Stochastic Modeling and Operational Optimization in District Heating Systems
(2001) In Doctoral theses in mathematical sciences 2001:3. Abstract
 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... (More)  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. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/record/20194
 author
 Arvastson, Lars ^{LU}
 opponent

 Prof Egardt, Bo, Chalmers
 organization
 publishing date
 2001
 type
 Thesis
 publication status
 published
 subject
 keywords
 Statistik, actuarial mathematics, programming, operations research, Statistics, Matematik, optimization, Mathematics, simulation, prediction, District heating, greybox modeling, operationsanalys, programmering, aktuariematematik, Systems engineering, computer technology, Data och systemvetenskap
 in
 Doctoral theses in mathematical sciences
 volume
 2001:3
 pages
 240 pages
 publisher
 Centre for Mathematical Sciences, Lund University
 defense location
 MH:C
 defense date
 20010615 13:15
 ISSN
 14040034
 ISBN
 9162848550
 language
 English
 LU publication?
 yes
 id
 a705f393e77742bd8d9abe5e085f8e5f (old id 20194)
 date added to LUP
 20070528 08:27:43
 date last changed
 20180529 10:10:07
@phdthesis{a705f393e77742bd8d9abe5e085f8e5f, abstract = {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.<br/><br> <br/><br> 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.<br/><br> <br/><br> 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.<br/><br> <br/><br> 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.<br/><br> <br/><br> 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.}, author = {Arvastson, Lars}, isbn = {9162848550}, issn = {14040034}, keyword = {Statistik,actuarial mathematics,programming,operations research,Statistics,Matematik,optimization,Mathematics,simulation,prediction,District heating,greybox modeling,operationsanalys,programmering,aktuariematematik,Systems engineering,computer technology,Data och systemvetenskap}, language = {eng}, pages = {240}, publisher = {Centre for Mathematical Sciences, Lund University}, school = {Lund University}, series = {Doctoral theses in mathematical sciences}, title = {Stochastic Modeling and Operational Optimization in District Heating Systems}, volume = {2001:3}, year = {2001}, }