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Model Predictive Control of District Heating Systems

Rene, Anders Svenheim
Master thesis
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URI
http://hdl.handle.net/11250/2404711
Date
2016
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Abstract
A district heating system is a network of pipelines where heating is delivered to a number

of customers from a centralized heating station. For the operators of these systems, the

ideal operating scenario is to deliver enough heat to satisfy the heating demand of every

customer on the network with the lowest possible costs.

District heating systems are slow processes, and the heated water may travel for several

hours from it leaves the heating station until it reaches the most distant customer. Today,

many district heating systems in Norway are operated largely based on operational

experience, without any available tools for predicting future states or demands.

Model predictive control on district heating systems has been applied in other countries,

and has been reported to be very effective. Motivated by its success, this thesis has

reviewed the benefits and challenges of applying model predictive control to district heating

systems in Norway.

In this thesis, a model predictive controller has been simulated on a small-scale district

heating system in Klæbu, a small urban area approximately 19 km south of Trondheim,

Norway. This system consists of a single plant which delivers heating on two separated

lines. The simulation has been performed on the line that covers the central areas of Klæbu.

The line only has five customers, making it a very simple district heating network, well

suited for initial testing of predictive control.

The model predictive controller implemented on the district heating system in Klæbu

controls the supply temperature of the water delivered from the plant. The controller uses

a model that predicts the future heat demand and the transport delay in the network. Based

on this model, the simulated controller generates values that meet the predicted heat demand

without violating the system constraints. However, the actual performance of the

controller has been found challenging to assess. This is due to a low amount of reliable

measurements on the network, unclear methods of how to interpet the available measurements,

and a lacking definition of economic optimization.
Publisher
NTNU

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