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Modelling and Control of a District Heating System

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Modelling and Control of a District Heating System UPTEC ES08 007 Examensarbete 20 p Mars 2008 Modelling and control of a district heating system Linn Saarinen Abstract Modelling and control of a district heating system Linn Saarinen Teknisk- naturvetenskaplig fakultet UTH-enheten The aim of this study was to investigate whether the supply temperature to a district heating system could be decreased if a dynamic model of the system is used to Besöksadress: determine the supply temperature control point, and to what extent the decrease of Ångströmlaboratoriet Lägerhyddsvägen 1 the supply temperature would improve the electricity efficiency of the connected Hus 4, Plan 0 combined heat and power plant. This was done by a case study of the district heating system of Nyköping and its 58 MW heat and 35 MW electricity CHP plant. The Postadress: district heating network was approximated as a point load with a variable time delay. Box 536 751 21 Uppsala Prediction models of the heat load, return temperature and transport time of the system were estimated from operational data of the heat plant. The heat load was Telefon: modelled with an ARX model using the 24 hour difference of the outdoor 018 – 471 30 03 temperature as input signal and the 24 hour difference of the load as output signal. A Telefax: comparison between using a regular control curve and using the dynamic model for 018 – 471 30 00 controlling the supply temperature indicated that with the same risk of heat deficit, the dynamic control strategy could increase the electricity production with 390 MWh Hemsida: per year, most of it during the winter months. This would correspond to an increased http://www.teknat.uu.se/student annual income of about 200 000 SEK for the owner. Handledare: Andreas Lennartsson Ämnesgranskare: Bengt Carlsson Examinator: Ulla Tengblad ISSN: 1650-8300, UPTEC ES08 007 Sponsor: Vattenfall Research and Development Sammanfattning Fjärrvärmenät finns i de flesta större samhällen i Sverige. I ett eller flera centrala värmeverk förbränns exempelvis skogsflis, avfall eller torv och värmen distribueras via ett isolerat vattenledningsnät till kunder runt om i samhället, som växlar över värme från fjärrvärmenätet till sina egna radiator- och varmvattensystem. I vissa fall har värmeverket också en ångturbin för elproduktion, och kallas då kraftvärmeverk. Värmen från förbränningen i pannan värmer vatten till ånga, som får passera ångturbinen och därefter kyls av fjärrvärmenätets vatten. Elproduktionen från en ångturbin beror av skillnaden mellan temperaturen på ångan före och efter turbinen. För att få en hög elproduktion vill man alltså ha en låg temperatur efter turbinen. Å andra sidan ställer värmebehovet på fjärrvärmenätet krav på att temperaturen inte får vara för låg, för ju lägre temperaturen på fjärrvärmevattnet blir, desto mindre värme per kubikmeter vatten transporteras från värmeverket till kunden. Vanligen styrs framledningstemperaturen (temperaturen på vattnet som skickas ut på fjärrvärmenätet från värmeverket) av utomhustemperaturen. När det är kallt ute behöver ju kunderna mer värme. Sedan systemet med gröna elcertifikat infördes i Sverige har elproduktion på biobränsleeldade kraftvärmeverk blivit mycket lönsam. Detta har gett incitament till industrin att konvertera värmeverk till kraftvärmeverk och även att höja elproduktionen på befintliga anläggningar. Problemet om man vill höja elproduktionen på ett kraftvärmeverk är att för en viss mängd producerad el produceras också en större mängd värme. Därför är elproduktionen beroende av efterfrågan på värme. I Nyköping har man till exempel börjat kyla bort värme från returledningen för att kunna öka elproduktionen på kraftvärmeverket vid tidpunkter då efterfrågan på värme är låg men elpriserna höga. Att sänka framledningstemperaturen innebär att man ökar andelen producerad el jämfört med producerad värme, och skulle kunna vara en billigare och mer energieffektiv metod att öka elproduktionen. I denna studie har en modell för värmebehovet och dynamiken på fjärrvärmenätet tagits fram för att kunna styra framledningstemperaturen mer precist. Exempelvis tar modellen hänsyn till att värmebehovet följer ett speciellt dygnsmönster som beror av kundernas beteende – som att mest varmvatten används under morgontimmarna. Med hjälp av driftdata från kraftvärmeverket i Nyköping har en modell skattats som både använder mätningar av utomhustemperaturen och beräkningar av värmeförbrukningen några timmar tillbaka för att förutsäga vilket värmebehovet kommer att bli de närmaste timmarna, och därigenom vilken framledningstemperatur som krävs. Detta innebär att framledningstemperaturen kan sänkas när värmebehovet inte är så stort, och då pressas verkningsgraden på elproduktionen upp. Resultaten av den här studien indikerar att elproduktionen på kraftvärmeverket i Nyköping skulle kunna höjas med omkring 390 MWh per år utan att värmeproduktionen ökas, vilket skulle innebära cirka 200 000 kr i ökade intäkter för ägaren Vattenfall. 2 Acknowledgements I would like to express my gratitude to the people who have given me their time and support during this degree project. First of all, thanks to my supervisor Andreas Lennartsson at Vattenfall and my examiner Bengt Carlsson at Uppsala University, who have given me feedback and good discussions. A special thanks also to Katarina Boman and Jozef Nieznaj at Vattenfall, for their help and answers to my numerous questions. And finally, thanks to all the other people at Vattenfall who have taken an interest in my project and assisted me in different ways, among them Christer Andersson, Majjid Mohammadi, Anna Helgesson, Rolf Abrahamsson and Peter Herbert. 3 Contents Abstract.......................................................................................................................................1 Sammanfattning ......................................................................................................................... 2 Acknowledgements .................................................................................................................... 3 Contents ...................................................................................................................................... 4 1 Introduction ............................................................................................................................. 6 1.1 Motivations ....................................................................................................................... 6 1.2 Methods ............................................................................................................................ 6 1.3 This report ........................................................................................................................ 7 2 Theory ..................................................................................................................................... 8 2.1 District heating ................................................................................................................. 8 2.2 Supply temperature and electricity output........................................................................ 9 2.3 Dynamics of district heating networks ........................................................................... 10 2.3.1 Time delays ............................................................................................................. 10 2.3.2 Loading/unloading the system ................................................................................ 10 2.3.3 Calculation of the heat load ..................................................................................... 11 2.3.4 Heat load models ..................................................................................................... 12 2.3.5 Dynamics of the return temperature ........................................................................ 12 2.4 Feed forward control ...................................................................................................... 13 2.5 Empirical modelling ....................................................................................................... 14 2.5.1 The ARX model ...................................................................................................... 15 2.5.2 Prediction with the ARX model .............................................................................. 16 2.5.3 Time dependent variations ...................................................................................... 18 2.6 Previous research ............................................................................................................ 18 3 Case study ........................................................................................................................ 21 3.1 Idbäcken’s CHP-plant .................................................................................................... 21 3.2 The district heating network of Nyköping ..................................................................... 22 3.3 Electricity output and supply/return temperature ........................................................... 23 3.4 Maximal flow on the DH network ................................................................................. 26 3.5 Load and return temperature characteristics .................................................................. 28 4 Modelling of the heat load .................................................................................................... 30 4.1 A static model ................................................................................................................
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