OPTIMIZTION OF DEVELOPMENT OF DISTRICT HEATING SYSTEM

1. OPTIMIZTION OF DEVELOPMENT OF DISTRICT HEATING SYSTEM ANDRZEJ REŃSKI PhD Department of Power Engineering TECHNICAL UNIVERSITY of GDANSK

2. 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)

3. Professional CHPs 19,0 CHPs & industrial heating plants 4,0 Municipal boilers 11,0 Local boilers (solid fuels) 26,5 Local boilers (fuel oil, gas) 6,0 Accumulative electric heating systems 0,5 Coal furnaces 33,0 100,0 % The share of meeting the demand on district heat (industrial utilities are excluded)

4. The projection of demand on district heat in the reference scenario (Poland) PJ 1400 1200 1000 Other consumption 800 Industry 600 400 Residential sector 200 0 1997 2005 2010 2015 2020

5. 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

6. Main thesis and goals • Small increase of heat demand or even demand decrease in large DHS as a result of modernizations on demand side

7. 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

8. 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

9. 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

10. MP Definition of DHS CHP REGION tz tp pz pp Gz Gp consumer SR housesubstation CHP – combined heat and power plant MP – main pipelines (transport line) SR – distribution system

11. DHS share in heat supply to consumers in residential sector

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

13. Hierarchic structure of DHS CHP2 CHP1

14. Tasks of the DHS optimization • Medium term optimization (month) • Short term optimization (one day) • A few year optimization • Strategic planning of the development

15. 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

16. Medium term optimization Time horizon: 1 week ÷ 1 year Expected effects: primary energy demand  plans of starts and stopsof heat source anddistribution net timetable of repairs distribution of heat and power costs

17. A few year optimization Time horizon : 1 year ÷ 5 years Expected effects:  primary energy demand  financial schedules  timetable of repairs  distribution of heat andpower costs  polluting emissionsfrom heat sources

18. Strategic planning of the development Time horizon : 5 years ÷ 20 years Expected effects:  primary energy demand  financial schedules  timetable of repairs  distribution of heat andpower costs  polluting emissions from heat sources power and energy balances investment plans

19. 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

20. Methods for the choice of large energy supply systems structure • Multivariant analysis • Mathematical programming (linear, mixed integer programming)

21. Optimization criterion of DHS development • Criterions • classic: • unit heat supply cost • annual costs of DHS • modern: • net present valuemethod( 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

22. 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

23. 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

24. Condensing Power Plant cooperating with peak load boiler in district heating system EK t 1 ZS EK ZS ZS t 1s EK t 2s t 2 Heat supply 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

25. Schematic heat flow diagram of power plant WP S P NP The unit with condensing turbine adapted to heat production

26. Characterization of supply region and heat transport system ZS EK EK t 1 t 1 t 1 oC t1, t2 Permanent annual curve of heat output q and outflow and return flow temperatures t1,, t2at sliding operation for the supply region t1s 125 q 120 % 100 100 t 80 80 q 60 60 t2s 50 50 t2 40 40 20 20 τ 0 0 1000 2000 3000 4000 5000 6000 7000 8000 h/a 8760

27. Economic criterion and methodology of heat parameters calculation Specific cost of heat supplied to the end-consumers K K r r = = k PLN/GJ & × × W 3,6 Q T r s s k  min where: K r • annual delivery costs, PLN/yr • annual amount of delivered heat, GJ/yr Wr . Qs ,Ts • peak load in MJ/s and annual peak load utilization period in hrs/yr

28. Elements of objective function Specific costs: K(t) = kEK + kEK + kCC + kMP + kZS + kZS PLN/GJ P A Q W where: kMP = kL + kP + kstr where: t • difference between supply and return water temperatures during peak load, K kEK, kEK • fixed and variable costs of heat production in condensing power plant P A 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 • fixed and variable cost of heat production in peak load boiler Q W

29. Costs of heat production in power plants Specific fixed cost: es  kSE  rcSE n kP = s PLN/GJ 3,6 Ts where: es • relative electrical power loss in condensing power plant, MW/MW kSE • specific capital cost of equivalent power plant in electrical power system, PLN/MW n rcSE • the rate of fixed costs for equivalent power plant , 1/yr s • cogeneration factor Ts • annual peak load utilization period, hrs/yr

30. Costs of heat production in power plants Specific variable cost: eA  kSE B kEK = 103 A PLN/GJ EK  Wu where: eA • relative electricity loss in condensing power plant, MWh/(MWh) kSE • standard fuel (coal equivalent) price for equivalent power plant, PLN/t ce B A • annual cogeneration factor EK • overall efficiency of equivalent power plant Wu • calorific value for standard fuel, kJ/kg ce

31. Hot water temperature 0,623 B2 L0,623 ( ) topt = 0,731 K   B1 Qs0,246 where : • distance of heat transmission in main pipeline, m L  Qs • peak load of heat power, MJ/s • constants for heating system and dependent on method of operation B1, B2

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

33. 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

34. Proposed optimization criterion in research of the developing DHS Variables: - constant and variable costs in year i Bottom indexes / sets: i – years; o, or – units;m –modernizations;b – construction technologiesof buildings; r – consumers regions; mp – sections of main pipe lines

35. Constraints Variables: - power and loss of power - annual heat production and heat losses Uppper indexes defineparts of DHS (distribution net, house substations, main pipe lines, consumers)

36. Scheme of DHS balance CHP QEC, WEC region losses: QMP WMP MP Qod Wod consumer Qr, Wr SR house substation Qod Wod QSR WSR Qwc Wwc WEC = Wod + Wod + WSR + WMP QEC = Qod + Qod + QSR + QMP

37. + + + + + + + min ( C C C C C C C ... ) A B C I II III IV Optimization of modernization and development of DHS Heat demand forecasts Small CHP A CA CI DHS Heat demand from DHS CII CB Heat only boilers B (Supply side) demand CIII side Individ. sources. C CC CIV Modernizations and development technologies in DHS DSM Supply and demand optimization

38. 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

39. 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 W, Ael-,E – variablesBW,ξ, σ–objective function parameters σ TP Ael- SB Bp Ep WB Bs Es Ws- Wp Wp Wod

40. Objective function component on supply side

41. Development/modernization technology within centralized heat source: combined steam and gas (stag) cogeneration plant ε ξ σ Ael- TP HRSG ξ WB Bs Wp Ws-Wp TG Bp Wod ε

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

43. Development technology in the decentralized district heating system: simplified view of small unit with gas engine cooperating with peak load boiler σ Wp Bp Bs Es W s Wod

44. Modernization activities on the demand side a,b,e,Δem,Am– parameters & variables concerning demand devices and modernization activities 120 Am1 a1,b1 Δe m a2,b2 Am2 120 e1 = 180 • Modernization technologies: • roof and wall insulation • windows replacement • thermostatic valves • heat consumption measurement on the demand side • complex thermo-renovation e2 = 240 180 Am3 a3,b3 180 e3 = 300 kWh/(m2·yr) Energy savings costs

45. Objective function component on demand side

46. Optimization problem and constraints If the objective function are linear functions, andxj are integer varaibles, then the objective function is minimized: under constraints where: J – vectors with real and integer elements A - matrix and it is a mixed integer programming problem

47. Flow chart of calculations Defining development options = j 1 Data, charts 0 c = + Initial value j : j 1 = k 1 DEMAND SUPPLY T&D = + k : k 1 0 k = c c NO k 0 - < e c c c YES NO > j n YES Results

48. 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

49. Example of district heating system optimization algorithm • Option no. 3: modernization activities undertaken in whole supply system and on the demand side (thermo-renovationin 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-renovationin buildings),at the level of 20% of whole dwelling resources, in the base year

50. ZR CR2 CR1 ZR CHP The model of district heating system for the agglomeration