Upgrading the system in Rottne

During the last months, the Rottne demo site has taken a significant step to increase the use of bio-fuel in the system. Its smart grid controller has been upgraded and parts of the STORM modules have been tested. Rottne, a district heating network operated by Växjö Energi, is based on two bio-fuel boilers, complemented with a traditional oil boiler running on RME (rapeseed oil) for peak load usage. The purpose of the STORM project is to minimize the oil usage and optimise the operational behaviour of the bio-fuel boilers. This installation is a classic set-up for temperature distribution and the general problem context is common within many district heating systems in Europe.

Therefore, it is an ideal demonstrator for a generic evaluation of the project technology. It will ensure replicability for other systems in Europe beyond the two demonstration sites. This is why the installation of the STORM controller was first performed at this more common demo site.

District heating network in Rottne

 (click here for enlarged view)

Upgrading the STORM controller

Before the project’s start, a smart heat grid technology was already installed in the network and performed by NODA.  This existing platform was upgraded during the project. The manageable size of this network ensures that it is possible to study full-scale benefits of the project technology in relation to production and distribution.

During the heating season of 2015/2016 the demonstration site in Rottne has been used to deploy and test initial prototype implementations of certain parts of the STORM controller framework. The dispatch functionality, building on the existing NODA platform in Rottne, has been further developed and has been evaluated. This part of the STORM controller is responsible for coordinating the operational behaviour of the individual consumer substations included in the smart grid. In parallel with this, a first version of the head load forecaster functionality has also been evaluated. The forecaster is based on several machine-learning techniques coordinated through an ensemble mechanism. This provides a robust solution for operational heat demand forecasting, which in turn is crucial for the overall optimisation technology.

Heat load forecaster Rottne

Screenshot from operational system showing three different forecasting algorithms in relation to the actual heat demand (green)

Initial results from the forecaster evaluation were published in a peer-reviewed scientific paper at the International Symposium for District Heating and Cooling in Seoul, Korea, in September of 2016. The paper was also awarded an award for scientific excellence (read more here!). In the last quarter of 2016, a first version of the central optimisation mechanism of the STORM controller is added to the functionality in Rottne. The complete range of modules, including forecasting, optimisation and dispatch functionality, will be in operation throughout the heating season of 2016/17.

Find out what has been going on in the other demonstration site in Heerlen here!