Potential of District Cooling in Singapore: From Micro to Mesoscale

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(1)DELIVERABLE TECHNICAL REPORT Version 01 - 30/09/2020 D1.2.2.5 – Potential of District Cooling in Singapore: From Micro to Mesoscale. Project ID. NRF2019VSG-UCD-001 Cooling Singapore 1.5:. Project Title. Virtual Singapore Urban Climate Design. Work Package ID. WP1.2.2 – Anthropogenic Heat Assessment. Deliverable ID. D1.2.2.5 – Potential of District Cooling in Singapore: From Micro to Mesoscale. Contributors. Emanuel Riegelbauer, Luis Guilherme R. Santos, Vivek Kumar Singh, Ido Nevat. DOI (ETH Collection). 10.3929/ethz-b-000445484. Date of Report. 30/09/2020. Version Date 1. 22.09.20. Modifications. Reviewed by. -. Jimeno A. Fonseca.

(2) Abstract District cooling is a proposed strategy to mitigate climate change and urban warming. The technology’s economic and environmental viability for different district types is an open research question. This study addresses this question by modeling 41 sample districts across Singapore, comparing building-site cooling with district cooling technology utilizing a part load adjusted COP model. The sample results are aggregated by district type and extended using machine learning to establish a city-wide business as usual (BAU) and district cooling scenario. The economic and environmental potential of district cooling is estimated and the sample districts and district types within Singapore are ranked according to their potential. A Singapore-wide district cooling scenario can be considered more sustainable with lifecycle greenhouse gas emission reductions of 1.7 MtCO2e/yr (17% reduction vs. BAU). In commercial areas, district cooling is profitable, with a mean return on investment of 13%. For private and public housing, the cost benefits do not compensate the high investment costs for the centralized air conditioning systems and the cooling network. Anthropogenic heat (AH) emissions would reduce by 2.6 TWh/yr (7% reduction vs. BAU) in an all-district-cooling scenario with a significantly different spatio-temporal AH distribution. The mean Urban Heat Island effect (UHI) at 2m height would subsequently be reduced by 0.5 ˝ C (36% reduction vs. BAU), while the maximum UHI at 2m height would increase by 2 ˝ C (42% increase vs. BAU). The increase of maximum UHI is highly localized in the district cooling plant locations. Validation against statistical data indicates an overestimation of the predicted effect of district cooling on the UHI. Adjustments in the mesoscale model and the analysis of a larger sample of districts would increase the accuracy of the predictions. The proposed framework has shown to effectively model the economics and environmental impact of different cooling technologies on a city-scale and can serve as a basis for model variations and foresight analysis.. i.

(3) Contents List of Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix 1 Introduction 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Methodology 2.1 Scope . . . . . . . . . . . . 2.2 Technology scenarios . . . 2.3 Microscale modeling . . . . 2.4 Selected evaluation metrics 2.5 Mesoscale modeling . . . . 2.6 Selected ranking methods .. 1 1 2 3. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. 8 10 20 21 35 38 43. 3 Results 3.1 Efficiency of cooling systems 3.2 Microscale . . . . . . . . . . 3.3 Mesoscale . . . . . . . . . . 3.4 Urban heat island effect . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. 44 44 53 68 84. . . . . . . . .. 86 86 88 91 94 99 99 100 102. 4 Discussion 4.1 Energy efficiency . . . . . . 4.2 Anthropogenic heat . . . . 4.3 Costs . . . . . . . . . . . . 4.4 Greenhouse gas emissions 4.5 Plant allocation . . . . . . . 4.6 Urban heat island effect . . 4.7 Validation . . . . . . . . . . 4.8 Limitations and future work. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. 5 Conclusion. 104. A Appendix A. 113. B Appendix B. 118. iii.

(4) List of Acronyms AC AH CAPEX CEA COP DC DCN DCP DCS DT DX EAC EGHG-E ETS EUI FAR FC GFA GHG LCGHG-E LCOC LCZ LEOC LU MACC MAVT MV NV OGHG-E OPEX O&M UHI VCC WCT. Air Conditioning Anthropogenic Heat Capital Expenditure City Energy Analyst Coefficient of Performance District Cooling District Cooling Network District Cooling Plant District Cooling System District Type Direct Expansion Equivalent Annualized Cost Embodied Greenhouse Gas Emission Energy Transfer Station Energy Use Intensity Floor Area Ratio Fan Coil Gross Floor Area Greenhouse Gas Lifecycle Greenhouse Gas Emission Levelized Cost of Cooling Local Climate Zone Levelized Emissions of Cooling Land Use Marginal Abatement Cost Curve Multi-Attribute Value Theory Mechanical Ventilation Natural Ventilation Operational Greenhouse Gas Emission Operational Expenditure Operation and Maintenance Urban Heat Island Vapor Compression Chiller Wet Cooling Tower. iv.

(5) List of Figures 1.1 Cross-border Metropolitan Region Singapore . . . . . . . . . . . . . . . . . 1.2 District cooling system components . . . . . . . . . . . . . . . . . . . . . . . 2.1 Overview of the framework to assess the potential of district cooling in Singapore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 List of Local Climate Zones . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Local Climate Zone (LCZ) map . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Map of local climate zones within the population area . . . . . . . . . . . . 2.5 Map of land use categories within the population area . . . . . . . . . . . 2.6 Map of district types in the population area . . . . . . . . . . . . . . . . . 2.7 Map of sample districts (in black) . . . . . . . . . . . . . . . . . . . . . . . 2.8 Sample district LCZ4 NUS UTown, Commercial . . . . . . . . . . . . . . . 2.9 Sample district LCZ4 JurongWest2, Residential(HDB) . . . . . . . . . . . 2.10 Sample district LCZ9 WestbourneRoad, Residential . . . . . . . . . . . . 2.11 Floor area ratio of the sample districts. . . . . . . . . . . . . . . . . . . . . 2.12 Floor area ratio of the sample districts by land use category. . . . . . . . . 2.13 Part load adjustment curve for DX, screw chillers and centrifugal chillers . 2.14 Type of district cooling plant heat rejection. Heat rejection area is indicated with a blue/white border. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. 9 10 11 11 13 14 15 17 17 18 18 19 32. . 41. 3.1 Detailed COP modeling results for DX units against the capacity per household with the fitted isotonic regression line. . . . . . . . . . . . . . . . . . . . 3.2 Detailed COP modeling results for Decentral VCC units against the capacity per building with the fitted isotonic regression line. . . . . . . . . . . . . . . 3.3 Isotonic regression: predicted vs. actual values for the decentral VCC model. 3.4 Detailed COP modeling results for Central VCC against the capacity per district with the fitted isotonic regression line. . . . . . . . . . . . . . . . . . 3.5 Central VCC adjusted COP up to a capacity of 14 MW. . . . . . . . . . . . . 3.6 Summary of conversion COPs in relation to the installed capacity for small capacities up to 4000 kW. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Mean district conversion COP of all districts for Central and Decentral scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 Boxplot of coincident peak demand by land use category . . . . . . . . . . 3.9 Coincident Peak demand density in [MW/km2 ] plotted at logarithmic scale . 3.10 Mean district System COP of all districts for Central and Decentral scenario. 3.11 Boxplots of the system COP by land use category . . . . . . . . . . . . . . 3.12 Ventilation share of primary cooling energy consumption per scenario across all districts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.13 DCN share of primary cooling energy consumption for all sample districts. .. v. 3 6. 44 45 45 46 46 47 47 48 48 49 49 50 50.

(6) 3.14 The energy use intensity (cooling) split up by source across all districts per scenario. Relative electricity consumption (cooling) savings in % are shown as a dotted line. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.15 Boxplot of the relative final energy consumption (cooling) savings in % . . . 3.15 Boxplot of the relative final energy consumption (cooling) savings in % . . . 3.15 Boxplot of the relative final energy consumption (cooling) savings in % . . . 3.16 The anthropogenic heat intensity split up by source across all districts per scenario. Relative AH savings in % are shown as a dotted line. . . . . . . . 3.17 Boxplot of EUI by land use category, compared to the reference EUI . . . . 3.18 Monthly seasonality of AH flux by land use category. Central tendency and confidence interval of 95% is depicted for each scenario. . . . . . . . . . . . 3.19 AH flux Diurnal Profile of individual districts in LCZ 5 of the Decentral scenario. 3.20 AH flux Diurnal Profile of individual districts in LU Residential of the Decentral scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.21 AH flux Diurnal Profile of individual districts in DT 5 Residential of the Decentral scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.22 AH density per district for Central and Decentral scenario. . . . . . . . . . . 3.23 AH density per LCZ for the Decentral scenario. . . . . . . . . . . . . . . . . 3.24 Life-Cycle GHG emissions split up by source across all districts per scenario. Relative LCGHG-E reduction in % is shown as a dotted line. . . . . . 3.25 Embodied GHG emissions split up by source across all districts per scenario. Relative EGHG reduction in % is shown as a dotted line. . . . . . . . 3.26 Levelized LCGHG emissions of Cooling versus the Final Cooling Energy intensity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.27 Levelized cost of Cooling against the cooling capacity density in a logarithmic scale. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.28 Levelized investment cost of Cooling against the cooling capacity density in a logarithmic scale. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.29 Capital expenditure by district for Decentral and Central scenario. . . . . . . 3.30 Levelized cost of cooling versus the Energy Use Intensity (Cooling). Cut-off point shown as a black line. . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.31 Levelized cost of cooling versus the Final Cooling Energy Consumption Intensity. Cut-off point shown as a black line. . . . . . . . . . . . . . . . . . 3.32 Return on investment of district cooling per district . . . . . . . . . . . . . . 3.33 Pareto Front plot of sample districts . . . . . . . . . . . . . . . . . . . . . . . 3.34 Plant allocation across the population area. The size of dots indicates the capacity of each DCP in MW. . . . . . . . . . . . . . . . . . . . . . . . . . . 3.35 Histogram of the DCP system capacities and their respective system COP as a colour. The kernel density estimate of capacities is overlayed. . . . . . 3.36 Histogram of the system DCP system capacity for smaller capacities up to 50 MW, with information on the system COP. . . . . . . . . . . . . . . . . . 3.37 Detail of the DCP allocation map, with the allocated DCP in the area of the NUS UTown in Orange, associated cells in gray. Overlayed with the microscale model district outline and zone shapefile. . . . . . . . . . . . . . 3.38 Histogram of the DCP system capacities and the DCS respective Cost reduction as a colour. Capacities up to 50 MW. . . . . . . . . . . . . . . . . . 3.39 Histogram of the DCP system capacities for ony Commercial systems and the DCS respective Cost reduction as a colour. Capacities up to 50 MW. . . 3.40 Marginal Abatement Cost Curve of GHG Emissions to assess the implementation of district cooling in different district types. . . . . . . . . . . . . . 3.41 Marginal Abatement cost curve of anthropogenic heat for the year 2016. . . vi. 51 52 52 52 53 54 56 56 57 57 58 58 59 60 61 62 63 63 64 64 65 66 68 69 69 70 71 72 73 74.

(7) 3.42 Building consumption anthropogenic heat profile of the BAU scenario for hour 19, in a 30 x 30m resolution. . . . . . . . . . . . . . . . . . . . . . . . 3.43 Building consumption anthropogenic heat profile of the BAU scenario for hour 19, in a 300 x 300m resolution. . . . . . . . . . . . . . . . . . . . . . 3.43 Building consumption anthropogenic heat profile of the BAU scenario for hour 19, in a 300 x 300m resolution. . . . . . . . . . . . . . . . . . . . . . 3.43 Building consumption anthropogenic heat profile of the BAU scenario for hour 19, in a 300 x 300m resolution. . . . . . . . . . . . . . . . . . . . . . 3.44 Building consumption anthropogenic heat profile of the DC scenario for hour 19, in a 300 x 300m resolution. . . . . . . . . . . . . . . . . . . . . . 3.45 Heat rejection anthropogenic heat profile of the BAU scenario for hour 19, in a 300 x 300m resolution. . . . . . . . . . . . . . . . . . . . . . . . . . . 3.46 Heat rejection anthropogenic heat profile of the DC scenario for hour 19, in a 300 x 300m resolution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.47 WRF input anthropogenic heat profile of the BAU scenario for hour 19, in a 300 x 300m resolution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.48 WRF input anthropogenic heat profile of the DC scenario for hour 19, in a 300 x 300m resolution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.49 UHI map of the BAU scenario for hour 19. . . . . . . . . . . . . . . . . . . 3.50 UHI map of the DC scenario for hour 19. . . . . . . . . . . . . . . . . . . .. . 77 . 78 . 78 . 78 . 79 . 80 . 81 . 82 . 83 . 84 . 85. 4.1 Variation A: Levelized LCGHG emissions of Cooling versus the Final Cooling Energy intensity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4.2 Annotated levelized lifecycle GHG Emissions of Cooling versus the Final Cooling Energy intensity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 4.3 Global GHG abatement cost curve beyond business-as-usual for 2030. . . 98 A.1 Script data flow of the solar radiation script in CEA . . . . . . . . . . A.2 Script data flow of the demand script in CEA . . . . . . . . . . . . . . A.3 Levelized cost of Cooling against the cooling capacity density in a logarithmic scale. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.4 Capital Expenditure per district and per land use category . . . . . . A.5 Operational Expenditure per district and per land use category . . . A.6 Equivalent Annual Cost per district and per land use category . . . .. . . . . . . non. . . . . . . . . . . .. B.1 UHI profile of the BAU scenario. . . . . . . . . . . . . . . . . . . . . . . . B.2 UHI profile of the DC scenario. . . . . . . . . . . . . . . . . . . . . . . . . B.3 Building consumption anthropogenic heat profile of the BAU scenario in a 33x33 m resolution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.4 Building consumption anthropogenic heat profile of the BAU scenario in a 333x333 m resolution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.5 Building consumption anthropogenic heat profile of the DC scenario in a 333x333 m resolution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.6 Heat rejection anthropogenic heat profile of the BAU scenario in a 333x333 m resolution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.7 Heat rejection anthropogenic heat profile of the DC scenario in a 333x333 m resolution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.8 WRF input anthropogenic heat profile of the BAU scenario in a 333x333 m resolution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.9 WRF input anthropogenic heat profile of the DC scenario in a 333x333 m resolution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. vii. . 113 . 113 . . . .. 114 115 116 117. . 130 . 142 . 154 . 166 . 178 . 190 . 202 . 214 . 226.

(8) List of Tables 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18 2.19 2.20 2.21. Significant Land use types and their coverage . . . . . . . . . . . . . . . . . Mapping of URA LU types to LU categories . . . . . . . . . . . . . . . . . . Share of district types across the population area in [%] . . . . . . . . . . . Count of districts per district type . . . . . . . . . . . . . . . . . . . . . . . . Specifications of sample districts (Where C = Commercial, R(HDB) = Residential(HDB), and R = Residential) . . . . . . . . . . . . . . . . . . . . . . . Technologies used in each scenario per sub-system and land use . . . . . Significant Land use types and their coverage . . . . . . . . . . . . . . . . . Architecture related input parameters for CEA modeling . . . . . . . . . . . Internal load related input parameters for CEA modeling . . . . . . . . . . . Supply system and HVAC related input parameters for CEA modeling . . . Indoor comfort related input parameters for CEA modeling . . . . . . . . . . Weekday schedules of commercial and residential buildings for CEA modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . GHG emission factors and refrigerant specifications per cooling sub-system GHG emission factors of DCN piping per nominal size . . . . . . . . . . . . Specific cost parameters per cooling sub-system . . . . . . . . . . . . . . . Investment cost of DCN piping per nominal size . . . . . . . . . . . . . . . . Number and type of chillers for the Decentral system design . . . . . . . . . Number and type of chillers for the Central system design . . . . . . . . . . Number and kind of DX units . . . . . . . . . . . . . . . . . . . . . . . . . . ASHRAE efficiency standards for water-cooled screw chillers at full load (Path B) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ASHRAE efficiency standards for water-cooled centrifugal chillers at full load (Path B) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ASHRAE efficiency standards for air cooled direct expansion units (DX) . .. 12 12 13 15 16 20 22 23 23 24 24 25 26 27 28 28 30 30 30 31. 31 2.22 32 2.23 Sample districts simulated with the detailed COP modeling approach by conversion system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.1 AH intensity statistics by land use category. . . . . . . . . . . . . . . . . . 3.2 Complete ranking of sample districts according to MAVT method. (R = Residential, R (HDB) = Residential (HDB), C = Commercial . . . . . . . . 3.3 Parameters of District cooling plant 1 derived from the DCP allocation algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Mesoscale cost estimations for Decentral and Central scenario . . . . . . 3.5 Validation of mesoscale results . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Spatio-temporal heat flux statistics of building consumption AH, BAU scenario, at 30 x 30m. All units are in W/m2, with the hour of min and max occurrence in brackets. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. viii. . 54 . 67 . 71 . 71 . 75 . 78.

(9) 3.7 Spatio-temporal heat flux statistics of building consumption AH, BAU scenario, at 300 x 300m. All units are in W/m2, with the hour of min and max occurrence in brackets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 Spatio-temporal heat flux statistics of building consumption AH, DC scenario, at 300 x 300m. All units are in W/m2, with the hour of min and max occurrence in brackets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 Spatio-temporal heat flux statistics of building consumption AH, DC scenario, at 300 x 300m. All units are in W/m2, with the hour of min and max occurrence in brackets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 Spatio-temporal heat flux statistics of building consumption AH, DC scenario, at 300 x 300m. All units are in W/m2, with the hour of min and max occurrence in brackets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.9 Spatio-temporal heat flux statistics of heat rejection AH, BAU scenario, at 300 x 300m. All units are in W/m2, with the hour of min and max occurrence in brackets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.10 Spatio-temporal heat flux statistics of heat rejection AH, DC scenario, at 300 x 300m. All units are in W/m2, with the hour of min and max occurrence in brackets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.11 Spatio-temporal heat flux statistics of heat rejection Ah input into the WRF simulation, BAU scenario, at 300 x 300m. All units are in W/m2, with the hour of min and max occurrence in brackets. . . . . . . . . . . . . . . . . 3.12 Spatio-temporal heat flux statistics of heat rejection AH input into the WRF simulation, DC scenario, at 300 x 300m. All units are in W/m2, with the hour of min and max occurrence in brackets. . . . . . . . . . . . . . . . . 3.13 Spatio-temporal UHI statistics, BAU scenario. All units are in ˝ C, with the hour of max occurrence in brackets. . . . . . . . . . . . . . . . . . . . . . 3.14 Spatio-temporal UHI statistics, DC scenario. All units are in ˝ C, with the hour of max occurrence in brackets. . . . . . . . . . . . . . . . . . . . . .. . 78 . 79 . 79 . 79 . 80 . 81 . 82 . 83 . 84 . 85. 4.1 Variation A: Statistics of the LEOC . . . . . . . . . . . . . . . . . . . . . . . 95 4.2 Uncertainty of the aggregated mesoscale results. . . . . . . . . . . . . . . . 101 A.1 Metrics generalized and scaled to mesoscale . . . . . . . . . . . . . . . . . . . 114. ix.

(10) Introduction 1.1. Motivation. Climate change impacts have onset and are expected to intensify in the form of environmental degradation, natural disasters, food and water insecurity, economic disruption, conflict, and terrorism [1]. Human activities are estimated to have caused 1 ˝ C of global warming above pre-industrial levels, which is close to the 2015 Paris Agreement goal of holding the warming ’well below’ 2 ˝ C, ideally limiting warming at 1.5 ˝ C [2][3]. Current national pledges are insufficient to reach this goal [4], which demonstrates the need for sustainable solutions that make business sense, independent of policies. District cooling is one of these technologies and a proposed strategy by the UN environment programme [5] and the European Commission to reduce GHG emissions due to its increased efficiency [6]. There is a knowledge gap on the total potential of district cooling to mitigate GHG emissions on a city scale and its characteristics in future scenarios. Urban warming leads to elevated heat stress and causes severe adverse effects on human health, from discomfort to increased mortality rates[7][8]. The nation of Singapore, as a highly dense city-state within the equatorial climate, has great exposure to this increasing threat [9]. Anthropogenic heat (AH) emissions partially cause urban warming [10], and in Singapore, 12% of those are emitted by buildings at the premises [11]. District cooling has shown to be a promising solution to reduce AH and urban warming [12], while little is known on the spatio-temporal distribution of building AH and the impact on the Urban Heat Island effect (UHI). The worldwide energy demand for air-conditioning is expected to triple by 2050 [13], which indicates a growing market and need for energy efficiency cooling technologies. District cooling has shown to yield up to 40% [14] and sometimes up to 86 % higher efficiencies [15] dependent on the technology. The literature describes district cooling to operate at a higher efficiency due to benefits in economies of scale of a larger system, expressed in higher isentropic efficiency [16]. However, these figures are often stated without specifying the district morphology or use of a district.. 1.

(11) 1.2. Objectives. The main objective of this report is: 1. to evaluate the economic and environmental potential of district cooling in Singapore. The specific objectives of this report are: 1. Determine the system efficiency of building-site and district cooling systems across a sample of at least 40 districts in Singapore. 2. Simulate the energy consumption and anthropogenic heat released by building-site and district cooling technology across a sample of at least 40 districts in Singapore. 3. Estimate the annualized life cycle costs of building-site and district cooling technology across a sample of at least 40 districts in Singapore. 4. Estimate the life cycle green house gas emissions of building-site and district cooling technology across a sample of at least 40 districts in Singapore. 5. Rank the sample of at least 40 districts according to their affinity to district cooling. 6. Simulate a cost optimal allocation for district cooling plants to cover the demand of all Singapore with a spatial resolution of at least 300 x 300m. 7. Estimate the anthropogenic heat release, lifecycle greenhouse gas emissions, and annualized life cycle costs for a business-as-usual and district cooling scenario of Singapore’s commercial and residential areas. 8. Evaluate the spatio-temporal anthropogenic heat profile for a business-as-usual and district cooling scenario of Singapore’s commercial and residential areas with a spatial resolution of at least 300 x 300m. 9. Simulate the urban heat island effect of a business-as-usual and district cooling scenario across Singapore with a spatial resolution of at least 300 x 300m.. 2.

(12) 1.3 1.3.1. Background Geographical location. Singapore is a high-density city-state situated near the equator. It has a tropical rainforest climate (Af) under the Köppen climate classification [17]. The temperature variation throughout the year is small, with daily mean temperatures of 26.0 ˝ C in the coldest month (December) to 27.8 ˝ C in the warmest month (June) [18]. Resulting from the steady climate, a small variation in the space cooling load of buildings throughout the year is expected. The state has one of the highest population densities in the world, with 7,894 pop./km2 [19]. Therefore floor area ratios (FAR) are relatively high, reaching 3 to 25 in high-density areas of the masterplan by the Urban Redevelopment Authority (URA) [20]. The state further has a large hinterland and influence on the entire region due to its strong economy. Figure 1.1 depicts the cross-border metropolitan area of Singapore and demonstrates the extent past its national borders. The water and air traffic to and from Singapore, labeled ’urbanized sea and air’ are marked in pink, while the ’metropolitan centres’ are marked in red.. Figure 1.1: Cross-border Metropolitan Region Singapore [21]. 3.

(13) 1.3.2. Climate change strategy. Global goals The IPCC fifth assessment report calculates a carbon budget of 475 GtCO2 (recalculated for Summer 2020) to stay below a warming of 1.5˝ C with an even chance (50% probability). Approximately 55 GtCO2e of greenhouse gas are globally emitted every year, mainly due to fossil fuel use and land use change [22]. A reduction in greenhouse gas emission down to 25 GtCO2e per year (-55%) by 2030 is required to meet the ambitious goal of 1.5˝ C warming, according to the UNEP Emission Gap Report 2019. Emission reductions will be challenged by a projected increase in global primary energy demand by 33% over the next 30 years, mainly due to growth in China and India [23]. The building and construction sector is identified as one of the essential mitigation opportunities due to its large contribution of 39% of energy-and process-related emissions in 2017 [24]. The IPCC states that ”1.5 ˝ C-consistent pathways require building [GHG] emissions to be reduced by 80–90% by 2050, new construction to be fossil-free and near-zero energy by 2020” and the need for ”an increased rate of energy refurbishment of existing buildings to 5% per annum in OECD countries” [25]. Singapore’s commitment Singapore reports total GHG emissions of 53 MtCO2e per year in 2017, of which commercial and residential buildings emit roughly 10.4 MtCO2e (20%). The state’s electricity is mainly generated by natural gas (95%) and has a renewable share of 0.7% [26][27]. Singapore’s emission reduction strategy involves installing 2 GWp solar systems by 2030, which could cover 4% of the current annual electricity consumption. In addition, the government also set goals for 80% of existing buildings to meet certified status by 2030 [28]. The state further introduced a carbon tax of 5 SGD/tonCO2e (3.7 USD/tonCO2e) for industrial facilities in 2019 [29]. This pricing is likely too low to incentivize low-carbon technologies, as the Report of the High-Level Commission on Carbon Prices defines a carbonpricing level to be consistent with achieving the Paris temperature target of at least 40–80 USD/tCO2 by 2020 and 50–100 USD/tCO2 by 2030 [30]. The city-state entered the 2015 Paris Agreement and submitted a Nationally Determined Contribution (NDC), committing to peak GHG emissions of 65 MtCO2e in 2030, which is 28% above 2014 emissions.[31] Aspirations further include a reduction to 33 MtCo2e by 2050 (36% decrease from 2017), which does not align with the 1.5˝ C Paris Agreement goal of net-zero carbon but is consistent with warming between 3˝ C and 4˝ C until the end of this century [29] according to the Climate Action Tracker [32] The apparent gap between global environmental goals and national policy response demonstrates the need for sustainable solutions that make business sense, independent of policies.. 1.3.3. Anthropogenic heat and urban warming. Anthropogenic heat (AH) is defined as the heat emitted to the environment as a result of human activities. It includes heat generated by industrial activities, by buildings, by transport, and human metabolism [11]. In Singapore, 171 TWh of AH was emitted in 2016, and buildings emitted 12% of those at their premises [11]. The sum of all building AH is estimated to be 28.3 TWh/yr. [33] 4.

(14) Anthropogenic heat released from air-conditioning is shown to increase urban warming significantly. A study by De Munck et al. [34] for Paris, France, showed an increase in ambient temperature of 0.5 ˝ C in the city center for current AC equipment and 2 ˝ C for a scenario with two times higher heat rejection into the atmosphere. Wen and Lian [35] conducted a study in Wuhan, China, and showed that residential air conditioning use increased the mean air temperature by 2.56˝ C under temperature inversion conditions and 0.2˝ C under normal conditions. A study by Mughal et al. [36] for Singapore quantified the impact of AH on the UHI for Singapore with an increase of 1.9 ˝ C of the maximum UHI intensity and localized impacts of up to 2.1 ˝ C in the Compact High Rise areas (LCZ1). The extent to which alternative air conditioning systems impact the outdoor environment is not as heavily discussed in the literature. Wang et al. [12] investigated the impact of decentralized and centralized water-cooled chillers (DCS) in comparison to air-cooled chillers and showed a reduction in 2 m air temperature of around 0.5 ˝ C – 0.8 ˝ C during the daytime of an extreme high temperature event in Hong Kong. However, the integrated building energy model to derive the anthropogenic heat profile in the used WRF models is restricted in its spatial and temporal modeling capabilities. To better understand the impact of building AH on urban warming, it is essential to characterize the spatio-temporal variation of AH to a greater detail across the city. It is further imminent to explore the impact of alternative air-conditioning technologies on the spatiotemporal distribution of AH to better inform on urban warming mitigation strategies.. 1.3.4. Air conditioning usage. Worldwide air-conditioning energy consumption is the fastest growing end use in buildings, and has more than tripled between 1990 and 2016 [13]. The energy demand for air-conditioning is expected to triple again by 2050 according to the baseline International Energy Agency (IEA) scenario, with 70% of this rise attributable to residential users [13]. The use of air conditioning is deeply ingrained in Singaporean society. When asked about the reasons behind Singapore’s success, the founding father of modern Singapore, Lee Kuan Yew, stated: ”Air conditioning was a most important invention for us, perhaps one of the signal inventions of history. It changed the nature of civilization by making development possible in the tropics.” Non-residential buildings are responsible for 31% of Singapore’s electricity consumption, of which 60 % is caused by air-conditioning.[37] Residential buildings are responsible for 18% of the electricity consumption, of which roughly 36% are consumed for air-conditioning. This sums to an overall electricity share of 25% for air-conditioning in Singapore. Currently, most of the air-conditioning demand is met by decentralized cooling systems that are installed at the building-site. Residential buildings predominantly use split-AC systems, a combination of direct expansion units and indoor fan coil units [38], while airand water-cooled chillers are common in residential buildings [39]. The share of watercooled chillers is considerably higher, with 89% according to Building and Construction Authority (BCA) statistics [39]. The cold energy is directly produced at the end-consumer site, with varying efficiencies [37]. System efficiencies of cooling systems in Singapore are attainable from the ’Listing of Performance Data’ published by the BCA [39]. The mean ’Centralised Air-conditioning Plant Efficiency’ in 2018 is 0.75 kw/RT (4.69 COP) for building-site water-cooled chillers, and 1.28 kW/RT (2.75 COP) for building-site air-cooled chillers. 5.

(15) 1.3.5. District Cooling Systems. System Components District cooling systems (DCS) produce chilled water in a central district cooling plant (DCP), which is distributed via a district cooling network (DCN). Energy transfer stations (ETS) at building-site serve as the interconnection between the network and the endconsumer building [40]. The system components are shown in figure 1.2. Various types of technologies produce the chilled water in the DCP, such as electrical vapor compression chillers (reciprocating, screw, or centrifugal), absorption chillers, engine-driven compression machines, or a combination thereof.[40]. Figure 1.2: District cooling system components. Benefits District cooling is more efficient than building-site cooling systems, due to large scale chillers, maximizing economies of scale [41][40][16]. The ’Listing of Performance Data’ by the BCA records 36 buildings supplied by district cooling, with only two efficiency entries of 0.59 kW/RT (5.96 COP) and 0.05 kW/RT (70 COP). An efficiency of 0.05 in this accounting method indicates that PV electricity is used to offset the energy consumed for cooling. The insufficient and embellishing data collection leaves a large knowledge gap of district cooling system’s efficiency in Singapore. The Singaporean Green Mark certification scheme, which is very successfully adopted, describes a minimum system efficiency of 0.65 kW/RT (5.4 COP) for DCS to achieve Gold, Goldplus , and Platinum certificate in the district certification scheme (V2.1, 0.75 kW/RT in V2.0). However, there is no disclosure on whether implemented district cooling systems reach these efficiencies, which demonstrates the need for an in-depth analysis of the DCS efficiency. Next to efficiency improvements, district cooling has lower maintenance and improved monitoring of refrigerants, leading to less refrigerant leakage, which is an important aspect 6.

(16) when considering GHG emissions.[40] Better maintenance further attributes to a longer life span than for conventional on-site air-conditioners.[42] Another advantage is a more efficient capacity use. The more diverse the cooling load profiles of buildings in a district, the lower the coincident capacity compared to the sum of building cooling capacities. This difference is called the diversity factor and can be as low as 0.6 [41]. Efficiency dependence on Density District cooling is only profitable at high cooling densities [41][40]. This comes since the profitability of district cooling is related to the energy savings which reduce the operational costs, compensating for the high investment costs associated with DCP, DCN, and ETS. The higher the energy consumption, the higher the operational cost savings. Alaa A. Olama [41] describes a cut-off point of 35 MW/km2 in cooling power density above which district cooling is profitable. This value is given for a mixed-use development in a high ambient temperature country. However, there is little knowledge on the cut-off for other land use types and different district morphologies. Risks District Cooling shows the risk of a direct rebound effect, where with increasing efficiency, the final cooling energy consumption increases. Guo et al. (2016) describes two main factors. First, a change in user behaviour, where the frequency and duration of airconditioning increased in buildings supplied by district cooling. And second, a different pricing scheme, where the cooling is priced per floor area (m2 ) instead of per energy consumed (kWh). The pricing scheme of cost per floor area does not incentivize reduced cooling consumption and can therefore influences the user behaviour. Cost-related risks concerning district cooling include a cost misconception, where developers often pass on the investment costs to the end-user through a tariff. This makes district cooling appear costly to the end-user, while the overall lifecycle costs might actually be lower compared to a building-site system. The cost of building-site air-conditioning systems are included in the property capital costs a user pays upfront, making the annualized cost appear lower to the user. Another risk factor is the early planning of DCS in the design phase, potentially leading to DCS being oversized and the developers carrying excess investment cost [44]. Better planning and a higher degree of collaboration between stakeholders can alleviate that risk.. 7.

(17) Methodology This report’s methodological framework is presented in figure 2.1 and consists of six steps. Step 1 consists of defining the study scope, district sampling, the development of cost and emission databases, weather files, building parameters, and schedules. In Step 2, we conduct the microscale modeling, where a business as usual (decentralized cooling) and a district cooling (centralized cooling) scenario are modeled using the City Energy Analyst (CEA) [45]. This step further includes the calculation of economic and environmental metrics at the microscale. The economic metric is the equivalent annualized cost, and the environmental metrics are lifecycle greenhouse gas emissions, building anthropogenic heat, and the Urban Heat Island (UHI) at 2m height. In step 3, we generalize the results and scale them up to a gridded map of the population area. Step 4 describes the mesoscale modeling, where a genetic algorithm allows for a cost-optimized district cooling plant allocation across the population area. In step 5, we perform a UHI simulation with the Weather and Research Forecasting model WRF. Finally, step 6 assesses the economic and environmental metrics for the business-as-usual and district cooling scenario at the mesoscale.. 8.

(18) 9 Figure 2.1: Overview of the framework to assess the potential of district cooling in Singapore.

(19) 2.1. Scope. The study area, Singapore, has a broad diversity in landscape morphology and land use. Due to the modeling methodology’s limitations, we can not analyze or model Singapore’s entire built-up area. In this context, we define a population area, which we ultimately want to make assertions about, and a sample area, which we model in detail using a bottom-up approach. The following sections describe the spatial and temporal scope of this study.. 2.1.1. Population area. The population area is described by the concepts of local climate zone and land use. This allows for an unambiguous definition of the area included and excluded from this analysis. Local Climate Zones Local climate zones (LCZ) are a concept developed by Stewart and Oke (2012) to characterize the morphology of districts. Attributes of each LCZ are the sky view factor, aspect ratio, building fraction, building height, and height variation. A total of 10 LCZ (named 110) describe built-up areas, and 7 LCZ describe non-building land cover types (described as A-G). The built-up types are shown in figure 2.2.. Figure 2.2: List of Local Climate Zones [46]. Figure 2.3 shows a LCZ map for Singapore developed by Mughal et al. (2019) [36] following WUDAPT methodology. In this approach, a Random Forest Classication algorithm based on satellite remote sensing imageries and building height data informs the spatial distribution of LCZ in Singapore.. 10.

(20) Figure 2.3: Local Climate Zone (LCZ) map [36]. The LCZ map by Mughal et al. is utilized to define the population area and scale up to a mesoscale. We exclude industrial areas of Singapore from the analysis. LCZ 10 (Heavy industry) is therefore excluded from the population area. LCZ 7 (Lightweight low-rise) areas are identified as shipping yards, which are therefore excluded from the population. The population area by LCZ is shown in the figure below.. Figure 2.4: Map of local climate zones within the population area. 11.

(21) Land use categories Utilizing the URA’s masterplan, we can classify the land use of 660,425 cells with 33.4m x 33.4m dimensions across Singapore.[47] The share of each significant land-use type (ą0.5% area coverage) as a percentage of the area of Singapore (« 730 km2 ) is shown in the following table. The URA masterplan’s detail is extended to further differentiate private residential areas (condominiums and landed houses) and areas of Housing Development Board (HDB) developments, which are subsidized public housing for Singaporeans. This separation is achieved through the application of geo-referenced statistical data on HDB developments. The approach is described in Santos et al. (2020) [33]. Table 2.1: Significant Land use types and their coverage [47] Land Use Type. Coverage [%]. Agriculture Business 1 Business 2 Cemetery Civic & Community Institution Commercial Commercial & Residential Educational Institution Open Space Park .. .. Land Use Type. Coverage [%]. .. . Port / Airport Reserve Site Residential Residential (HDB) Road Special Use Sports & Recreation Utility Waterbody Rest. 1.3 1.1 15 0.61 0.83 0.69 0.61 2.6 13 3.2 .. .. .. . 4.7 9.4 12 6.3 11 5.8 2.6 1.8 5.6 2.4. This study defines three categories of land use (LU) in Singapore. Category ’Residential’ refers to landed houses and condominiums and will be referred to as private housing. ’Residential (HDB)’ refers to the Housing Development Board (HDB) developments and will be called public housing. ’Commercial’ includes office, retail, hotel, and medical facilities but excludes all industrial buildings. The classification of LU types by the URA into the LU categories and their total land coverage within the local climate zones described in section 2.1.1 is shown in the table below. The spatial distribution of LU categories is depicted in figure 2.5. Table 2.2: Mapping of URA LU types to LU categories Area [km2 ]. LU Category. URA LU type. Commercial. Commercial; Business 1; Business Park; Hotel; Educational Institution; Health & Medical Care. 30. Residential. Residential; Commercial & Residential; Residential with Commercial at 1st Storey; Residential / Institution. 66. Residential(HDB). Residential(HDB). 44. 12.

(22) Figure 2.5: Map of land use categories within the population area. District Types We define the concept of district type (DT) as the combination of LCZ and Land Use. Twenty-four district types result from the combination of the eight LCZ and three LU categories. Hereinafter they are called according to their combination, connected with an underscore ’ ’ (i.e., LCZ LU). District Types occur with different frequency across the population area as shown in table 2.3 and figure 2.6. Most prominent is DT 4 Residential(HDB) (LCZ 4 & LU category Residential(HDB)), which covers 15% of the population area. Table 2.3: Share of district types across the population area in [%]. 13.

(23) Figure 2.6: Map of district types in the population area. 14.

(24) 2.1.2. District Sampling. We analyze 41 districts across all LCZ and LU categories. The districts are selected based on the frequency of district types across the population area described in 2.1.1. The higher the occurrence of a particular district type in the population, the higher the emphasis to represent this type in the sample, if suitable districts can be found.. Figure 2.7: Map of sample districts (in black). The count per district type is shown in table 2.4. Fifteen out of the 24 possible combinations of LCZ and LU are represented in the sample data. The 9 missing DT make up a minority of the population area and cover 8.7% collectively. The most represented LU category is Residential with 19 districts, which also has the highest frequency in the population. Table 2.4: Count of districts per district type. 15.

(25) Key specifications of the districts are shown in table 2.5. The total site area of sample districts sums up to 9 km2 , and the GFA sums up to 20.4 km2 , the largest sample area modeled with the City Energy Analyst tool. Table 2.5: Specifications of sample districts (Where C = Commercial, R(HDB) = Residential(HDB), and R = Residential) District Name. Latitude. Longitude. LCZ. LU Category. LCZ1 LCZ1 LCZ2 LCZ2 LCZ4 LCZ5 LCZ8 LCZ1 LCZ1 LCZ2 LCZ4 LCZ4 LCZ4 LCZ4 LCZ4 LCZ4 LCZ4 LCZ4 LCZ5 LCZ5 LCZ5 LCZ5 LCZ2 LCZ4 LCZ6 LCZ6 LCZ9 LCZ9 LCZ9 LCZ3 LCZ3 LCZ3 LCZ5 LCZ5 LCZ5 LCZ6 LCZ6 LCZ6 LCZ6 LCZ9 LCZ9. 1.27958 1.28401 1.29557 1.298528 1.306409 1.305236 1.335979 1.30465 1.27514 1.29821 1.3457 1.3418 1.28276 1.39673 1.45053 1.38806 1.37737 1.35392 1.37775 1.34936 1.37297 1.37919 1.28149 1.35738 1.32661 1.36287 1.33163 1.37625 1.29631 1.3143 1.32453 1.36279 1.31211 1.30741 1.30141 1.31489 1.31742 1.31902 1.29572 1.28158 1.29857. 103.84931 103.85168 103.78205 103.771806 103.773 103.891755 103.861 103.83655 103.84551 103.85177 103.71176 103.70275 103.82693 103.91198 103.8196 103.9017 103.73939 103.95003 103.77283 103.72156 103.9597 103.75244 103.84365 103.7646 103.76684 103.87931 103.83111 103.7127 103.7953 103.91976 103.79485 103.8626 103.91195 103.91081 103.88819 103.81294 103.91626 103.80026 103.81912 103.79916 103.8119. 1 1 2 2 4 5 8 1 1 2 4 4 4 4 4 4 4 4 5 5 5 5 2 4 6 6 9 9 9 3 3 3 5 5 5 6 6 6 6 9 9. C C C C C C C R(HDB) R(HDB) R(HDB) R(HDB) R(HDB) R(HDB) R(HDB) R(HDB) R(HDB) R(HDB) R(HDB) R(HDB) R(HDB) R(HDB) R(HDB) R R R R R R R R R R R R R R R R R R R. -. -. -. -. Total. LauPaSat RafflesPlace KentRidge NUS Engineering NUS UTown ChungCheng HS TaoPayoh Parangon TanjongPagar BrasBasah BoonLay JurongWest2 KimTianRoad Punggol Sembawang Sengkang SunshinePlace TampinesPark Bangkit JurongWest PasirRis TeckWhye Chinatown Hillview ClementiPark GlasgowRoad MountPleasant TengahRiver WestbourneRoad FrankelAve Namly Serangoon Bedok EastCoast MarineParade ClunnyHill FrankelAve LeedonPark MountEchoPark CanterburyRoad KaiSiangRoad. 16. Site Area [m2 ]. GFA [m2 ]. FAR. 68,102 70,562 122,215 248,101 170,716 64,343 89,090 143,186 71,352 122,008 593,784 501,796 121,504 396,628 70,032 437,442 156,446 671,681 221,838 651,719 349,996 340,910 149,481 97,144 370,552 95,003 224,143 402,718 174,114 40,911 100,584 179,537 137,634 118,734 110,553 111,855 38,431 189,634 293,801 219,203 282,182. 495,903 676,673 405,046 437,482 590,833 78,970 163,262 577,167 393,131 391,046 1,077,897 2,065,769 551,583 1,404,534 446,028 1,262,216 411,108 1,697,585 601,868 1,200,653 810,564 1,096,255 482,772 252,945 393,222 164,474 76,826 290,845 65,740 73,621 210,534 422,136 205,735 157,602 89,551 120,543 55,572 182,281 173,207 57,680 78,167. 7.28 9.59 3.31 1.76 3.46 1.23 1.83 4.03 5.51 3.21 1.81 4.12 4.54 3.54 6.37 2.89 2.63 2.52 2.71 1.84 2.31 3.21 3.23 2.61 1.06 1.73 0.34 0.72 0.38 1.80 2.09 2.35 1.49 1.33 0.81 1.08 1.45 0.96 0.58 0.26 0.28. 9,019,665. 20,389,040. -.

(26) (a) CEA model. (b) Google Maps 3D view. Figure 2.8: Sample district LCZ4 NUS UTown, Commercial. The sample is a diverse set of districts that differ in multiple district characteristics, such as average building height, building fraction, site area, land use, and building geometries. A subset of three sample districts depicted in figures 2.8 to 2.10 demonstrates this diversity. The images are derived from the interface of the CEA modeling software (further described in section 2.3.1) and compared to images from the 3D view of Google Maps [48].. (a) CEA model. (b) Google Maps 3D view. Figure 2.9: Sample district LCZ4 JurongWest2, Residential(HDB). 17.

(27) (a) CEA model. (b) Google Maps 3D view. Figure 2.10: Sample district LCZ9 WestbourneRoad, Residential. Density of sample district The density of a district can be represented by the floor area ratio (FAR) of a district, which is the share of a building’s gross floor area (GFA) to the size of land upon which it is built. The FAR of sample districts is shown in figure 2.11. The following representation of district parameters is further used throughout the report, where the left side up to the yellow bar contains districts of land use category Commercial, the middle part up to the blue vertical line are Residential(HDB) districts, and the righthand side districts up to the green line are Residential districts.. Figure 2.11: Floor area ratio of the sample districts.. It is apparent that there is a wide range of densities sampled, with district LCZ1 Raffles showing the largest FAR of 9.59 and LCZ9 Canterbury showing the lowest FAR of 0.26. A statistical analysis of the FAR by land use is shown in figure 2.12, shows Residential(HDB) and Commercial districts to have the same median FAR, while Commercial districts are potentially of much higher FAR.. 18.

(28) Figure 2.12: Floor area ratio of the sample districts by land use category.. 2.1.3. Temporal Scope. The analysis is performed for 2016, as the most accurate weather data is available for this year. To analyze 2016 further enables the comparison to results of the Anthropogenic Heat Report (Kayanan et al., 2019 [11]), which is a detailed analysis of AH for 2016 based on the Energy Market Authority’s (EMA) energy statistic for Singapore [49]. The building energy simulations use hourly weather data as input and produce hourly results for the entire year (8760 hours). The WRF simulations simulate the month of April 2016 on an hourly basis.. 19.

(29) 2.2. Technology scenarios. This study analyses two technology scenarios to evaluate the economic and environmental potential of district cooling in Singapore. Each technology scenario is juxtaposed in each one of the 41 sample areas/districts. The scenarios differ in the cooling system of buildings within the area. The cooling system is a combination of three main sub-systems. These are the conversion system, the distribution air-conditioning system, and the ventilation system. Table 2.6 summarizes the type of technologies used per sub-system, scenario, and land use category. The scenarios apply to both micro and mesoscale. Table 2.6: Technologies used in each scenario per sub-system and land use. Scenario ’Decentral’ describes the business-as-usual case, where buildings are fitted with the most commonly used cooling systems of Singapore’s building stock. For residential buildings, the ’Decentral’ scenario’s conversion system uses direct expansion units (DX) to reject heat into the outdoor environment. The air conditioning system uses a fan coil or mini-split unit (FC), which is typical in residential units. The ventilation system consists of natural ventilation (NV). For commercial buildings, the ’Decentral’ scenario’s conversion system consists of a combination of vapor compression chillers (VCC) and wet cooling towers (WCT) to reject heat into the outdoor environment. The air conditioning system uses air handling units (AHU), typical in medium and large commercial units, including retail, hotel, and offices. The ventilation system consists of traditional mechanical ventilation with metallic ducting and electrical fans (MV). Scenario ’Central’ describes the district cooling scenario, where buildings are connected to a district cooling system (DCS). The DCS’s primary components are the district cooling plant (DCP), the distribution network, and the energy transfer station (ETS), connecting the customer to the network. The conversion system within the DCP consists of VCC and WCT. The distribution system is designed as a two-pipe system with supply and return. The air conditioning system uses AHUs, and all buildings in this scenario are mechanically ventilated.. 20.

(30) 2.3. Microscale modeling. The energy performance of case study districts, described in 2.1.2, is modeled for the Decentral and the Central technology scenarios. Detailed modeling requires the design of all energy systems within the sample districts, on a building and district scale. It also requires the building energy simulation of the year 2016 in an hourly resolution. Based on the modeling, system costs and greenhouse gas emissions are quantified per year, and anthropogenic heat emissions are quantified per hour. A powerful tool is required to analyze 9 km2 of site area to this detail.. 2.3.1. District modeling with the City Energy Analyst (CEA). The scenario modeling is conducted using the City Energy Analyst 3.4.0 [45]. The CEA is an open-source tool developed for the analysis and optimization of urban energy systems. It is based on an integrated model for characterizing spatio-temporal building energy consumption patterns developed by Fonseca and Schlueter, 2015 [50]. The tool consists of multiple scripts to design systems and simulate different operational metrics of a district. We utilize an extensive range of scripts, from which the main scripts are presented in the following paragraphs. District initialization An interactive script allows the selection of the case study district’s site area from a map, using a graphical user interface. Geo-referenced information on district morphology, building geometries and information (e.g., the number of floors), and street network within the selected site is subsequently derived from the open street map database (OSM) [51]. Solar radiation Within CEA, the solar irradiation onto the building envelope is simulated using an interface to DAYSIM [52]. A high-resolution virtual sensor grid, which is adjustable in its resolution, covers the building envelope and allows a detailed and time-efficient simulation of hourly solar irradiation. [45, 53] Inputs are building geometries, envelope properties, the surrounding terrain, and hourly weather data in an EnergyPlus-weather file format (epw). The detailed data flow of this script is shown in figure A.1. Energy demand The integrated building energy model, described in Fonseca and Schlueter, 2015 [50] calculates heat losses due to ventilation and transmission, internal heat gains due to occupancy, solar radiation, appliances, and lighting, and derives the resulting net space heating, cooling, and domestic hot water demands as well as electricity demands per building. Inputs are building geometries and properties, occupancy and system operation schedules, system technologies, weather data, and the solar radiation script results. The detailed data flow of this script is shown in figure A.2. Thermal network The thermal network characteristics and network energy consumption are calculated utilizing two scripts in CEA, the thermal network layout and the thermal network simulation.. 21.

(31) The network layout script is based on the Networkx python package [54], using a Steiner minimum spanning tree algorithm to find the shortest path to connect all buildings in the case study district. For simplicity, all pipes are assumed to be laid in trenches under the streets, and the network is assumed to be branched. The script’s inputs are a street layout shapefile (indicating all possible paths through which buildings can be connected), a shapefile comprising all buildings in the scenario, and the energy demand simulation results. The thermal network simulation performs thermal and hydraulic modeling of the designed network, where plant and consumer substations, piping routes, and pipe properties (length and heat transfer coefficients) are already specified. The simulation assumes one district cooling plant, located in the building with the highest cooling consumption. Given those specifications, the script optimizes the pipe diameters, calculates the mass flow and temperatures, and derives the thermal and pressure losses for each hour, utilizing the Epanet Simulator [55].. 2.3.2. Input data. CEA encompasses a Singapore-specific database containing construction and building envelope properties, internal load properties, occupancy schedules for different land-use types, supply systems and HVAC technology parameters. The parameters used for this study are based on this database and are manually calibrated. Manual calibration Manual calibration of energy performance parameters was conducted to reflect the energy use intensity of Singapore’s building stock in 2016 in the simulation results. For the calibration goal, the mean energy use intensity (EUI) per land use category was derived from statistical data. For commercial buildings, the Building and Construction authority benchmarking report [56] and the Singaporean energy statistics for 2016 [49] were used to derive a mean EUI. For land use categories Residential and Residential(HDB), the mean EUI is derived from the energy statistics report [49] and the statistics of properties under the Housing Development Board (HDB) management [57]. The exact methodology with which the reference EUI values are derived is described in Santos et al. 2020 [33]. The districts were not only calibrated to match the overall energy use intensity but also to match the split in energy use by cooling, appliances, lighting, ventilation, and warm water predominant in Singapore described in Building and Construction Authority [58] for commercial buildings and Department of Statistics Singapore [59] for residential buildings. A summary of the benchmarking values is shown in the table below. Table 2.7: Significant Land use types and their coverage Parameter 2. EUI [kWh/m .yr] Cooling share Ventilation share Lighting share Other. Commercial. Residential. Residential(HDB). 267 [56][49] 60% [58] 10% [58] 15% [58] 15% [58]. 70 [49][57] 36% [59] NA NA NA. 49 [49][57] 36% [59] NA NA NA. To calibrate the district model parameters according to the energy characteristics show in table 2.7, iterative adjustments to a set of initial parameters are performed until a satisfactory calibration was achieved. Wherever no characteristic is defined in table 2.7 (i.e., NA), preset CEA parameters in the Singapore context and the respective land use were used. 22.

(32) It is assumed that input parameters are identical for buildings of one land use category. The following paragraphs describe a comprehensive listing of the calibrated simulation parameters and inputs. Weather database Data from 12 different weather stations in different locations of Singapore are used. The data is provided by the National Environment Agency (NEA) for the year 2016. Data from the closest station to each separate case study is used to attain an accurate simulation input. Architecture database The CEA architecture database contains building envelope characteristics and defines the area fractions for a specific building service (i.e., area share cooled and area share with appliance use). The parameters are adapted to the Singaporean context and reflect the status-quo building stock. Table 2.8 contains the parameters and the related settings per land use category. Table 2.8: Architecture related input parameters for CEA modeling. Parameter. Commercial. Fraction w/ electrical demand Fraction w/ cooling demand Net floor share WWR North [-] WWR South [-] WWR East [-] WWR West [-] Shading type Window type Wall type Roof type Floor type Air tightness Construction type. Residential. Residential(HDB). 0.9 0.95 0.8 0.7 0.7 0.7 0.7. 0.8 0.6 0.45 0.2 0.8 0.8 0.39 0.39 0.39 0.39 0.39 0.39 0.39 0.39 no shading single glazing white paint over plaster over clay brick concrete or rock pebbles finishing - Singapore concrete floor medium (ACH (at 50 Pa) = 3 [1/h]) medium. Internal load database Internal loads influence the cooling as well as the appliance and lighting energy demand. Those parameters, described in table 2.9, are the most influential when manually calibrating the model. Table 2.9: Internal load related input parameters for CEA modeling. Parameter Peak elec. load appliances [W/m2 ] Peak elec. load lighting [W/m2 ] Occupancy density [m2 /pax] DHW consumption [l/d.pax]. Commercial. Residential. Residential(HDB). 15 10 3 5. 4 3 40 35. 4 3 45 35. 23.

(33) Supply system and HVAC database This study employs different supply systems and HVAC systems dependent on the scenario and land use category, as described in section 2.2. All technologies implemented in the scenarios are represented in the CEA database. However, this study’s supply system efficiencies are not based on the CEA database or literature but are derived from a detailed efficiency modeling approach described in section 2.3.3. The air conditioning and ventilation systems are decisive for the ventilation electricity demand simulation. Table 2.10: Supply system and HVAC related input parameters for CEA modeling Scenario. Decentral Residential + Residential (HDB). Central. LU category. Commercial. Supply system. VCC and WCT (building scale). DX (building scale). VCC and WCT (district scale). Air conditioning system. Central AC (6/15). Mini-split AC (6/15). Central AC (6/15). Ventilation. Mechanical ventilation with demand control and economizer. Natural Ventilation. Mechanical ventilation with demand control and economizer. All. Indoor comfort and schedule database The indoor comfort settings are expressed in setpoint and setback temperatures for the airconditioned space and in ventilation rate. These parameters were adjusted to fit the EUI as well as the energy consumption split described in paragraph ’Manual calibration’. Table 2.11: Indoor comfort related input parameters for CEA modeling Parameter Cooling setpoint T [ C] Cooling setback T [˝ C] Ventilation rate [l/s.pax] ˝. Commercial. Residential. Residential(HDB). 20 24 1. 26 26 10. 26 26 10. Occupancy and energy consumption schedules dictate the temporal profile of energy consumption and are therefore especially important to this study. The used schedules are dependent on the land use of the building and are assumed to be identical across all buildings of one land use. The occupancy, cooling, lighting, and appliances schedules by land use category for a weekday are shown in table 2.12. The cooling schedule for residential buildings is derived from Wong et al. 2002 [60], which states that air conditioning use for residential buildings in Singapore is primarily at nighttime. Further residential schedules for occupancy, lighting, and appliances are based on the CEA database. The commercial schedules are a weighted mix of all land use type schedules under the commercial land use category (i.e., an average of office, hotel, hospital, retail, and school schedules), derived from the CEA database and are shown in the table below.. 24.

(34) Table 2.12: Weekday schedules of commercial and residential buildings for CEA modeling Hour 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23. Occupancy 0.04 0.03 0.03 0.03 0.03 0.04 0.07 0.16 0.43 0.47 0.56 0.62 0.64 0.74 0.68 0.64 0.7 0.52 0.44 0.45 0.36 0.29 0.12 0.09. Commercial Cooling Lighting SETBACK SETBACK SETBACK SETBACK SETBACK SETBACK SETPOINT SETPOINT SETPOINT SETPOINT SETPOINT SETPOINT SETPOINT SETPOINT SETPOINT SETPOINT SETPOINT SETPOINT SETBACK SETBACK SETBACK SETBACK SETBACK SETBACK. 0.26 0.26 0.26 0.26 0.26 0.26 0.27 0.28 0.56 0.69 0.87 0.87 0.84 0.87 0.87 0.87 0.86 0.73 0.71 0.71 0.59 0.59 0.27 0.26. Appliances 0.28 0.27 0.28 0.27 0.28 0.3 0.31 0.33 0.65 0.68 0.72 0.73 0.67 0.72 0.71 0.71 0.71 0.48 0.43 0.43 0.41 0.4 0.32 0.28. Residential+Residential(HDB) Occupancy Cooling Lighting Appliances 1 1 1 1 1 1 1 0.9 0.4 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.3 0.5 0.9 0.9 0.9 1 1 1. SETBACK SETBACK SETBACK SETBACK SETBACK SETBACK OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF OFF SETBACK SETBACK SETBACK SETBACK SETBACK SETBACK. 0.5 0.4 0.4 0.4 0.4 0.4 0.5 0.7 0.7 0.4 0.4 0.4 0.4 0.4 0.4 0.5 0.7 1 1 0.9 0.9 0.8 0.7 0.6. 0.5 0.4 0.4 0.4 0.4 0.4 0.5 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.8 1 1 0.9 0.9 0.8 0.7 0.6. Thermal network database For the Central scenario, we set multiple thermal network parameters relevant for the thermal network simulation script. All pipes in the area are assumed to be made out of steel (λ pipe = 76 W/m.K) with a polyurethane insulation (λ insulation = 0.023 W/m.K). As a pressure loss parameter, the Hazen-Williams friction coefficient of 110 is chosen, which corresponds to ’new riveted steel’ according to Brater et al., page 6.29 [61].. 25.

(35) GHG emission database Embodied greenhouse gas emission factors are collected for the cooling system components of both scenarios. Emission factors define the embodied GHG emissions per specific unit of a sub-system. In the case of conversion systems (i.e., DX+FC, VCC+WCT, DCP), the emission factors are reported per kW of thermal capacity installed. Factors for the DCN are reported per meter of piping installed, and emission factors of the airconditioning system and building management system (BMS) are reported per m2 of GFA (see table 2.13. Any emissions due to equipment maintenance were neglected, and all sub-systems were assumed to be replaced after their respective lifetime. The cooling sub-systems of both scenarios operate on electricity. The emission factor of electricity was calculated based on Singapore’s electricity mix in 2016 and the lifecycle emission factors for each electricity source according to the IPCC fifth assessment report, appendix III [62]. The conversion systems utilize different refrigerants and entail specific refrigerant leakage percentages per year, adapted according to the IPCC Good Practice Guidelines [63] and [64]. Direct Expansion units are assumed to operate on HCFC 22 refrigerant gas, a common refrigerant gas for household units in the year 2016. Water-cooled vapor compression chillers are assumed to operate on the more environment friendly refrigerant HFC 134a, commonly used in vapor compression chillers according to ASHRAE 2013 [40]. A refrigerant charge of 0.33 kg/kW of capacity installed is assumed for all conversion systems [64]. Table 2.13: GHG emission factors and refrigerant specifications per cooling sub-system Sub-System. Specific Emissions. Lifetime. Refrigerant Type. Refrigerant Leakage. Direct Expansion Unit + FC. 49 kgCO2e /kWth [65]. 15 yr [66]. HCFC 22 (GWP = 1760) [63]. 3%/year + 0.5% one-off [67]. Decentral VCC + WCT. 232 kgCO2e /kWth [68]. 20 yr [66]. HFC 134a (GWP = 1300) [63]. 7.5%/year + 0.5% one-off [67]. District Cooling Plant. 232 kgCO2e /kWth [68] + 25 kgCO2e /m2 technical [69]. 35 yr [45]. HFC 134a (GWP = 1300) [63]. 2%/year + 0.5% one-off [67]. District Cooling Network. 127 – 3460 kgCO2e /m (by DN, see table 2.14) [70][68]. 50 yr [45]. -. -. Energy Transfer Station. 25 kgCO2e /m2 technical [69]. 50 yr. -. -. 50 kgCO2e /m2 commercial [68][71] 28 kgCO2e /m2 residential [68][71]. 50 yr. -. -. 0.4901 kgCO2e /kWh [62][27][49]. -. -. -. Central AC + BMS. Electricity. The embodied emissions associated with district cooling networks have typically been neglected in previous assessments [72]. This is due to previous findings that showed that a district cooling network would lead to a much lower greenhouse gas emissions overall [73]. Studies of revealed that with low-carbon and highly efficient systems, the relative and absolute contributions of embodied GHG emissions rise.[74] Therefore, embodied emission factors of DCN piping, based on the manufacturing of materials, excavation, and transport of excavated soil, are estimated using a simplified approach. The volume of excavated soil transported to disposal for each pipe size was extrapolated from values from Frischknecht et al. [70] for pipe sizes DN 100 and DN 200. Emission factors for pipe materials (i.e.,galvanized steel, polyurethane, and geo26.

(36) textile material) and for the transport (0.412 kgCO2e /m3 by truck) were derived from the KBOB database [68]. The emission factors per nominal pipe size are presented in table 2.14. Table 2.14: GHG emission factors of DCN piping per nominal size Pipe DN. Dext [m]. Dint [m]. Dinsulation [m]. Vdotmin [m3 /s]. Vdotmax [m3 /s]. GHG Intensity [kgCO2e /m]. 20 25 32 40 50 65 80 100 125 150 200 250 300 350 400 450 500 600 700 800 900 1000. 0.0269 0.0337 0.0434 0.0484 0.0603 0.0761 0.0889 0.1143 0.1397 0.1683 0.2191 0.2730 0.3239 0.3556 0.4064 0.4572 0.5080 0.6100 0.7110 0.8130 0.9140 1.0160. 0.0216 0.0285 0.0372 0.0431 0.0545 0.0703 0.0825 0.1071 0.1325 0.1603 0.2101 0.2630 0.3127 0.3444 0.3938 0.4446 0.4954 0.5958 0.6950 0.7954 0.8940 0.9940. 0.11 0.11 0.13 0.13 0.14 0.16 0.18 0.23 0.25 0.28 0.36 0.45 0.50 0.56 0.63 0.67 0.80 0.90 1.00 1.10 1.20 1.30. 0.0001 0.0002 0.0003 0.0004 0.0007 0.0012 0.0016 0.0027 0.0041 0.0061 0.0104 0.0163 0.0231 0.0280 0.0365 0.0465 0.0577 0.0834 0.1133 0.1481 0.1869 0.2306. 0.0005 0.0012 0.0024 0.0035 0.0062 0.0112 0.0161 0.0287 0.0457 0.0692 0.1244 0.2026 0.2956 0.3653 0.4907 0.6421 0.8172 1.2379 1.7578 2.3982 3.1475 4.0378. 127 129 152 157 177 206 239 311 354 416 555 734 867 973 1157 1256 1492 1807 2160 2532 2992 3460. Cost database The investment cost (CapEx) of each cooling system component is calculated based on the installed quantity and the respective specific cost, equivalent to GHG emission calculations. Aside from the cost of energy, there will be other significant operational costs that should be considered. All non-fuel related Operation and Maintenance costs (O&M) are represented as a percentage of the initial investment per year. All systems are assumed to be replaced after the lifetime for the same investment as the initial cost. A discount rate of 3% is employed to determine the present value of future cash flows. The collected cost parameters are summarized in table 2.15. Costs include design fees, contingencies, and taxes. The costs do not include retrofitting costs due to a lack of data on retrofitting with district cooling. The investment cost factor of the system ’Direct Expansion Unit + FC’ comprises costs for condenser units, indoor units, and piping. It does not include the costs of concealing piping, which is allocated to building developers’ inherent construction costs. This system has high reported operation and maintenance costs (O&M) of 12% due to frequent servicing, chemical washing, and refrigerant gas top-ups, mainly invoked by refrigerant leakage. The investment cost factor of the system component ’Decentral VCC + WCT’ comprises costs for vapor compression chillers, cooling towers, pumps and pipework of condenser and chilled water and the controls. Its lifetime is estimated to be 20 years according to the mean lifetime for water-cooled chillers of the ’ASHRAE Owning and Operating Cost Database’ [66].. 27.

(37) Table 2.15: Specific cost parameters per cooling sub-system System Component. Specific Cost. Lifetime. O&M. Discount Rate. Direct Expansion Unit + FC. 350 USD/kWth [75]. 15 yr [66]. 12% [76]. 3% [77]. Decentral VCC + WCT. 620 USD/kWth [78]. 20 yr [66]. 4% [79]. 3% [77]. District Cooling Plant. 600-1,200 USD/kWth (depending on size) [40] [78]. 35 yr [45]. 1.5% [40]. 3% [77]. District Cooling Network. 550 – 16,000 USD/m (depending on DN) [40][45]. 50 yr [45]. 2.5% [40]. 3% [77]. Energy Transfer Station. 110-500 USD/kWth ,station. 50 yr. 2.5% [40]. 3% [77]. 59 USD/m2 [78]. 50 yr. 4%. 3% [77]. 0.18 / 0.135 USD/kWh* [80]. -. -. 3% [77]. Central AC + BMS. Electricity. The investment cost of the ’District Cooling Plant’ includes the building, chillers, cooling towers, pumps, piping, and controls. Its operational and maintenance costs of 1.5% of the initial investment is low compared to decentralized cooling systems due to reduced operating personnel and reduced maintenance work.[40] Table 2.16: Investment cost of DCN piping per nominal size Pipe DN. Dext [m]. Dint [m]. Dinsulation [m]. Vdotmin [m3 /s]. Vdotmax [m3 /s]. Investment Cost [USD2015 /m]. 20 25 32 40 50 65 80 100 125 150 200 250 300 350 400 450 500 600 700 800 900 1000. 0.0269 0.0337 0.0434 0.0484 0.0603 0.0761 0.0889 0.1143 0.1397 0.1683 0.2191 0.2730 0.3239 0.3556 0.4064 0.4572 0.5080 0.6100 0.7110 0.8130 0.9140 1.0160. 0.0216 0.0285 0.0372 0.0431 0.0545 0.0703 0.0825 0.1071 0.1325 0.1603 0.2101 0.2630 0.3127 0.3444 0.3938 0.4446 0.4954 0.5958 0.6950 0.7954 0.8940 0.9940. 0.11 0.11 0.13 0.13 0.14 0.16 0.18 0.23 0.25 0.28 0.36 0.45 0.50 0.56 0.63 0.67 0.80 0.90 1.00 1.10 1.20 1.30. 0.0001 0.0002 0.0003 0.0004 0.0007 0.0012 0.0016 0.0027 0.0041 0.0061 0.0104 0.0163 0.0231 0.0280 0.0365 0.0465 0.0577 0.0834 0.1133 0.1481 0.1869 0.2306. 0.0005 0.0012 0.0024 0.0035 0.0062 0.0112 0.0161 0.0287 0.0457 0.0692 0.1244 0.2026 0.2956 0.3653 0.4907 0.6421 0.8172 1.2379 1.7578 2.3982 3.1475 4.0378. 557 570 589 612 642 690 742 791 962 1032 1263 1573 1948 2376 2728 3001 3468 4133 5382 6138 8103 9699. Since all systems are electrically powered, the electricity price is pivotal in analyzing the costs. In Singapore, large consumers with a monthly electricity consumption above 2000 kWh are allowed to purchase electricity from the open electricity market, benefiting from reduced rates [80]. We assume that cooling operators, who meet these criteria, benefit from a 25% price reduction compared to the standard electricity tariff, as indicated in table 2.15. [81] 28.