Credit: Erlend Øye
Credit: Erlend Øye

A zero-emission society cannot use an electricity distribution grid design for a high emission world. We must plan, build, and operate the grid in completely new ways.

Therefore, CINELDI and FME ZEN (Zero Emission Neighbourhoods) has joined forces in a pilot project together with Arva, testing new distribution grid planning methodology. 

It is being tested on a neighbourhood called Molobyen (formerly known as Breivika Vest) currently under development in Bodø. The entrepreneur and the municipality aim at achieving a zero emission neighbourhood, where energy may largely be supplied by solar PVs and district heating (DH).

The grid planning framework for active distribution grids developed in CINELDI has in this pilot been adapted to incorporate

  1. load and generation modelling from FME ZEN,
  2. a model for optimal operation of neighbourhood batteries,
  3. time series power flow analysis using an existing, commercial Network Information System (NETBAS), and
  4. optimization of grid development plans using a tool previously developed at SINTEF Energy Research (DYNKO).

The work is documented in a master thesis and demonstrates how both traditional and new methods and tools can be combined and incorporated in the grid planning methodology.

The results in the master thesis shows how using PVs and DH dramatically reduces the maximum need for electricity grid capacity, from 3.93 MW to 0.61 MW. Because energy for heating is supplied by DH, PV generation becomes the dimensioning factor for the distribution grid. One implication of this analysis is that design and dimensioning of the electric distribution grid should consider the design of the internal energy system in neighbourhoods and the coordination with other energy carriers and energy sources. The dimensioning and design of the electric and the thermal energy system are dependent on each other so that different solutions for both should be considered during the design phase.

Contact persons:

  • , SINTEF Energi
  • , Arva