Optimizing night cooling for two systems in Stockholm

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Master of Science Thesis

KTH School of Industrial Engineering and Management Department of Energy Technology

SE-100 44 STOCKHOLM, Sweden

Optimizing night cooling for two

systems in Stockholm

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Master of Science Thesis EGI 2019: 421

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Abstract

Buildings in the commercial sector in Sweden accounts for around 12 % of the final energy consumption of the whole country. Utilizing energy efficient methods for heating, cooling and ventilation without compromising the indoor environment is therefore important. The incentives for energy conserving techniques are not only related to cost but also to lower the environmental impact. The cooling demand is highest during summer and to meet the demand indirect or direct cooling methods could be utilized. Night cooling is an indirect method and operating a night cooling system could be an option to lower the cooling demand.

Utilizing night cooling means that the building is ventilated with cold air during nighttime, when the outdoor air temperature is lower than the indoor air temperature. The thermal mass of the building and equipment is therefore cooled down and could then act as a heat sink during daytime. The night cooling system is controlled with several conditions that needs to be fulfilled to allow the system to activate.

Several previous studies and experiments have concluded that utilizing night cooling results in energy and cost savings, but some studies have established the opposite. Discomfort in the indoor environment has also been reported when operating night cooling systems in colder climates. Since the prediction of the performance also depends on several parameters the decision to install a night cooling system is not obvious. This study investigates two existing buildings in Hammarby sjöstad, Stockholm with night cooling systems installed. The aim of the study is to establish if night cooling should be utilized in the two facilities and discus if the obtained results are relevant for night cooling systems in general. To determine the performance of the systems the energy demand, the energy cost, the indoor environment, and the environmental impact will be analyzed.

The method in the study has been to create building models of the two buildings in a building simulation performance software called IDA ICE. The initial information of the two buildings had different levels of detail. Information about one of the buildings was well documented from previous projects, while the information about the other building was limited. The missing information was compensated with standardized values provided from research on existing buildings in Sweden. In IDA ICE the buildings’ information and details was then imported to create the building models. A parametric study was then performed to test the effect of the night cooling.

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Sammanfattning

Fastigheter inom den kommersiella sektorn, i Sverige, står för omkring 12 % av den slutgiltiga totala energianvändningen för hela landet. Användandet av energieffektiva metoder för kyla, uppvärmning och ventilation utan att kompromissa på inomhusklimatet är därför viktigt. Incitamenten för att använda energieffektivare metoder är inte bara relaterat till kostnaden utan också att sänka miljöpåverkan. Kylbehovet är som högst under sommaren, och indirekta eller direkta metoder för kylning kan användas för att möta behovet. Nattkyla är en indirekt metod och kan vara ett alternativ för att sänka den totala energianvändningen för en fastighet.

Nattkyla är en energibesparingsmetod där en byggnad ventileras med kall luft under natten när utomhustemperaturen är lägre än inomhustemperaturen. Detta kyler ner byggnadens konstruktion som sedan kan sänka kylbehovet under dygnets varmare timmar. Nattkylningssystemen styrs motflera olika villkor som skall vara uppfyllda innan systemen aktiveras.

Ett antal tidigare studier och experiment har konstaterat att användandet av nattkyla har resulterat i energi - och kostnadsbesparingar, medan andra studier har påvisat motsatsen. Försämrat inomhusklimat har också rapporterats i fall där nattkyla används. Eftersom nattkylningssystemen beror av många olika parametrar för att prestera lönsamt är valet att använda sig av metod inte uppenbar.

Detta projekt undersöker två existerande byggnader som ligger i Hammarby sjöstad i Stockholm där nattkyla är installerat. Syftet med projektet är att fastställa om de två fastigheterna ska använda sig av nattkyla och att diskutera om de slutsatserna gäller generellt. För att fastställa prestandan av nattkylningssystemen kommer systemens energibehov, energikostnad och miljöpåverkan att analyseras.

Metoden i projektet har varit att skapa simuleringsmodeller av de två byggnaderna i ett simuleringsverktygsprogram som heter IDA ICE. Tidigare information om byggnaderna hade varierad detaljnivå. Den ena byggnaden hade mer detaljerad information från tidigare projekt, medan informationen om den andra byggnaden var begränsad. Avsaknad information blev ersatt med standardiserade värden som är framtagna ur studier på ett stort antal existerande byggnader i Sverige. I IDA ICE skapades sedan byggnadsmodellerna baserat på den tillgängliga informationen och de standardiserade värdena. En parameterstudie utfördes sedan för att testa effekten av nattkyla.

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1 Introduction

1.1 Background

Buildings in the commercial sector in Sweden consumes around 12 % of the final energy consumption of the country (Energimyndigheten, 2017). It is therefore of interest to lower the energy consumption of the buildings, while maintaining the indoor environment at a comfortable level. Incentives for energy reducing techniques is not only cost related, but also to lower the environmental impact. The cooling demand for buildings in Sweden is highest during the summer. To meet the cooling demand indirect or direct methods could be used. Night cooling is an indirect method, where the building is ventilated with cool air during nighttime when the temperature is lower than the required indoor temperature. This cools down the thermal mass of the building which could then serve as a heat sink during daytime.

Several previous studies exist which covers the potential that night cooling brings. Night cooling systems used in office buildings have been proven to lower the cooling demand (Zhaojun, et al., 2009) but since the operation of a night cooling system depends on many parameters it could sometimes be difficult to predict the performance. Discomfort in the indoor environment has also been observed for systems operating in colder climates (Wittchen, et al., 2005) and increased energy consumption has been reported when mechanical systems was utilized (Kolokotroni & Aronis, 1999).

1.2 Scope

The two buildings that will be examined is owned by the property company Fabege. Fabeges’ properties are developed to be sustainable and modern and it is therefore in their interest to have properly working systems installed in the buildings.

Bengt Dahlgren AB works with building projects related to energy performance. It is therefore in their interest to examine and determine methods to lower the energy consumption of a building. This thesis aims to increase the knowledge about night cooling systems, which could lead to it being utilized more in future energy related projects.

1.3 Objective and research questions

The objective of this project is to outline the conditions that needs to be fulfilled for the night cooling systems to work properly. The reason for poor indoor quality will also be evaluated for two existing buildings with night cooling installed, located in Hammarby sjöstad, Stockholm. The conditions that will be examined initially for the two buildings are the design details, the performance of the operating systems and the climate during the cooling period (May to September).

The thesis will be executed by investigating the following research questions:

 What parameters of the two analyzed night cooling systems have the largest influence on the systems’ performance i.e. the energy demand, the energy cost, the indoor environment and the environmental impact? Is it the same parameters for both systems, and how does it relate to previous research?

 Will the potentially reduced cooling demand be beneficial from a cost perspective since the price for district cooling is lower than the price for electricity?

 How should the systems be operated to reduce the energy demand without compromising the thermal comfort of the buildings’ indoor environment?

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1.4 Limitations

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2 Literature review

Passive heating and cooling methods have been practiced during the entire history of building development. The application of these methods has in some extent been neglected in modern building engineering, where mechanical systems are more commonly used. Night cooling or night ventilation is a passive or semi-passive method that could be operated to reduce the energy use. In this chapter, previous research and experiments related to night cooling is discussed and evaluated. The present reviewed work will mainly have focus on the performance of night cooling systems in terms of energy reduction and methods for predicting how the systems will affect the indoor environment.

Artmann, Manz, and Heiselberg (2007) discusses in their study that the current trend for energy related building projects is to find energy efficient methods for operation. Another discussed trend is the increasing cooling demand. This is due to the increasing internal gains and the extensive glazing of new modern buildings (higher solar gains). The effect of global warming is also contributing, slowly, to the increased cooling demand with warmer climate. They also state in their study that Scandinavian countries have a large potential for using night cooling. The potential is important because the climate is the parameter that would be the hardest to influence for an efficient performance of a night cooling system. On the other hand, the article does not evaluate other parameters which also have significant effect on the performance. The problems that night cooling systems could induce are also not discussed.

Wittchen, Løgberg, Pedersen, Djurtoft and Thiesen (2005) states a problem that could occur in colder climates when operating night cooling systems, which is a discomfort in the indoor environment. This could be due to an under-cooled construction by over ventilation, and the consequence of this could be that the system is turned off. Since night cooling system depends on several parameters in order to perform well, it results in complex predictions of the indoor environment and energy reduction. Building developers therefore have skepticism to implement such systems.

Several previous studies exist which covers the potential that night cooling brings. The parameters that affect the effectiveness and the potential for implementation have also been studied. Kolokotroni and Aronis (1999) performed a study for air-conditioned offices utilizing night ventilation. In the study a building model was created in order to alter different parameters, one at a time, and simulate the potential energy reduction. Both different building and cooling-system parameters was investigated. The building parameter that had the largest effect on the energy reduction was the thermal mass of the building. A light compared to a heavy building resulted in an energy reduction of ~4 % respective ~15 %. When mechanical ventilation was operated in the model the energy required for operating the fans resulted in an energy increase, though the total cooling capacity for the plant could be decreased. This study concludes the importance of analyzing the conditions for the night ventilation system, because it does not always result in an energy reduction. Zhaojun, Lingli and Fusheng (2009) researched different control strategies for night ventilation systems operating in offices. The method of the study was to create a building model in an energy analysis software and assess the performance of the system. The results from the simulations predicted an energy reduction when utilizing mechanical night ventilation. During June the energy reduction was 19 % compared to August when it was 9.1 %. The climatic conditions used in the simulations was of a moderate climate in northern China. From the study it can be concluded that mechanical night cooling systems could significantly decrease the total energy consumption. On the other hand, the research does not evaluate the convective surface heat flux of the building energy model. Accurate predictions of the simulations are highly sensitive in this matter.

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4 transfer in this study was simplified compared to the real situation. The simulation model did not consider direct heat transfer between surfaces and heat transfer through radiation was accounted for by increasing the convective heat transfer coefficient.

Goethals, Breesch and Janssens (2011) addressed the issue of simplifying the convective surface heat flux in building energy simulation software. The accuracy of predictions from simulations is according to the study related to a correctly described convective heat transfer. In the study they concluded that the operation time of the night cooling systems mainly was dependent on a correct prediction of the convective heat transfer. The cooling demand and the indoor temperature reduction was more dependent on the properties for the thermal mass of the building. The research also stated, through a sensitivity analysis, that the choice of the convective heat transfer coefficient can influence decision regarding the design of the night cooling systems. In order to obtain more accurate results, it is therefore important to assess how energy building software simulate the convective heat transfer.

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3 Theoretical background

3.1 Night cooling

Night cooling is a passive or a semi-passive method for decreasing the cooling demand by ventilating the building during nighttime when the outdoor air temperature is lower than the indoor air temperature. The cold is stored in the structural elements of the building and acts as a heat sink during warmer hours when cooling is required.

Figure 1 – Specific cooling power in the air during the summer months.

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Figure 2 – Relative humidity during summer

The relative humidity of the outdoor air is an important aspect to consider when utilizing night cooling. If the building is ventilated with air that is very humid the indoor environment could be affected. Figure 2 contains readings from Bromma flygplats in Stockholm, Sweden of the relative humidity. The 21st_{of July} the relative humidity is 100 % during nighttime, which is also the cases for a few other dates.

The night cooling system could be controlled with several conditions. This is to ensure that the system operates with the intended effect. The conditions could be an outdoor temperature limit, a benefit limit temperature and a time schedule.

The outdoor temperature limit is a limit where the night cooling system is allowed to be activated. If the outdoor temperature limit is set too low the night cooling system could be active during the wrong season where cooling is not required.

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3.2 IDA ICE

IDA ICE is a building performance simulation (BPS) software developed by EQUA simulation AB. The software enables complex simulations of a building’s energy use, thermal comfort, daylight etc. Building models could be created by importing 3D-objects in IFC-format or from the detail plan in 2D. During simulation the software creates a mathematical model of all the components in the building model. The mathematical model consists of a large equation system which is solved numerically for each time step. Larger models with many components will result in time consuming simulations. In addition, a parametric optimization feature is available in IDA ICE. The software is coupled with GenOpt and available directly in the program. GenOpt is an optimization program developed for optimizing problems which are computationally expensive (GenOpt, 2016).

The output from simulations is presented in tables, report, charts and plots. The report from the simulation covers several outputs:

 Delivered energy, which includes purchased energy and peak demand  Energy balances for each zone or the whole model

 System energy – Zone heating, zone cooling, AHU heating, AHU cooling and domestic hot water Many other outputs are available from the results, mainly related to thermal comfort (PPD, zone temperatures etc.) (EQUA, 2019).

3.3 Energy simulation data

In projects it is common that the initial information provided is not considerably detailed. Construction details and the specific fan power for the fans in the air handling unit are some examples of parameters that needs measurements to obtain the right values. Usually it is not time and cost efficient to perform these measurement and therefore other methods needs to be applied. Sveby and Boverket provides standardized values that has been compiled from research of existing properties in Sweden. Sveby is an industry-wide program that produce aids for agreements on the energy use in properties and Boverket is an agency that provides Swedish construction regulations.

Table 1 – Density of occupants and air supply according to activity.

Activity Density of occupants

[m2_/occupant]

Supply air [l/sm2_]

Normal office 20 1.3

Stores 3 3

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Table 2 – Effect of internal gains for different activities.

Activity Equipment [W/m2_{] Lighting}_[W/m2_]

Normal office 12 7

Stores 40-45 1-5

Lighting has the same scheduled hours as the occupancy, while the equipment operates from 08.00-17.00 during weekdays with 70 % capacity. The zone cooling setpoint are recommend being 24.5 ± 1.5 ° C in a normal office (Sveby, 2013).

Table 3 – U-values for the building’s envelope.

U-value [W/m2_K]

Uroof 0.13

Uexternal wall 0.18

Uwindow 1.2

The U-values in Table 3 could be used if the investigated property doesn’t provide specific construction details.

The specific fan power is recommended to not be above 2 kW/m3_{/s when having air supply and return air} with heat exchange. Wind driven infiltration of the building’s envelope should not be above 0.6 l/sm2 (Boverket, 2015).

3.4 Cost of energy and environmental impact

The cost for district cooling and electricity is different. In a cost analysis it is important to consider the subscription costs, the cost per energy unit and distribution costs.

3.4.1 District cooling

Stockholm Exergi is a Swedish company that delivers, among other services, district cooling. The company has a large distribution network in Stockholm.

The price for district cooling is related to the peak cooling demand effect, subscription costs and energy costs.

Table 4 – Effect costs

Yearly effect [kW] Fixed cost [SEK/year] Effect cost [SEK/kW]

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Table 5 – Energy cost

Period Energy price [SEK/MWh]

Jun-Aug 450

Apr-May, Sep-Oct 180

Jan-Mar, Nov-Dec 0

Table 5 demonstrated that the energy cost is different during the year. During the summer months when the demand peaks the price increases and when the demand is lower the price reduces (Exergi, 2019) . The environmental impact from district cooling is 31 g/kWh of CO2 (Energi, 2017).

3.4.2 Electricity

The electricity cost consists of three part. The distribution cost of the electric grid, electricity subscription cost and taxes and government fees.

Figure 3 – Electricity cost (Ellevio, 2019).

The electricity trade could be chosen by the costumer while the distribution cost depends on which supplier owns the electricity grid in the area.

Table 6 – Electricity trade agreement

Price [SEK/kWh]

Electricity cost 0.638

Energy taxes 0.293

Total price 0.931

An example of an electricity trade agreement is stated in Table 6. The agreement is from the current electricity trade for Kanalhuset. Since the distribution cost is 25 % the total electricity price is 1.16 SEK/kWh. The environmental impact for electricity is given to 125 g/kWh of CO2 (Naturvårdsverket, 2018).

45% 25%

30%

Electricity cost

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3.5 Thermal comfort

The indoor environment is important to maintain at a comfortable level. Heating, cooling and ventilation should be sized to meet the requirements for thermal comfort. Thermal comfort depends on many factors which includes the metabolic activity of the occupants, the relative humidity, the operative temperature and several other factors. The thermal comfort could be expressed by the predicted percentage of dissatisfied index (PPD-index).

The metabolic activity is the heat production per unit of body surface area and is expressed in the unit Met.

Table 7 – Met rates for different activities in offices.

Activity in offices Met rate Reading, seated 1.0

Typing 1.1

Filing, seated 1.2

Walking about 1.7

In a standard office most people are seated and typing but people are also walking around, which results in a slightly higher Met rate than 1.1 in average.

The operative temperature is defined as a uniform temperature in an enclosure where the occupant would exchange the same amount of body heat by convection and radiation as in the actual environment. It could be expressed as the average of the mean radiant temperature and ambient temperature. The operative temperature gives a better indication of the experienced temperature in an enclosure rather than only measuring the ambient temperature.

The relative humidity is defined as the ratio between the amount of water vapor in the air and amount of water vapor that the air could hold at the given temperature and pressure. When the body sweats it tries to release heat by evaporation and if the relative humidity is high and the air is almost saturated the heat transfer by evaporation decreases. This causes a sensation of thermal dissatisfaction. Low relative humidity also causes discomfort because of the effect on the human’s mucous membranes.

Since the experienced thermal comfort of a person is subjective it becomes a challenge to set the indoor conditions to be satisfactory to every occupant. A tool to measure the experience thermal comfort of the occupants is the predicted percentage of dissatisfied index (PPD index). The PPD index gives a percentage of the fraction of dissatisfied occupants in an enclosure. The PPD index takes the mentioned parameters into account but also the relative air velocity and clothing details of the occupant (Havtun, et al., 2017).

3.6 Global warming and Urban heat islands

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4 Building descriptions

This chapter describes the two buildings that will be examined in the study. Kanalhuset had data from previous projects while Fartygstrafiken had less information available.

4.1 Kanalhuset

Kanalhuset was constructed in 2014 and is located in Stockholm, at Hammarby kajgata 12. The building has seven stories and a basement. On top of the building a machine room is located, that contains the two air handling units that provides ventilation. The cooling is provided by district cooling from the company Stockholm Exergi. On each floor the office space is an open landscape with, on some floors a few meeting rooms. The area of the building was measured, including the basement and the air handling unit room, to 3303 m2_.

4.1.1 Construction details

Specific details of the construction materials were provided from previous studies if the building.

Table 8 – Construction details.

Construction detail U-value [W/m2_K] External wall 0.16 Roof 0.1 Internal wall 3.47

In Table 8 the U-values for the walls and roof of the building is given.

Table 9 – Window details.

Window Dimensions [m] U-value [W/m2_K] _{Solar heat gain}

coefficient, g-value [-]

Small window 0.9 x 2.4 0.7 0.28

Middle size window 1.5 x 1.5 0.7 0.28

Large window 1.5 x 2.4 0.7 0.28

The building has three different window sizes. The total area of the windows of the building was measured to 660 m2_{, which is 23 % of the total envelope area (Bengtsson, 2012).}

4.1.2 Internal gains

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Figure 4 – Detail plan of the office space.

Figure 4 displays the workplaces for each floor. Every floor has 48 workplaces and the area of the office space per floor is 361.4 m2_{. This results in 0.133 occupants per m}2_{. The presence of occupants is scheduled} to 07.00-19.00 with a 70 % occupancy during weekdays.

The lighting of building was given to 10 W/m2_{during 08.00-17.00 on weekdays and the energy consumption} of equipment was given to 8.1 W/m2_{during the same hours (Bengtsson, 2012).}

4.1.3 Air handling units

The building is equipped with two air handling units that runs in parallel. They supply the building with a constant air flow (CAV) of around 1.2 l/sm2_{. The AHUs have a rotary heat exchanger installed with an} efficiency of 80 %. The supply temperature of the AHUs is set to 18 °C and the specific fan power has been measured to 1.6 kW/m3_{/s (Bengtsson, 2012). The air is cooled with district cooling in the AHUs.}

4.1.4 Cooling

The air and cooling in the building is supplied by chilled beams. The installed chilled beams are from the company Halton (see appendix ii). The air supply for each chilled beam is 18 l/s and the cooling coil capacity is 116 W/m. The installed chilled beams are 2.4 m long (Fabege, 2013). The placement of the chilled beams is also given from previous projects by blueprints of the piping system.

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4.2 Fartygstrafiken

Fartygstrafiken was constructed in 1955 and is located near Kanalhuset, at Hammarby allé 91 and 93. The building has stores at ground level and then five stories with offices. Four air handling units provides the ventilation and the cooling is supplied by district cooling. The stores have large windows and high ceilings. The offices have different space plan arrangements, with open offices, meeting rooms and smaller office rooms. The total area of lettable space in the building is 8760 m2_{(Fabege, 2017), this area only includes the} stores and offices, excluding staircases etc. From blueprints the total area was measured to 9957 m2_{. Specific} construction details and information about the occupancy, lighting and equipment was not available, which is a common situation in these kinds of projects.

4.2.1 Air handling units

The building has four air handling units installed. The AHUs supply air with a constant air flow (CAV) to different sections of the buildings.

Figure 5 – Air supply distribution for Fartygstrafiken.

In Figure 5 the distribution of the air supply is depicted. AHU1 supplies all the offices and the staircase for the right side of the building if facing it from the main street. AHU2 supplies one large store and a bank. The offices and staircase on the left side of the building have ventilation from AHU3. The last AHU supplies the remaining stores at ground level.

The AHUs have different operation schedules and AHU1, 2 and 3 have a rotary heat exchanger while AHU4 has a liquid heat exchanger installed. The efficiency for a rotary heat exchanger is 80 % and for the liquid heat exchanger 70 % (Venitlation, 2019).

Table 10 – Operation schedules for the AHUs.

Schedule Weekdays Saturday Sunday

AHU1 07.20 - 17.00 - -

AHU2 07.00 - 18.00 9.45 – 16.30 10.45 – 15.30

AHU3 07.30 – 17.30 - -

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15 The supply temperature is set to 19 °C for each AHU. The night ventilation system is currently not in operation, but the operation conditions was available from the control system.

Table 11 – Operation conditions for night ventilation.

Outdoor temperature limit [°C] Benefit limit temperature [°C] AHU1 9 4 AHU2 9 5 AHU3 9 4 AHU4 9 4 4.2.2 Cooling

In Fartygstrafiken the air and cooling are supplied by chilled beams. In the building there are three types of chilled beams installed. Two is from the company Swegon and the other one is from Lindab.

Table 12 – Manufacturing data from the different chilled beams.

Model Air supply [l/s] Cooling coil capacity [W] Swegon, Adriatic (Swegon, 2018a) 22.6 353 Swegon, Pacific (Swegon, 2018b) 25.1 634 Lindab, PREMAX (Appendix iii) 27 240

The Adriatic model is installed in most of the building. The other two models are only installed in a few parts of the building. All the chilled beams are 2 m long.

Figure 6 – District cooling demand for Fartygstrafiken.

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5 Method

This chapter describes how the building models were created with the building performance software IDA ICE to obtain the results. The two building objects have been created with a different approach. Kanalhuset was well documented and had more specific information that could be used when the building model was created. Fartygstrafiken had less specific information, which resulted in that more standard values were used when creating the model.

Thermal bridges, infiltration and distribution losses have been added for both models. To obtain the thermal bridges one simulation without any additional losses is performed and then 20 % of the total thermal bridges’ losses is added, this assumption is based on the study performed by (Bengtsson, 2012). Wind driven infiltration is set according to the energy simulation data and distribution losses in the cooling system is set to 10 %. The zone setpoints for heating and cooling was set to 21 °C and 25 °C for both models. The simulation period of the two building models is from the 1st of May to the 1st of September, this results in that the heating demand can be neglected in this project since almost no heating is required during this period. The Met rate of the occupants has been set to 1.2 in the baseline case for both building.

5.1 Kanalhuset

Building the model starts with creating the geometry according to blueprints. Detailed blueprints were provided from previous projects on Kanalhuset, that could be imported into IDA ICE.

Figure 7 – Building model of Kanalhuset

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Figure 8 – Surrounding buildings of the model.

The other details of the model were created according to the building information presented in the chapter Building description. This includes the information about the construction details, internal gains, air handling unit details and the cooling system’s specifics.

Since the two air handling units runs in parallel, they could be modeled as a single AHU. The night ventilation is modeled to operate with a benefit limit temperature of 2 °C, an outdoor temperature of 9°C and a schedule that runs from 00.00 to 06.00 (night ventilation schedule 2). With these conditions the baseline case is created for Kanalhuset.

5.2 Fartygstrafiken

The same process when creating the model for Fartygstrafiken was used as for Kanalhuset. Fartygstrafiken is a significantly larger building than Kanalhuset which results in that the zones in the model need to be as few as possible. If a building model has to many zones the simulation time and complexity will be too high.

Figure 9 – Building model of Fartygstrafiken.

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Figure 10 – Surrounding buildings of the model.

The front façade of the building is facing southwest at 205 ° which results in high solar gains in the building. Manufacturing information about the windows was not available. The windows at the floors one to five, had three glaze-panes, blinds and solar films installed (See appendix i for the solar film details). The blinds where modelled to run according to the solar activity. The larger store windows were modelled to have the same type of glazing but with integrated shading.

Since some details about the construction, internal gains and the air handling units were not available, standard values from Sveby and Boverket was used, see chapter Energy simulation data. The available information was also added to the model and the baseline case for Fartygstrafiken was created.

5.3 Parameter study

The parameter study was performed to analyze different cases and the influence of different parameters on the energy demand, the thermal comfort, energy cost and the environmental impact. All the cases originate from the baseline case with one changing parameter.

Five different cases have been studied for both models. The influence of changing the Met rate for the occupants, different operation schedules for the night cooling and altering zone cooling setpoints have all been simulated. For each case simulated a case without any night cooling has been performed for comparison. The output for each case was the energy demand for the fan energy (HVAC aux) and the district cooling demand. In each case the maximum relative humidity, maximum operative temperature and the maximum PPD for different zones have been obtained. Average PPD and the peak cooling demand for each case have also been attained.

The Met rate was changed to 1.4, which would correspond to when most of the occupants is seated and typing (60%) but some of the activity is walking about. In the cases with no night cooling the air handling units was modelled to not run any ventilation during nighttime. Two different schedules for the operation of the night ventilation were also tested. The duration of night ventilation schedule 1 was modelled to run from 21.00 to 06.00 and night ventilation schedule 3 was modelled to run between 02.00 and 04.00. The zone cooling setpoints were also altered with one case where the cooling setpoint was set to 24 °C (strict case) and one case with cooling setpoint set to 26 °C (high case).

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19 The energy cost and the environmental impact were calculated according to the presented values in the chapter cost of energy and environmental impact.

Additional parameters have also been analyzed. The benefit limit temperature, the outdoor temperature limit, the supply temperature, the specific fan power, and the U-value of the concrete floor. Each parameter change is derived from the baseline case. The conditions for operating the night cooling system (Benefit limit temperature and outdoor temperature limit) are simulated to determine what conditions the systems should be operating with. The supply temperature and the specific fan power is tested to make a sensitivity analysis on the energy demand and energy cost for the night cooling systems. The U-value of the concrete floor is also altered to make a sensitivity analysis of the energy demand and cost. This decision is based on the previous study from Goethals, Breesch and Janssens (2011) study of how the convective heat transfer coefficient could influence the results in building simulation software. The choice of altering the concrete floors’ U-value is based on that this structural element is not adjacent to the outdoor air, so the heat transfer is contained inside the buildings’ envelope.

The output from the simulations were the energy demand for HVAC aux and district cooling and the energy cost. Since Fartygstrafiken has four different AHUs and they have been simulated separately to examine the influence from each AHU.

5.4 Indoor environment during 24h

The indoor environment was also examined further. The relative humidity’s, PPD’s, operative temperature’s and mean air temperature’s variation over 24 hours were analyzed. The chosen parameters were analyzed for the case where night cooling was operated during the most hours (Night ventilation schedule 1) and compared with the case where no night cooling where utilized (Baseline No NC). Each parameter will be analyzed in one zone of the building based on the maximum result for the relative humidity, PPD and operative temperature.

5.5 Validation of the building models

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6 Results

6.1 Kanalhuset

The results from the simulations of Kanalhuset is presented in this chapter. The first section is the results from the individually changed parameter.

Figure 11 – Energy demand for different cases in Kanalhuset.

The lowest total energy is achieved when the zone setpoint is calibrated to high and compared to the same case with no night cooling the total energy demand is decreased with 9.6 %. Utilizing the night ventilation schedule 1 has a 9.0 % decrease in total energy demand compared to the baseline case where no night cooling is utilized (Baseline No NC). In all the cases utilizing night cooling, the total energy demand decreases compared to the case with no night cooling. The energy demand for the HVAC aux increases when the time duration of the night cooling systems is longer, while the district cooling demand decreases.

0 5 10 15 20 25 Baseline Night ventilation schedule 1 Night ventilation schedule 3 Baseline No NC MET rate 1.4 MET rate 1.4 No NC Zone setpoint high Zone setpoint high No NC Zone setpoint strict Zone setpoint strict No NC kWh/m 2

Energy demand ‐ Kanalhuset

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21

Table 13 – Maximum relative humidity for different zones in different cases.

Case Zone Maximum relative

humidity [%]

Baseline 6th North 71.78

Night ventilation schedule 1 GF South West 70.49

Night ventilation schedule 3 1st South 71.51

Baseline No NC 1st North 80.93

MET rate 1.4 GF South West 75.72

MET rate 1.4 No NC GF South West 74.2

Zone setpoint high GF South West 70.02

Zone setpoint high No NC GF South West 68.21

Zone setpoint strict GF South West 76.35

Zone setpoint strict No NC GF South West 74.87

In most of the cases the zone GF South West (see appendix v for the location in the building) has the highest relative humidity. The maximum relative humidity is higher in most of the cases where night cooling is utilized compared to the case where it is not.

Table 14 – Maximum operative temperature for different zones in different cases.

Case Zone Maximum operative

temperature [°C]

Baseline 6th South 26.21

Night ventilation schedule 1 5th South 26.27

Baseline No NC 5th South 26.42

MET rate 1.4 5th South 26.31

MET rate 1.4 No NC 5th South 26.41

Zone setpoint high 5th South 27.25

Zone setpoint high No NC 5th South 27.36

Zone setpoint strict 5th South 25.32

Zone setpoint strict No NC 5th South 25.4

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22

Table 15 – Maximum PPD for different zones in the different cases.

Case Zone Maximum PPD [%]

Baseline 5th South 14.8

Baseline No NC 5th South 15.79

MET rate 1.4 5th South 21.94

MET rate 1.4 No NC 5th South 22.46

Zone setpoint high 5th South 22.03

Zone setpoint high No NC 5th South 22.9

Zone setpoint strict 5th South 10.17

Zone setpoint strict No NC 5th South 10.43

The predicted percentage of dissatisfied people is also highest in the top floors in southern direction. The maximum PPD is not significantly higher in the comparison between the cases where night cooling is utilized and not, but it is a slight decrease.

Table 16 – Average PPD and peak cooling demand for the different cases.

Case Average PPD [%] Peak cooling power

[kW]

Baseline 10.0 95.57

Night ventilation schedule 1 10.0 91.96

Night ventilation schedule 3 11.0 100.9

Baseline No NC 12.0 106.7

MET rate 1.4 16.0 115.6

MET rate 1.4 No NC 17.0 126.2

Zone setpoint high 14.0 90.5

Zone setpoint high No NC 16.0 116.2

Zone setpoint strict 8.0 99.4

Zone setpoint strict No NC 8.0 110

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23

Figure 12 – Energy cost for the different cases.

The energy cost is highest when the MET rate of the occupants is modelled to be higher. The total energy cost is lower for all the cases when night cooling is utilized. The baseline case compared to the case with no night cooling (Baseline No NC) has almost none cost difference. Operating the night cooling system with night ventilation schedule 1 has an energy cost decrease with 2.9 % compared to the baseline No NC case. The highest cost decrease occurs in the comparison when the zone set point is set to high, with cost savings of 12.7 %. 0 20000 40000 60000 80000 100000 120000 140000 160000 Baseline Night ventilation schedule 1 Night ventilation schedule 3 Baseline No NC MET rate 1.4 MET rate 1.4 No NC Zone setpoint high Zone setpoint high No NC Zone setpoint strict Zone setpoint strict No NC SEK

Energy cost ‐ Kanalhuset

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24

Figure 13 - Emissions for district cooling and HVAC aux.

The highest emissions occur in the cases with the highest electricity demand. The lowest total emissions are in the cases when no night cooling is utilized. In the case when night ventilation schedule 1 is applied and when the zone set point is set to strict the highest emissions are obtained.

0 200 400 600 800 1000 1200 Baseline Night ventilation schedule 1 Night ventilation schedule 3 Baseline No NC MET rate 1.4 MET rate 1.4 No NC Zone setpoint high Zone setpoint high No NC Zone setpoint strict Zone setpoint strict No NC [gCO 2 /m 2]

Emissons from energy source ‐ Kanalhuset

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25

6.1.1 Benefit limit temperature

This section presents the results from the effect of the benefit limit temperature on the HVAC aux and district cooling demand and energy cost.

Figure 14 – Energy cost and energy demand for HVAC aux regarding the benefit limit temperature.

Small temperature differences in the benefit limit temperature increases the energy demand and cost. If the temperature difference is larger than 6.5 °C the energy demand and cost decrease significantly.

Figure 15 – Energy cost and energy demand for district cooling regarding the benefit limit temperature.

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26

Figure 16 – Total energy demand and cost for different benefit limit temperatures.

The total energy cost decreases when the district cooling demand increases. The intersection of the energy cost and the energy demand occur with a benefit limit temperature of around 8 °C.

6.1.2 Outdoor temperature limit

The results from alternating the outdoor temperature limit is presented in this section. The energy demand and cost are depicted for HVAC aux and district cooling.

Figure 17 – Energy cost and energy demand for HVAC aux regarding the outdoor temperature limit.

Low outdoor temperature limits increase the energy demand and cost. The decrease of energy demand and cost with increasing outdoor temperature is almost linear until around 18 °C. Above 18 °C the energy demand and cost stabilize.

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27

Figure 18 - Energy cost and energy demand for district cooling regarding the outdoor temperature limit.

The energy demand and cost for district cooling increases with the outdoor temperature until it stabilizes around 18 °C.

Figure 19 - Total energy demand and cost for different outdoor temperature limit.

The total energy demand and cost is lower when the outdoor temperature limit is lower. The total energy cost increases with increasing total energy demand.

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28

6.1.3 Supply temperature

In this section the energy demand and energy cost for HVAC aux and district cooling are presented with altering supply temperature.

Figure 20 - Energy cost and energy demand for HVAC aux regarding the supply temperature.

The supply temperature doesn’t affect the HVAC aux energy demand and cost significantly. Between 15 °C and 25 °C the energy demand decreases 200 kWh and the energy cost 175 SEK.

Figure 21 - Energy cost and energy demand for district cooling regarding the supply temperature.

The supply temperature has larger influence on the energy demand and cost for district cooling. The energy demand and cost increase almost linear with increasing supply temperature.

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29

Figure 22 - Total energy demand and cost for different supply temperatures.

The total energy cost is lower when the supply temperature is lower, the total energy cost increases with increasing total energy demand.

6.1.4 Specific fan power

The effect of the specific fan power on the energy demand and cost for HVAC aux is presented in this section.

Figure 23 - Energy cost and energy demand for HVAC aux regarding the supply temperature.

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30

6.1.5 U-value concrete floor

The results from changing U-value for the concrete floor in the building model is presented in this section.

Figure 24 – Energy cost and energy demand for HVAC aux with changing U-value.

The effects of altering U-value on the energy cost and energy demand for HVAC aux is small. The largest fluctuation in the energy demand is around 20 kWh with increasing U-value.

Figure 25 - Energy cost and energy demand for district cooling with changing U-value.

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31

6.2 Fartygstrafiken

In this chapter the results from the simulations of Fartygstrafiken is presented. The energy demand and cost for HVAC aux and district cooling are evaluated for different parameters.

Figure 26 – Energy demand for different cases in Fartygstrafiken.

The total energy demand is lower in in all the cases where night cooling is utilized compared to the case with no night cooling. The largest total energy decrease is in the comparison where the zone set point is set to high, with an energy decrease of 14.9 %. Operating the night cooling system according to night ventilation schedule 1 decreases the total energy demand with 12.3 % compared to the case where no night cooling is utilized (Baseline No NC). The same results as the simulations for Kanalhuset can be observed where utilizing night cooling lowers the district cooling demand but increases the energy consumption of the AHUs. 0 5 10 15 20 25 30 Baseline Night ventilation schedule 1 Night ventilation schedule 3 Baseline No NC MET rate 1.4 MET rate 1.4 No NC Zone setpoint high Zone setpoint high No NC Zone setpoint strict Zone setpoint strict No NC kWh/m 2

Energy demand ‐ Fartygstrafiken

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32

Table 17 - Maximum relative humidity for different zones in different cases.

The maximum relative humidity is attained in the basement of Fartygstrafiken. The highest relative humidity is acquired in the case when night ventilation schedule 1 is operated. The maximum relative humidity is higher when night cooling is utilized compared to when it is not in all the cases.

Table 18 - Maximum operative temperature for different zone in different cases.

Case Zone Maximum Operative

Temperature [°C]

Baseline 1/2F Handelsbanken 27.02

Night ventilation schedule 1 1/2F Handelsbanken 26.63

Baseline No NC 1/2F Handelsbanken 27.2

MET rate 1.4 1/2F Handelsbanken 27.1

MET rate 1.4 No NC 1/2F Handelsbanken 27.28

Zone setpoint high 1/2F Handelsbanken 28

Zone setpoint high No NC 1/2F Handelsbanken 28.18

Zone setpoint strict 1/2F Handelsbanken 26.07

Zone setpoint strict No NC 1/2F Handelsbanken 26.22

In all cases the maximum operative temperature is obtained in the zone 1/2F Handelsbanken (see appendix vii). The highest temperature is acquired in the case when the zone setpoint is set to high, while the lowest temperatures are in the case when the zone setpoint is set to strict. When no night cooling is utilized the maximum operative temperature is slightly higher, but as in the results of Kanalhuset not significantly.

Case Zone Maximum relative humidity

[%]

Baseline Basement 93 75.79

Night ventilation schedule 1 Basement 93 80.71

Night ventilation schedule 3 Basement 93 74.09

Baseline No NC Basement 93 73.17

MET rate 1.4 Basement 93 76.02

MET rate 1.4 No NC Basement 93 73.9

Zone setpoint high Basement 93 78.13

Zone setpoint high No NC Basement 93 71.04

Zone setpoint strict Basement 93 77.48

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Table 19 – Maximum PPD for different zones in different cases.

Case Zone Maximum PPD [%]

Baseline 1/2F Handelsbanken 18.79

Baseline No NC 1/2F Handelsbanken 20.91

MET rate 1.4 1/2F Handelsbanken 27.25

MET rate 1.4 No NC 1/2F Handelsbanken 29.78

Zone setpoint high 1/2F Handelsbanken 28.32

Zone setpoint high No NC 1/2F Handelsbanken 31.4

Zone setpoint strict 1/2F Handelsbanken 12.98

Zone setpoint strict No NC 1/2F Handelsbanken 13.97

Maximum percentage of dissatisfaction is in all cases obtained in the zone 1/2F Handelsbanken. The maximum PPD is lower in all cases when night cooling is operated compared to when it is not.

Table 20 – Average PPD and peak cooling demand for the different cases.

The average PPD is lower in the cases where night cooling is operated compared to the cases where night cooling is not utilized. Operating the night cooling during a longer time influences the average PPD, when comparing with the night ventilation schedule 3. Utilizing night cooling decreases the peak cooling power and operating the night cooling system for a longer time decreases it further.

Case Average PPD [%] Peak cooling power

[kW]

Baseline 8 295.3

Night ventilation schedule 1 8 282.1

Night ventilation schedule 3 9 329.6

Baseline No NC 9 347.7

MET rate 1.4 16 304.5

MET rate 1.4 No NC 17 362.9

Zone setpoint high 10 278.1

Zone setpoint high No NC 13 343.5

Zone setpoint strict 7 319.4

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34

Figure 27 – Energy cost for different cases in Fartygstrafiken.

The total energy cost is lower when night cooling is utilized. When the zone set point is set to high the energy cost is decreased with 7.1 % compared between the case with night cooling operating and not. The cost reduction between operating night ventilation schedule 1 and the baseline case with no night cooling is 5.4 %. 0 50000 100000 150000 200000 250000 300000 350000 400000 450000 Baseline Night ventilation schedule 1 Night ventilation schedule 3 Baseline No NC MET rate 1.4 MET rate 1.4 No NC Zone setpoint high Zone setpoint high No NC Zone setpoint strict Zone setpoint strict No NC SEK

Energy cost ‐ Fartygstrafiken

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35

Figure 28 – Emissions for district cooling and HVAC aux.

The total emissions are higher when night cooling is utilized. The largest emissions occur when the electricity demand for the AHUs is highest. In the case when night ventilation schedule 1 is operated and when the zone set point is set to strict, the largest total emissions occur.

0 200 400 600 800 1000 1200 1400 1600 Baseline Night ventilation schedule 1 Night ventilation schedule 3 Baseline No NC MET rate 1.4 MET rate 1.4 No NC Zone setpoint high Zone setpoint high No NC Zone setpoint strict Zone setpoint strict No NC gCO 2 /m 2

Emissions from energy source ‐ Fartygstrafiken

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36

6.2.1 Benefit limit temperature

In this section the effect of the benefit limit temperature is presented for the energy demand and cost for HVAC aux and district cooling. The results are depicted for each air handling unit.

The energy demand and cost for HVAC aux decrease with larger temperature differences. If the temperature difference is larger than 6.5 °C, the energy demand and cost decrease significantly. With smaller temperature differences the energy demand and cost are more stable. Air handling unit 4 is least effected by increasing temperature difference, while the other AHUs have larger fluctuation in the energy demand and cost.

0 5 10 83000 84000 85000 86000 87000 97000 98000 99000 100000 101000 Temperature difference [°C] kWh SEK

Benfit Temperature limit

AHU1 ‐ HVAC aux

Energy Cost Energy demand 0 5 10 85000 85500 86000 86500 99000 99500 100000 100500 101000 Temperature difference [°C] kWh SEK

Benfit Temperature limit AHU4 ‐

HVAC aux

Benfit Temperature limit

AHU3 ‐ HVAC aux

Energy Cost Energy demand 0 5 10 83000 84000 85000 86000 87000 97000 98000 99000 100000 101000 Temperature difference [°C] kWh SEK

Benfit Temperature limit AHU2

‐ HVAC aux

Energy Cost Energy demand

(43)

37 With increasing temperature difference of the benefit limit temperature, the energy demand and cost for district cooling increases. By 6.5 °C the energy demand and cost increase significantly. Lower temperature differences have smaller impact on the energy demand and cost. AHU2 has the largest effect on the energy demand and cost with altering temperature differences, while AHU4 has the lowest influence.

0 2 4 6 8 110000 112000 114000 116000 248000 249000 250000 251000 Temperature difference [°C] kWh SEK

Benfit Temperature limit AHU1

‐ District cooling

Energy Cost Energy demand 0 5 10 111000 112000 113000 114000 115000 116000 248000 249000 250000 251000 Temperature difference [°C] kWh SEK

Benfit Temperature limit AHU2 ‐

District cooling

Energy Cost Energy demand 0 5 10 110000 112000 114000 116000 248000 249000 250000 251000 Temperature difference [°C] kWh SEK

Benfit Temperature limit AHU3

‐ District cooling

Energy Cost Energy demand 0 5 10 111000 112000 113000 114000 248500 249000 249500 Temperature difference [°C] kWh SEK

Benfit Temperature limit AHU4 ‐

District cooling

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38 The total energy cost decreases with larger temperature difference in the benefit limit temperature and increasing total energy demand. The intersection between the total energy cost and the total energy demand occur at a temperature difference between 7-9 °C for the different AHUs.

0 5 10 197700 198000 198300 198600 347000 348000 349000 350000 Temperature difference [°C] kWh SEK

Benfit Temperature limit

AHU1 ‐ Total energy

Benfit Temperature limit

AHU2 ‐ Total energy

Benfit Temperature limit

AHU3 ‐ Total energy

Benfit Temperature limit AHU4 ‐

Total energy

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39

6.2.2 Outdoor temperature limit

In this section the effect of the outdoor temperature limit is presented for energy demand and cost. The result from each air handling unit is depicted for HVAC aux and district cooling.

The energy demand and cost for HVAC aux decreases with increasing outdoor temperature limit until it stabilizes around 18 °C. AHU1 has the largest fluctuation in energy demand and cost, while AHU4 has the lowest. Lower outdoor temperatures than 18 °C have an almost linear relation between the energy demand and cost and the outdoor temperature limit.

0 10 20 30 75000 80000 85000 90000 80000 90000 100000 110000 Outdoor temperature [°C] kWh SEK

Outdoor temperature limit

AHU1 ‐ HVAC aux

Energy Cost Energy demand 0 10 20 30 80000 82000 84000 86000 88000 92000 96000 100000 104000 Outdoor temperature [°C] kWh SEK

Outdoor temperature limit

AHU2 ‐ HVAC aux

Outdoor temperature limit

AHU3 ‐ HVAC aux

Energy Cost Energy demand 0 10 20 30 84000 85000 86000 87000 97500 99000 100500 102000 Outdoor temperature [°C] kWh SEK

Outdoor temperature limit

AHU4 ‐ HVAC aux

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40 The energy demand and cost increases with the outdoor temperature limit for district cooling. By 18 °C the energy demand and cost stabilize, while lower outdoor temperatures decrease the energy demand and cost. AHU1 has the largest influence on the energy demand and cost and AHU4 has the least.

0 10 20 30 100000 120000 140000 245000 250000 255000 260000 Outdoor temperature [°C] kWh SEK

Outdoor temperature limit

AHU1 ‐ District Cooling

Energy Cost Energy demand 0 10 20 30 110000 120000 130000 248000 250000 252000 254000 Outdoor temperature [°C] kWh SEK

Outdoor temperature

limit AHU2 ‐ District

cooling

Energy Cost Energy demand 0 10 20 30 110000 115000 120000 125000 248000 250000 252000 254000 Outdoor temperature [°C] kWh SEK

Outdoor temperature limit

AHU3 ‐ District cooling

Outdoor temperature limit

AHU4 ‐ District cooling

(47)

41 The total energy demand increases with increasing outdoor temperature limit until around 18° C for all the different AHUs. By around 14° C the intersection between the total energy cost and the total energy demand occur for all the AHUs.

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42

6.2.3 Supply temperature

In this section the supply temperature’s influence on the energy demand and cost for HVAC aux and district cooling is presented. The results are depicted for each air handling unit.

The energy demand and cost for HVAC aux increases with increasing supply temperature. AHU1 and AHU2 have the largest influence on the energy demand and cost, while AHU4 has the lowest.

14 19 24 84000 85500 87000 88500 98000 100000 102000 104000 Temperature [°C] kWh SEK

Supply temperature AHU1‐

HVAC aux

Energy Cost Energy demand 14 19 24 84000 86000 88000 90000 98000 100000 102000 104000 Temperature [°C] kWh SEK

Supply temperature AHU2‐

HVAC aux

Supply temperature AHU3‐

HVAC aux

Supply temperature AHU4‐

HVAC aux

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43 The energy demand and cost for district cooling mainly increases with increasing supply temperature. AHU 1 and AHU2 have a decrease in energy demand and cost from 15 °C to 17 °C, while the other AHUs have a stagnation in the same interval.

14 19 24 100000 120000 140000 160000 240000 250000 260000 270000 Temperature [°C] kWh SEK

Supply temperature AHU1‐

District Cooling

Energy Cost Energy demand 14 19 24 100000 110000 120000 130000 140000 245000 250000 255000 260000 Temperature [°C] kWh SEK

Supply temperature AHU2‐

District cooling

Supply temperature AHU3‐

District Cooling

Supply temperature AHU4‐

District cooling

(50)

44 The total energy cost and demand follows a similar trend. The lowest energy demand and cost for the different AHUs occur when the supply temperature is set to around 16-17° C.

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45

6.2.4 Specific fan power

In this section the effect of the specific fan power on the energy demand and cost is presented.

The energy demand and cost increase linear with increasing SFP. AHU1 has the largest influence on the energy demand and cost, while AHU4 has the lowest. AHU2 has the second largest influence with AHU3 following.

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46

6.2.5 U-value concrete floor

The results from altering the U-value on the concrete floor of the building model is presented in this section.

Figure 39 - Energy cost and energy demand for HVAC aux with changing U-value.

The U-value has a low impact on the energy demand and cost for HVAC aux. Increasing the U-value from 1.7 W/m2_{K to 2.5 W/m}2_{K increases the energy demand with 630 kWh and the energy cost with 730 SEK.}

Figure 40 - Energy cost and energy demand for district cooling with changing U-value.

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47

6.3 Indoor environment during 24h

This section presents the results from both building model’s indoor environment during 24h. Relative humidity, predicted percentage dissatisfied, operative temperature and the mean air temperature are the indoor environment parameters displayed. The relative humidity simulation is based on weather data from a humid day (21st_{of July 2018, see Figure 2) and the other indoor environment parameters is simulated} based on weather data from a relative warm day (27th_{of June 2018, see Figure 1).}

6.3.1 Relative humidity

The result from simulation of the relative humidity during one humid day is presented in this section.

In Kanalhuset operating night ventilation result in lower relative humidity compared to the case without night cooling. The result from Fartygstrafiken displays that the relative humidity is slightly higher in the case where night cooling is utilized. Maximum relative humidity was obtained in the basement of Fartygstrafiken but was not investigate due to lower occupancy in the basement.

6.3.2 Predicted percentage dissatisfied

The result from simulation of the PPD for both building models in one zone during 24h is displayed in this section.

Both building models displays the same result that with operating night cooling result in lower PPD. The building model from Kanalhuset displays a higher and faster spike in PPD in the morning when occupants of the building arrives in the case where no night cooling is utilized compared to the case where night cooling is operated.

Figure 41 – Relative humidity during 24h for both buildings in one zone.

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6.3.3 Operative temperature

This section displays the result of the operative temperature simulated during 24h in one zone for both building models.

The simulations from both building models displays similar results. In the cases where night cooling is utilized the operative temperature is slightly lower during the whole day, and significantly lower during early mornings and late evenings.

6.3.4 Mean air temperature

The result from simulations during 24h of mean air temperature for one zone and both building models is displayed in this section.

In the case where no night cooling is operated the mean air temperature remains constant. In both building models utilizing night ventilation decreases the mean air temperature during the morning and late evening hours but remains the same constant temperature during the day as in the case of no night cooling.

Figure 43 – Operative temperature during 24h for both building models in one zone.

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49

6.4 Validation of the building models

The result from the comparison from measured data for the district cooling demand for one week compared to the modelled results for the same period is presented in this chapter.

Figure 45 – Measured cooling demand for Kanalhuset compared to simulated results in the baseline case.

The correlation between the measured district cooling demand and the modelled results is 81 % for Kanalhuset.

Figure 46 – Measured cooling demand for Fartygstrafiken compared to simulated results in the baseline case.

Optimizing night cooling for two systems in Stockholm

Optimizing night cooling for two

systems in Stockholm

Abstract

Sammanfattning

Table of Contents

1 Introduction

1.1 Background

1.2 Scope

1.3 Objective and research questions

1.4 Limitations

2 Literature review

3 Theoretical background

3.1 Night cooling

3.2 IDA ICE

3.3 Energy simulation data

3.4 Cost of energy and environmental impact

Electricity cost

3.5 Thermal comfort

3.6 Global warming and Urban heat islands

4 Building descriptions

4.1 Kanalhuset

4.2 Fartygstrafiken

5 Method

5.1 Kanalhuset

5.2 Fartygstrafiken

5.3 Parameter study

5.4 Indoor environment during 24h

5.5 Validation of the building models

6 Results

6.1 Kanalhuset

Energy demand ‐ Kanalhuset

Energy cost ‐ Kanalhuset

Emissons from energy source ‐ Kanalhuset

6.2 Fartygstrafiken

Energy demand ‐ Fartygstrafiken

Energy cost ‐ Fartygstrafiken

Emissions from energy source ‐ Fartygstrafiken

Benfit Temperature limit

AHU1 ‐ HVAC aux

Benfit Temperature limit AHU4 ‐

HVAC aux

Benfit Temperature limit

AHU3 ‐ HVAC aux

Benfit Temperature limit AHU2

‐ HVAC aux

Benfit Temperature limit AHU1

‐ District cooling

Benfit Temperature limit AHU2 ‐

District cooling

Benfit Temperature limit AHU3

‐ District cooling

Benfit Temperature limit AHU4 ‐

District cooling

Benfit Temperature limit

AHU1 ‐ Total energy

Benfit Temperature limit

AHU2 ‐ Total energy

Benfit Temperature limit

AHU3 ‐ Total energy

Benfit Temperature limit AHU4 ‐

Total energy

Outdoor temperature limit

AHU1 ‐ HVAC aux

Outdoor temperature limit

AHU2 ‐ HVAC aux

Outdoor temperature limit

AHU3 ‐ HVAC aux

Outdoor temperature limit

AHU4 ‐ HVAC aux

Outdoor temperature limit

AHU1 ‐ District Cooling

Outdoor temperature

limit AHU2 ‐ District

cooling

Outdoor temperature limit

AHU3 ‐ District cooling

Outdoor temperature limit

AHU4 ‐ District cooling

Supply temperature AHU1‐