OPTIMIZTION OF DEVELOPMENT OF DISTRICT HEATING SYSTEM

ANDRZEJ RE ŃSKI 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)

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

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

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

PJ 1400 1200 1000 800 600 400 200 0 1997 2005 Industry Other consumption Residential sector 2010 2015 2020

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

CHP t z p z t p p G z G p p MP SR REGION consumer house substation CHP – combined heat and power plant MP – main pipelines (transport line) SR – distribution system

DHS share in heat supply to consumers in residential sector

coal-fired furnaces 33 % electric heating with accumulation 0,5 % oil or gas-fired boiler stations 6 % solid fuel boiler stations 26,5 % DHS 34 %

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

coal-fired furnaces 25,9% electric heating with accumulation 0,7% oil or gas-fired boiler stations 3% solid fuel boiler stations 17,4% DHS 53%

Hierarchic

structure

of DHS

CHP1 CHP2

Tasks of the DHS optimization



Short term optimization (one day)



Medium term optimization (month)



A few year optimization



Strategic planning of the development

Short term optimization

Time horizon: Expected effects: 1 day ÷ 1 week  load timetable of heat generation units  flows of water in distribution net  pressures in distribution net

Medium term optimization

Time horizon: Expected effects: 1 week ÷ 1 year  primary energy demand  plans of starts and stops of heat source and distribution net  timetable of repairs  distribution of heat and power costs

A few year optimization

Time horizon : Expected effects: 1 year ÷ 5 years     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 : Expected effects: 5 years ÷ 20 years        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

ZS ZS EK t 1 EK ZS t 1s t 2 EK t 2s Heat supply system EK – electric power plant as base load heat source; ZS – peak load boiler; t 1 , t 2 – temperature of water in main pipelines: supply and return

Schematic heat flow diagram of power plant WP S P

NP The unit with condensing turbine adapted to heat production

o C 125 120

Characterization of supply region and heat

t 1 , t 2 t 1s q % t 1 ZS t 1 EK

transport system

100 100 t 1 EK 80 80  t q 60 50 40 t 2s 60 50 40 t 2 20 20 0 0 1000 2000 3000 4000 5000 6000 7000 8000 8760 Permanent annual curve of heat output q and outflow and return flow temperatures t 1, , t 2 at sliding operation for the supply region τ h/a

Economic criterion and methodology of heat parameters calculation

Specific cost of heat supplied to the end-consumers k = K W r r = 3,6  K r s  T s k  min PLN/GJ where: s K r W r Q ,T s    annual delivery costs, PLN/yr annual amount of delivered heat, GJ/yr peak load in MJ/s and annual peak load utilization period in hrs/yr

Elements of objective function

Specific costs: K(  t) = k EK P + k EK

A

+ k CC + k MP + k ZS + k ZS Q W PLN/GJ where:  t k MP = k L + k P + k str where:  difference between supply and return water temperatures during peak load, K k EK , P k CC k MP  k EK A  fixed and variable costs of heat production in condensing power plant cost of heating unit in power plant  cost of main pipeline including the following: k k k L P str    fixed cost of pipeline cost of water pumping station cost of heat losses due to pipeline transmission k ZS Q , k ZS W  fixed and variable cost of heat production in peak load boiler

Costs of heat production in power plants

Specific fixed cost:

k P

=

e s  k SE  n r c SE  s PLN/GJ 3,6 T s where: e s  relative electrical power loss in condensing power plant, MW/MW k SE n  specific capital cost of equivalent power plant in electrical power system, PLN/MW r c SE  s   the rate of fixed costs for equivalent power plant , 1/yr cogeneration factor T s  annual peak load utilization period, hrs/yr

Costs of heat production in power plants e

Specific variable cost:

where: k EK

= 10 3

e A   EK k SE B  W u A   A PLN/GJ relative electricity loss in condensing power plant, MW  h/(MW  h) k SE B  standard fuel (coal equivalent) price for equivalent power plant, PLN/t ce  A  annual cogeneration factor  EK  overall efficiency of equivalent power plant W u  calorific value for standard fuel, kJ/kg ce

Hot water temperature

 t opt = 0,731 ( B 2 B 1 ) 0,623  L 0,623  Q s 0,246 K where : L  distance of heat transmission in main pipeline, m  Q s B 1 , B 2   peak load of heat power, MJ/s constants for heating system and dependent on method of operation

Sample calculation results

K 150  t sliding operation constant operation 100 80 50 30 20 10 10 20 30 100 MJ/s 100 MJ/s 500 MJ/s 1000 MJ/s 500 MJ/s 1000 MJ/s 40 L km 32 Optimal temperature difference at sliding and constant 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

C

=

i

 ( 1 

p

) 

i

     

o

(

C i c

, 

m

  

o



o



C i v

,

o

(

C i c

,

o

) ,

m

  

or

(

C i c

,

or C i v

,

o

,

m

)   

b C i v

,

or C i c

,

b

)  ,

m

        min

Variables :

C i c

,

C i v

- constant and variable costs in year

i

Bottom indexes / sets:

i

– years;

o, or

– units;

m

– modernizations; construction technologies of buildings;

r b

– – consumers regions;

mp

– sections of main pipe lines



z Q sz i, z

= 

r

Constraints



Q r s, SR

 

Q r s, wc

 

b Q s, od i, b, r

 

or Q s i, or, r

 

mp



Q s, MP i, mp



z W i, z z

= 

r



W r

SR

 

W r wc

 

b W od i, b, r

 

or W i, or, r

 

mp



W i, MP mp

Variables:

Q W

, , 

Q



W

- power and loss of power - annual heat production and heat losses Uppper indexes define parts of DHS (distribution net, house substations, main pipe lines, consumers)

Scheme of DHS balance

losses:



Q MP



W MP Q r , W r CHP MP Q EC , W EC



Q SR



W SR SR region consumer house substation



Q od



W od Q od W od



Q wc



W wc W EC = W od +



W od +



W SR +



W MP Q EC = Q od +



Q od +



Q SR +



Q MP

Optimization of modernization and development of DHS

A B C C B C A C C

DHS (Supply side)

Heat demand forecasts C I

Heat demand from DHS demand side

C II C III

Small CHP Heat only boilers

C IV

Individ. sources.

Modernizations and development technologies in DHS

DSM

Supply and demand optimization

min

( C A  C B  C C  C I  C II  C III  C IV  ... )

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 b B p W p SB E p TP σ A el W, A el ,E – variables B W , ξ, σ – objective function parameters b W s B s WB



s E s W s - W p W p W od

Objective function component on supply side

K i zm

= 

z

           

f k i, B f A i, el z

  

k o

        

p o



s W i, p z, o

  

p W s i, z, o



s b W i, z, o



p f, W i, p z, o



s k

 

i, ez



z, o p b W i, z, o



s i A

 

A i, el z

 

k i A

  

s



E i, n z, s



k i, en s f,



k ez i, z, o s

        

E i, k z, s



k i, ek s

          

Development/modernization technology within centralized heat source: combined steam and gas (stag) cogeneration plant ε

ξ

TP σ A el HRSG W p b W s WB B s

ξ

W s -W p ε b W p B p TG W od

Simplified view of district heating system presenting moderniziation activities distribution network CHP CHP Plant Main pipeline MP House substation wc buildings b DHS

Development technology in the decentralized district heating system: simplified view of small unit with gas engine cooperating with peak load boiler W od σ

(

A n el

 ) 

c p

B p



s E s W s



c s B s W p

Modernization activities on the demand side

a,b,e, Δe m ,A m – parameters & variables concerning demand devices and modernization activities 120 a 1 ,b 1 a 2 ,b 2

Δe m

A m1 A m2 e 1 = 180 e 3 = 300 kWh/(m 2 · yr) e 2 = 240 a 3 ,b 3 costs Modernization technologies:

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

120 A m3 180 180 Energy savings

Objective function component on demand side

W i, od r

= 1 / 1000  

b

   

e b max

    

a b, r



i' i'

 

i

= 1

Δ a i, r, b

 

m



e b max

 5

, m



i' i'

 

i

= 1

A i' , r, b

 5

, m

       

Optimization problem

If the objective function are linear functions, and

x j f

(

x

) and constraints

g i

(

x

) ,

i

= 1 , 2 ,...,

m

are integer varaibles, then the objective function is minimized:

f

(

x

) 

min

under constraints

A



x x



J



b

m



n

,

x



R n

,

b



R m

where:

J

– vectors with real and integer elements

A -

matrix and it is a mixed integer programming problem

Flow chart of calculations

Defining development options j = 1 Data, charts Initial value c 0 k = 1 SUPPLY T&D NO c k  c 0 < e c YES j > n YES Results NO DEMAND k : = k  1 c 0 = c k j : = j  1

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 and for no.

2: modernization undertaken for centralized heat sources transmission and activities 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

ZR CHP CR2 CR1 ZR

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



=0,5

100,0

Qsz=100 MJ/s

80,0 60,0

source

40,0

Qs Ws-Wp Qpz=Qsz/2

20,0

Qp=QsQpz/Qsz

Wp 0,0 0 1000 2000 3000 4000 5000 6000 7000 8000 9000

unit

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

W

GWh/a 300,0 250,0 200,0 150,0 100,0 50,0 0,0 0,0 20,0 40,0

W=f(Q)

60,0 80,0

source unit Q

100,0 MJ/s

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

0 0

1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9

BC50-I Rozpr

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

0 0,0

1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9

BC50-I WP70-ID WP70-III

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

300 300 250 250 200 200 150 150 100 100 50 50 0 0

1 4 5 5 6 6 7 7 8 8 9 9 10

BC50-I

12 12 13 13

WP70-IB Rozpr

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

0 0

1 1 2 2 3 3 4 4 5 5

BC50-I 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 modernization the demand activities side have resulting impact on from 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 supply side module including demand side module, transmission and distribution (T&D) system module, and 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