Stochastic Modeling and Operational Optimization in District Heating Systems
Centre for Mathematical Sciences
Lund Institute of Technology
Operation of a district heating system is accomplished via a sequence of
decisions by the operators controlling the system. These decisions are
based on expectations of conditions in the system that are not known at decision
time. The operators could be helped by a decision support
system that computes predictions of future system variables and suggests
appropriate control actions given the available information.
This thesis presents a new model that gives a both physical and stochastic
description of a district heating system. The model describes both
technical and economical information of the system that are important for
the control decisions. It is easy to calculate predictions based on this
model as well as performing simulations.
The ambient temperature is the single most important explanatory variable
for the heat demand in a district heating network. A model that can be used
to calculate reliable temperature predictions are presented where the full
advantage of both local measurements and forecasts from a meteorological
institute are utilized.
A heuristic approach to the operational optimization problem is presented
and it is shown in simulations to be superior to a traditional control, based
on a priority scheme. The operational optimization problem is a complex
stochastic optimization problem and the heuristic approach gives a solution
that can be calculate instantly.
An online computer program, EnerPlan, is developed where the described models
are used to calculate predictions and simulate alternative future scenarios.
The program is currently used in the control room at the Heleneholm power
plant in Malmö, Sweden.
District heating, grey-box modeling, simulation, prediction, optimization