cooling at the Festo company

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1 CEP CLEAN ENERGY POWER 2009 Large capacity solar adsorption cooling at the Festo company Dirk Pietruschka, Antoine Dalibard, Ursula Eicker Folie 1

2 Content Part A: Solar cooling system FESTO office building and cooling system Solar collector field Research Projects, funding and institutions involved Part B: Simulation based performance analysis during the planning phase of the solar collector field Analysed cases and control options Simulation results and conclusions Folie 2

3 Content Part C: Measured performance of the collector field 2008 compared to predicted values Part D: Online simulation i tool Comparison online simulation tool / measured performance data Conclusions and Outlook Folie 3

4 A) Office building, FESTO AG & Co. KG Solar collector field m² gross floor area Heat- and Cold distribution through thermal activated ceilings and ventilation system Cold production with three 353 kw MYCOM adsorption chillers and thermally activated bored piles of the buildings foundation Utilisation of compressor waste heat Folie 4

5 A) Solar collector field; FESTO AG & Co. KG Collector area: Collector type: Connection: Orientation: 1218 m² (aperture area) 1330 m² (brutto area) Paradigma CPC collectors with evacuated tubes - 58 Paradigma CPC 30 with 3,0 m² aperture area each Paradigma CPC 45 with 4,5 m² aperture area each all parallel South Tilt angle: 30 Guaranteed result: 500 MWh/a Folie 5

6 A) Integration of the solar collector field in the existing heat distribution 1218 m² TSA CPC evacuated tube collectors max. 200 kw Activated ceilings Stat. heating grid. Cooling tower 1 3 Heat storages 8.5 m³ 8.5 m³ 50/30 C V 28/25 C M 70/50 C ADCM 1 3 TWU TSE Heat recovery compressors ( kw) 3 gas boilers 5.5 MW max. 600 kw 70/60 C 14/9 C Heat distribution Cold distribution Folie 6

7 A) Reaserch Projects, Funding and Institutions Involved Cooperation Project: Simulation based control optimisation of buildings with sustainable cooling systems Design, implementation and commissioning of the monitoring system of the plant Performance analysis and graphical presentation of the monitoring data Simulation based performance observation and proof of guaranteed solar energy yield Evaluation of optimisation potentials Simulation based control optimisation of the partly solar driven adsorption chillers of the FESTO AG & Co. KG Online simulation based performance observation of the absorption chillers and supporting solar collector field Folie 7

8 B) Simulation based performance analysis of the collector field Questions analysed during the planning phase of the collector field: How do different collector start up temperature limits influence the annual performance of the collector field? Mass flow control of the collector pump or simple on-/off control? Performance improvement through supply temperature reduction in the primary heating circuit of the activated ceilings? Folie 8

9 B) Dynamic simulation model in INSEL Folie 9

10 B) Analysed control options Bezeichnung Description Regelung der Kollektorpumpe Control of the collector pump Kollektorsolltemperatur Start-up temperature Supply Vor-/Rücklauf / return solarer Primärkreis primary solar circuit Winter Summer Sommer Activated Bauteilaktivierung ceilings Heizungsverteiler Heat distributer Case 1 Variante 1 ON / OFF 70 C 50 C / 30 C 70 C / 60 C Variante Case 2 2 ON / OFF 50 C 70 C 50 C /30 C 70 C /60 C Variante Case 3 3 Volumenstrom mass flow 50 C 70 C 50 C / 30 C 70 C / 60 C Variante Case 4 4 Volumenstrom mass flow 40 C 70 C 40 C / 30 C 70 C / 60 C Folie 10

11 B) Boundary conditions of the simulations Weather data: 1. Annual simulations: Insel Weather data base location Stuttgart 2. Summer 2006: Nearby weather station UNI-Hohenheim Heating load/degree of utilisation of solar heat: 1. Annual simulations: Assumption: Q h,building Q Collector 2. Summer 2006: Real measured heating energy consumption of the three AdCM Shading losses: Geometric shading model Folie 11

12 B) Results of annual simulations : - Influence of setpoints and pump control ergy / MW Wh/a. Use eful solar heating en ,9% ,4% ,0% Performan nce improv vement / %. 620 Case 1 (Tset = 70 C Case 2 (Tset = 50 C Case 3 (Tset = 50 C Case 4 (Tset = 40 C winter and summer, winter / 70 C summer, winter / 70 C summer, winter / 70 C summer, On/Off-control of On/Off-control of collector pump with collector pump with collector pump) collector pump) mass flow control) mass flow control) 0 Useful solar heating energy / MWh/a Performance improvement Folie 12

13 B) Results of annual simulations : - Influence of setpoints and pump control seful sola r heating energy / kw Wh/m²a iency / % Solar sys stem effici spec. u Case 1(Tset = 70 C Case 2 (Tset = 50 C Case 3 (Tset = 50 C Case 4(Tset = 40 C winter and summer, winter / 70 C summer, winter / 70 C summer, winter / 70 C summer, On/Off-control of On/Off-control of collector pump with collector pump with collector pump) collector pump) mass flow control) mass flow control) 30 Spec. useful solar heating energy Solar system efficiency Folie 13

14 B) Results of annual simulations : - Influence of different system losses, Case 2 Usefu ul solar he eating ener rgy / MWh/ /a Not considered: - losses through frost protection - losses through system start-up / % Syste em losses 640 Case 2, incl. only storage heat losses Case 2, incl. heat losses throug storage and tubing Case 2, incl. storage heat losses and shading losses Case 2, incl. storage and tubing heat losses and shading losses 1 Useful solar heating energy / MWh/a System losses Folie 14

15 B) Simulation results summer 2006: - Meteorological conditions Weather station UNI Hohenheim 2006 kwh/m² Month hly solar irr radiation / Tempe rature / C 0 Mai June July August September 0 Monthly solar irradiation External mean temperature Folie 15

16 B) Simulation results summer 2006: - Solar fraction ACM heating energy demand MWh Sola ar fraction / % 20.7% 14.8% 14.9% 15.0% Cooling g-/heating energy cons sumption / 0 Mai June July August September Solar fraction on the ACM heating energy consumption Case 2 Cooling energy consumption, FESTO 2006 Heating energy consumption ACM, FESTO Folie 16

17 B) Conclusions Due to permanent use of the produced solar heating energy: high specific useful heating energy > 530 kwh/m²a high solar system efficiency % 7 % increase of solar heating energy possible for different collector temperature setpoints for summer and winter operation (70 C summer / 50 C Winter) 2,5 % increase in solar heating energy for collector pump with mass flow control instead of On-/Off control Temperature reduction in the primary heating circuit it of the activated ceilings revels in additional 2,6 % increase of solar heating energy 15 to 25 % solar fraction of the heating energy demand of the adsorption chillers expected. Folie 17

18 C) Measured performance of the collector field 2008 compared to predicted values Objectives: Demonstration of the quality of dynamic simulation based performance predictions. - Prediction with long term weather data (Part B, Case 2) - Real measured performance data 2008 Discussion of possible reasons for deviations between prediction and measured data Data source: Measured performance data of the solar collector field from March to December 2008 (Monitoring system: Hochschule Offenburg, Solarthermie 2000plus 5 min mean values ) Folie 18

19 C) Measured performance compared to predicted values Wrong hydraulic brake Problems with heat flow to the building and stagnation Discharge vol. flow sensor problems Intensive measurement phase Solarthermie 2000plus Folie 19

20 C) Possible reasons for large difference between prediction and measured data Wrong hydraulic brake Intensive measurement phase Folie 20

21 Solar therm mal energy / MWh. C) Possible reasons for large difference between prediction and measured data Heat Production and Utilisation of the FESTO Solar Collector Field in Wrong hydraulic brake Problems with heat flow to the building and stagnation Discharge vol. flow sensor problems / % utilisation Heat 0 0 March April May June July August September October November December Solar energy deliverd by the collector circuit including losses Utilisation of delivered solar heat Measured useful solar heat delivered to the building / ACM Folie 21

22 C) Possible reasons for large difference between prediction and measured data Solar thermal energy / MW Wh March April Heat Distribution of the Solar Heat Delivered by the FESTO Solar Collector Field in 2008 Mai June July Much lower heat input to the low temperature heating circuit of the activated ceilings than assumed! Optimisation potential? August September October November December Measured useful solar heat delivered to the building / ACM Heat delivered to activated ceilings of the building Heat delivered to distributer 16 (ACM and building) Intensive measurement phase Folie 22

23 C) Conclusions Reasons for deviations between prediction and measurement: Losses through system startup and frost protection not considered in the simulation tool 10 % heat losses! Additional heat losses in March and April due to a wrongly dimensioned hydraulic brake backflow of warm water into the collector field during night time 3 % lower measured solar irradiation Problems with the heat flow to the building in August 2008 and resulting stagnation of the collector field lower efficiency Much h lower heat input to the low temperature t heating circuit it of the activated ceilings than assumed by the ideal control of the simulation model! Higher collector temperatures and lower system efficiency Possible optimisation potential for the implemented control Together around 30% losses not considered in the simulations Folie 23

24 C.1) Measured performance of the adsorption chillers 2008 Adsorption chillers Qh,boilers ) Ene erg y (MWh April May June July AugustS eptemberoctobernovember Q h,c hillers Qwaste_heat 0.30 COP Qh,solar Adsorption chillers heat source period COP AKM April November % % % Qsolar Qwaste heat Qboilers Folie 24

25 D) Online simulation tool and measured performance data Objectives: Validation of the simulation models (collector field and ACM) Implementation of an online simulation based performance observation Simulation based control optimisation of the ACM cascade Data source: Measured performance data of the solar collector field from April to December 2008 (Monitoring ing system: stem Hochschule h Offenburg, Solarthermie 2000plus 5 min mean values ) Measured detailed performance data of the adsorption chillers (FESTO building management system, 10 s mean values) Folie 25

26 D.1) Validation dynamic solar system model Example, one day in July T_amb Tcol_out Tcol_out_sim TPS2U_sim TPS1O TPS1O_sim TPS2U Vdot_col Vdot_tank Gt Heat delivered to ACM Tstorage1,top top 80 C Tstorage1,top top < 72 C Solar irradiation / W/m² Volume flow rate / m³/h storage 1,top storage 2,bottom T col,out G t Vdotload Temperature / C Vdotcol t T amb :05 00:45 01:25 02:05 02:45 03:25 04:05 04:45 05:25 06:05 06:45 07:25 08:05 08:45 09:25 10:05 10:45 11:25 12:05 12:45 13:25 14:05 14:45 15:25 16:05 16:45 17:25 18:05 18:45 19:25 20:05 20:45 21:25 22:05 22:45 23:25 Folie 26

27 D.1) Validation dynamic solar system model Results and conclusions Comparison of measured and simulated results Qsol Qcol_m Qcol_sim Qload_m Qload_sim Dev. col Dev. load kwh/m² kwh/m² kwh/m² kwh/m² kwh/m² % % 5 th July Conclusions - Good representation of the solar system performance by the developed dynamic system model - Improvements: - shorter time steps 10 s instead of 5 min mean values Data transfer via OPC and Labview from the BMS - Improvement of the stratified solar storage model fixed heat input at predefined storage layers Folie 27

28 D.2) Development and validation adsorption chiller model Problem: Discontinuous dynamics of adsorption/desorption cycle causes variable cooling effects Dynamic model required which accounts for the transient behaviour [Saha simplified silicagel model according to Henry s law proposed by Ng and Chua 2001] Objectives: Analysis of optimisation potentials through the implementation of variable cycle times in the ACM control Implementation of an online simulation tool for plant observation and control optimisation with e.g. variable generator mass flow rates at part load or optimised control of the chiller cascade Folie 28

29 D.2) Comparison to measured data at standard operation conditions Generator / C Temp perature Absorber / C ondenser 10 0 Evaporator Time / Sek Tg,in Tg,out, meas Tg,out sim Ta,in / Tc,in Ta,out meas Ta,out sim Tc,out meas Tc,out sim Te,in Te,out meas Te,out sim Folie 29

30 D.2) Comparison to real measured data with different generator volume flow rates Theat_out Tcool_out Tchill_out Theat_out_sim Tcool_out_sim Tchill_out_sim Theat_in Tcool_in Tchill_in Vdot_heat = 90 m³ / h Vdot_cool = 200 m³/h Vdot_chill = 58 m³ / h Generator Case Adsorber / Condenser Temperature / C 20 Evaporator 10 0 Folie 30 :03:36 :04:26 :05:16 :06:06 :06:56 :07:46 :08:36 :09:26 :10:16 :11:06 :11:56 :12:46 :13:36 :14:26 :15:16 :16:06 :16:56 :17:46 :18:36 :19:26 :20:16 :21:06 :21:56 :22:46 :23:36 :24:26 :25:16 :26:06 :26:56 :27:46 :28:36 :29:26 :30:16 :31:06 :31:56 :32:46 :33:36 :34:26 :35:16 :36:06

31 D.2) Comparison of results and conclusions Comparison of measured and simulated data Theat_in Vdot_heat P_cool,m P_cool,sim COP_m COP_sim Dev. Pcool C m³/h kw kw - - % % Dev. COP Case Case Case Conclusions - Very good agreement between simulation model and measurement also for variable operation conditions - Implementation as online simulation tool in February 2009 Folie 31

32 D.3) Implementation of online simulation tools in the local BMS Local Data Logging Supervisory Control Module BMS OPC-Clients DS- Server Online Simulation Tool Citadel SQL Database Daily archiv vpn FTP Remote Desktop PC Anywhere zip Local Et External OG / Stgt Folie 32

33 Outlook Automated data-transfer between BMS and online simulation tool with high time resolution (OPC-interface, LabView, DataSocket) finished Implementation of a permanent and automated performance observation of solar plant and adsorption chillers February / March 2009 Primary energy optimised simulation based control of the adsorption chillers including all subsystems May / June 2009 Folie 33

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