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## OPTIMIZTION OF DEVELOPMENT OF DISTRICT HEATING SYSTEM

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**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)**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)**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**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**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**DHS share in heat supply to consumers in residential sector**in cities**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 stopsof heat source anddistribution net timetable of repairs distribution 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 andpower costs polluting emissionsfrom 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 andpower 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 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**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 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**Schematic heat flow diagram of power plant**WP S P NP The unit with condensing turbine adapted to heat production**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**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**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**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**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, MWh/(MWh) 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**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**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**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 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**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)**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**+**+ + + + + + 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**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 W, Ael-,E – variablesBW,ξ, σ–objective function parameters σ TP Ael- SB Bp Ep WB Bs Es Ws- Wp Wp Wod**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**Simplified view of district heating system presenting**moderniziation activities distribution network - CHP Main pipeline MP CHP Plant 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 σ Wp Bp Bs Es W s Wod**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**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**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**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-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**ZR**CR2 CR1 ZR CHP The model of district heating system for the agglomeration