BUILDINGS BASED ON BUILDING ENERGY SIMULATION. Public Doctoral Defence April 12, Wout Parys

Transcription

1 COST OPTIMIZATION OF CELLULAR OFFICE BUILDINGS BASED ON BUILDING ENERGY SIMULATION Public Doctoral Defence April 12, 213 Wout Parys Supervisor: Prof. dr.ir. Hugo Hens Co-supervisor: Prof.dr.ir. Dirk Saelens 1 Contents Introduction Background Overall aims Methodology Reference office building Dynamic building simulation High-resolution behavioural model HVAC system analysis Feasibility analysis of passive cooling in Belgium Optimization results Conclusions 2 1

2 Introduction: Background Background: Shift towards decarbonization and energy independency Several eral possible paths Supply side - Wind energy - Solar energy - Biofuels -... Demand side - Transport - Industry - Building stock -... Limited amount of resources Distribution of societal effort based on cost-efficiency Quantification necessary 3 Introduction: Background Demand side End energy use per sector in Flanders (21) Agriculture 3% T Transportt 2% Industry 41% Buildings 36% residential 25% Offices 4% Education 1% Commerce 3% Health care 1% Hotels and Other restaurants 1% 1% 1% Office buildings: - Small (< 1 m 2 floor area) - Medium (< 1 m 2 floor area) - Large (> 1 m 2 floor area) 4 2

3 Introduction: Overall aims Overall aims: Define economical optimal configuration for medium-sized office buildings in terms of energy efficiency Quantify the costs and hierarchy in measures taken to lower the energy use beyond this optimum Bi-dimensional optimization Operating energy use: -Heating - Cooling - Ventilation - Auxiliary - Lighting Net present cost: (~ Life cycle cost): - Investment cost - Life cycle energy use - Maintenance cost - Replacement cost 5 Introduction: Overall aims Bi-dimensional optimization Net present cos st Building variants Operating energy use Building variants - Envelope properties - HVAC system selection - Lighting system 6 3

4 Introduction: Overall aims Bi-dimensional optimization Net present cos st Δcost ΔE Building variants Economic optimum Pareto solutions Operating energy use Pareto solutions (or non-dominated solutions): define the optimal path (measures taken) to lower to energy use beyond the economic optimum 7 Contents Introduction Background Overall aims Methodology Reference office building Dynamic building simulation High-resolution behavioural model HVAC system analysis Feasibility analysis of passive cooling in Belgium Optimization results Conclusions 8 4

5 Methodology: Reference building Reference office building Generic office building defined based on statistical data Individual id office cells 6.6 m 6 m 32.4 m 5 m Storage, Archive, Sanitary Individual offices Circulation Individual offices Conference room 1 m Plan view external variable width fixtures (with daylight sensor points) 2.7 m Side view external variable height suspended ceiling.5 m 2.8 m 4 m.8 m 9 Methodology: Reference building Reference office building: optimization variables - Building variables: - U-value opaque envelope (walls:.6 W/m 2 K 15W/m.15 2 K) - Glazing-to-wall-ratio (21% - 71%) - Glazing type (triple vs. double low-e argon; absorbing vs. reflective vs. clear) - Air tightness (1 ACH 2.5 ACH) - External shading device (automated screen or fixed slats) - Lighting system variables: - Installed lighting power - Control (daylight dimming) - HVAC system selection - Primary system: traditional (boiler, chiller) vs. innovative (geothermal heat pump) - Secondary system: hydronic vs. all-air vs. TABS - Passive cooling (night ventilation, window operation) - Ventilation 1 5

6 Contents Introduction Background Overall aims Methodology Reference office building Dynamic building simulation High-resolution behavioural model HVAC system analysis Feasibility analysis of passive cooling in Belgium Optimization results Conclusions 11 Methodology: Dynamic simulation Net presen nt cost Operating energy use Building variants Calculation of operating energy use of large number of building variants necessary Dynamic building simulation Calculate energy use for entire year Time steps between 9s and 1h Both daylight and thermal simulations 12 6

7 Methodology: Dynamic simulation Dynamic building energy simulation: thermal Building model Q vent T Q zone sol Q int HVAC system model Q Q H/C trans Internal boundary conditions: building use External boundary conditions: weather data Methodological refinements implemented: - Internal boundary conditions: behavioural model - HVAC system model: regression analysis 13 Contents Introduction Background Overall aims Methodology Reference office building Dynamic building simulation High-resolution behavioural model HVAC system analysis Feasibility analysis of passive cooling in Belgium Optimization results Conclusions 14 7

8 Methodology: Behavioural model Internal boundary conditions: - Occupancy Q int - Lighting use - Shading use - Appliances - Window operation - Heating/cooling set point control Influence energy use + thermal comfort Current building energy simulation practice: 1 - Deterministic - Building design independent - Often overestimated Methodology: Behavioural model... But we know from research, occupant behaviour is - Adaptive - Stochastic - Individual bability Action prob Environmental variable A comprehensive behavioural model is implemented incorporating these features based on empirical models from 1. the literature Switch-on probabaility at arrival [-] Hunt 1979 Reinhart 23 Mahdavi Workplane Illuminance [lux] 16 8

9 Methodology: Behavioural model Occupancy pattern Zone daylight calculations Preprocessing Shading positions Lighting heat gains Appliances heat gains Behavioural profile Behavioural model Window positions Zone thermal calculations Building simulation 17 Methodology: Behavioural model Each submodel implemented as a Markov chain e.g. lighting use Time t t + 5 Occupant arrives ( occupancy model) 15 lux on workplane ( daylight simulation) ity at Switch-on probabaili arrival [-] Workplane Illuminance [lux] 18 9

10 Methodology: Behavioural model Each submodel implemented as a Markov chain e.g. lighting use Time t t + 5 t ity at Switch-on probabaili arrival [-] 1. Occupant arrives ( occupancy model) 15 lux on workplane ( daylight simulation) Adaptive Workplane Illuminance [lux] Compare probability to random number < x < 1 e.g. x =.23 Stochastic Individual? 19 Methodology: Behavioural model Individual aspect representative active and passive users bility Action probab bility Action probab Active Average Passive Environmental variable Environmental variable Ratio of active vs. passive users serves as input for uncertainty analysis N Room 11 Room 12 Room 13 Room 14 Room 15 A P A P A P A A P A Room 16 Room 17 Room 18 Room 19 Room 2 4 m 1 m Circulation Room 1 A Room 2 A Room 3 A Room 4 P Room 5 A Room 6 P Room 7 P Room 8 P Room 9 A Room 1 P 2 1

11 Methodology: Behavioural model Application results uncertainty analysis Monthly net he eating demand [MJ/m m2] Building 1 Building 2 Building Month Annual heating demand: stdev = ± 1% Monthly net cooling demand [MJ/m2] Building 1 Building 2 Building Month se Monthly lighting energy u [kwh/m2] Annual cooling demand: stdev = 1% 2% Building 1 Building 2 Building Month Annual lighting demand: stdev = 1% 15% 21 Contents Introduction Background Overall aims Methodology Reference office building Dynamic building simulation High-resolution behavioural model HVAC system analysis Feasibility analysis of passive cooling in Belgium Optimization results Conclusions 22 11

12 Methodology: HVAC system analysis Inclusion of HVAC system in dynamic thermal model Water flow rate Water temperature Water flow rate Water temperature Emitted power Radiative fraction Boiler Radiator Zone temperature Type56 Building Water flow rate Water temperature Water flow rate Water temperature Flow rate fraction Zone temperature Takes into account part load performance and performance under non-nominal conditions Very time-consuming - Small time steps (minutes) - Resizing required for each building variant Infeasible for all building variants! TRV 23 Methodology: HVAC system analysis Alternative: standardized subsystem efficiencies Secondary HVAC system Primary HVAC system Calculate l only Net Energy Demand AHU Q gross,ahu E fin,pref E fin,npref Q gross,buil Emission subsystem Distribution subsystem Storage subsystem Generation subsystem Final Energy Use = Net Energy Demand η η η η em dis Currently available values defined on subsystem level Neglect interaction building occupants - HVAC Refinement: redefine subsystem efficiencies based on integrated building and HVAC system dynamic simulation of selection of building variants stor gen 24 12

13 Methodology: HVAC system analysis Considered HVAC systems: Secondary HVAC system Primary HVAC system Ventilation Radiator heating Passive cooling Radiator heating Chilled ceilings Fan coil units Condensing gas boiler Condensing gas boiler Compression chiller Condensing gas boiler Compression chiller Extraction Balanced hygienic Balanced hygienic Variable air volume (VAV) with local hydronic reheating Concrete core activation (TABS) Condensing gas boiler Compression chiller Geothermal heat pump with free cooling Balanced Balanced hygienic 25 Methodology: HVAC system analysis Innovative GEOTABS-model: validated with real case 26 13

14 Methodology: HVAC system analysis All sub-models verified with measured data: Calibration of ground model 8 7 Ground temperature [ C] Ground inlet - measurement Ground outlet - measurement ground outlet - simulation Time [h] 27 Methodology: HVAC system analysis All sub-models verified with measured data: Calibration of ground model Verification of building model (cooling down test) Zone temperature [ C] Ground floor - South 3 Floor 1 - South 1 Floor 2 - South 4 External Time [h] External temperature [ C] Zone temperature [ C] Ground floor - zone A_S Floor 1 - zone C_N Floor 2 - zone A_N Time [h] 28 14

15 Methodology: HVAC system analysis All sub-models verified with measured data: Calibration of ground model Verification of building model (cooling down test) Verification of TABS equivalent model eating power [W/m 2 ] He Ceiling Original model Equivalent model eviation from original model [%] De Time [min] 29 Methodology: HVAC system analysis All sub-models verified with measured data: Calibration of ground model Verification of building model (cooling down test) Verification of TABS equivalent model Calibration/verification of AHU 28 Air temperatu ure [ C] Measured Simulated Time [h] 3 15

16 Methodology: HVAC system analysis Validation of integrated building and HVAC systems model Targeting monthly energy use Simulated thermal comfort = measured thermal comfort Based on measured data of 21 Criteria: MBE < 1% and C v (RMSE) < 3% nergy use [kwh] Electricity en Measured Simulated Month MBE = 2.% C v (RMSE) = 1.9% days 15/15 Degree gy output [kwh] Ground energ Measured Simulated Month MBE = 5.% C v (RMSE) = 18.3% 31 Methodology: HVAC system analysis Goal: define monthly efficiencies ~ heat-balance ratio γ Heat gains γ = = Heat losses Q sol + Q int Q + Q + trans Defined on building level Incorporates climate, building characteristics and building use Represents the system s part load ratio Can be calculated without knowledge of HVAC system properties Closely related to net energy demand vent Q inf 32 16

17 Methodology: HVAC system analysis Results: Generation efficiency Condensing ggas boiler Air-cooled compression chiller η gen,heat [%] Integrated model EPR standard γ [ ] 35 η gen,cool [ ] Integrated model EPR standard γ [ ] Boiler thermal losses [%] 3 Environment Flue gas sensible 25 Flue gas latent γ [-] Methodology: HVAC system analysis Results: Generation efficiency Geothermal heat pump p Free cooling 34 17

18 Methodology: HVAC system analysis Results: Secondary HVAC system efficiency Hydronic (radiator, FCU) Heating Cooling η sys,heat,buil[%] η sys,heat,buil[%] Integrated model EPR default γ [ ] Hydronic (chilled ceiling, FCU) Integrated model 3 EPR default γ [ ] TABS TABS 35 Methodology: HVAC system analysis Results: Total primary energy use on Primary energy use proportio [%] FCU NHD [kwh/m 2 a] Primary energy use proportion [%] Others Fans FCU Pumps Fans AHU Chiller Boiler gas GEOTABS n Primary energy use proportio [%] Scenario Fans Pumps All-air NHD [kwh/m 2 a] PU cooling Boiler PU heating Others Pumps Fans AHU Chiller Boiler gas 36 18

19 Contents Introduction Background Overall aims Methodology Reference office building Dynamic building simulation High-resolution behavioural model HVAC system analysis Feasibility analysis of passive cooling in Belgium Optimization results Conclusions 37 Methodology: Passive cooling Feasibility analysis of passive cooling of office buildings in temperate Belgian climate Manual window operation Manual window operation + passive stack night ventilation Deduction of simplified feasibility criterion 38 19

20 Methodology: Passive cooling Adaptive thermal comfort criterion Main sources of uncertainty taken into account: Air tightness Air flow rates (through window, night ventilation, hygienic ventilation) Internal convective heat transfer coefficient Internal heat gains Manual window operation Real-time e coupling thermal model manual window 1 operation model Opening probability at arrival [-] Inside temperature [ C] 39 Methodology: Passive cooling Results Probability monthly WET < 5 % [%] 1 9 Window operation 8 7 Window operation + Night ventilation Average daily heat gains [kj/m 2 ] Average daily heat gains [kj/m 2 ] Internal gains - surplus no daylighting Internal gains - daylighting Solar gains Total diffuse solar energy transmittance * GWR [-] 4 2

21 Contents Introduction Background Overall aims Methodology Reference office building Dynamic building simulation High-resolution behavioural model HVAC system analysis Feasibility analysis of passive cooling in Belgium Optimization results Conclusions 41 Net present cost Optimization results Operating energy use Bi-dimensional optimization of reference office building: - Minimum net present cost - Minimum annual operating primary energy use Basic assumptions: - Primary energy conversion factor for electricity Micro-economical analysis without subsidies - Life span 2 years - Discount factor 4% - Constant increase above inflation energy cost of 1.75% 42 21

22 Optimization results 1 Net pre esent cost [ 212/m2] Results building optimization per HVAC system HVAC system 2 (FCU) All building variants 2 Pareto solutions 1 Nett present cost [ 212/m2] Operating primary energy use [kwh/m2a] Operating primary energy use [kwh/m2 a] Economic optimum: - Very low glazing-to-wall ratio (21%) - Air tightness 2.5 ACH - Low-e argon g filled double g glazing g - Low g-value glazing E-S-W - Uavg =.6 W/m2K (Uavg =.45 W/m2K for GEOTABS) 43 Optimization results 1 Net pre esent cost [ 212/m2] Results building optimization per HVAC system HVAC system 2 (FCU) All building variants 2 Pareto solutions 1 Nett present cost [ 212/m2] Operating primary energy use [kwh/m2a] Operating primary energy use [kwh/m2 a] 7 First set ofoptimum: Economic measures: - Air Very tightness low glazing-to-wall 1. ACH ratio (21%) - Automated Air tightnessdaylight 2.5 ACH dimming - Low-e External argon shading g filled g device doublesg glazing g 2K E-S-W - U Low = g-value.35 W/m glazing avg - Uavg =.6 W/m2K (Uavg =.45 W/m2K for GEOTABS) 44 22

23 Optimization results 1 Net pre esent cost [ 212/m2] Results building optimization per HVAC system HVAC system 2 (FCU) All building variants 2 Pareto solutions 1 Nett present cost [ 212/m2] Operating primary energy use [kwh/m2a] Operating primary energy use [kwh/m2 a] Second Economic First set set ofoptimum: measures: of measures: - External Very Air tightness low shading glazing-to-wall 1. ACH device ratio W (21%) 2 - U Airavgtightness Automated 2.5 ACH =.25 daylight W/m K dimming - Low-e External argon shading g filled g device doublesg glazing g 2 - U Low g-value.35 W/m glazing K E-S-W avg = 2 - Uavg =.6 W/m K (Uavg =.45 W/m2K for GEOTABS) 45 Optimization results 1 Net pre esent cost [ 212/m2] Results building optimization per HVAC system HVAC system 2 (FCU) All building variants 2 Pareto solutions 1 Nett present cost [ 212/m2] Operating primary energy use [kwh/m2a] Operating primary energy use [kwh/m2 a] 7 Thirdset Economic First Second setset ofofoptimum: measures: of measures: measures: - External Very Air tightness low shading glazing-to-wall 1. ACH device ratio W (21%) E - Triple Airavgtightness Automated U argon 2.5 2ACH dimming glazing = low-e.25 daylight W/m Kfilled - Low-e External argon shading g filled g device doublesg glazing g 2K E-S-W - U Low = g-value.35 W/m glazing avg - Uavg =.6 W/m2K (Uavg =.45 W/m2K for GEOTABS) 46 23

24 Optimization results Results building optimization per HVAC system Results are robust for: Net pre esent cost [ 212/m 2 ] HVAC system 2 (FCU) All building variants Pareto solutions Operating primary energy use [kwh/m 2 a] - Primary energy conversion factor for electricity Life span 3 years - Discount factor 2.5% and 5.5% - Constant increase above inflation energy cost of.% and 3.5% 8 - Long term scenario Short term evaluation Net present cost [ 212/m 2 ] Long term evaluation Operating primary energy use [kwh/m 2 a] First set (partially) integrated in economic optimum 47 Optimization results Comparison of HVAC systems Net present cost [ 212 2/m 2 ] Rad-NV FCU Operating primary energy use [kwh/m 2 a] ent cost [ 212/m 2 ] 45 Rad-CC Air GEOTABS Net pres Net present cost [ 212/m 2 ] 7 65 Rad-CC 6 FCU Operating primary energy use [kwh/m 2 a] 7 Rad-NV 65 FCU Operating primary energy use [kwh/m 2 a] Traditional hydronic secondary system economic optimum 48 24

25 Optimization results Comparison of HVAC systems Net present cost [ 212 2/m 2 ] Rad-NV FCU Operating primary energy use [kwh/m 2 a] Rad-CC 7 GEOTABS (lower floor-to-floor height) 65 Air FCU 6 GEOTABS Net present cost [ 212/m 2 ] Operating primary energy use [kwh/m 2 a] GEOTABS (and passive cooling) are able to lower the energy use beyond reach of traditional systems 49 Contents Introduction Background Overall aims Methodology Reference office building Dynamic building simulation High-resolution behavioural model HVAC system analysis Feasibility analysis of passive cooling in Belgium Optimization results Conclusions 5 25

26 Conclusions Methodological refinements in building energy simulation: Development of high-resolution behavioural model, incorporating adaptive, stochastic and individual characteristics Uncertainty on net energy demand modest Refined definition of HVAC subsystem efficiencies as a function of building s heat-balance ratio, based on integrated dynamic simulation Feasibility of passive cooling for office buildings in a temperate t climate Simplified criterion deduced based on uncertainty analysis 51 Conclusions Bi-dimensional optimization of medium-sized cellular office building Economic optimum found at relatively standard building measures Hierarchy in building measures defined and quantified to go beyond towards lower operating energy use Traditional HVAC systems economic optimum Innovative systems GEOTABS and passive cooling can enable lower operating energy use 52 26

27 Thank you! 53 27