FACULTY OF ENGINEERING AND SUSTAINABLE DEVELOPMENT
Department of Building Engineering, Energy Systems and Sustainability Science
Petter Berner Wik
2020
Perspectives of a climate-neutral urban district
Evaluation of greenhouse gas emissions, exergy and energy
balances
Student thesis, Advanced level (Master degree, two years), 30 HE Energy Systems
Master Program in Energy Systems Supervisor: Mattias Gustafsson
Preface
This report is a master thesis in the 120-credit master program in Energy system at Gävle University. The report is a pre-study for a financially supported study about a climate-neutral urban district, through district heating and heat pump supplied buildings. The study is financed by several stakeholders like Gävle University, Gävle Energy AB, Gävle Municipality, and Future heat. The expectation is that a climate-neutral district will be
constructed to 2040 since the government and Gävle municipality have a contract with financial support to accomplish the vision of a carbon neutrality urban district during the operation phase.
Personally, I want thank my supervisor Mattias Gustafsson, who has helped me to brainstorm and discuss ideas concerning this master thesis. I also want to thank Marita Wallhagen. With her knowledge of city development as an architect, she guided me in the development of a grey box city model. Finally, a special thanks to my little sister Elin Berner Wik. That is the fantastic artist of the illustration “Petter’s sustainable mansion”.
Petter Berner Wik Gävle 2020-05-12
Abstract
A climate-neutral city can be viewed at in many different aspects. This report investigates the greenhouse gas, exergy and energy balance for both heat pumps and district heat supply at local, national and methane gas perspectives of the energy conversion processes. Through a numerical grey box model of a geographical information system based urban district. There seven different passive-, nearly zero-, and plus-energy residential buildings are implemented.
That are developed and annually simulated in the IDA ICE software. There, thermal transmittance and building geometry are the most urgent parameters that impacts the space heating demand and energy performance. They are estimated by current and proposed primary energy weight factors where the geometry shape is undefined, while the altitude impact’s the building's energy, exergy, and greenhouse gas balance. Therefore high-rise building's energy performance are poorer than low-rise buildings, simultaneously as heat pump supply enables higher altitude than district heating. Other energy savings occur through additional energy-efficient technologies, energy generating technologies and soft tools that change residents’ behavior.
The investigated urban district is placed in the Swedish city Gävle, which meets residents’ demand for approximately 6000 apartments without
additional service. It is a plus energy district for heat pump supply and passive energy for district heating supply. Although the district heated urban district electricity-saving towards heat pump corresponds to 32 percent of the urban district's total facility and household electricity utilization. The energy analysis include the perspective of the facility’s energy utilization and generation, and the perspectives of residents’ energy utilization and recovery from their waste resource production. This makes the urban district exergy productive and carbon-negative during the operating phase, regardless of emission value and heat supply technology, since the facility perspective compensates for the residents’ electricity utilization and consumption of goods. Therefore, there are no need for tree plantation as compensation of greenhouse gas pollution since the carbon negativity corresponds to between 2 to 154 hectares of forest. The study is therefore relevant for other geographical locations in Sweden depending on geographical location, heat supply technology and emission value from the primary energy conversion processes.
Key words: Carbon neutral district, energy passive district, sustainable cities, exergy positive district, passive-, nearly zero-, and plus-energy buildings.
Table of contents
1 Introduction ... 11
1.1 Background... 14
1.2 Aim ... 22
1.3 Limitations ... 22
1.4 Method ... 23
2 Theory ... 28
2.1 Passive-, nearly zero- and plus-energy houses ... 28
2.2 Heating supply perspectives ... 35
2.3 Waste resources ... 38
3 Results ... 40
3.1 Part 1 ... 40
3.1.1 EP of BES models ... 41
3.1.2 Perspectives of facility & residents model approach ... 43
3.1.3 Waste resources perspective of models ... 46
3.1.4 Accumulation of buildings energy & exergy ... 48
3.2 Part 2 ... 49
3.2.1 EP of urban districts ... 49
3.2.2 Perspectives of facility & residents urban district approach ... 50
3.2.3 Waste resource perspective of urban district approach ... 52
3.3 Part 3 ... 54
3.3.1 Energy & exergy comparison ... 54
3.3.2 Electricity saving ... 56
3.3.3 GHG emission reduction ... 56
4 Discussion ... 59
5 Conclusions ... 63
6 Future research ... 65
References ... 66
Appendix A ... 1
Appendix B ... 1
Appendix C ... 2
Appendix D ... 4
Appendix E ... 5
Appendix F ... 6
Appendix G ... 10
Appendix H ... 11
Table of figures
Figure 1. Detailed plan for district Näringen (Gävle Kommun, 2019). 11 Figure 2. The Kyoto pyramid illustrate how buildings should be designed. 12 Figure 3. The accumulated cost for buildings during its lifetime. 13 Figure 4. Example of an annual duration graph for DH and electricity generation. 16 Figure 5. The division of environmental impacts from buildings. 19 Figure 6. Visualization of the urban district’s energy flows. 19 Figure 7. Visualization of where GHG pollution and reduction occur by the energy flows. 20 Figure 8. The different electricity balance cases over the Smart meter. 25 Figure 9. Illustration of how the forecast of GHG, energy and exergy balances occur. 25 Figure 10. Parameters that affect the building energy balance (Berner Wik E. , 2020). 29 Figure 11. A schematic illustration of the DHW and space heating system. 33 Figure 12. Exergy factor of space heat demand and heat supply technology access. 36 Figure 13. DHW demanded exergy factor and accessible exergy factor. 37 Figure 14. Models EP of current and proposed PE weight factors. 41 Figure 15. The models unweighted energy utilization, generation and recovery per Atemp. 42 Figure 16. The models unweighted exergy consumption and production per Atemp. 42 Figure 17. The facility’s energy balance, for DH and HP. 43 Figure 18. The residents’ energy utilization, for DH and HP. 43 Figure 19. GHG pollution by facility and residents of different cases. 44 Figure 20. Accumulated GHG pollution by facility and residents. 45 Figure 21. Energy recovery by residents’ waste resources. 46 Figure 22. GHG pollution by waste resources caused by other sectors. 47 Figure 23. Accumulated GHG pollution of the whole chain and various emissions. 47 Figure 24. Accumulated energy balance by source, model, and heat supply technology. 48 Figure 25. Accumulated exergy balance by form, model, and heat supply technology. 48 Figure 26. Residential buildings of each district inside the urban district model Näringen. 49 Figure 27. The facility's energy balance inside district Näringen. 50 Figure 28. The residents’ energy utilization inside district Näringen. 51 Figure 29. Urban districts GHG pollution by facility and residents of different cases. 51 Figure 30. Accumulated GHG pollution by the urban district's facilities and residents. 52 Figure 31. The urban districts energy recovery by residents’ waste resources. 52 Figure 32. GHG pollution by the urban district’s waste resources. 53 Figure 33. Accumulated GHG pollution of the urban district different emission cases. 53 Figure 34. Accumulated energy recovery per district for DH and HP. 54 Figure 35. Näringen's exergy balance comparison between DH and HP. 55 Figure 36. Näringen's accumulated energy and exergy balance for DH and HP. 55 Figure 37. Näringen's accumulated GHG pollution by facilities and residents. 57
Figure 38. Näringen's accumulated GHG pollution. 57
Figure 39. The amount of hectares the GHG reduction equals in forest plantation. 58
Table of charts
Table 1. BBR’s weight factors for different PE sources (Boverket, 2018; Boverket, 2020). 15 Table 2. The modeled building’s land area, Atemp and the number of expected residents. 23 Table 3. Exergy factor for each energy form (Ertesvåg, 2005; Gong & Werner, 2017). 26 Table 4. The applied CO2 emission data (Naturvårdsverket, 2019b; Gode, et al., 2011). 26 Table 5. The number of trees per hectare for different regions in Sweden (Fridman & Wulff,
2018). 27
Table 6. The annual energy recovery of each residents in district Näringen. 39
Table 7. Different parameters that affect EP. 40
Table 8. The urban districts EP, with current and proposed PE weight factors. 49 Table 9. Näringen's electricity saving between DH and HP. 56 Table 10. Total electricity saving by a DH supplied Näringen. 56
Nomenclature
Atemp Tempered floor area (>10°C) m2
Aencl.e Inner enclosing exterior area m2
Aencl.i Inner enclosing interior area m2
Awindows Window surface m2
CO2 Carbon dioxide Kg CO2 eq
Cp Specific heat capacity of air kJ/kg, °K
d Relative operating hours -
DVUT Dimensioning of the winter outdoor temperature °C
∈tree CO2 absorption from trees Kg CO2/year
ɳ Efficiency -
e Wind protection coefficient -
E Energy kWh
E(h) Power, transient energy with hourly steps kW
EP Energy Performance kWh/m2 Atemp
E/Q Exergy factor -
f Wind protection coefficient -
Fgeo Geographical adjustment factor -
GDP Gross Domestic Product -
h Heating value kWh/Nm
Hectare Number of hectares hm2
HLN Heat Loss Number W/m2, Atemp
HT Heat loss coefficient W/K
λ Thermal conductivity W/m, °K
m Mass kg
npopulation Number of inhabitants -
PE Primary Energy -
Prodbiogas Residents production of biogas Nm
Prodow Residents organic waste production Kg/person
ρ Density m3/kg
ρ forest Forest density for different geographical locations Trees/ha
q Flowrate l/s
SF Shape factor -
τ Thermal time constant h
T Temperature °K
TOE Tonne of oil equivalent GJ
Um Envelopes overall thermal transmittance coefficient W/m2, °K
V Volume m3
WWR Windows to wall ratio %
x Thickness of layer or wall m
Glossary
Definitions
Conventional buildings EP ≤ 85 kWh/m2, Atemp.
Grey box model Apply real data with theoretical assumptions.
Nearly Zero Energy Building A passive house where Eexport equals Eimport. Passive house A building that meets specific energy and indoor
environmental classifications.
Plus energy house A passive house where Eexport > Eimport.
BBR Boverkets building regulations
BES Building Energy Simulation
CB Conventional buildings, EP ≤ 85 kWh/m2, Atemp
CCT Carbon Capture Technology
CHP Combined Heat and Power
COP Coefficient of performance
DH District heating
DHW Domestic hot water
FE Facility electricity
FEBY Forum for energy efficient building
GHG Greenhouse gas
GIS Geographical Information System
HE Household electricity
HP Heat pump
IDA ICE IDA Indoor Climate and Energy, simulation software
LCA Life Cycle Analysis
PV Photovoltaic
SVEBY Standardized and verified energy performance for buildings
1 Introduction
Buildings there you can measure lower energy utilization and sustainable targets than conventional buildings (CB) are often constructed as a statement of how buildings can be designed and built, although they only benefit the environment from a micro perspective. If you instead focus on urban districts or whole cities it would benefit the environment from a macroscopic perspective and conduct to synergy effects. Two examples are Tudela1, that is energy self-sufficient through intermittent energy generation and passive house dwellings (ECO-City, 2020) and Masdar2, that has a similar approach as Tudela with a wider perspective and with lower sustainability aims (Masdar, 2020). However, none of these climate-neutral urban districts are developed in cold climates, like Gävle3 and therefore a study will be done in an urban district in Gävle called Näringen4, Figure 1. The investigation due to financial aid from different stakeholders (Regeringskansliet, 2017). Näringen shall be rebuilt from a company area to a residential area through six urban districts, including approximately 6000 apartments with additional services (Gävle Kommun, 2019). Näringen and seven other urban districts in Sweden shall function as models, for future climate-neutral urban districts.
Figure 1. Detailed plan for district Näringen (Gävle Kommun, 2019).
1 Eco-city in Spain.
2 Eco-city in Abu Dhabi.
3 City in Sweden, where the study is performed.
Successfully passive house dwellings exist in cold climates. They meet energy and thermal comfort requirements from Forum for energy-efficient construction (FEBY) (Rohdin, Molin, & Moshfegh, 2014) where the main idea is to minimize energy utilization to benefit the environment, society, and the end-utilizers economy. Since the fundamental part are to minimize the imported energy utilization demand and maximize the free heat surplus from internal and external heat gains passive-, nearly zero-, and plus-energy buildings shall be designed from the Kyoto pyramid, Figure 2 (Andersen, et al., 2008). Then the building's imported energy supply decreases further through dynamical real-time control technologies and locally placed renewable energy sources, with or without additional energy storage. While conversion of primary energy (PE) sources to renewable energy sources, with such little environmental impact as possible, is implemented lastly to minimize the exported environmental footprint (Heiselberg, et al., 2006).
Figure 2. The Kyoto pyramid illustrate how buildings should be designed.
Due to the implementation of energy minimizing and generating technologies contribute passive-, nearly zero-, and plus-energy buildings to increasing initial costs and decreased operating costs compared to CB, Figure 3. But by analyzing the accumulated economic cost during the building’s lifetime passive, nearly zero-, and plus-energy buildings are more lucrative to build (Zalejska-Jonsson, Lind, & Hintze, 2012). Their lifetime cost is lower, with or without inflation and interest rate, since the demolition cost is excluded, as the building certification and EP does not impact the demolition cost. Additionally, certified buildings are not only sustainable in an economic approach, but also from an environmental approach compared to CB. The economic advantage contributes to incitements for urban districts with passive-, nearly zero-, and plus-energy houses because it is fundamental to ensure that
climate-neutral societies can be constructed without subsidies from the government, European Union, or stakeholder organizations.
Figure 3. The accumulated cost for buildings during its lifetime.
A key in decreasing the urban district’s environmental impact is to approve energy supply from third-party access to the electricity and district heating (DH) grid by either other energy companies or "micro-producers"5 (Frenning & Ljungberg, 2014). That increases the grid competitiveness, as more energy resources are accessible and are particularly interesting for heat supply by either DH or heat pumps (HP). This recover and supply low-temperate waste heat though 4th generation DH pipes or electrical work by HP (Paardekooper, et al., 2018).
In the Gävle region waste heat from local pulp industry or other local businesses can be supplied to decrease greenhouse gas (GHG) pollution and biofuel resource demand. With new technology his can be used in Näringen. The present district heating infrastructure in Gävle cannot recover waste heat bellow 80°C (Broberg Viklund & Johansson, 2014). This will contribute to synergy effects as the local pulp industry and energy company share the profit without consuming additional
resources. That contributes to social, economic, and ecological sustainability as less biomaterial is utilized in the boilers. Local GHG pollution inside the Gävle region and fuel expense will decrease simultaneously as more profit is gained per fuel expense. Then the reduced biomaterial can phase out unconventional fuels like coal, peat, natural gas, or non-recycled municipal waste in other cities and contribute to macro perspective synergies (Schmidt & Kallert, 2017).
1.1 Background
Fossil fuel dependency contributes to both social, environmental, and economic problems and possibilities. The unsustainable increasing wealth is based on the cost of society’s health, nature’s biodiversity, and extraction of natural resources (Alier, 2009). At current state the global economy is based on 81 percentages of fossil fuels that contribute to GHG emissions and its side effects as global warming, increasing sea level, wars, immigration streams, etcetera (IEA, 2019). Buildings utilize 22 percentage of the global energy conversion and emit most GHG as a sector
(IEA,2018; EIA 2011). Due to the buildings and residents’ environmental impact it is necessary to construct buildings with lower energy utilization like passive-, nearly zero-, and plus-energy houses. But also, with other environmental assessments, as materials, transport and, energy conversion methods (Goymann, Wittenwiler, &
Hellweg, 2008). There are different environmental assessment tools that focus on Leadership6 in Energy and Environmental Design (LEED) and BRE7 Environmental Assessment Method (Wallhagen, 2016). Boverket’s building regulations (BBR) and forum for energy-efficient building (FEBY) only focus on resource demand to keep buildings operating that meets the United Nations sustainable development goal 7 (sustainable energy for everybody), goal 11 (sustainable cities and society), goal 12 (sustainable consumption and production), and goal 13 (fight climate change ) (UN, 2019a). Then a deeper perspective of exergy, GHG pollution, and geographical possibilities are excluded at the same time as unequal PE weight factors between heat pumps (HP) and DH are applied, Table 1. Therefore, HP have an energy utilization advantage towards DH in urban areas although DH may be more
environmentally friendly, like in Gävle (Gustafsson, Thygesen, Karlsson, & Ödlund, 2017). Their DH grid conducts to more sustainable energy and less GHG pollution per MWh, than HP implication Table 4. Simultaneously the air quality increases and less virgin resource extraction is needed. The PE weight factors do not consider the parameters:
• Population
• Gross domestic product
• Energy intensity
• Energy conversion carbon dependency
That is related to GHG pollution and known as the KAYA identity, Equation 1 (Gudipudi, et al., 2019).
𝐶𝑂2 = 𝐶𝑂2
𝑇𝑂𝐸∙𝑇𝑂𝐸
𝐺𝐷𝑃∙ 𝐺𝐷𝑃
𝑛𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛∙ 𝑛𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 Equation 1
6 LEED, American environmental assessment tool.
7 BREEAM, British environmental assessment tool.
Where:
Sweden has shown that is it possible to have an increasing population and gross domestic product growth while the GHG pollution decrease (Naturvårdsverket, 2019a; SCB, 2016). The energy conversion’s carbon dependency has decreased through energy efficiency and phasing out fossil PE sources towards renewable and low carbon dependent PE sources. A great potential still exists according to the heat road map Europe that can be partly utilized by implementation of more energy efficient DH grids in Swedish societies (Paardekooper, et al., 2018). Additionally, the PE weight factor does not consider the carbon dependency, exergy and geographical possibilities, but as Table 1 indicates, newly proposed PE weight factors might be implemented to decrease the gap between HP and DH (Boverket, 2018; Boverket, 2020). Although DH utilizes more energy than HP, there exists an exergy factor difference between low temperate heat and electricity that must be considered in the evaluation of supply sources (Gong & Werner, 2017).
Table 1. BBR’s weight factors for different PE sources (Boverket, 2018; Boverket, 2020).
The energy conversion’s GHG pollution for DH and electricity generation depends on the applied PE source and fuel that differs through the season of the year and time of the day, as the energy demand fluctuates with weather fluctuations,
industries, and circadian rhythm (Hagberg, et al., 2017). This is specifically obvious in DH energy conversion there the amount of carbon dependent fuel differs with the season Figure 4. During the summer it is lower in cold climates and higher in hot climates, simultaneously as some energy conversion processes can be unable to implement from a water-energy nexus perspective due to lack of water or too warm cooling water sources, that becomes a more crucial problem because of the increase of global warming (IEA, 2016).
CO2 Carbon dioxide [kg CO2 eq]
GDP Gross Domestic Product [-]
TOE Tonne of oil equivalent [GJ]
npopulation Number of inhabitants [-]
Non-weight Current weight Proposed weight
Electricity 1 1.6 1.8
DH 1 1 0.7
Biogas 1 1 1.1
Figure 4. Example of an annual duration graph for DH and electricity generation.
Due to energy conversion processes, international grid connections, fuel costs, subsidies, and emission certificates inside the European Union the competition inside the European energy market increase. This promotes low carbon dependent renewable and waste energy recovery primary, since the energy conversion price is lower than fossil and nuclear dependent energy conversion processes. Although the international high voltage cables enable importation of coal-based electricity from Great Britain, Netherlands, Germany, Russia, Poland, and the Baltic states to the Nordic region both Sweden and Norway are net energy exporters and have a less carbon dependent electricity generation than continental Europe Appendix B (ENTSO-E, 2019). The Nordic electrical mix increases the competition towards carbon dependent PE sources and reduce GHG emissions from electricity generation outside the Nordic region (Nord pool, 2019; Capgemini, 2019).
Simultaneously the electricity supply security and stability increase. The transition towards cleaner PE sources in the electricity mix does not impact the combustion of biogas and “dry” non-recycled municipal waste notably, as the biogas is utilized as a propellant in the transport sector with less CO2 eq than other fossil-based
propellants (Naturvårdsverket, 2019b). The “dry” non-recycled waste is utilized for energy recovery in the energy sector that reduces land fields but mostly EU obey emission trade certificates or implement CCT and CCS in the CHP plant (Avfall Sverige, 2018; European Commission, 2015).
The European energy market incitements impact is specifically obvious in Gävle, there the local DH grid only emit 4.3 kg CO2 eq/MWh of heat due to waste heat from a local pulp industry in combination with 100 percent of renewable biofuels (Gävle Energi, 2018). Biofuels have a low environmental impact if forest are replanted. The Swedish DH average emits 69 kg CO2 eq/MWh of heat and waste heat assumes to emit 0 kg CO2 eq/MWh of heat (Naturvårdsverket, 2019b).
Although the DH pumps contribute to a small amount of GHG pollution (Hagberg, et al., 2017). Simultaneously the industrial processes contribute to GHG emissions when a product for demand is produced. Then the waste heat is categorized as a carbon-neutral waste heat resource because the product for demand should be produced anyway, either at the current location or another one (Paardekooper, et al., 2018; Naturvårdsverket, 2019b).
The electricity supply to the residential building proceeds on PE source obligations for electricity utilization in the form of wind, hydro, solar, bio, nuclear, or different unconventional fuels (Nord pool, 2019). Their shares are a part of the Nordic electricity mix that contributes to 125 kg CO2 eq/MWh, while the obligations show 0 kg CO2/MWh as it only includes the PE sources that the obligations include (Naturvårdsverket, 2019b). Furthermore, marginal electricity assumes to become the highest GHG pollution source inside the Nordic electricity grid as the electricity conversion’s coal and oil dependency are phased out in favor of Methane gas
(Hagberg, et al., 2017).
As aforementioned the municipal and residential sector utilize 22 percentage of the world’s energy conversion, simultaneously as approximately 55 percent of the present world’s population lives inside urban areas (IEA, 2018; UN, 2019b). That shows the way for grid technologies that require high customer density to be economically lucrative (Paardekooper, et al., 2018). These energy grids decrease the building’s electricity demand in urban areas and contribute to a decreased risk of electricity grid capacity shortage without new infrastructure investments (Wang, et al., 2019). The competition inside electrical grids increase, especially in
international connected grids like Europe because of the annual net surplus of Nordic electricity mix valued electricity from Scandinavia to continental Europe (Hagberg, et al., 2017; Capgemini, 2019). So, in one perspective Sweden and Norway dump cheaper and environmentally friendlier electricity on the European market too increase the competition towards carbon dependent energy conversion industries in continental Europe.
Is it smarter to supply low exergy demanding purposes, like space heating with low exergy factor energy form as DH (Averfalk & Werner, 2020). High exergy
demanding purposes can access high exergy factor forms, like electricity supply to energy-demanding industries, servers and infrastructure, which are the main components in keeping the economy and society going. The electricity demand is forecasted to increase with megatrends like electrification, Internet of things,
urbanization and decentralization of national and international energy systems (Fulli, et al., 2019). Currently Internet communication technologies and water pumping each utilize 10 percent of the global electricity conversion (Malmodin & Lundén, 2018; IEA, 2016). Along with these megatrends the electricity demand and gap between key actors and insignificant users may increase, as electricity price increase during high energy demand. That impact the insignificant users. The high utilization industries obtain energy tax reductions since electricity generally it is one of the energy-intensive industries' greatest expenses (Trinomics, 2017). Consequently, energy price might contribute to uncompetitive business and therefore many energy-intensive industries invest in energy efficiency and energy conversion technologies to become more robust towards energy price fluctuations. (Haller Pedersen, Beck Nellemann, & Feveile Adolfsen, 2014).
Through fractionalizing of the building's environmental impact in the perspectives of resident and facility it is clear that resident has the highest environmental footprint, Figure 5. As their consumption and energy utilization is the most important post that must be addressed and the facility's operation impact is therefore secondary to the residents’ behavior. It is easier to address the facility perspective environmental impact than the resident perspective as the facility operates almost in the same manner annually over the years while residents’ material consumption and energy utilization pattern are harder to estimate (Yan, et al., 2015). This opens for implementation of other tools like nudging8, or administrative or economical tools that impact the residents’ behaviors (Thaler & Sunstein, 2008; Pihl, 2014).
8 A physiological tool that impacts the residents’ behaviour.
Figure 5. The division of environmental impacts from buildings.
The energy flow of urban districts proceed from the energy utilization, energy generation, and energy recovery, Figure 6. The flow are divided into:
• Facility perspective
• Household perspective
• Waste resource perspective
Normally only perspectives of the facility and household energy flows should be estimated into energy analysis in buildings and urban districts. This depend on whether fluctuations and residents’ behavior, where residents waste generation as a by-product of materialistic consumption, should be considered. (Boverket, 2020).
That are valued as waste resources, which are ennobled to different energy recovery forms and utilized by actors in other sectors.
Figure 6. Visualization of the urban district’s energy flows.
Additionally, the urban district's energy flows contribute to GHG pollution, Figure 7. That originates from electricity’s PE conversion sources, biogas production and combustion, 46 kg CO2 eq/MWh, and incinerators energy conversion of both renewable biofuels and “dry” non-recycled waste, 144 kg CO2 eq/MWh (Naturvårdsverket, 2019b).
All GHG pollution occurs outside the boundary of Näringen system, as the electricity and DH are distributed from the energy conversion process location to Näringen. The biogas GHG pollution occur at the production facility, through anaerobic retting, and in the transport sector through combustion to mobility (Afran, 2019).
The electricity and DH GHG pollution depend on the emission value. All new building’s electricity utilization shall be classified as marginal electricity, to ensure that the building’s GHG pollution does not exceed the forecast value. Similarly, electricity saving and exported electricity shall be classified as marginal electricity since it assumes to minimize marginal electricity that equals Methane gas at another location (Gode, et al., 2014).
Figure 7. Visualization of where GHG pollution and reduction occur by the energy flows.
However, electricity-savings and export from PV generated electricity might not compensate for the GHG pollution of the whole Näringen's energy utilization and recovery. Therefore, carbon capture technology (CCT) with additional carbon capture storage (CCS), and tree plantation can be implemented, to eliminate and absorb GHG pollution. The CCT could probably become mandatory in a close future in incinerators, to eliminate GHG pollution from the energy conversion processes. Then CCS has the potential to become a new economic sector in the gas and oil industry as GHG must be pumped back and storage into the ground (Koelbl, et al., 2015). Tree plantation compensate for deforestation and GHG emission due to the utilization of organic based fuels in CHP plants (Andersson, 2015). Beyond this tree plantation fight climate change and heat islands, with an increased albedo value.
1.2 Aim
To investigate if it is possible to design residential passive-, nearly zero-, or plus energy houses with additional 4th generation DH or HP and PV systems. To ensure that Näringen with approximately 6000 apartments can be rebuilt as a carbon- neutral and energy or exergy passive district during the operating phase.
The relation between the buildings EP, shape factor (SF), and thermal time constant will be investigated for different azimuth. It will clarify if the parameters limit the technical demands of the building models to ensure passive-, nearly zero-, or plus- energy buildings.
Through this procedure following questions will be answered:
• Is it possible to construct passive-, nearly zero-, or plus-energy buildings for residential buildings with DH or HP in a Nordic climate as Gävle?
• Can a climate-neutral urban district be designed for either DH or HP?
• How will the different sub-systems impact the climate? There are facility perspective, residential perspective, and energy resource perspective.
• DH and HP by comparison, which is most climate-neutral?
1.3 Limitations
Life cycle analysis (LCA) perspective and economic investment cost are not included in the study and therefore the buildings’ operating impact will be evaluated only from an environmental, energy, and exergy perspective. Water-energy nexus and supply chains from apply technologies are excluded, like silicon in PV systems and coolant fluids in heat pumps. Also, the residents’ traffic impact within district Näringen is neglected, as it is estimated to be finished in 2040. Then utilization of unconventional propellant and approximately zero parking lots can be a reality.
Due to decision-making, uncertainties and lack of time to complete a master thesis another delimitation of the project is neglecting of additional services, that come along with residential facilities and current companies that may stay inside the district. Although principal standards between residential and municipal buildings are similar, municipal buildings demand higher ventilation rate, thermal satisfaction, and air quality with fewer operating hours than residential buildings, and therefore the difference between residential and municipal buildings are diminutive.
The investigation applies Gävle’s historical weather data and therefore is global warming not considered as a parameter that impacts the building’s energy utilization and thermal comfort of the coming years. Land rise through organic material and historical landfill, as well as dumped municipal and industrial waste, make soft and contaminated soil practical problems in Näringen. Therefore Gävle municipality wants to exploit as little surface as possible of Näringen.
1.4 Method
Seven different grey box models of newly developed residential buildings in Gävle were created, and building energy simulations (BES) were performed with Gävle’s climate data, by the software IDA ICE, (EQUA, 2020). The model's information can be viewed in Table 2 and Appendix A. It is based on a literature review and standardized standards from FEBY and standardized and verified energy
performance for buildings (SVEBY). FEBY’s and SVEBY’s input data are empirically verified and validated by a numerous number of buildings the standard has been developed on. Errors that the standards have been developed on are implemented in the BES. The models must have a marginal of at least 90 percent of maximum accepted energy utilization to ensure weather fluctuation, building errors and the users’ unsustainable behavior, like long showers and airing while external heat surplus is supplied through the heat ventilation air conditioning system (Yan, et al., 2015). These fluctuations and standard errors can impact up to 10 percent of energy utilization annually (Boverket, 2020).
Table 2. The modeled building’s land area, Atemp and the number of expected residents.
The IDA ICE models input data are controlled through time channels that control the energy utilization after the residents’ circadian rhythm and weekday, Appendix C. Additionally, PV systems without additional electrical storage are included in all BES models that are assembled at the BES model’s geometries with a 30-degree skillion and 370W10 peak monocrystalline modules, Appendix D. The PV system capacity and design were developed and decide by identically BES models in the Solar11 Edge design software, Appendix C (Solar Edge, 2020). The solar edge developed PV systems annual hour-based electricity generation was estimated and standard correlated by the software WINSUN12. That is an anisotropic sky model with annual hourly measured temperature, diffuse and direct irradiation for a
location (Hay, 1993). In this study that is Gävle, there shading and albedo influenced by the surrounding environment are neglected.
9 According to SVEBY standard and the grey box BES’s floor area (SVEBY, 2012).
10 Corresponds to 218 Wpeack/m2, Appendix D.
11 Software for PV capacity planning and design.
Building Land area [m2] Atemp [m2] Number of residents9
1 200 400 12
2 268 2144 63
3 284 1500 48.5
4 315 945 26
5 338 5070 180
6 356.5 1700 60
7 1070 3925 102.5
To estimate the electricity saving by self-utilization and solar electricity export the PV system must the generated electricity be transient subtracted and sorted hourly, Equation 2.
𝐸𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑏𝑎𝑙𝑎𝑛𝑐𝑒(ℎ) = 𝐸𝐹𝐸 & 𝐻𝐸 𝑑𝑒𝑚𝑎𝑛𝑑(ℎ) − 𝐸𝑃𝑉(ℎ) Equation 2 Where:
EElectricity balance (h) Electricity power balance per hour [kW]
EFE & HE demand (h) FE and HE power demand per hour [kW]
EPV (h) Power generation per hour from PV system [kW]
The hour sampled electricity saving is summed up annually to a self-utilization factor and implemented towards the total annual electricity demand, Equation 3.
𝐸𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑖𝑚𝑝𝑜𝑟𝑡 = ∑ 𝐸𝐹𝐸 & 𝐻𝐸 𝑑𝑒𝑚𝑎𝑛𝑑 − ∑ 𝐸𝑃𝑉 ∙ ɳ𝑠𝑒𝑙𝑓 𝑢𝑡𝑖𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 Equation 3 The self-utilization factor is also implemented in the estimation of the annual
electricity export to the electricity grid, Equation 4.
𝐸𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑒𝑥𝑝𝑜𝑟𝑡 = ∑ 𝐸𝑃𝑉∙ (1 − ɳ𝑠𝑒𝑙𝑓−𝑢𝑡𝑖𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛) Equation 4 Where:
EElectricity import Imported electricity generation [kWh]
EElectricity export Exported electricity generation [kWh]
EPV Energy generation from PV system [kWh]
ɳself-utilization Fraction of self-utilization [-]
Because of hourly-based time channels of the model’s electricity demand and electricity generation by the PV system, it is possible to make an estimation of electricity savings and exports which is balanced over the Smart meter13, where three different cases occur, Figure 8. These cases are:
• 100 percentage of electricity import and 0 percent solar electricity supply.
• 100 percentage of self-utilization through solar electricity supply with solar electricity supply back to the electricity grid.
• Partly electricity import and partly self-utilization from the solar electricity supply.
13 Electricity meter that import & export electricity to the grid. With additional communication technology.
Figure 8. The different electricity balance cases over the Smart meter.
The BES’s are performed for different azimuths, namely -90 to 90 degrees with a 15-degree difference. That ensure that the numerical grey box geographical information system (GIS) developed city model, Figure 9, consider Näringen’s inhabitant demand of each subdistrict and energy utilization and generation fluctuations for different orientations, which impact the EP, Appendix F.
Figure 9. Illustration of how the forecast of GHG, energy and exergy balances occur.
The numerical grey box model provides necessary data to evaluate the urban
district’s energy, exergy, and GHG balance for buildings’ and residents’ perspective with either DH or HP supply. This enables different environmental analyses with and without PE weight factors, for local and national scenarios to clarify the advantages and disadvantages of DH and HP in different geographical areas.
The exergy analysis are based on the energy balances for different cases and the exergy factor (E/Q), Table 3. Because of the irreversibility within energy distribution and utilization processes can be hard to estimate (Gong & Werner, 2015).
Table 3. Exergy factor for each energy form (Ertesvåg, 2005; Gong & Werner, 2017).
Due to the limitation of accessible emission data GHG pollution is estimated
through annual average emission values instead of applying dynamic emission values, which change with the seasons and hours of energy demand. Table 4 presents the emission rate from energy conversion processes for Gävle's and Sweden’s DH supply, and electricity supply from the Nordic electricity mix and the marginal electricity.
Table 4. The applied CO2 emission data (Naturvårdsverket, 2019b; Gode, et al., 2011).
CCT and CCS implementation are not included in the grey box urban district model but tree plantation is estimated to realize the urban district’s GHG pollution
Equation 5, in relation to where carbon neutrality occurs.
𝐻𝑒𝑐𝑡𝑎𝑟𝑒𝑠 = ∑ 𝐶𝑂2
𝜌𝑓𝑜𝑟𝑒𝑠𝑡∙𝜖𝑡𝑟𝑒𝑒 Equation 5
14 Swedish marginal electricity’s emission value (Gode, et al., 2011).
15 End utilization GHG pollution, including production chain.
16 Assumed to be applied with the Swedish average CHP energy efficiency 0.92 (Gong & Werner, 2017).
Electricity 4th generation DH Biogas Irradiation Vehicle mobility
E/Q 1.00 0.09 1.04 0.93 1.05
Gävle Sweden Nordic
electricity mix Marginal electricity DH
[kg CO2 eq/MWh] 4.3 69 - -
Electricity
[kg CO2 eq/MWh] - - 125 42514
Biogas15
kg CO2 eq/MWh] 46 46 46 46
Municipal waste16
[kg CO2 eq/MWh] 144 144 144 144
Where:
Hectares Number of hectares [hm2]
ρforest Forest density [tree/hm2]
εtree CO2 absorption from trees [kg CO2 eq]
A single tree absorbs approximately 22kg CO2 eq annually (European Environment Agency, 2012). That is the net absorption, as trees inhale CO2 during spring and exhale CO2 during autumn. The forest tree density per hectare, Table 5, differs within Sweden due to that the Swedish forest consists of both conifer and deciduous trees. (Fridman & Wulff, 2018).
Table 5. The number of trees per hectare for different regions in Sweden (Fridman & Wulff, 2018).
Gävle is located in south Norrland19 where the tree density per hectare is the highest in Sweden. But in this investigation all locations in Table 5 will be included since different geographical perspectives will be investigated for different cases and climate-neutral urban districts could be implemented in other Swedish regions.
17 Geographical area in south Sweden.
18 Geographical are in middle Sweden.
Götaland17 Svealand18 South Norrland North Norrland Sweden
𝝆𝒇𝒐𝒓𝒆𝒔𝒕 3 163 3 034 3 441 3 267 3 235
2 Theory
A sustainable city interacts with the ecological system on a micro and macro level where light, sounds, materials, albedo, water and air pollution impact the
surrounding environment and biodiversity notably (Oikarinen, 2012). Therefore, several parameters must be addressed to design a truly sustainable city. This study only address energy utilization and GHG pollution from municipal buildings’
operating phase, while other sustainable parameters are excluded from the study.
2.1 Passive-, nearly zero- and plus-energy houses
Passive-, nearly zero-, and plus-energy houses are key components in constructing truly sustainable cities since they utilize at least 70 percent less energy compared to CB (Oikarinen, 2012). They also gets tougher energy demands, as the energy utilization for newly constructed CB shall not exceed an EP of 8520 kWh/m2, Atemp. There the EP is decided through the Swedish BBR, Equation 621 (Boverket, 2018).
𝐸𝑃 =∑(
𝐸𝐻𝑒𝑎𝑡𝑖𝑛𝑔
𝐹𝐺𝑒𝑜 +𝐸𝐶𝑜𝑜𝑙𝑖𝑛𝑔+𝐸𝐷𝐻𝑊+𝐸𝐹𝐸−𝐸𝑃𝑉∙ɳ𝑠𝑒𝑙𝑓−𝑢𝑡𝑖𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛)∙𝑃𝐸
𝐴𝑡𝑒𝑚𝑝 Equation 6
Where:
Atemp Tempered floor area (>10°C) [°C]
EDWH Energy for domestic hot water [kWh]
ECooling Energy for cooling [kWh]
EFE Energy for facility electricity [kWh]
EHeating Energy for space heating [kWh]
EP Energy performance [kWh/m2 Atemp]
Fgeo Geographical correction factor [-]
PE Primary energy per energy source [kWhPE/kWhsource] The EP is adjusted after the climate zone the building is located at, and includes all energy utilization processes that keep the building operating, as space heating, DHW and facility electricity (FE) (FEBY, 2018). The space heating is dependent on the building’s energy balance, Figure 10.
20 Valid for current PE weight factor. The proposed PE weight factor cannot exceed 75 kWh/m2 annually.
21 Valid for HP, if DH or district cooling is applied must each energy source be multiplied to its PE weight factor.
Figure 10. Parameters that affect the building energy balance (Berner Wik E. , 2020).
Furthermore, nearly zero-, and plus energy-houses are an extension of passive houses where renewable energy generation technologies are implemented in the facility to compensate for imported energy utilization. Nearly zero-energy buildings achieve almost a net-zero energy balance between imported and exported energy (European Union, 2010). If a building achieves a surplus energy balance it is classified as a plus energy building instead. Passive houses are defined by multiple parameters like heat loss number (HLN), solar heat surplus, audio levels,
infiltration, moisture, energy-efficient installations of the facility and household appliances, transparent reporting of annual energy utilization and a facility protocol22 (FEBY, 2018).
The main purpose of passive houses is the minimizing of external heating or cooling demands. Therefore, according to BBR, the envelope’s overall thermal
transmittance cannot exceed 0.4 W/m2, K (Boverket, 2020). Experience from passive houses points out that the envelope’s overall thermal transmittance shall not exceed 0.15 W/m2, K (Oikarinen, 2012). To evaluate the envelope’s quality and passive house standard the HLN is applied there HLN values below 14 W/m2, Atemp
is valued as gold standard, 19 W/m2, Atemp silver standard, and 22 W/m2, Atemp
bronze standard, Equation 7 (FEBY, 2018).
𝐻𝐿𝑁 = 𝐻𝑇∙(21−𝐷𝑉𝑈𝑇)
𝐴𝑡𝑒𝑚𝑝 Equation 7
22 A protocol that records all relevant information about the facility. Together with different executed
Where:
DVUT23 Dimensioning of the winter outdoor temperature [°C]
HLN Heat loss number [W/m2 Atemp]
HT Heat loss coefficient [W/°K]
The HLN depends mainly on the building’s heat loss coefficient, Equation 8. That depends on the envelopes’ transmission, ventilation, and infiltration losses.
𝐻𝑇 = 𝑈𝑚∙ 𝐴𝑒𝑛𝑐𝑙.𝑖 + 𝜌 ∙ 𝐶𝑝∙ 𝑞𝑙𝑒𝑎𝑘+ 𝜌 ∙ 𝐶𝑝 ∙ 𝑑 ∙ 𝑞𝑣𝑒𝑛𝑡∙ (1 − ɳ𝑣) Equation 8 Where:
Aencl.i Envelopes enclosing interior area [m2]
Cp Specific heat capacity of air [kJ/kg, °K]
d Relative operating hours [-]
ρ Density of air [kg/m3]
qleak Infiltration leakage from enclosing envelope [l/s, m2]
qvent Ventilation flow [l/s]
Um Envelopes overall thermal transmittance [W/m2, °K]
ɳv Heat exchanger efficiency (ventilation) [-]
The infiltration loss, Equation 9 cannot exceed 0.6 1/h24 at 50Pa under pressure (FEBY, 2018; Oikarinen, 2012). But research proves that infiltration losses can be minimized to below 0.3 1/h what is applied in the developed BES models (Bankvall, 2013; Sikander, 2010). The infiltration losses are dependent on the wind profile factors e and f, which are determined by the surrounding environment and in this current study the urban area is applied, Appendix C. The ventilation rate is set to 0.35 l/s, m2 for residential buildings (FEBY, 2018).
𝑞𝑙𝑒𝑎𝑘 = 𝑞50∙𝐴𝑒𝑛𝑐𝑙.𝑒∙𝑒
1+𝑓𝑒∙(𝑞𝑠𝑢𝑝−𝑞𝑒𝑥 𝑞50∙𝐴𝑒𝑛𝑐𝑙.𝑒)
2 Equation 9
Where:
Aencl.e Envelopes enclosing exterior area [m2]
f Wind protection coefficient, Appendix E [-]
e Wind protection coefficient, Appendix E [-]
qsup Supply air [l/s]
qex Exhaust air [l/s]
q50 Infiltration leakage from enclosing exterior
envelope at 50Pa under pressure [l/s, m2]
23 The lowest temperature the building is projected for and must sustain for at least 72 hours.
24 Number of indoor volume air changes per hour.
A cost-effective application to minimize external heating and cooling demand is the adoption of passive solar design to gain thermal heat radiation through windows, Trombe walls, or thermal sluices during wintertime and eliminate the external cooling load through shading during summertime (Candanedo & Athienitis, 2009).
However, multiple variables must be considered in passive solar house design, like orientation, window to wall ratio (WWR), and SF. A southwest to southeast
orientation can minimize space heating utilization with approximately 1 to 5 percent mainly due to insulation and WWR because it determines the amount of direct irradiation radiation that can enter the building's indoor environment, Equation 10 (Aksoy & Inalli, 2006). The SF, Equation 11, increases or decreases the space heating utilization within 1 to 7 percentage (Cutland Consulting Ltd & Eco Design Consultants Ltd, 2016; Aksoy & Inalli, 2006). Space heat utilization decreases when SF’s value decrease and vice versa.
𝑊𝑊𝑅 =∑ 𝐴𝑤𝑖𝑛𝑑𝑜𝑤𝑠
𝐴𝑒𝑛𝑐𝑙.𝑒 Equation 10
𝑆𝐹 =𝐴𝑒𝑛𝑐𝑙.𝑒
𝐴𝑡𝑒𝑚𝑝 Equation 11
Where:
Awindows Window surface [m2]
SF Shape factor [-]
WWR Window to wall ratio [-]
The transmission operates through the wall in both directions. Due to the size of the surface area that is exposed to direct solar irradiation, and depth of the concrete slab, the building’s envelope can operate as sensible heat storage, there the
temperature difference and thermal mass impact the amount of thermal energy that can be stored in the envelope. The thermal inertia can be expressed as a thermal time constant, Equation 12, that estimates how many hours it takes before the building has lost 63 percent of its stored sensible thermal energy as the envelope’s thermal mass delays indoor temperature fluctuations. Heavier buildings store more thermal energy and contribute to less indoor temperature fluctuations. (Johra, Heiselberg, & Le Dréau, 2019)
𝜏 = 𝜌∙𝑉∙𝐶𝑝
∑λ
𝑥 ∙𝐴𝑒𝑛𝑐𝑙𝑜𝑠𝑖𝑛𝑔 Equation 12
Where:
λ Thermal conductivity [W/m, °K]
τ Thermal time constant [h]
V Volume [m3]
x Thickness of layer or wall [m]
Although heavier buildings possess a more stable indoor temperature more thermal energy is utilized to heat the indoor environment. That can contribute to both higher and lower space heating utilization then free heat surplus continuously is supplied in combination with a dynamic indoor temperature, even if it manages to be the most economical solution to utilize external heating (Berner Wik P. , 2018).
But since BBR and FEBY do not certificate after economical energy utilization trade pattern it can impact the validation of EP and HLN negative.
The adoption of passive solar design for roofs is a key component in passive heating and cooling. By implementing an oblique roof that enables direct irradiation flows through the building’s windows when the sun stands bellow equinox, and shades the windows and eliminates the direct irradiation when the sun stands above equinox, Figure 10. So, the building becomes naturally heated and cooled with the seasons, as diffuse irradiation does not impact the building’s heat balance (Hachem, Athienitis,
& Fazio, 2011; Candanedo & Athienitis, 2009).
Additionally, thermal indoor climate must be acceptable to avoid the sick building syndrome that impacts the residents’ health, productivity, and wellbeing (Arif, et al.,2016). Therefore, FEBY requires that passive-, nearly zero-, and plus-energy buildings’ thermal climate must be acceptable and demands that the indoor temperature not exceed 26°C for more than 10 percentages of the operating time from April to September (FEBY, 2018). Therefore, the set point temperature for the seven model’s indoor environment in this study is 21°C in all apartments and 19°C in the stairways and open spaces.
The utilization of domestic hot water (DHW) is moderately constant at 25kWh/m2, Atemp annually (SVEBY, 2012). Fresh cold water heats up from 6°C to 55°C
(Persson, 2015; IEA DHC CHP, 2017). That causes problems for building with low exergy supplied energy forms, like DH, because the DHW count for approximately 30 percent of the total EP usages that the building is allowed to use (FEBY, 2018;
Boverket, 2018). Therefore, additional energy-efficient technologies must be applied at the DHW post to dump overproduced electricity to DHW generation or drain water heat recovery systems. But the exergy destruction from a high exergy energy form is a waste of resources as it conducts to better system utility for high exergy demanded processes. Greywater25 at 20 to 60°C is seen as a waste resource that can decrease the DHW utilization with 30 percent annually (Bertrand,
Aggoune, & Maréchal, 2017; FEBY, 2018). The greywater temperature depends on the supply source, like showering, washing, cooking etcetera. The annual average greywater temperature is approximately 30°C (Niewitecka, 2018). So, a drain water heat recovery system should be implemented before the freshwater enters the DH heat exchanger, Figure 11. Then another DH heat exchanger supplies heat to the temperate surface area without any preheating from the drain water heat recovery system since the inlet temperature usually is too high in relation to the greywater.
Figure 11. A schematic illustration of the DHW and space heating system.
To avoid legionella in the DHW supply a separated heat exchanger must be installed at each apartment (Schmidt & Kallert, 2017). That contributes to neglected thermal losses for DHW, as each exchanger is placed close to the users. There is also a health risk with drain water heat recovery systems. If cracks occur bacteria and dirt can enter the DHW’s freshwater supply (Niewitecka, 2018). That contaminates the DHW as the temperature might be too low to eliminate all bacteria. That contributes to health issues for exposed residents, who consume it.
The building’s electricity utilization is divided into two sections, FE and household electricity (HE). The FE includes all pumps, fans, additional control systems
etcetera, and is assumed to correspond to 10kWh/m2, Atemp annually (FEBY, 2009).
HE includes all electricity utilized inside the apartment and corresponds to
20kWh/m2, Atemp annually (Westin, 2019). Both FE’s and HE’s equipment have an energy factor of 0.7 and contribute to 21 kWh/m2, Atemp internal waste heat annually (SVEBY, 2012). But since FE and HE are operating in a stochastic matter with different quantities, an accurate specific pattern cannot be generalized for all buildings. That is obvious for apartments where HE fluctuates a lot, although similar sized and orientated apartments are compared to each other (Yan, et al., 2015).
To minimize the imported electricity different control technologies, nudging, and PV-systems can be implemented. However, BES models’ apply annual energy utilization assumptions. Therefore control technologies and time channel control after the residents’ circadian rhythm that conduct to electricity utilization savings cannot be applied in the virtual buildings, which it does in physical buildings.
Nudging has energy-saving potential for both environmental and economic reasons but cannot be implemented in the building’s design phase as every operator will adopt nudging differently (Thaler & Sunstein, 2008). Additionally, the PV-system contributes to electricity savings during the operating phase since the electricity generation primarily supplies the FE and HE's self-utilization. The overproduction is exported back to the electricity grid and utilized by buildings in the local district which minimizes the society's GHG pollution and electricity demand. Furthermore, every building's self-utilization fraction is individual due to the building's heat supply technology and electricity utilization profile. This makes electricity storage lucrative for some buildings where the optimal investment cost and battery size fluctuates (Lorenzi & Silva, 2016). That makes it impossible to generalize the electricity-saving potential by PV systems, with or without batteries, regardless of the aim is to supply all FE through PV generated electricity, since it becomes easier to accomplish the passive house, nearly zero-, and plus-energy building’s EP requirement (FEBY, 2018).
2.2 Heating supply perspectives
A joint problem with BBR and FEBY are that they only measure the energy utilization towards the Atemp. Therefore HP is more energy efficient to apply for space heating and DHW supply than DH, since HP’s Coefficient of Performance (COP) value is estimated to equal 4 and DH’s to 1 annually (Boverket, 2018; FEBY, 2018). HP’s COP is usually overrated as the COP value commonly is below 326 annually for residential buildings (Boverket, 2020). COP fluctuates during the season and is usually valued from its maximal COP value, which only occurs partly over the year, and therefore are seasonal COP values more transparent to
implement into energy analysis. However, more parameters than energy utilization for space heating and DHW supply from DH and HP are of interest, which BBR should consider in the valuation of the PE weight factors.
HP’s most energy-efficient mediums usually contain chlorofluorocarbon chains in combination with high global warming potential gases. That exceeds the global warming and break down the ozone layer if emission leakage occurs (Botticella &
Viscito, 2015; Heikkilä, 2008). DH’s GHG emissions on the other hand are strongly related to the combusted fuel, were flue gas condensation or CCT, together with CCS can be applied to minimize GHG pollution (Gode, et al., 2015; IVA, 2020).
It is more exergy efficient to apply low exergy energy forms, like low temperate water for low exergy demanding processes, as space heating. So, a more accessible capacity of high exergy energy forms, like electricity and petroleum, can be utilized for high exergy demanding processes in the industry and transportation sectors. The minimum exergy factor needed to accomplish a thermodynamically process is explained by the Carnot efficiency, Equation 13 (Gong & Werner, 2015). There the reference temperature is put in relation to heat or a cold source, depending on the thermodynamically process purpose.
ɳ𝑐𝑎𝑟𝑛𝑜𝑡 = 1 −𝑇𝑐𝑜𝑙𝑑
𝑇ℎ𝑜𝑡 Equation 13
Where:
Tcold Cold source temperature [°K]
Thot Hot source temperature [°K]
ɳCarnot Carnot efficiency [-]
Demanded exergy factor for buildings’ space heating and cooling and heat supply technologies available exergy factor in relation to the outdoor temperature is explained by a modified Carnot efficiency, Equation 14. T0 is the fluctuating outdoor temperature and T the indoor temperature, with a constant setpoint temperature at 21°C in apartments and 19°C in open spaces and stairways. The denominator composes the supply and return flow temperate difference, and the natural logarithm constitutes the factor between the supply and return flow temperature (Gong & Werner, 2015).
𝐸
𝑄 = |1 − 𝑇0
𝑇−𝑇0ln (𝑇
𝑇0)| Equation 14
Both the available and demanded exergy factor changes with the outdoor
temperature since the exergy difference increases and decreases with temperature and pressure difference relative to the reference state, Figure 12. Although the HP’s available exergy factor depends on distance towards the reference state, is it
powered by electricity and consumes an exergy factor 1 energy form. That is the most valuable energy source since it can be applied for more applications and purposes then other sources and forms of energy that can be converted into electricity to the cost of anergy. Additionally, direct irradiation’s exergy factor is 0.93 and therefore the building can heat up the whole building partly annually (Gong & Werner, 2017). That can also be utilized in DHW generation, like DH and HP supply which is heated from an annual average groundwater temperature of 6°C to 50°C, Figure 13 (Persson, 2015). The groundwater is preheated by the drain water heat recovery system from approximately 6°C to 19°C.
Figure 12. Exergy factor of space heat demand and heat supply technology access.
Figure 13. DHW demanded exergy factor and accessible exergy factor.
Benefits with the 4th generation of DH are that electricity generation and Rankine cycle overall efficiency increase in combined heat and power (CHP) plants.
Simultaneously as more waste heat from industries can serve a purpose by supply to the DH grid as waste heat from pulp, steel, or chemical industries (Averfalk &
Werner, 2020). Furthermore, since the 4th generations DH grid enables a lower minimum supply temperature it is more energy efficient than the previous generations (Schmidt & Kallert, 2017).
