crucial key component when meeting societies expectation, and how grid owners and electric distribution companies can mitigate climate change.

Meet the expert
Jonas Jacobsson is CEO at Digpro Solutions AB, and has numerous years’ experience in the power and electric industry. He holds a Master of Science in Industrial Engineering andJonas Jacobsson Management from Linköping’s University, and a Master of Science in System and Control Engineering from Case Western Reserve University, Cleveland USA. After a trainee position at ABB in 1996, Jonas continued his career focusing on IT solutions for electric distributions. He has worked at Digpro for over thirteen years, focusing on sales and management.

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