Optimal Strategy of Large Cities District Heating Systems

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					  OPTIMAL STRATEGY OF LARGE CITIES DISTRICT HEATING SYSTEMS
                       T e c h n iic a ll U n iiv e r s iit y o f G d a n s k — F r a u n h o f e r I n s t iit u t U M S I C H T
                       Techn ca Un vers ty of Gdansk — Fraunhofer Inst tut UMSICHT
Development of an energy management system (EMS) suitable for modern DH systems was a goal of common Polish —German research project. Design and implementation of different required mathematical and software tools
have been carried out to achieve the purpose. Appropriate adaptatio n to practical DH conditions was reached with help of industrial partners – Gdansk DH Enterprise (GPEC) and Energy Supply Company of Oberhausen (EVO).

AUTOMATIC AGGREGATION OF                                           MODELING AND SIMULATION OF                                         PUMPING POWER MINIMIZATION                                           OPTIMAL ARRANGEMENT OF HEAT
DISTRICT HEATING NETWORKS                                          DISTRICT HEATING SYSTEMS                                           IN DISTRICT HEATING SYSTEMS                                          SUPPLY ALTERNATIVES
by Achim Loewen                                                    by Andrzej Augusiak                                                by Michael Wigbels                                                   by Andrzej Reński

Solving very complex systems of equations in DH simulation         A very crucial in optimal operation of large DH systems is their   Target of the pumping power minimization in DH systems is            A research method has been formulated to support effective-

programs often requires long computing times or can in some        proper design and exact pressure and temperature distribution      to determine operational mode of all pumps and valves that           ness evaluation in modernization and development process

cases be even impossible. Thus a methodology has been deve-        over the whole net.                                                minimizes the total energy costs for pumping. In order to            within large DH systems. A mathematical model and prepared

loped to simplify the structure of models for complex district                                                                        achieve the absolute minimum of the costs, a DH network has          software consisting of several modules that describe main ele-
                                                                   To improve the quality and ease of thermo–hydraulic analysis,
heating networks (‘aggregation’) without causing any                                                                                  to be regarded as a total system. That is why the objective          ments of district heating system has been prepared and tested.
                                                                   an integrated computer system DBNet for modeling and
essential alteration in their mathematically modeled behavior.                                                                        function is formulated by the sum of the pumping power
                                                                   simulation of complex DH structures has been created, with two                                                                          It is based on a Mixed-Integer-Programming (MIP) optimization

Calculations for different networks have shown very good           main functions implemented in:                                     requirements Pi of all pumps in the network:    Pi = MIN!           algorithm with an objective function defined as a sum of

results. Where the input values are the same (total load,               Computer tools for modeling of different DH network          For the safe supply of all consumers the following boundary          annual total costs of heat supply service in a considered

supply temperature and mass flow), up to a high degree of                elements that utilize object-relational database             conditions are regarded:                                             period of time:      (KS + KD) = MIN!
aggregation the pressures, temperatures, and supply times at             connectivity and GUI interface communication in multi-             Operational limits of the pumps and armatures,                The mathematical model takes into account costs of:
the remaining nodes match those of the original network. Also            user and open-platform environment,                                Design pressure of the network components,
                                                                                                                                                                                                                      Heat generation characteristics (supply side) —          KS,
parameters of the whole network, such as heat storage and               New and very efficient computational procedures for                Minimum static pressure head of the pumps,                               Heat consumption factors (demand side), including
pumping energy are subject to only minor divergence.                     running steady-state simulations of DH systems based               Boiling point of the heating water,                                       market economy transformation driven ones —              K D.
The aggregated networks can be calculated almost without                 on general-graph theory and sparse matrix algebra.                 Minimum pressure difference at the substations and
                                                                                                                                                                                                           With case study of a typical large city area in Poland supplied
limitation for steady state and up to a degree of aggregation of   The software tool has been implemented within a DH network               Difference between middle pressure and static pressure.
                                                                                                                                                                                                           by a district heating system, essential problems concerning
75% also for non-steady state thermo–hydraulic simulations.        of a large Polish company, where it has fully proven its           Mixed-Integer-Programming (MIP) has been used for the                development of its heat supply alternatives has been analyzed.
Thus a considerable reduction in computation time (compared        functional features and has been regarded as a very helpful        solution of the optimization problem in case of German and           It has proven that optimal arrangement of heat supply costs
to the non-simplified network) of 85% on average up to 98%         means of computer aided operational analyses in DH                 Polish DH systems. By the application of the optimization tool,      can largely increase the competitiveness of DH systems
in some cases can be achieved.                                     systems under modernization and re-development.                    cost savings up to 20% are possible.                                 and contribute to a sustainable development.
                                                                                                                                       Pumping
                                                                                                                                       power [kW]
                                                                                                                                      90
                                                                                                                                                                                                                       350          M od ern ize                         In stall n ew
                                                                                                                                                    BeforeOptimization                                                               u n it n o I                        u n it n o III
                                                                                                                                      80
                                                                                                                                                    AfterOptimization                                                  300
                                                                                                                                      70                                                                               250




                                                                                                                                                                                                               M J/s
                                                                                                                                      60                                                                               200
                                                                                                                                      50                                                                               150

                                                                                                                                      40
                                                                                                                                                                                                                       100
                                                                                                                                                                                                                         50
                                                                                                                                      30
                                                                                                                                                                                                                          0
                                                                                                                                      20
                                                                                                                                                                                                                                1      3      5      7   9   11 13 15 17 19
                                                                                                                                      10
      Aggregation of Gdansk DH net                                                                                                                                                                              M od ern ize
                                                                                                                                                                                                                u n it n o II                       T im e h o riz o n
                                                                                                                                       0
                                                                                                                                             Pump A          Pump B      Pump C    Pump D     total




 Tech nica l U niv ers ity o f Gd ans k, De p t.                                                In te rna ti o nal C oo p era ti on o n R e searc h                                                  Frau nh of er I nsti tu t fü r Um we lt- ,
   Pow er P lan ts & En er g y Ec on o mics                                                      in En vir on m en tal Pr o tec tio n, Pr oces s                                                  Sich erh ei ts-, En er gi etech nik UMS I CH T
  Narutowicza 11 /12, 8 0-952 Gdans k                                                                 Safety a nd E n er gy T ec hn ol ogy                                                        Osterf el de r S tr . 3 , 4 6 047 Ob er hau se n

				
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