12th International Symposium on District Heating and Cooling

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The <strong>12th</strong> <strong>International</strong> <strong>Symposium</strong> on District Heating and Cooling,September 5 th to September 7 th , 2010, Tallinn, Estoniato implement the schemes, the performance data forthe commercial products, operation efficiency in fulland part load condition, has been extensivelyinvestigated and the database has been realized on thesimulation program.One can consider a variety of CHP systemconfigurations with various CHP prime movers andtypes of cooling chillers. If the type of CHP primemovers is being selected, the capacity of it is to bedetermined in the form of any percentage on the basisof the maximum value of annual hourly electricitydemand. Then, the feasible options, which can matchthe condition entered by the user, are compiledaccording to the relevant algorithm as shown in Fig. 5.(a) Heating load(b) Electricity loadFig. 4. Prediction of annual hourly energy consumption forthe apartmentthe number of units, and the load factor in terms of unitcapacity. When an option is selected by the user asdescribed above, its corresponding technical data forCHP product will be linked automatically in thesubsequent operation simulation procedures. Thesettlement of the system configuration for the coolingsystem can also be performed in a similar manner byproviding the data for the ratio of being in charge ofturbo or absorption type chillers.2. Modelling of DHC system for networkingoperationIn contrast with small cogeneration or CES system, theDHC system is not authorized to sell the electricity tothe customer directly in Korea [6]. As a result, theoperation mode differs from that of cogeneration orCES system, i.e. the facilities are operating dependingon the heat loads, and CHP facilities stop operatingduring summer to reduce waste heat production.Instead, the hot water load during the summer seasonis usually supplied from incinerators nearby, or heatonly boilers (HOB). However, the operation schemes ofDHC system for stand-alone operation are bound to bemodified to some extent by networking operation withCHP system on-site and the appropriate modelling forsuch an effect of networking operation on DHC systemis a key element for a reliable prediction of theoperation behaviours due to thermal network operation.In this study, the changes of operation schemes andcorresponding variations for physical or mechanicalaspects on existing DHC system side have beenrealized by employing mathematical correlations for thesake of simplicity. The mathematical correlations forenergy productions as a function of energyconsumption are developed based on the annualoperation data of a branch of Korea District HeatingCorporation (KDHC). By applying a simple, but credibleempirical correlations instead of performing anadditional cycle simulation for the existing DHC system,the calculation load and the complexity from thestandpoint of simulation are considerably alleviated.The procedure to obtain the correlations for energyproduction in terms of energy consumption are givenas follows,Fig. 5. Parametric entry of option for CHP system productThe user is to select the most desirable one among thelist of options by referring to the technical specificationfor each option such as the unit capacity of the product,159The required data for the establishment of themathematical correlation is given by,– Annual, heat and electricity production and the salesper day according to the facilities of heat production(CHP, HOB, Incinerator)– Annual, fuel consumption per day according to thefacilities of heat production (CHP, HOB)
The <strong>12th</strong> <strong>International</strong> <strong>Symposium</strong> on District Heating and Cooling,September 5 th to September 7 th , 2010, Tallinn, EstoniaThe functional form of the mathematical correlation isgiven as follows,F f ( H,P)(1)Where,F: Fuel consumptionH: Heating loadP: Electricity loadcorrelations is a certain time, not a specific time duringthe year as in the original data. For example, if theDHC system is requested to produce more heataccording to the request from CHP system to theamount of Q , the heat load of DHC system can beregarded to be changed from Q 1 to Q 2 , i.e. Q 2 =Q 1 + Q .Then, the operation behaviour for DHC system at themoment can be estimated simply from themathematical correlations by simply referring the valueof F 2 *, corresponding to Q 2 * and P* corresponding toF 2 *. It means that one can reconstruct the operationbehaviour of the DHC system as a function ofsequential time reflecting the effects of thermal energynetworks. The correlations for the heat and electricityproduction vs. fuel consumption are shown in Fig. 7.(a) Time vs. events(a) Electricity production vs. fuel consumption(b) Events vs. eventsFig. 6. Illustrative diagram for the correlation betweenenergy production and fuel consumptionFig. 6 shows the illustrating diagram for themathematical correlation between energy productionand consumptions. For any time t 1 , an optimizedoperation scenario already exists and correspondingheat and electricity production, and fuel consumptionhas been fixed according to the operation scenario andfor any time t 2 , it is the same as above. On the basis ofthe operation data for a year, the behaviour of systemoperation can also be described between dependentvariables (e.g. F: Fuel consumption, H: Heatproduction, P: Electricity production). In the correlationsbetween dependent variables, the time t is reflectedwith implicit manner and the meaning of time t in the(b) Fuel consumption vs. Heat productionFig. 7. Developed correlations for the energy productionsvs. fuel consumptionSIMULATION OF THE THERMAL NETWORKINGOPERATION1. Operation Conditions and SchemesThe operation of the overall system should be carriedout by the order of priority of operation for the variousheat sources. In this study, the basic schemes in orderof priority for supplying the energy demands in newlydeveloped area are established as shown in Fig. 8,160
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12th International Symposium on District Heating and Cooling

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