OPTIMIZTION OF DEVELOPMENT OF DISTRICT HEATING SYSTEM

ANDRZEJ REŃSKI PhD

Department of Power Engineering

TECHNICAL UNIVERSITY of GDANSK

Introduction

The share of district heat demand in domestic district heating systems

Projections of meeting the demand on district heat

The role of combined heat and power production (cogeneration)

The share of meeting the demand on district heat(industrial utilities are excluded)

Professional CHPs 19,0CHPs & industrial heating plants 4,0Municipal boilers 11,0 Local boilers (solid fuels) 26,5Local boilers (fuel oil, gas) 6,0Accumulative electric heating systems 0,5Coal furnaces 33,0 100,0 %

The projection of demand on district heat in the reference scenario (Poland)

0

200

400

600

800

1000

1200

1400

1997 2005 2010 2015 2020

Residential sector

Industry

Other consumption

PJ

Scope of the research work

Presentation of research methods to anable effectivness optimization of a large DHS

Presentation of computer based software to analyze and optimize complex DHS

Main thesis and goals

Small increase of heat demand or even demand decrease in large DHS as a result of modernizations on demand side

Main thesis and goals

The first issue to protect competitiveness of the DHS with other heat supply systems is modernization of DHS but usually not completely new investments

Main thesis and goals

Development of centralized heat sources should go towards higher level of heat and electricity cogeneration and effectiveness of primary energy use

Energy Supply and DHS

Definition and parameters of DHS

Heat supply from DHS to consumers in residential sector on background of other heat supply systems 

Structures of DHS in large urban areas

Definition of DHS

REGION

CHP – combined heat and power plantMP – main pipelines (transport line)SR – distribution system

SR

MP

CHP

tz tp

pz pp

Gz Gp

consumer

house substation

DHS share in heat supply to consumers in residential sector

DHS34 %

solid fuel boiler stations26,5 %

oil or gas-fired boiler stations

6 %

electric heating with

accumulation 0,5 %

coal-fired furnaces

33 %

DHS share in heat supply to consumers in residential sector in cities

coal-fired furnaces 25,9%

solid fuel boiler stations17,4%

DHS53%

oil or gas-fired boiler stations

3%

electric heating with

accumulation 0,7%

Hierarchic structure of DHS

CHP2

CHP1

Tasks of the DHS optimization

Medium term optimization (month)

Short term optimization (one day)

A few year optimization

Strategic planning of the development

Short term optimization

Time horizon: 1 day ÷ 1 week

Expected effects: load timetable of heat generation units

flows of water in distribution net

pressures in distribution net

Medium term optimization

Time horizon: 1 week ÷ 1 year

Expected effects: primary energy demand plans of starts and stops of heat source and distribution net timetable of repairsdistribution of heat and power costs

A few year optimization

Time horizon : 1 year ÷ 5 years

Expected effects: primary energy demand financial schedules

timetable of repairs distribution of heat and power costs polluting emissions

from heat sources

Strategic planning of the development

Time horizon : 5 years ÷ 20 years

Expected effects: primary energy demand financial schedules

timetable of repairs distribution of heat and power costs polluting emissions

from heat sources power and energy

balances investment plans

Algorithms for the choice of optimal parameters in a developing DHS

Cogeneration factor

Supply water temperature in the transport system

Operation at constant or sliding outflow temperature

Methods for the choice of large energy supply systems structure

Multivariant analysis

Mathematical programming (linear, mixed integer programming)

Optimization criterion of DHS development

Criterions classic:

unit heat supply cost annual costs of DHS

modern: net present value method ( NPV ) internal rate of return method ( IRR )

Proposed optimization criterion : objective function as discounted sum of total DHS costs

taking into account supply and demand sides of the system

Choice of optimal parameters in DHS with condensing power plant

Hot water temperature at the plant outlet

Operation at constant or sliding outflow temperature of hot water

Technical capabilities of applying power plants in district heating systems

The scale of activities undertaken in Poland Electric power plants cooperating with existing

(or future) heating systems Modification of heating system in power plant

is necessary and changes in turbine system are required

Condensing Power Plant cooperating with peak load boiler in district heating

system

EK – electric power plant as base load heat source; ZS – peak load boiler; t1, t2 – temperature of water in

main pipelines: supply and return

Heat supply system

t 1

EK t 1s

t 2st 2

EK

ZS

ZSEK

ZS

The unit with condensing turbine adapted to heat production

NPS PWP

Schematic heat flow diagram of power plant

Permanent annual curve of heat output q and outflow and return flow temperatures t1,, t2 at sliding operation for the supply region

1000 2000 3000 4000 5000 6000 7000 8000 8760 h/a

τ00

2020

40

5060

80

100

120125

40

60

80

100

50

t1, t2

t2

t 1

EK

t 1

EK

t 1

ZS

t

t1s

t2s

Characterization of supply region and heat transport system

%

oC

q

q

Economic criterion and methodology of heat parameters calculation

Specific cost of heat supplied to the end-consumers

where:

ss

r

r

r

TQ3,6

K

W

Kk

PLN/GJ

annual delivery costs, PLN/yr

k min

K r annual amount of delivered heat, GJ/yrWr

Qs peak load in MJ/s and annual peak load utilization period in

hrs/yr

.,Ts

Elements of objective function

PLN/GJ where:

Specific costs:

K(t) = kEK + kEK + kCC + kMP + kZS + kZS

difference between supply and return water temperatures during peak load, K

P A Q W

kMP = kL + kP + kstr where:

t

kEK,P

kEK

A

fixed and variable costs of heat production in condensing power plant

kCC cost of heating unit in power plant

kMP cost of main pipeline including the following:

kL fixed cost of pipeline

kP cost of water pumping station

kstr cost of heat losses due to pipeline transmission

kZS , kZS

Q W

fixed and variable cost of heat production in peak load boiler

Costs of heat production in power plants

Specific fixed cost:

where:

kP =

es kSE rcSE

3,6 Ts

relative electrical power loss in condensing power plant, MW/MW

n s PLN/GJ

es

kSE

n

specific capital cost of equivalent power plant in electrical power system, PLN/MW

the rate of fixed costs for equivalent power plant , 1/yrrcSE

s cogeneration factor

Ts annual peak load utilization period, hrs/yr

Costs of heat production in power plants

Specific variable cost:

where:

kEK = 103

eA kSE

EK Wu

relative electricity loss in condensing power plant, MWh/(MWh)

B A PLN/GJ

eA

kSE

B

standard fuel (coal equivalent) price for equivalent power plant, PLN/t ce

A annual cogeneration factor

EK overall efficiency of equivalent power plant

Wu calorific value for standard fuel, kJ/kg ce

Hot water temperature

topt = 0,731

where :

B2

distance of heat transmission in main pipeline, m

B1

0,623

( ) L0,623

Qs0,246

K

L

Qs peak load of heat power, MJ/s

B1, B2 constants for heating system and dependent

on method of operation

Sample calculation results

Optimal temperature difference at sliding and constant operation

102030

50

80

100

150

10 20 30 40 km

L

K t

100 MJ/s

100 MJ/s500 MJ/s

1000 MJ/s500 MJ/s

1000 MJ/s

32

sliding operationconstant operation

First conclussions

Results of sample calculations: lower level of temeprature for supply water in

main pipeline lower temperature of hot water when constant

operation occurs

Comments: condensing power plants are competitive heat

source in district heating systems detailed research in specifying transmission and

distribution losses is justified tThe role of cogeneration factor

Proposed optimization criterion in research of the developing DHS

min)(

)()(

)1(

,,,,,,

,,,,

m b

cmbi

o

vmoi

cmoi

o or

vori

cori

voi

coi

i

i CCC

CCCC

pC

Variables: - constant and variable costs in year i

Bottom indexes / sets: i – years; o, or – units; m – modernizations; b – construction technologies of buildings; r – consumers regions; mp – sections of main pipe lines

vi

ci CC ,

Constraints

Variables:

mp

MP s,mp i,

r b or

sror,i,

ods,rb,i,

wcs,r

SRs,r

z

szz i, QQQQQQ

mp

MPmp i,

r b orr or, i,

odr b, i,r

z

zz i, WWWWW wc

rSR W

QQ , - power and loss of power

- annual heat production and heat lossesWW ,

Uppper indexes define parts of DHS (distribution net, house substations, main pipe lines, consumers)

Scheme of DHS balance

region

WEC = Wod + Wod + WSR + WMP

QEC = Qod + Qod + QSR + QMP

Qod

Wod

SR

MP

CHP

losses:QMP

WMP

Qr, Wr

QSR

WSR

Qwc

Wwc

consumer

QEC, WEC

house substation Qod

Wod

Optimization of modernization and development of DHS

Small CHP

Heat only boilers

Individ. sources.

DSM

Heat demand

from DHS

Heat demand forecasts

demandside

DHS

(Supply side)

Modernizations and development technologies in DHS

A

B

C

CI

Supply and demand optimizationmin ( ... )C A C B C C C I C II C III C IV

CII

CIII

CIVCC

CB

CA

Algorithm of optimization of DHS development

General characteristic of mathematical models of basic DHS components model of centralized heat source model of transport and distribution net model of demand structures model of decentralized heat sources

Methodology Computer tool

Simplified heat flow diagram of combined heat and power unit BC-50 with back-pressure turbine TP and steam boiler

SB in cooperation with peak load water boiler WB                              

 

σ Ael-

WpWs- Wp

Wsb

Bs

Wod

TP

Bp

Wpb

WB

Ep

p

Es

s

W, Ael-,E – variablesBW, ξ, σ – objective function parameters

SBp

Objective function component on supply side

z

s

eksi,

ksz,i,

ensi,

nsz,i,i

elzi,i

elzi,

f

o

ezoz,i,f,oz,i,

p

oz,i,soz,i,

ezoz,i,f,oz,i,

p

oz,i,

fi,zmi

kEkEkAkA

kbWW

kbW

k

K

AA

ss

Wsss

pop

Wpp

B

Development/modernization technology within centralized heat source: combined steam and

gas (stag) cogeneration plant

                         

 

                         

 

                         

 

TP

ε

σ

Ael-

ε

Bp

Wpb

TG

HRSG

Wp Ws-Wp

Wsb

Bs

WB

Wod

ξ

ξ

Simplified view of district heating system presenting moderniziation activities

-

                    

 

distribution network

buildings b

House substation wc

Main pipeline MP

CHP

DHS

CHP Plant

Development technology in the decentralized district heating system: simplified view of small unit with

gas engine cooperating with peak load boiler                         

                        

                        

                       

 

σ

)( elnA

Bp

cp

Es

s Bs

cs

Wp

WodsW

Modernization activities on the demand side

e2 = 240e1 = 180

e3 = 300 kWh/(m2·yr)

120180

180

120

costsEnergy savings

Modernization technologies:-roof and wall insulation -windows replacement-thermostatic valves- heat consumption measurement on the demand side-complex thermo-renovation

a1,b1a2,b2

a3,b3

Am1

Am2

Am3

a,b,e,Δem,Am– parameters & variables concerning demand devices and modernization activities

Δem

Objective function component on demand side

b m

ii'

i'm,br,,i'

maxm,b

ii'

i'br,i,rb,

maxb

odri, AeaΔaeW

155

11000/1

Optimization problem

If the objective function )(xf and constraints mi ,...,2,1,)(xigare linear functions, and xj are integer varaibles, then the objective function is

minimized:

minx )(f

under constraints

Jx

bxA

where: J – vectors with real and integer elements A - matrix

and it is a mixed integer programming problem

mn RbRx ,,nm

Flow chart of calculations

Data, charts Initial value

Defining development options

1j

0c1k

T&D DEMAND

NO

NO

YES

YES

Results

SUPPLY

1j:j

1k:k

k0 cc

c0k cc

nj

Example of district heating system optimization algorithm

Basic assumptions and input data – calculations for development and modernization technology options

Scope of research – development options are analyzed Option no. 1: modernization activities

undertaken only for centralized heat sources Option no. 2: modernization activities

undertaken for centralized heat sources and for transmission and distribution system

Example of district heating system optimization algorithm

Option no. 3: modernization activities undertaken in whole supply system and on the demand side (thermo-renovation in buildings), at the level of 10% of whole dwelling resources, in the base year

Option no. 4: modernization activities undertaken in whole supply system and on the demand side (thermo-renovation in buildings), at the level of 20% of whole dwelling resources, in the base year

The model of district heating system for the agglomeration

CHP

CR1CR2

ZR

ZR

source

unit

Permanent annual curve of heat output for given centralised heat source with peak capacity Qsz=100 MJ/s, and cogeneration factor =0,5

0,0

20,0

40,0

60,0

80,0

100,0

0 1000 2000 3000 4000 5000 6000 7000 8000 9000

Qpz=Qsz/2

Qp=QsQpz/Qsz

Qsz=100 MJ/s

Qs

Wp

Ws-Wp

Cumulative annual heat production curve for given centralized heat source with peak capacity Qsz=100 MJ/s and for unit with capacity Qs=30 MJ/s

W=f(Q)

0,0

50,0

100,0

150,0

200,0

250,0

300,0

0,0 20,0 40,0 60,0 80,0 100,0

Q

GWh/aW

MJ/s

source

unit

Option no. 1 system development: modernization of centralized heat source

OPCJA 1

0

50

100

150

200

250

300

350

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

MJ/

s

BC50-I WP70-II WP70-III WP70-I WP70-IB Rozpr

OPCJA 1

0

50

100

150

200

250

300

350

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

MJ/

s

Budynki Rozdział Przesył Rozpr

Option no. 4 system development: modernization of whole supply system along with demand side modernization (thermo-renovation in buildings)

OPCJA 4

0,0

50,0

100,0

150,0

200,0

250,0

300,0

350,0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

MJ/

s

BC50-I WP70-II WP70-IIC WP70-I WP70-ID WP70-III

OPCJA 4

0

50

100

150

200

250

300

350

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

MJ/

s

Budynki Rozdział Przesył

Modified option no. 4 – system development under following conditions: fuel prices lowered to 60% of baseline price level, electricity prices lowered to 80% of baseline price level

OPCJA 4b

0

50

100

150

200

250

300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

MJ/

s

BC50-I WP70-II WP70-IIB WP70-III WP70-I WP70-IB Rozpr

OPCJA 4b

0

50

100

150

200

250

300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Budynki Rozdział Przesył Rozpr

Modified option no. 4: system development –share of annual thermo-renovation activities on the demand side increased to 60%

OPCJA 4c

0

50

100

150

200

250

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

MJ/

s

BC50-I WP70-II WP70-IIC WP70-III WP70-I Rozpr

OPCJA 4c

0

50

100

150

200

250

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

MJ/

s

Budynki Rozdział Przesył Rozpr

Conclusions – the analysis

The most effective option is based on complex undertaking of modernization and development activities with regard to whole elements of examined supply system

Changes on the demand side resulting from modernization activities have impact on the formulation of optimization criterion on the demand side

Modernization activities on the demand side anticipate efforts aiming for heat source extension (they are more effective than activities undertaken in the whole source of heat)

Conclusions – the analysis

The level of investment has great impact on modernization/development technology choice

New peak units are introduced to the system prior to new base loaded units, and the sequence of introducing and loading peak units depends on techno-economic factors of these utilities

The method enables to calculate optimal value of cogeneration factor for centralized source in the following years of considered time horizon

Summary and prospects

The essential advantage of this method is that it includes both supply and demand sides of heat supply system functioning under market conditions to large extent

The usefulness of modular structure applied for the mathematical model and of the structure of computer application program including demand side module, transmission and distribution (T&D) system module, and supply side module

Applying GAMS system ver. 2.25 and running the sample model using mixed integer programming (MIP)

The elaborated mathematical model is a kind of compromise between the exact image of actual structures and relationships, and the solution providing effective obtaining of the results and their easy interpretation

Summary and prospects

New formulation of objective function Proposed optimization criterion enables to calculate

specific heat delivery cost per unit of product from the examined modernized or developed supply system in considered time horizon, which makes model formulation and assumed input data a subject to revision

One of the most significant aspects of this research is to proof that the essential impact of the demand side on the obtained solution exists (solution means the choice of optimal development strategy for the system supplying heat to agglomeration)

Summary and prospects

Useful tool for many companies that are engaged in heat supply planning or concerned with investment in heat generation utilities

Proving of slight increase in peak load of heat supply system in examined time horizon; after initial decrease in cogeneration, gradual increase occurs

Among modernization/development activities, the most effective are in order: 1) activities based on thermo-renovation in buildings, 2) modernization activities of heat generation units and T&D systems, and 3) activities related to investments in new base loaded utilities supplying heat