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Faculty of Technology
Demand Side Management potential of using electric
heating in Finnish buildings – current and prospective
technology
Jari Pulkkinen
Environmental Engineering
Master’s Thesis
December 2018
Faculty of Technology
Demand Side Management potential of using electric
heating in Finnish buildings – current and prospective
technology
Jari Pulkkinen
Supervisors:
Jean-Nicolas Louis, D.Sc. (Tech)
Professor Eva Pongrácz
Mika Ruusunen, D.Sc. (Tech)
Environmental Engineering
Master’s Thesis
December 2018
TIIVISTELMÄ
OPINNÄYTETYÖSTÄ Oulun yliopisto Teknillinen tiedekunta Koulutusohjelma (kandidaatintyö, diplomityö) Pääaineopintojen ala (lisensiaatintyö)
Ympäristötekniikan tutkinto-ohjelma
Tekijä Työn ohjaaja yliopistolla
Pulkkinen, Jari
Louis J, TkT
Pongrácz E, Professori
Ruusunen M, TkT
Työn nimi
Kysynnänhallinnan potentiaali rakennusten sähkölämmityksessä Suomessa – nykyiset ja tulevaisuuden teknologiat
Opintosuunta Työn laji Aika Sivumäärä
Teollisuuden energia- ja
ympäristötekniikka Diplomityö Joulukuu 2018 148 s. + 2 liitettä
Tiivistelmä
Työn tavoitteena on erilaisten rakennusten sähkölämmitystekniikoiden kysynnänhallintamekanismien arviointi.
Tähän tarkoitukseen luotiin rakennusten lämmöntarvetta ja sähkölämmityksen käyttäytymistä kuvaava mallinnus,
joka liitetään olemassa olevaan älykkäiden rakennusten malliin. Testatuilla kysynnänhallintamekanismeilla pyrittiin
optimoimaan rakennuksen sähkön käytön kulut, ympäristövaikutukset ja asukkaiden lämpöviihtyvyys sekä
nykyisessä että vuoden 2050 sähköverkossa. Lisäksi testattiin sekä mekanismien kannattavuutta että rakennusten
lämmöntarpeen ja -eristyksen vaikutuksia tuloksiin.
Luotu malli mahdollistaa lämmöntarpeen ja rakennusten sähkölämmityksen simuloinnin tunneittain hyödyntämällä
muokattuja Rakennusmääräyskokoelman ja standardien laskentamenetelmiä. Testatut sähkölämmitystekniikat
sisältävät suoran sähkölämmityksen erilaisilla lämpötila-asetuksilla, manuaalisesti säädetyn lämmityksen, kulujen
minimoinnin lineaarisen optimoinnin avulla, aurinkosähkö-, ja akkujärjestelmän mallintamisen sekä varaavan
lattialämmityksen lataamisen erilaisilla menetelmillä. Lämpöviihtyvyyttä mallinnettiin hyödyntämällä PMV
menetelmää standardista. Vuoden 2050 sähköntuotannon ja -hinnan sekä lämpötilamuutoksien profiilit luotiin
tunneittain nykyisten ennustusten sekä toteutuneen sähkön tuotannon ja ulkolämpötilojen pohjalta.
Vuosittaiset tulokset osoittavat optimointitehtävän haastavuuden, sillä yksikään tekniikka ei ollut muita selkeästi
parempi kaikissa kolmessa kategoriassa. Sähkönhinnassa sekä hiilidioksidipäästöissä suurimmat vähennykset saatiin
aikaan hyödyntämällä aurinkosähköjärjestelmää varaavassa lattialämmityksessä. Yöllä varaavalla
lattialämmityksellä taas saavutettiin korkein lämpöviihtyvyys. Ainoastaan suoraa sähkölämmitystä ja varaavaa
lattialämmitystä hyödyntävät optimointiteknologiat havaittiin taloudellisesti kannattaviksi nykyisessä tilanteessa.
Aurinkosähköjärjestelmien avulla saatiin lupaavia tuloksia optimointiongelmaan vuonna 2050, samalla kun
aurinkosähkö sekä varaavassa lattialämmityksessä että suorassa sähkölämmityksessä lämmittimen kuorman siirron
kanssa tulivat kannattaviksi. Näin ollen aurinkosähköjärjestelmien hyödyntämisellä lämmityksessä vaikuttaa olevan
potentiaalia, mutta nykyisellä hintatasolla ne eivät ole vielä kannattavia. Rakennusten lämmöneristyksen määrällä ei
vaikuttanut olevan suurta vaikutusta optimiratkaisuun, mutta sen parantamisella voitiin vähentää sähkön käytön
kuluja sekä hiilidioksidipäästöjä.
Diplomityö tehtiin osana Smart Energy Networks 2050 (SEN2050) projektia, joka on Suomen Akatemian rahoittama
ja tutkii energiajärjestelmien siirtymää kohti vähähiilistä yhteiskuntaa vuoteen 2050. Tämä työ auttaa projektin
tavoitteissa osallistumalla älykkäiden rakennusten mallintamiseen ja pyrkimällä rakentamaan vähähiilistä
tulevaisuutta.
Muita tietoja
ABSTRACT
FOR THESIS University of Oulu Faculty of Technology Degree Programme (Bachelor's Thesis, Master’s Thesis) Major Subject (Licentiate Thesis)
Degree Programme in Environmental Engineering
Author Thesis Supervisor
Pulkkinen, Jari Louis J, D.Sc. (Tech.)
Pongrácz E, Professor
Ruusunen M, D.Sc. (Tech.)
Title of Thesis
Demand Side Management potential of using electric heating in Finnish buildings – current and prospective
technology
Major Subject Type of Thesis Submission Date Number of Pages
Industrial Energy and
Environmental Engineering Master’s Thesis December 2018 148 p. + 2 appendices
Abstract
The objective of this work is to evaluate the flexibility of electric space heating technologies with their respective
demand side management programs. To reach this goal, a thermal model was formulated to be integrated into an
existing smart house model. The demand side management programs include three parameters: electricity costs,
environmental impact and thermal comfort in today and 2050’s networks. The economic feasibilities of the simulated
demand side management programs are estimated and the impacts of the heat demand and thermal insulation on the
optimal solution are tested.
To create the thermal model, hourly heat demand and electric space heating models were created based on the
modified calculation methods of the National Building Code of Finland and relevant standards. The created electric
space heating model includes several modelling blocks: direct electric space heating with different temperature
settings, manually controlled heating, linear programming optimization tool to minimize costs, PV generation, battery
systems and underfloor heating with their respective charging scenarios. Thermal comfort is estimated by utilizing
predicted mean vote (PMV) method from relevant standard. For testing 2050 scenarios, hourly profiles for electricity
generation and price together with the temperature changes were created from existing estimation scenarios and
current profiles.
The annual results from the simulations show the difficulty in optimizing the system by the three parameters, as no
technology could show good results in all three of them. The biggest reductions in electricity costs and CO2 emissions
can be achieved by underfloor heating with photovoltaic charging, and the highest thermal comfort achievement rate
with set charging hours in underfloor heating. The economically feasible solutions were cost optimized direct electric
space heating and cheapest charging hours for underfloor heating. The photovoltaic systems showed the best
optimization results in 2050, and the underfloor charging and direct load shifting with them became profitable.
Therefore, PV generation seems to have potential in electric space heating in the future but first needs to become
economically feasible. The thermal insulation level did not seem to impact significantly on the optimal solution but
increasing it was efficient in reducing costs and CO2 emissions.
This thesis is part of the Smart Energy Networks 2050 (SEN2050) project funded by the Academy of Finland. The
core aspect of the project is to study the transition towards decarbonized energy systems in 2050. This work
participates in the project’s objectives of modelling smart buildings in a bottom-up approach and optimizing them
for decarbonization.
Additional Information
Acknowledgements
This work was funded through an Academy of Finland project Smart Energy Networks
2050 (SEN2050) and conducted on the Energy and Environmental Engineering Research
Group at the University of Oulu. The work started at March 2018 and took nine months
to finnish in December 2018.
Firstly, I want to sincerely thank my supervisor Dr. Jean-Nicolas Louis for his great
support during the process and for sharing his insights on the topic. I also want to express
my gratitude to my supervisor and head of the unit, Professor Eva Pongrácz for providing
me this oportunity to work with this subject and for supporting me along the process.
Many thanks go also to my supervisor Dr. Mika Ruusunen for his knowledge and the help
he provided along the process.
I would like to express my gratefulness to all my co-workers in the office for providing
help with everyday issues and for the good atmosphere we had. I also want to thank Kalle
Herva and Alec Svoboda for all the great conversations we had and for keeping me
company throughout the process. Everyone in the Research Group also deserves my
gratitudes for providing a nice working environment, which was a pleasant to work in. I
also want to thank Petri Hietaharju for helping me with the issue on the thermal mass of
the building, and therefore helping me progress onward with my work.
Finally, I want to thank my friends and family for their support and time they had. Their
presences helped me a lot during the process and gave something else to think about. That
really helped me along the process.
Oulu, 17.12.2018 Jari Pulkkinen
Table of Contents
Tiivistelmä ........................................................................................................................ 3
Abstract ............................................................................................................................. 4
Acknowledgements ........................................................................................................... 5
Table of Contents .............................................................................................................. 6
Markings and abbreviations .............................................................................................. 9
1 Introduction .................................................................................................................. 11
2 Smart Buildings ............................................................................................................ 14
2.1 Smart Building Concept ........................................................................................ 14
2.2 Smart Building Functions ..................................................................................... 15
2.3 Home Energy Management System ...................................................................... 17
2.4 Measuring Devices ................................................................................................ 18
2.5 Thermal Comfort ................................................................................................... 19
2.6 Local Generation ................................................................................................... 21
2.7 Energy Storage ...................................................................................................... 22
3 Demand Side Management .......................................................................................... 25
3.1 General .................................................................................................................. 25
3.2 Residential DSM ................................................................................................... 27
3.3 Evaluation and barriers ......................................................................................... 29
4 Projections .................................................................................................................... 31
4.1 Energy Mix & Related Emissions ......................................................................... 31
4.1.1 Today ........................................................................................................... 31
4.1.2 In 2050 ......................................................................................................... 35
4.2 Price of Electricity ................................................................................................. 40
4.2.1 Today ........................................................................................................... 41
4.2.2 In 2050 ......................................................................................................... 44
4.3 Climate .................................................................................................................. 46
4.3.1 Today ........................................................................................................... 46
4.3.2 In 2050 ......................................................................................................... 49
4.4 Building specificities ............................................................................................. 51
4.4.1 Today ........................................................................................................... 51
4.4.2 In 2050 ......................................................................................................... 55
5 Own Model .................................................................................................................. 57
5.1 General on thermal models ................................................................................... 57
5.2 Current smart house model ................................................................................... 58
5.3 Assumptions .......................................................................................................... 59
5.4 General information and inputs to the simulation ................................................. 59
5.5 Heat Demand ......................................................................................................... 64
5.5.1 Heat gains from appliances .......................................................................... 67
5.5.2 Heat gains from people ................................................................................ 68
5.5.3 Heat gain from solar radiation on transparent materials .............................. 69
5.5.4 Solar Shading ............................................................................................... 70
5.6 Ventilation ............................................................................................................. 71
5.6.1 Heat flow and heating calculations .............................................................. 74
5.7 Forecasting of model variables ............................................................................. 76
5.8 Thermal comfort .................................................................................................... 77
5.9 Photovoltaic generation ......................................................................................... 79
5.10 Battery system ..................................................................................................... 80
5.11 Heat production technologies .............................................................................. 83
5.11.1 Heating Calculations .................................................................................. 83
5.11.2 Design Heat Power Calculations ............................................................... 85
5.11.3 General Description of the heating model ................................................. 85
5.11.4 Direct Electric Space Heating from Grid-based Electricity ...................... 90
5.11.5 Heating with photovoltaic electricity generation ....................................... 92
5.11.6 Heating with battery from grid .................................................................. 94
5.11.7 Thermal Storage Heaters ........................................................................... 96
5.12 Operative Temperature ........................................................................................ 98
5.13 Feasibility Test .................................................................................................... 99
5.14 Selected Simulation Scenarios .......................................................................... 100
6 Results ........................................................................................................................ 102
6.1 Today scenario .................................................................................................... 102
6.1.1 Reference scenario ..................................................................................... 102
6.1.2 Heating from the grid ................................................................................. 104
6.1.3 PV panels ................................................................................................... 105
6.1.4 Battery from grid ....................................................................................... 107
6.1.5 Underfloor heating ..................................................................................... 108
6.1.6 Overall ....................................................................................................... 109
6.1.7 Feasibility test ............................................................................................ 111
6.2 2050 Scenario ...................................................................................................... 113
6.2.1 Effects of the scenarios .............................................................................. 113
6.2.2 Technology comparison ............................................................................. 117
6.2.3 Zero and net zero building ......................................................................... 120
6.2.4 Overall ....................................................................................................... 121
6.2.5 Feasibility test ............................................................................................ 122
7 Discussion .................................................................................................................. 125
8 Conclusions ................................................................................................................ 128
References ..................................................................................................................... 131
Appendix A – Occupancy Detection of a single person household .............................. 149
Appendix B – Occupancy Detection of a multiple persons household ......................... 150
Appendices:
Appendix A – Occupancy Detection of a single person household
Appendix B – Occupancy Detection of a multiple person household
Markings and abbreviations
BPIE the Buildings Performance Institute Europe
CCS Carbon capture and storage
CFD Computational fluid dynamics
CHP Combined heat and power
CO2 Carbon dioxide
CPP Critical peak pricing
DR Demand response
DSM Demand side management
EPBD Energy Performance of Buildings Directive
FMI Finnish Meteorological Institute
GHG Greenhouse gas
HAN Home area network
HEMS Home energy management system
HVAC Heating, ventilation, and air conditioning
ICT Information and communications technology
IoT Internet of Things
LCOE Levelized-cost of electricity
nZEB Nearly zero energy building
NZEB Net zero-energy building
met Metabolic equivalent of task
PMV Predicted mean vote
PPD Predicted percentage dissatisfied
PV Photovoltaic
RTP Real-time pricing
SOC State-of-charge
SRES Special Report on Emissions Scenarios
TOU Time-of-Use
TRY Test reference year
VAT Value-added tax
VRES Variable renewable energy sources
ZEB Zero-energy building
A Area [m2]
C Total heat capacity [Wh/K]
c Specific heat capacity [J/kgK]
E Energy [Wh]
F Fuel cost [€]
FW Non-scattering glazing correction factor [-]
f fraction of outdoor air in the supply air flow [-]
g Total solar radiation transmittance value [-]
gdirect Total solar energy transmittance values on direct solar incidence [-]
I Investment cost [€]
m mass
N Number of items
n50 Building’s air-leak number [1/h]
OM Operation & maintenance costs [€]
P Power [W]
p Price [€cent/kWh]
q Air-flow [m3/s]
Q Energy flow [Wh]
q50 Building envelope’s air-leak number [m3/(h m2)]
t Time [h]
T Temperature [°C]
U Overall thermal transmittance [W/m2K]
ŋ Efficiency
ρ density [kg/m3]
11
1 INTRODUCTION
Climate change is impacting the world and its effects are becoming more visible on
regular basis, influencing the building energy consumption in near and mid-term future.
At the same time, smart technologies are developing in an increasing rate, which can help
in providing smart functions to customers. To mitigate the effect of climate change,
carbon dioxide (CO2) emissions need to be reduced and society decarbonized. Thus,
achieving the carbon reduction targets require changes to the current energy system. On
the other hand, information and communications technologies (ICT) allow better
utilization of variable renewable energy sources (VRES) through demand side
management (DSM) and improve quality of life in buildings through smart functions. All
these characteristics are intrinsic to smart buildings, which should additionally empower
customers, reduce electricity costs and decarbonize building stock. (De Groote et al. 2016,
Haider et al. 2016, De Groote et al. 2017a)
An important parameter in decarbonizing the building stock is the environmental impact
reduction of the heating of buildings (De Groote et al. 2016). As a Northern European
country, Finland’s energy system is highly influenced by space heating, which accounts
27 % of the total energy consumption (Statistics Finland 2018a). From the total space
heating, residential buildings then account 64 % (Statistics Finland 2018b). Especially in
detached houses, electricity-based heating is very common as almost 43 % of them have
an electric heating system (Statistics Finland 2018c). This would add up to slightly under
1400 MW heating load in 0°C outdoor temperature (Järventausta et al. 2015). Therefore,
electric space heating impacts the future energy system, which needs to change to match
the CO2 reduction targets. Similarly, electric space heating has potential in balancing the
electricity consumption and supply.
DSM aims at improving the energy efficiency of the system and reducing energy
consumption, which results in lower electricity costs and environmental impact (Palensky
and Dietrich 2011). Improving energy efficiency is also the direction to which European
Union (EU) legislation is heading towards, as new building should be nearly zero-energy
buildings (nZEB) soon, and the member states should create pathways in improving the
energy efficiency of existing building stock through renovations (The European
Parliament and The Council of the European Union 2010, 2018). Therefore, future
buildings will be more energy efficient than today. Thermal loads, such as heating, are
12
considered to be flexible, and can be used in shifting electricity loads (Sharifi et al. 2016).
This is subject to the thermal characteristics of the building; temperature in a low thermal
mass building with poor insulation drops faster than in a building with high thermal mass
and good insulation. Conversely, poorly insulated buildings provide higher reduction
potential due to higher heat demand than well insulated building. (Le Dréau and
Heiselberg 2016) Generally, the DSM markets for frequency control and balancing power
need higher loads than what a single household can provide, and therefore they need to
be aggregated to be utilizable. Yet, Rautiainen et al. (2009) found that aggregated heating
loads can bring frequency control, and Lu (2012) that aggregated residential heating,
ventilation and air conditioning (HVAC) loads can operate on balancing power. For
optimizing the system from the single household’s point-of-view, optimization of just
costs can increase CO2 emissions and vice versa. When the optimization is handled as a
combination of both, they both can be reduced. (Dahl Knudsen and Petersen 2016)
Therefore, the optimization needs to take several parameters into account.
The work is part of the Smart Energy Networks 2050 (SEN2050) project, which is funded
by Academy of Finland and aims at creating a tool for planning and evaluating energy
networks in 2050. More precisely it contributes to model a smart house using a bottom-
up approach for smart grid infrastructure (WP1) and it participates in optimizing
decarbonization (WP3). The objectives of the study include the creation of electric space
heating and heat demand models to test out DSM programs, and integrating the created
models with the existing smart house model. Research questions for the thesis for both
today and 2050 scenarios are as followed:
1. Which demand side management (DSM) technologies are useful for optimizing
the electric space heating between price, environmental parameters, and thermal
comfort?
2. What DSM technologies are profitable?
3. How does heat demand and thermal insulation affect the heating of buildings and
DSM programs?
The work is separated into two sections, to theoretical and experimental parts. First
theoretical part discusses the characteristics of smart buildings and Home Energy
Management Systems (HEMS). Second part is about DSM, its utilization in residential
sector and its markets. Third theoretical part is combined with experimental part as it
13
includes a description about today and 2050’s energy systems, climates and building
specifications. This is extended to the experimental part by creating hourly 2050 scenarios
for electricity generation and real-time-price and utilizing climate change scenarios for
estimating hourly outdoor temperatures from existing prediction scenarios and current
profiles.
Experimental part includes the creation and description of the electric space heating and
heat demand models. The electric space heating model includes direct and electric thermal
storage space heating technologies with electric radiators and underfloor heaters,
respectively. The model also includes the utilization of photovoltaic (PV) panels and
battery storage technologies, which are tested from the load shifting point-of-view. This
is followed by results from the simulations and economic feasibility test. The thesis work
is then concluded, and future research, limitations of the study and the benefits to the
project are described.
14
2 SMART BUILDINGS
The following chapters describe some smart building fundamentals by firstly looking into
the concept and the smart building functions, and then discussing about some of the smart
building devices.
2.1 Smart Building Concept
The smart or intelligent building concept was introduced already in the 1980s and was
emphasizing more automatically controlled system. The concept has developed since and
has changed its key features and definitions more towards a thorough management
system, which would provide both comfort and efficiency. (Ghaffarianhoseini et al. 2016)
The difficulty in defining the concept causes some issues in the subject as varying
definitions can cause slightly different specifications. An intelligent building may be
regarded as one, which should be smart and aware of technology, economically and costly
efficient, have sensitivity towards personal and social preferences as well as be
environmentally responsive. (Ghaffarianhoseini et al. 2016) The Buildings Performance
Institute Europe (BPIE) (De Groote et al. 2017a) on the other hand regards the smart
building to consider the occupant’s preferences and to provide both higher-quality and
lower costs, simultaneously with providing flexibility to the building’s energy use. The
European Parliament’s Amendment Directive (EU) 2018/844 describes three
functionalities in buildings to assess its smart readiness. The Amendment Directive
emphasizes the ability to integrate renewable energy sources to the buildings energy
system, the capability to participate in demand response actions, and the occupant’s
empowerment on the system to generate healthy indoor climate and receive information
on the energy use. Even though these indicators do not directly give a description of the
smart building, they show what the measures in assessing it are. Furthermore, smart
buildings may also generate energy efficiency and flexibility, which would provide health
and comfort in the building while lowering both energy consumption and carbon impact
through ICT. (The European Parliament and The Council of the European Union 2018,
Verbeke et al. 2018)
Based on these definitions and measures on the smartness of the building, a smart building
shall improve the occupant’s quality of life while generating and making renewable
energy integration possible by controlling and maintaining building’s energy
15
performance and providing demand response possibilities. Consequently, the building
would be a healthy and comfortable living space where occupant can influence the system
and control it. Similarly, the energy efficient performance should generate economic
benefits and help in the renewable energy transition. Achieving these functions would
likely require some sort of building automation system together with ICT technologies.
2.2 Smart Building Functions
The Smart Building concept includes improvements to both building’s energy system and
to occupants living conditions and quality of life. It can be seen as an enabler in
decarbonizing the building stock in order to meet the carbon dioxide reduction
requirements set by EU. (De Groote et al. 2016, European Commission 2018) De Groote
et al. (2016) further considers 10 different principles which will allow buildings to
function as micro-energy hubs. These micro energy-hubs are defined as a single or group
of buildings, which can integrate their electricity production and storage to the energy
system, enable DSM and enhance energy efficiency. At the same time, the concept
includes the possibility to reduce electricity invoices of customers and could be an enabler
in increasing the number of electric vehicles. Similarly, smart buildings have been seen
as a step towards decarbonized residential sector as they improve the energy efficiency
of the buildings and by allowing the integration of renewable energies (De Groote et al.
2016, 2017a, 2017b).
Smart buildings are considered to offer economic savings through their functions, which
include e.g. the possibility to have DSM and to participate in demand response (DR)
programs. The potential financial savings in electricity invoices are presented for instance
by Lee and Bahn (2013) who studied scheduling of appliances by conducting
experimental simulations. They noted that using genetic algorithm to optimize the usage
of appliances through a smart device could generate savings in electricity costs by 25.6
% with dynamic pricing. In Hussain et al. (2018) the electricity cost deductions, peak to
average ratio and user comfort through waiting time were studied by using heuristic
optimization methods as well as creating and testing a new one. They noted that using
heuristic optimization methods, the electricity costs of appliances can be reduced as much
as 56 % for multiple homes compared to non-optimized scenario. Zhao et al. (2013)
noticed reductions in electricity costs as well as lower peak to average values by using
genetic algorithm to schedule appliances in a combination of real-time pricing and
16
inclining block models. The decrease in average daily electricity cost was found to be
approximately 10 cents.
Healthy living environment and occupant’s comfort were considered as another smart
building feature in De Groote et al. (2017a) and therefore, indicators and their measures
determining the healthy living environment needs to be assessed. Several pollutants like
carbon dioxide (CO2) and carbon monoxide (CO), sulphur dioxide (SO2), benzene, ozone
(O3), nitrogen oxides (NO) and (NO2), particulate matter (PM10 and PM2.5) and different
volatile organic compounds (VOCs) have an effect to the indoor air quality, and they can
be sourced from outdoors or indoors. Smart buildings can improve the indoor air quality
through monitoring the pollutant concentrations and indoor temperature with sensors and
controlling ventilation accordingly with the building automation. (Schieweck et al. 2018)
However, Schieweck et al. (2018) points out the difficulty in controlling the indoor air
quality based on the measured values due to the complexity of the environment. Another
health related improvement in smart house is the possibility to provide assisted living for
the elderly and the disabled, by providing different kinds of remote monitoring systems
to keep track of the state of the inhabitant (Deen 2015, Schieweck et al. 2018).These
functions come with an increasing amount of information sharing between parties and
increasing amount of smart devices that arises some security and privacy issues. The
privacy issue was also found to be one of the public concerns by Balta-Ozkan et al. (2013)
who surveyed experts and public in the UK about smart homes. Similarly, smart buildings
need to address not only privacy issues, but also potential security breaches and have
countermeasures to tackle them (Komninos et al. 2014). E.g. smart meter related privacy
issues may be tackled by using privacy enhancing technologies, like anonymization,
trusted computation, cryptographic computation, perturbation, verifiable computation or
data obfuscation techniques (Jawurek et al. 2012, Komninos et al. 2014).
At the same time, building performances are set and monitored in the EU legislation for
Energy Performance of Buildings Directive (EPBD) (2010/31/EU). It provides
frameworks to the building-stock and drives the new buildings towards nearly zero energy
buildings (nZEB). (The European Parliament and The Council of the European Union
2010) Karlessi et al. (2017) studied the principles and design parameters of smart
buildings in regards with the EU policy about nZEBs. The study considered three main
stages of the implementation and design of nZEBs: 1. building design, 2. the design of
the operation and 3. integrating the nZEBs in smart grids. The focal points of the study
17
were modelling the nZEB in design and operational mode to design them correctly, apply
smart technologies, like smart meters and controllers to the building, and secure the
efficiency and reliability of the building through control strategies which will not neglect
the needs and comfort of the occupants. (Karlessi et al. 2017) Therefore, the smart
building concept seems to relate to the nZEBs, which will more likely increase the
importance of smart buildings.
2.3 Home Energy Management System
Home energy management systems (HEMS) make it possible for consumers to manage
and control their energy usage by providing either information or allowing the HEMS a
direct control over the appliances and devices consuming energy. HEMS combines
interconnected hardware and software to provide various objects to consumers. These
objects include some of the smart building principles by providing energy management,
comfort, convenience, and economic gains to occupants while decreasing their
greenhouse gas emissions. (Karlin et al. 2015) Karlin et al. (2015) and Ford et al. (2017)
conducted comprehensive reviews on smart home technologies, which were classified
into three groups: user interfaces, smart hardware and software platforms. The user
interface group included devices like in home displays and energy portals, which are used
to present information. Some of these also provide a control possibility either with or
without the information presentation. Smart hardware covers appliances, lights,
thermostats, plugs and hubs that have some intelligent function embedded. Finally, the
software platforms are used to enable the information communication between the parties.
These platforms can be for smart homes, data analytics and web services. The smart home
technologies can be linked together through Home Area Network (HAN) by using
different communication protocols which are varying from Wi-Fi and Bluetooth to
Zigbee and Zwave. These protocols are generated to make communication of devices
possible, yet not all these protocols offer interoperability of devices. (Karlin et al. 2015)
At the same time, there does not seem to be any individual protocol which could be able
to fulfill the smart home criteria (Karlin et al. 2015, Mendes et al. 2015). This lack of a
common standard can even restrict the utilization and development of smart home
management automation technologies (Toschi et al. 2017).
Energy efficiency and management are one of the main principles of the smart house
concept and therefore, the energy saving and shifting potential achieved by applying
18
HEMS needs to be assessed. Karlin et al. (2015) reviewed the HEMS saving potential
when the systems provide information and feedback to the user and analyzed these results.
The study estimated that the common energy saving varies from 4 to 7 % when the user
is given feedback on their actions, but that on general there was more variation in the
results. This would indicate that the effectiveness is dependable on the user and the
feedback type. (Karlin et al. 2015)
The control of HEMS, on the other hand, can be either user’s remote control or an
automation and optimization based control, which may be applied by the user or the utility
(Karlin et al. 2015). Ullah and Kim (2017) presented an experimental simulation study,
which was conducted to optimize energy efficiency and comfort parameters. The results
of the study show that with an optimization scheme, energy savings are possible, varying
between 27 and 31 %, depending on the optimization method. At the same time, the user
comfort was found to have improved when compared to the non-optimized case.
On the contrary, Ford et al. (2017) reviewed energy saving and DSM potential of smart
technologies, and noted that currently not all home management technologies provide
energy consumption information, interact with DSM programs and some may even
increase the energy usage through their control functions. Hence, even though HEMS
have been found to reduce energy consumption, some of the smart home devices do not
seem to yet provide the energy efficiency and information exchange principles of the
smart houses.
2.4 Measuring Devices
To gain full benefits from HEMS and provide high quality living conditions in a building,
smart sensors and smart meters are required to measure and monitor the environment and
electricity consumption. As discussed in section 2.2, there are several different
environmental parameters, which can be measured with sensors and that can be important
for the high quality of life. In general, Fugate et al. (2011) divided measuring devices in
to three categories based on the measurement: 1. occupant comfort, 2. energy
consumption and 3. machinery characteristics, with each of them having their own
characteristics. Measuring devices can provide information about electricity and gas
consumption, air and mean radiant temperatures, air velocity, relative humidity, indoor
air quality, pollutant gases, like CO2, CO and VOC, occupancy and about the amount of
19
the daylight. (Ahmad et al. 2016) Similarly, they have different usages, communication
protocols, and sensor types. This can be used then to present the performance of a building
and possibly even improve it.
Occupancy detection and determination is one way to utilize sensors and measurements.
In Chen et al. (2018) the most common types of occupancy detection are either different
kinds of sensors like passive infrared, environmental parameters, like e.g. CO2,
temperature, light, humidity, pressure or a combination of these, or other methods like
WiFi signals, smart meters, cameras or Bluetooth low power technology. The review
concluded that all these methods have their own benefits and downsides, making the
selection of the measurement type more objective based. Furthermore, they concluded
that the accuracy could be increased with the selection of multiple methods, as they are
likely to complement each other. Detection can then be combined with prediction and
used in managing the heating of the building based on the estimated times of occupancy
(Kleiminger et al. 2014). In Kleiminger et al. (2014) the actual energy saving amount was
found to vary from 6 to 17 % based on the insulation level of the building. They also
noted that the impact on the annual energy consumption from the occupancy prediction
was higher with less insulated buildings. On the other hand, the buildings with lower
occupancy rates have higher potential in energy savings with occupancy prediction
methods (Kleiminger et al. 2014). Occupancy measuring using environmental parameters
can also provide savings in demand control of ventilation when the air flow is controlled
using CO2 and moisture threshold values, so that higher ventilation rates are used at times
of higher CO2 and moisture content to ensure proper indoor air quality for the occupants.
(Nielsen and Drivsholm 2010).
2.5 Thermal Comfort
One important function of a smart building is its ability to increase the comfort level and
satisfaction of the resident. An important parameter in that is thermal comfort, which
describes the person’s thermal sensation and contentment with the current thermal status.
The affecting parameters are based on the thermal balance of a body, which is eventually
connected to person’s own actions through clothing and metabolic rate of the physical
activity. Environmental parameters like radiant and air temperatures, and velocity and
relative humidity of the air impact the thermal sensation as well. Similarly, a local climate
20
like cold or warm surfaces and draught can create thermal discomfort, which will
influence the thermal sensation and cause dissatisfaction. (SFS-EN ISO 7730 2005)
The thermal comfort estimations can be done using a Predicted Mean Vote (PMV) and
the predicted percentage dissatisfied (PPD) models first created by Fanger (1970) and
further developed and presented in SFS-EN ISO 7730 (2005) standard (Rupp et al. 2015).
The aim of these methods is to present the average thermal sensation of the people and
whether they are content with the thermal environment. The PMV index shows the scale
of the perception of the thermal sense. It presents the sensations in a seven-point scale
where each of the points presents a certain thermal feeling. The scale goes from cold to
hot sensation so that the middle value presents the most neutral sense. The final value is
a prediction of the average vote from the group to present the general thermal comfort.
PPD value is then an estimation of the proportion of dissatisfied people based on the
PMV. According to the model, it is impossible to create an environment where everyone
is satisfied to the situation, since with the neutral PMV value there are 5 % of dissatisfied
individuals. In addition to the two models, the local thermal discomfort is also indicated
with a percentage of dissatisfied. These values can then be used to determine a thermal
environment where majority of the people feel content with the thermal climate. (SFS-
EN ISO 7730 2005) Rupp et al. (2015) presented two other standards to also assess the
thermal comfort; an adaptive model which was made by Nicol et al. (2012) and the
Chinese Evaluation Standard by Li et al. (2014).
Wang et al. (2018) investigated the differences related to e.g. gender and age by
conducting a comprehensive literature review from researches using climate chambers
and field studies. They noted that the results from the previous studies showed no clear
inference on preferred thermal conditions by gender or age, but it is believed that females
and elders are more critical of the thermal climate and susceptible to the deviation in the
conditions due to the differences in clothing and physiology. Without these differences,
they concluded that there are no clear differences in the preferred thermal environments.
Differences in fitness, circadian rhythm, disabilities and so on were found to influence
the individual’s thermal comfort in the same review. Thus, Wang et al. (2018) suggested
a time varying and personalized air conditioning with the help of using smart wearable
devices and machine learning to address the thermal comfort better.
21
2.6 Local Generation
One of the smart building characteristics is considered to be the potential to integrate local
generation in to the building and thus create more dynamic and resilient energy system in
the building while promoting prosumerism (De Groote et al. 2016). PV panels, small-
scale wind turbines, µCHP, small-scale hydro power, biogas and fuel cells can be used in
small-scale electricity generation in residential level (Pesola et al. 2014, Motiva Oy
2017a). Out of these, PV panels seem to be achieving the most attention as the most
prominent small-scale generation method in Finland (Pesola et al. 2014).
From the economic point-of-view, PV panels decrease the costs in the buildings as the
electricity generated and self-consumed in them is not associated to the grid-based
electricity costs, including the cost of electricity, distribution costs and taxes. The PV
panel costs are just direct investment costs and operation and maintenance costs.
(Auvinen and Jalas 2017) Therefore, the feasibility of the system is related to its size,
financial aid and support from the government, and to the purchase cost of energy
(Auvinen 2016). Different kinds of support schemes for PV in Europe include feed-in
tariffs and premiums, investment subsidies, tax reductions, soft loans, tradable green
certificates, R&D incentives, calls for tenders and net-metering and self-consumption
schemes (Ramírez et al. 2017). From these, feed-in tariffs, net metering and net purchase
and sale were studied as residential support schemes to sell electricity to the grid by
Yamamoto (2012). The improvements on social welfare were found to differentiate by
the electricity consumption reduction assumed to be happening under net metering
scheme, favoring net metering, and net purchase and sale when the reductions are large,
and feed-in tariffs in cases when the reductions in electricity consumption are small. On
the other hand, no clear indication was found on which scheme provides the lowest
electricity rate.
To optimize the economic gains from the PV system, the amount of self-consumption
should be increased as it provides more savings than what the profit would be from selling
the electricity to the grid in Finland (Auvinen and Jalas 2017). The amount of the self-
consumption can be increased by utilizing load shifting if applicable or a battery system
at the times, the generation is higher than the base consumption (Luthander et al. 2015,
O’Shaughnessy et al. 2018). Luthander et al. (2015) found that with the utilization of load
shifting, relative self-consumption can be increased by 2-15 %-points and with a battery
22
system 13-24 %-points (capacity of 0.5-1 kWh per 1 kW of installed PV). O’Shaughnessy
et al. (2018) presented comparable results with the increase in self-consumption, but
found load controlling devices to be more cost-efficient than battery systems currently.
On the other hand, it was found that batteries provide more flexibility and efficiency in
self-consumption than load controlling. Cerino Abdin and Noussan (2018) conversely
found battery systems as economically unfeasible at current prices in Italian case,
emphasizing the net metering as a better solution than battery storage systems to provide
economic gains, as well as discusses the inefficiencies due to higher losses related to
battery storage system. Similarly, McKenna et al. (2013) and Uddin et al. (2017)
considered the unfeasibility of battery systems integrated with PV panels in the UK,
adding also higher environmental impacts with lead-acid batteries integrated to grid-
connected PV panels in residential buildings
2.7 Energy Storage
One way to handle the variations and intermittency of the generation and consumption in
residential level is to utilize energy storage technologies. In general, these technologies
can be divided in to electricity and thermal storages (Baker 2008, Ibrahim et al. 2008,
Kousksou et al. 2014). The electricity storage technologies utilize either electrochemical,
kinetic or potential energy, or electromagnetism, while the thermal energy storages have
either latent or sensible storage medium, or it utilizes thermochemical reactions (Ribeiro
et al. 2001, Baker 2008, Kousksou et al. 2014). Examples of electricity storage
technologies are presented on Table 1 below.
23
Table 1. The different electricity storage methods.
Electricity storage type Reference
Electrochemical storages Baker 2008
Kousksou et al. 2014 - Battery
- Flow cell
Kinetic storage Baker 2008
Kousksou et al. 2014 - Flywheel
Potential energy storage Baker 2008
Kousksou et al. 2014 - Pumped Hydro
- Compressed Air
Electromagnetic storages Ribeiro et al. 2001
Gualous et al. 2003
Rafik et al. 2007
Ibrahim et al. 2008
Kousksou et al. 2014
Nielsen and Molinas 2010
- Capacitators
- Super capacitators
- Superconducting magnet
Medium storage Ibrahim et al. 2008
Kousksou et al. 2014 - Hydrogen
Thermal energy storages store energy in the form of heat and utilize either sensible, latent,
or chemical way of storing energy. Sensible energy storage utilizes the temperature
difference between the medium in time, where the medium can be in either liquid or solid
form. On the contrary, the latent energy storage includes a material changing its phase,
while storing the energy in the phase change. The third thermal energy storage type uses
thermochemical method in which the heat is used to generate new chemical compounds,
then they are stored separately and combined again at the discharge state in which the
compounds react with each other and that generates heat (Kousksou et al. 2014).
Currently, five battery technologies exist in the residential scale: lithium-ion, lead-based,
flow, nickel-based and sodium-based batteries (van de Kaa et al. 2018). Van de Kaa et al.
(2018) conducted an analysis on the residential scale battery technologies by using the
best worst method and considered lithium-ion batteries to have the best chance of
succeeding in the future.
24
Thermal storage is another energy storing technology and it includes sensible, latent, and
thermochemical storages divided further into passive and active methods. The passive
methods include thermal mass as sensible storage and phase change materials in the
building envelope as latent storage. Active methods can then heat up the hot water tanks,
utilize snow or water storages nearby the building, have latent storage in ventilation or
equipment, or include thermochemical storage. The building structures could also be
thermally activated and passively distributed. (Heier et al. 2015) Heier et al. (2015)
considers sensible heat storages like water tanks to be the most common heat storages in
residential buildings, while phase change materials and thermochemical storages are
raising a lot of interest. On the other hand, in Heier et al. (2015), the thermal mass in
buildings was found to stabilize the indoor temperatures with only small reduction in the
energy consumption. Contrary, Zhu et al. (2009) found that high thermal mass can
actually increases the cooling demand in zero energy buildings in desert climate as the
heat is not released outdoor fast enough during the night and is therefore accumulating
inside. Yet, combining partial thermal storage with DR and direct electric heating in
dynamic pricing scheme could provide energy cost savings (Ali et al. 2014).
25
3 DEMAND SIDE MANAGEMENT
The demand side management (DSM) section discusses DSM programs in both general
and residential level. These are followed by markets and price signals, and evaluation and
barriers of DSM.
3.1 General
Demand Side Management (DSM) is a measure to adapt the electricity consumption of
the customer to better match the state of the system. In general, DSM techniques can be
divided in to two categories: Energy Efficiency (EE) and Demand Response (DR). Energy
Efficiency improvement increases the efficiency of the system in terms of energy use and
this can be achieved by having more energy efficient devices and materials, e.g.
improving thermal insulation of the building, or changing old lightbulbs to newer light-
emitting-diode (LED) lamps. DR then is used to change the consumption pattern of the
end-user or customer by applying either price or incentive-based DR programs. The price
based DR programs can still be divided into three groups by basing them on their dynamic
behavior: Time-of-Use (TOU) tariff presents different electricity prices during the day by
certain time slots, Critical Peak Pricing (CPP) is similar to TOU, but it also adapts to peak
times by increasing the peak price, and Real-Time Pricing (RTP) gives the most dynamic
electricity prices as it variates during the day based on the market price of electricity. RTP
can be either a day or an hour ahead price. The incentive-based DR programs, then, are
based on some incentives by the action. The schemes are divided into six categories:
Direct Load Control (DLC), Interruptible/curtailable service, Demand Bidding or
Buyback Programs, Emergency Demand Response Programs, Capacity Market Programs
and to Ancillary Services Market Programs. These price and incentive-based DR
categories are presented more on Table 2 below. (US Department of Energy 2006,
Palensky and Dietrich 2011, Behrangrad 2015, Sharifi et al. 2017)
26
Table 2. The definitions and presentations of different DR actions (US Department of
Energy 2006).
Demand Response
Programs Definition and actions
Price-Based
Time-of-Use (TOU) Different electricity tariffs based on the time of use.
Critical Peak Pricing (CPP) Different electricity tariffs like TOU, but an extra peak
cost in certain conditions.
Real-Time Pricing (RTP) Dynamic pricing scheme where the electricity price
variates, usually on hourly basis, and by reflecting the
wholesale electricity markets.
Incentive-Based
Direct Load Control (DLC) Program operator has direct control over certain
electricity appliances or devices.
Interruptible/curtailable
(I/C) service
A service where the participant agrees to decrease the
load when necessary and gets some form of discount
or credit from it. Possible to offer penalties for not
participating.
Demand Bidding or
Buyback Programs
Participant bids to curtail the load.
Emergency Demand
Response Programs
Participant reduces load during network emergency
shortfall and receives incentive from it.
Capacity Market Programs Participant receives day ahead notices of capacity
restrictions. Possible to have penalties for failure of
load reduction.
Ancillary Services Market
Programs
Participants bid load reductions in markets, and act as
reserves if needed.
In Palensky and Dietrich (2011) another kind of division of the DR systems is presented.
They conclude that DR can be parted in to Market and Physical DR, where Market based
DR includes pricing schemes and signals as well as incentives. Physical DR then would
consider grid emergencies and management which cannot be handled through markets.
These physical DR programs would have mandatory requests so that the load reduction
would happen even without being included into the market-based prices. (Palensky and
Dietrich 2011)
TOU, CPP and RTP are potential dynamic pricing schemes for DSM providing the
customers different prices at different times according to the characteristics of the pricing
schemes as described in Table 2. The price signals are the ones that will direct the
27
consumers behavior as the price of electricity changes the consumption profile by making
the customers consider their preferences (Herter 2007, Behrangrad 2015). Issues may also
arise from the automated control, if it is based on dynamic pricing. These issues were
presented in Muratori et al. (2014) who used HEMS to create a local optimum on using
TOU rating and deferrable loads. The study concluded that by simply creating a local
optimum e.g. waiting for the cheaper TOU rate and turning the deferrable loads on
immediately after that, could create a peak on the consumption as the deferrable loads
will aggregate. Thus, this phenomena might create a local optimum, but not a global
optimum. (Muratori et al. 2014) Muratori et al. (2014) also discuss the problem of the
price signal and how pricing might not provide enough incentives to customers to adapt
to the state of the system, or how the dynamic pricing might shift the consumption, but
not reduce it.
3.2 Residential DSM
Residential customers have different potential in DSM than industrial and commercial
customers due to their different characteristics, which is seen in the difference of the
applicable DSM programs of residential customers. The residential sector can have DR
measures from price-based DR and from direct load control incentives while the load
shifting is either manual or automated by HEMS. (Paterakis et al. 2017) Haider et al.
(2016) considers that for an efficient load shifting, accurate optimization method and a
good communication infrastructure are needed. Yet, Internet of Things (IoT) devices and
applications can also participate and provide new chances for load monitoring and control
(Haider et al. 2016).
The residential sector includes many kinds of loads from various appliances and devices.
In Sharifi et al. (2017) and Sharifi et al. (2016) these loads have been parted in to flexible
and non-flexible ones based on their behavior and characteristics for load shifting. Non-
flexible loads were considered to include e.g. lighting and cooking appliances. Flexible
loads on the other hand were divided in to deferrable and temperature-based loads by their
features; deferrable loads including washing machines and dishwashers and temperature-
based loads HVAC technologies. (Sharifi et al. 2016) Hence, heating applications in
residential sector are flexible loads based on temperature, and are able to participate in
residential DR. E.g. in Chassin et al. (2015) a new thermostat was found to be able to
provide consumer comfort even with fast reactions to price chances and DR signals, and
28
by providing demand elasticity between 10 and 25% from the total residential load. On
the other hand, Lu (2012) suggests that direct load control from the aggregator can be
used to provide DR on aggregated HVAC units, and that with a wide enough deadband,
the load of a single HVAC unit can be controlled. Similar results were discovered by Hao
et al. (2015) while studying the potential of thermochemically controlled loads to provide
regulation services as an aggregate resource by modelling them as a stochastic battery
with losses. Both of the studies (Lu 2012, Hao et al. 2015) stated the potential of the
thermal load, either in heating as in Lu (2012) or air conditioning as in Hao et al. (2015),
to be relative to the outdoor temperature and varying the regulation power accordingly.
A study by Rautiainen et al. (2009) discovered that heating devices can also be used as a
frequency reserve due to their low effect on indoor temperature on short disturbances,
while an aggregated load reduction of multiple households can reduce the load from the
grid and slightly balance the frequency until other mechanisms are applied. Samarakoon
et al. (2012) on the other hand argues that with the current smart meters, a primary
frequency response through direct load control scheme is unlikely due to the delays in
communication. The study presented another frequency control scheme which utilizes
frequency measurement of smart meters, and control of different appliances accordingly.
Yet, they concluded that the current smart meters need to increase the speed of the
measurement if they are to provide any primary frequency response control. (Samarakoon
et al. 2012) Thus, it seems that the heating devices have potential in frequency control,
but still face some technical restrictions.
As the residential sector may also have their own electricity generation, DSM methods
like load shifting can be used to make consumption match their own generation
(Luthander et al. 2015, O’Shaughnessy et al. 2018). On the other hand, the excess
generation and electricity from the grid can be stored and used later on for DSM from the
storage system (Wu et al. 2015). Wu et al. (2015) then discusses the possible problems
from the renewable energy generation with a battery system from the DSM side,
specifying especially peak shaving, direct load control, capacity market programs and
time-of-use rates as challenging programs.
Battery storages can also be used individually without local generation. Leadbetter and
Swan (2012) modelled and simulated typical Canadian houses and could reduce the
consumption peak from the grid by 42-49 % with a battery storage system size of 5-8
kWh in houses without electric space heating. Electrically heated houses required
29
significantly higher storage capacity (22kWh) to provide proper peak reduction. Zheng et
al. (2015) conducted research on the potential of electricity storage systems on peak
shaving and DR without altering the appliance consumption and found out that by
integrating electricity storages to buildings it is possible to reduce the electricity invoice
and provide feasible DR, hence providing comparable results to the potential of battery
only systems as well.
3.3 Evaluation and barriers
Important issues regarding the DSM technologies lie in estimating their efficiency and
barriers in utilizing them. This is vital as there is lack of understanding DR in terms of
utilization and economic benefits as noted in Nolan and O’Malley (2015). There are also
difficulties for customers and residents in estimating the effect from energy efficiency
measures as in some countries the residents may overestimate the effect while in the
others it can be vice versa (Attari et al. 2010, Iwata et al. 2015). Hence, conducting
research on the topic and studying the effects would generate better evaluations on the
DSM measures, especially in DR (Nolan and O’Malley 2015). The lack of data and
behavior of consumers can therefore be barriers to DR. Similarly, the difficulty in
defining the baseline scenario for the DR activity and calculating the DR impact from the
considered baseline, are barriers that undermine the importance of evaluation. (Nolan and
O’Malley 2015, Sharifi et al. 2017)
Good et al. (2017) derived the DR barriers in to fundamental and secondary barriers. The
fundamental barriers were economic, social, and technological while the secondary
barriers were divided in political and regulatory, market structures, physical and
understanding. The economic barriers were still divided in to market failures, like
imperfect information, incomplete markets and to imperfect competition, and to market
barriers, like lack of access to capital, uncertainty, hidden costs and to both system value
and demand for DR. The imperfect information can be traced back to the lack of data
mentioned earlier as it describes problems occurring from situations where the
information might be unknown or uncertain. (Good et al. 2017) On the other hand, the
lack of data could produce situations in which the product might not meet the demand of
the second party or act against the second party’s interest, called ‘split incentive’ situation
and ‘principal-agent problem’ (Brown 2001, Thollander et al. 2010, Good et al. 2017).
Incomplete markets may then give unfair benefits or costs to third parties by e.g. not
30
noticing the polluter or not excluding the benefits of the DR action from the third parties.
The other fundamental barriers are yet parted in to organizational and behavioral as social
barriers and to sensing, computing and communication as technological barriers. These
barriers were possibly tackled to some extent with automation systems including opting-
out possibility and policy certainty. An idea of a separate metering infrastructures for
consumption, generation and storage were also introduced so that different taxation
schemes can be applied to them. (Good et al. 2017)
Residential sector has its own specific barriers from the above presented ones. Gyamfi et
al. (2013) found that there are behavioral issues in the residential sector in applying DR.
The study argued against price only signals as some households are not responding to
them, and some customers may be more prone to respond to other kinds of signals than
just price. The lack of responsiveness to prices was found to be related to the richness of
the household, the competence of the customer and to the lack of options or capacity. Yet,
all consumers have been found to benefit from residential demand response programs and
Gyamfi et al. (2013) suggests explaining these benefits better to the consumers in order
to implement the DR better in residential sector. At the same time, the difficulty in
considering the baseline scenarios can restrict residential sector DR as well, since the
residential consumption is unpredictable, making the baseline estimation difficult (Nolan
and O’Malley 2015, Mohajeryami et al. 2017, Sharifi et al. 2017).
31
4 PROJECTIONS
To simulate the thermal model, the simulation environment needs to be described.
Therefore, the following sections define the energy mix and related operational CO2
emissions, electricity costs, climate and building specificities today and in 2050. The
simulations are based on imaginary buildings located in Oulu and therefore information
used is related to the location. The 2050 scenarios present both the projections used from
literature, and then the creation of their hourly profiles.
4.1 Energy Mix & Related Emissions
Here the energy mix and related operational CO2 emissions are presented for today and
2050 scenarios, respectively.
4.1.1 Today
An important part of determining the environmental parameters from the electricity
consumption is the energy mix of the electricity generation. Finland’s total electricity
consumption in 2016 was 85.2 TWh, from which 66.2 TWh was produced in Finland,
resulting in a net import of 19 TWh. Consumption increased 2.7 TWh from 2015, while
production stayed basically the same. Thus, increase in consumption was covered mainly
by importing electricity. (Statistics Finland 2018d) The development of Finnish energy
mix from 2005 to 2016 is presented below on Figure 1.
32
Figure 1. The development of Finnish energy mix from 2005 to 2016 (Statistics Finland
2018e, 2018f).
Figure 1 shows that the energy mix in Finland has stayed rather constant through the last
decade, as nuclear and hydro power and import of electricity are covering over half of the
electricity generation in Finland. Similarly, consumption has been stable, while energy
production has decreased slightly, resulting in an increase of import. The decrease in the
production is due to reduction in condensing power production. At the same time, some
wind power has entered the market. The technology mix could be described as diverse,
as electricity is supplied from several sources. Many of them are not operated on
balancing electricity demand and supply; combined heat and power (CHP) plants are
mainly run by heat demand, and nuclear power plants provide base power. On the other
hand, as the electricity mix does not have high proportions of variable renewable energy
sources (VRES), there is less need for complementary balancing power or demand
shifting in the system. Balancing power then is mainly delivered by condensing and hydro
power plants or through importing electricity.
The direct CO2 emissions from the electricity production come from combustion-based
technologies and to assess their environmental impacts, their fuel usage needs to be
defined. The fuel usage by the generation technology is presented on Figure 2.
33
Figure 2. The usage of fuels on generating electricity. “Others” variable includes both
renewable and fossil fuels which are not separately defined here. (Statistics Finland
2018g)
As Figure 2 shows, there is a high variability on the used fuels, as industrial CHP plants
use mainly wood fuels, while condensing and district heating CHP rely more on the usage
of coal. There is basically no natural gas used in condensing power plants, nor oil used at
all. Overall, wood-based fuels are used the most with the share of 42 % of the total
combustion-based electricity generation. Wood fuels are followed by coal, which has a
share of 25 %, and natural gas with a 12 % share. The generation with peat is close to the
natural gas production (11 %). (Statistics Finland 2018g) Next, the aggregated CO2
emissions from electricity generation are defined in Figure 3.
34
Figure 3. CO2 emissions from the combustion of peat and fossil fuels in the electricity
generation in Finland in 2016 by technologies. The values are from the energy method
from Statistics Finland (2018g).
Figure 3 shows that almost half of the CO2 emissions from the combustion of fossil fuels
and peat come from condensing power generation, even though it produces only 5 % of
the electricity consumed in Finland. Thus, condensing power is very emission intensive
method to generate electricity in Finland. District heating CHP plants also have high CO2
emissions, but they generate more electricity and have higher efficiencies as they combine
electricity and heat productions. Industrial CHP plants then have the smallest electricity
generation CO2 emissions from the three. This is likely linked to the high proportion of
wood fuels used in the industries. (Statistics Finland 2018g) On average, the total
electricity generation produced 114 gCO2/kWh in 2016 when considering energy method
(Statistics Finland 2018h).
The model uses hourly emission factors on the consumed electricity in Finland and only
takes operational emissions into account. These are created by first taking annual
emissions and electricity productions per technology from Statistics Finland (2018g) and
then calculating a fixed emission factors for a produced electricity per technology by
assuming that electricity production per technology has a fixed fuel usage and
35
disregarding the ramping effect on emissions. Finally, the cumulated hourly emissions
are calculated by utilizing the hourly production profiles per technology from Finnish
Energy (2018). The downside of this method is that it does not consider that power plants
use different fuels and operate on different times. Yet, the approximation is used, as
condensing power is generally used to meet peak demands, which on the other hand
usually generates the most emissions. This impact is modelled even with the fixed
emission factors as condensing power has the highest emissions per produced electricity.
This increases the hourly emissions every time condensing power is used. The impact of
electricity import is neglected here, as they are considered to only contribute into the
origin country’s emissions in the model.
4.1.2 In 2050
The Finnish national energy and climate roadmap 2050 describes the greenhouse gas
(GHG) reduction targets for Finland in total and by the sectors by 2050. In general, the
total GHG reduction target was set to 80-95 % by 2050 from the 1990 levels and more
specifically the energy sector should be nearly emission free at the same time. (Finnish
Ministry of Employment and the Economy 2014) Thus, there will be changes in the
electricity mix. Koljonen et al. (2012), Lehtilä et al. (2014) and Child and Breyer (2016)
were all able to present different future scenarios providing the required GHG reductions
to the energy sector by 2050. Out of these, two scenarios, Growth (“Jatkuva Kasvu”) and
Save (“Säästö”), from Lehtilä et al. (2014) are selected to test different energy systems
for the electricity supply to buildings in 2050. The scenarios are created as possible
pathways for the future with the VTT TIMES model and they were chosen as they present
different pathways for the future; Growth relies on technological development and higher
share of VRES while energy efficiency has more important role on Save scenario. Hence,
they provide different pathways of achieving the same goal, and with them some
uncertainties on the future direction can be tested.
Next, the characteristics of the chosen scenarios are presented. The annual energy mixes
of these scenarios are presented in Figure 4, followed by presentation of the emissions
from the scenarios on Figure 5.
36
Figure 4. The energy mixes of Growth and Save scenarios in 2050. The values are
estimations from Lehtilä et al. (2014).
There are some differences on the energy mixes of the scenarios. Save scenario has more
nuclear power generation than Growth, but solar and district heating CHP productions
have higher shares in Growth scenario. In Save, the electricity production surpasses the
demand, and thus it includes exporting of electricity. Save scenario also has lower
electricity demand, which impacts the annual electricity balance. (Lehtilä et al. 2014)
Figure 5. Annual emission from Growth and Save scenarios (Lehtilä et al. 2014).
37
Both scenarios include carbon capture and storage (CCS), and therefore some of the
emissions are immediately captured. The rest are dispatched to the atmosphere resulting
in direct CO2 emissions. Bio-CCS is considered as a viable option in both scenarios and
could make them carbon neutral in whole (Lehtilä et al. 2014).
To test the impacts of these scenarios on energy supply and emissions, as well as on
electricity price, an hourly generation profile needs to be created. First, hourly
productions are created from the normalized productions of 2016 to present a possible
future scenario on the generation distribution, and second, the respective hourly CO2
emissions are calculated. The normalized production is created with the following
equation (1):
Enorm,t = Et
Ea (1)
Where Enorm,t is the normalized distribution of the production at time t [-], Et is the
measured production at time t [TWh], and Ea is the annual production [TWh]. This way
the distribution profile is created for production that equals to 1 TWh production, and the
hourly generations are created by multiplying the annual production by the normalized
hourly values. For the model the normalization is based on hourly production in 2016 by
Finnish Energy (2018). The created hourly production profiles for 2050 are in Figures 6
and 7.
38
Figure 6. The hourly electricity generation and consumption distribution in Growth
scenario based on normalized 2016 generation and solar electricity generation from Louis
et al. (2016) with Test Reference Year 2012 (TRY2012) radiation data from area III.
(Jylhä et al. 2012, Finnish Energy 2018)
Figure 7. The hourly electricity generation and consumption distributions in Save
scenario based on normalized 2016 generation and solar electricity generation from Louis
et al. (2016) with TRY2012 radiation data from area III. (Jylhä et al. 2012, Finnish Energy
2018)
39
The hourly generations show different patterns on both scenarios. In Growth scenario, the
generation varies a lot as there are high shares of production from VRES. In Save
scenario, there is less variation as the scenario includes high generation from nuclear
power, which generates steady base power. This likely affects the self-sufficiency of the
system as Save scenario exports electricity while in Growth some import is needed.
Similarly, the high share of variable generation needs either DSM mechanisms or
electricity storage technologies. Therefore, Lehtilä et al. (2014) includes compressed air
energy storage (CAES) technologies in both scenarios. The effect of the storage to the
supply and demand of the energy system on hourly basis is out of the scope of this work,
but the created artificial hourly electricity generation profiles highlight its importance.
The electricity demand distribution here does not consider any DSM mechanisms on the
system scale and follows the normalized 2016 pattern. At the same time, the lack of
balancing power matching the demand is present on the hourly distributions. Hence, the
operation of hydro power would likely be different from the current pattern to stabilize
the grid. It seems that Save scenario would rely on such a high base production that there
is less need for balancing power, while Growth scenario would need more balancing to
both sides. Yet, the proper operation of the system is not modelled here as the generations
are purely based on historic and climatic data, and the actual operation is likely different
creating uncertainties on the presentation of the system.
Next, the fuel consumptions for electricity generation needs to be estimated to create the
hourly CO2 emission profiles. First, the annual energy mix in Growth and Save scenarios
were used to determine the combustion-based productions. Then their fuel distributions
were calculated by estimating bio-based productions (estimated from Lehtilä et al.
(2014)) and considering both direct and captured annual emissions as the total CO2
emissions created. Next, industrial CHP generation was assumed to be fully from bio-
based fuels, and therefore all the created emissions are from district heating CHP. The
emissions are assumed to come only from the combustion of coal and natural gas, and
their share in district heating CHP was solved from their final energy delivery, share of
electricity from the total production of the plant, and from the total emissions. The
efficiencies of CHP plants were assumed to be around to 80 %, which is close to the
current efficiency (Statistics Finland 2018g).
Hourly CO2 emissions are based on fixed emission values from the efficiency of CHP
plants and the fuel’s carbon intensity from Statistics Finland (2018g, 2018i). CCS is
40
assumed to work on a fixed share from the production while in reality, it is likely included
in just some of the power plants and would be operated only during the operation times
of the plants, and not as fixed share from production. The scenarios include the utilization
of bio-CCS, but its impact is not considered in the model as their emission reductions
come in longer term, and they do not affect the direct emissions. Growth scenario is
assumed to include bio, coal, and natural gas-based district heating, while in Save only
bio and coal are assumed to be used. All the captured emissions come from the
combustion of coal, as it had the highest carbon intensity out of the fuels. As industrial
CHP is assumed to be only bio-based and there is no condensing power, all the emissions
come from district heating CHP plants. The hourly emission factors are calculated by the
generation of electricity and presented as emissions per consumed electricity. Average
value from Growth scenario is 4.6 gCO2/kWh from the production and in Save scenario
it is 4.7 gCO2/kWh. Therefore, there are no big differences on average, which is caused
by the lower total production of electricity in Save, even though it has higher direct annual
emissions than Growth.
The biggest uncertainties related to these scenarios come from the created assumptions,
reading the graphical presentations, lack of data on the fuel mixes, and the direct
transformation of the hourly distribution data from 2016 to 2050. Thus, the fuel mix and
emissions may be different in the original study and the results from them might be
different. Also, the hourly distributions include uncertainties in the operations of the
technologies, as they are likely operated differently due to different conditions. Similarly,
emissions are likely different due to changes in the maturity of the technologies, different
power plants may have different fuels and they are operated on separate times. Yet, the
simplified version is used as it shows similar patterns of base generation from nuclear
power and high fluctuation of VRES. This provides an approximated but detailed
representation of the hourly distribution profiles.
4.2 Price of Electricity
Next section describes the costs related to electricity in Finland and more specifically in
Oulu. After that the creation of the artificial real-time pricings for 2050 are presented.
41
4.2.1 Today
The total electricity price for customers in Finland is a combination of three separate
costs: the price of electricity, the distribution costs and taxes (Energiavirasto 2018a).
Taxes are fixed to similar customers and are based on the electricity consumption (Act
on excise tax on electricity and some fuels 1260/19961). Value-added tax (VAT) is also
added to the electricity price and distribution costs (Energiavirasto 2018a). Distribution
costs are related to the location and the distribution company and, therefore, they vary
over the country. There is no possibility for a customer to change the costs related to taxes
and distribution of electricity as taxes are set by the government and distribution costs by
the regional distribution companies, which are then regulated and monitored by the
energy market authority. Yet, the distribution costs can have TOU tariffs, varying over
the time of day, or by the season. Usually the distribution costs include a fixed monthly
fee added to a consumption based fee. (Energiavirasto 2018a) The distribution costs and
associated taxes are presented in Tables 3 and 4 below.
Table 3. The distribution costs in Oulu by the local distribution company Oulun Energia
Siirto ja Jakelu Oy in October 2018. The prices do not include the annual fees added to
the distribution price, nor the energy taxes. (Oulun Energia 2018)
Distribution Costs of Oulun Energia Siirto ja Jakelu Oy
Type of Distribution and the time of distribution Costs (€cents/kWh, incl.
VAT 24%)
Fixed Price 3.52
Daily Time-of-use, 7-22 2.96
Daily Time-of-use, 22-7 1.82
Seasonal Time-of-use, 1.11.-31.3 between 7-22 hours,
excl. Sundays
3.21
Seasonal Time-of-use, other times 2.16
1 Laki sähkön ja eräiden polttoaineiden valmisteverosta 1290/1996
42
Table 4. Taxes related to the electricity distribution and retail price for residential
customers in 2018. Class I is for e.g. residential customers. (Act on excise tax on
electricity and some fuels 1260/1996 2018, Energiavirasto 2018a, Verohallinto 2018)
Type of Tax Amount Variable
Energy Tax,
Electricity, Class I
2.24 €cent/kWh
VAT 24 %
Strategic Stockpile Fee 0.013 €cent/kWh
Unlike taxes and distribution costs, electricity prices are open for tendering for the
consumers. There are different electricity contracts available in Finland and they can be
divided either with their duration or by their type. Electricity consumers can choose
between fixed-term and non-fixed-term electricity contracts as well as from fixed priced,
TOU or RTP contracts. TOU price can be based on the time of the day, in which day and
night have different electricity prices, or on season, when the price is different in winter
days than in other times. The prices in fixed and TOU contracts are predefined and
tendered by the supply company, but RTP prices in Finland are defined on hourly-basis
in electricity stock market. (Energiavirasto 2018a)
The stock marketplace for electricity in Finland is Nord Pool, which is an electricity
trading market and a company providing electricity trading services. Currently it is
operating in the Nordic and Baltic countries. It provides hourly prices in day-ahead and
intraday markets called Elspot and Elbas, respectively. Some of their intraday markets
offer also half- or quarter-hourly products. (Nord Pool 2018a, 2018b) In day-ahead
market, an auction is held, where traders give bids on generation and consumption by
providing one of the four order types. The day-ahead market operates for each hour of the
day and the bidding procedure closes on 12.00 Central European Time (CET) the previous
day. After that, the hourly price is determined by matching created curves on demand and
supply for every hour, and by taking the network’s transmission restrictions into account.
Thus, all the transmission areas will have their own price area. (Nord Pool 2018b, 2018c)
Intraday market is then an hourly market for matching the consumption and production
during the on-going day by either continuous trading or by running an auction. The
bidding and trading for intraday market closes 30-minutes before the supply of electricity
in Finland. (Nord Pool 2018b, 2018d) Other balancing mechanisms on the Finnish
43
electricity market are handled by the Finnish transmission system operator (TSO) Fingrid,
which provides both generation and DSM markets. (Fingrid 2018a, 2018b)
A comparison of different electricity contract types and their costs to customers are
presented below on Table 5.
Table 5. Electricity prices by different contracts. Fixed and TOU prices are cheapest
found in November 2018 from Helen (2018) and RTP is average Elspot price in 2016
(Nord Pool 2018e).
Electricity contract Day tariff (7-22) Night tariff (22-7) Average daily
Fixed price - - 5.59 €cent/kWh
Time-of-Use 5.99 €cent/kWh 4.98 €cent/kWh 5.65 €cent/kWh
Real-Time-Price
(Elspot)
- - 3.24 €cent/kWh
Table 5 values for electricity prices for fixed and TOU contracts are currently higher than
what the real-time electricity price was in 2016. The difference in the electricity price
comes from the increase of the market price of electricity from 2016 to 2018. E.g. average
Elspot price in 2016 was 3.24 €cents/kWh while in 1.1.2018-30.9.2018 it was 4.59
€cent/kWh on average, increasing 42 %. (Nord Pool 2018e) Similarly, a fixed price
contract had an electricity price of 3.72 €cents/kWh in 2016, which is 50 % lower than
the current one (Energiavirasto 2018b). On the simulations, only RTP is used to calculate
the price of electricity.
The cost of electricity to consumer includes taxes, distribution costs and electricity price.
According to information from Energiavirasto (2018a) the hourly price to consumer can
be calculated with equation (2) below.
Cost = (EP + Distribution) × (1 + VAT) + TAX (2)
Where Cost is total cost of electricity to a customer [€/kWh], EP is the price of electricity
[€/kWh], VAT is the value-added-tax, Distribution is the respective distribution cost
[€/kWh] and TAX is the associated energy taxes [€/kWh]. For the cost calculation, fixed
distribution fee from Table 3 is used, energy taxes are combination of energy tax on
electricity and stockpile fee from Table 4 and electricity price is the hourly Elspot price
from 2016 (Nord Pool 2018e).
44
4.2.2 In 2050
Generally, the cost of generating electricity has an impact to the electricity price as it will
affect the supply price to the customers. Levelized Cost of Electricity (LCOE) describes
the unit price of electricity generation by technology and it can be used in comparing their
costs over the lifetime of the technology. LCOE [€/MWh] is calculated with equation (3)
below:
LCOE = ∑ ( ( It + OMt + Ft+ Dect ) × (1 + r) -t )
∑ (Et × (1 + r) -t )
(3)
where It is the investment costs at time t [€], OMt is operation and maintenance costs at
time t [€], Ft is fuel and carbon costs at time t [€], Dect is the decommissioning costs at
time t [€], (1+r)-t is discount factor and Et is the produced electricity at time t [MWh].
This calculation is done on by summing all the operational years together to achieve
lifetime LCOE. This equation is also used to determine the electricity price with which
the owner will break even on the operation. (International Energy Agency et al. 2010)
Thus, LCOE is used to determine the cost of electricity in 2050 by the generation
technology mix and their electricity generation distribution.
LCOE for different technologies in 2050 in Finland is calculated by using POLES model
values including extra costs and subsidies (Keramidas et al. 2017). POLES model gives
LCOE in US$/MWh which is exchanged to €/MWh with an average exchange rate from
October 2018 (Eurostat 2018). The calculated electricity price for the different scenarios
is presented in Figure 8. The electricity price is a weighted average of the LCOEs by the
technology and fuel mix of the on-going hour from chapter 4.2. Fuels are considered to
be consumed at fixed rate, which is equal to the fuel mix used in the technology. As there
are no direct LCOEs for coal and natural gas CHP plants, they are assumed to be the same
as respective combined cycle power plants using same fuels. This is assumed since
LCOEs for biomass CHP plant and biomass combined cycle plant are close in the POLES
model. The utilization of CCS is considered in the price similarly as in the emissions,
having a fixed share from the production.
The created hourly prices are then presented in Figure 8 below together with a
comparative hourly cost of 2016 created with using only LCOE values from the POLES
model.
45
Figure 8. Hourly electricity price calculated by using LCOE values of generation
technologies in 2016 and 2050.
Based on Figure 8, it seems that electricity price will increase in the future, as well as the
volatility of the price when compared to LCOE-based price from 2016. The future price
in Growth scenario is 9.9 €cent/kWh on average, whereas in Save scenario it will be
slightly higher at the 10.7 €cent/kWh level. Similarly, the volatility is higher in Save
scenario, which is due to the higher price of coal-based generation without CCS. Yet, in
both scenarios the increase in volatility come partially from the increase of VRES, which
has lower LCOE than nuclear or CHP plants.
There are some uncertainties in using LCOE in defining the electricity price. According
to International Energy Agency et al. (2010) LCOE prediction on the wholesale price
works better on regulated electricity markets, whereas in liberalized markets the real-time
price is defined by the marginal costs of the most expensive operated power plant of the
hour. Generally, VRES and CHP plants are not operated by the variation on electricity
demand, so they offer lower bids to the market during their operation. This impacts the
market-clearing price, as there is less need of more expensive generation. Similarly, the
margin costs may vary during the year according to the changes in fuel prices, for
46
instance. On the other hand, some unexpected events may cause spikes to the dynamic
price, as there may be a sudden lack of supply on the markets. This is also a downside on
LCOE-based price model as it cannot simulate the effect of the shifts in demand and
supply balance, which influences the market price. By using LCOE, the market price is
assumed to be more influenced by the supply price than by the demand levels, while their
balance is more important in dynamic markets. (International Energy Agency et al. 2010)
This uncertainty is visible on the mismatch between the average 2016 Elspot price in
Finland, which was 3.24 €cent/kWh, and the LCOE modelled price, which was on
average 8.16 €cent/kWh. This means that the modelled LCOE price was over 2.5 times
higher on average than the Elspot price. At the same time, LCOE price is unable model
the volatility of the current electricity market price, as it only had standard deviation of
0.86 while Elspot had 1.31. (Nord Pool 2018e)
In spite the lack of ability to model the dynamic market, LCOE is used in the creation of
the 2050 hourly price profile. International Energy Agency et al. (2010) considers that
LCOE is still a good method to estimate electricity prices, especially in comparing the
technologies, as it presents price of electricity when all the costs are delivered to the
customer. Hence, LCOE is used in creating the hourly model for comparing the costs of
the 2050 scenarios and their technologies. It should provide information on the volatility
of the electricity price on the supply-side as well. Yet, the level of uncertainty is naturally
high in the price scenarios as the LCOE model has its downsides on modelling the
dynamic behavior and deregulated electricity market.
4.3 Climate
Climate influences the building simulation and therefore, it is important to present the
environment in which the simulations are run. For this reason, representative climates for
today and 2050 for Oulu are presented on their respective sections.
4.3.1 Today
In the model 2016 values from the Finnish Meteorological Institute’s (FMI’s) open data
set are used in the simulations to present outdoor temperature and solar irradiance
(Finnish Meteorological Institute 2018a). Temperatures are from Vihreäsaari, Oulu and
global irradiance data is from Sotkamo, which is the closest solar irradiance measuring
point available in FMI’s open data set. Sotkamo is placed southeast from Oulu and is
47
located inland, whereas Oulu is on the sea shore. This means that the used solar irradiance
values are slightly different than what the Oulu climate would have. (Finnish
Meteorological Institute 2018a) The solar irradiance dataset was lacking 5 values from
December and were mainly from night and morning. The next observed values were in
the same magnitude than day before, and due to lack of starting values, the missing
datapoints were taken from the observed values from day before. The dataset also
included some global irradiance values from the winter nights, but their impact was
assumed to be little on the annual basis. Temperature and solar irradiation variations are
represented in Figures 9 and 10 and they are compared to monthly average values to show
the long-term variations in climate and to validate the data. Similarly, Test Reference
Year 2012 (TRY2012) for area III, which includes Oulu, is added as well, as it presents
an average year used in the building simulations (Jylhä et al. 2012).
Figure 9. Average temperatures in Oulu by test reference year 2012 (TRY2012),
measured average values from Oulunsalo Pellonpää in 1980-2010, and 2016 monthly
average temperatures (Jylhä et al. 2012, Pirinen et al. 2012, Finnish Meteorological
Institute 2018a).
48
Figure 9 shows that 2016 was slightly warmer year than what the average year in Oulu
is: the annual average temperature in 2016 was 4.1°C as the 1980-2010 average was
2.7°C. (Pirinen et al. 2012, Finnish Meteorological Institute 2018a) The only colder
month in 2016 was January, which was over 2°C colder than normally. The difference in
the average and measured temperatures may be subject to different measuring stations, as
2016 values are from Vihreäsaari, which is located on the seaside and measured average
values are from Pellonpää, which is located more on the inland. On the other hand, as the
aim is to simulate one representative year, the measured values are used instead of
TRY2012 values, as the climatic conditions can impact the energy mix of the year. In
general, the average monthly temperatures follow the same seasonal pattern in 2016 than
the measured average, and thus 2016 temperatures are used to represent a single year in
the current climate. The average monthly irradiance sums of the used data and its
comparison to data from Jyväskylä, TRY2012 and PVGIS are presented on Figure 10.
Figure 10. The average monthly irradiance sums of used data in the simulation from
Sotkamo 2016 compared to average monthly sums from Jyväskylä 1980-2009, to a
TRY2012 and data obtained from an interactive tool PVGIS near Oulu (Jylhä et al. 2012,
European Commission 2017, Finnish Meteorological Institute 2018a).
49
On Figure 10 the measured Sotkamo irradiance values show similar monthly patterns than
TRY2012 and PVGIS values on Oulu, and therefore Sotkamo data is assumed applicable
in the model. Only difference is the clearly lower irradiance sum values on April that can
be caused by natural variation as other months have compatible values to the compared
databases. On hourly level there might be some differences due to the location and
climatic differences, but due to lack of data from Oulu, the closest measuring station is
estimated to give acceptable values on global irradiance.
4.3.2 In 2050
The temperature changes in Oulu region by 2050 were created by using monthly average
temperature projections from Finnish Meteorological Institute and Ilmasto-opas (2018),
by calculating the projected temperature change from TRY2012 and using the monthly
temperature change values in Oulu with three Special Report on Emissions Scenarios
(SRES): B1, A1B and A2, which are described in Nakicenovic et al. (2000). As the data
is provided in every 10 years from 2015 onwards until 2085, the projected temperature
changes by 2050 were interpolated from the provided values. The 2050 temperatures for
the climate scenarios are created by multiplying the changes in temperatures from
TRY2012 to the projected mean monthly temperatures in Oulu in 2050, so that the
average monthly temperatures match. These changes from the TRY2012 are presented in
Figure 11.
50
Figure 11. The changes in mean temperatures from TRY2012 for area III to projected
2050 temperatures in Oulu by SRES (Jylhä et al. 2012, Finnish Meteorological Institute
and Ilmasto-opas 2018).
As Figure 11 shows, there is a clear increase in the average temperature. Yet, there are
some months like April, May, September, and October, where the future temperature in
Oulu is closer to the current TRY2012 temperature, or even under that. This highlights
the importance of using the values from Oulu.
Global radiation changes are harder to mimic due to lack of hourly and monthly data of
the changes, so the TRY2050 values were used from Finnish Meteorological Institute
(2012) for the area III. It is acknowledged that the TRY2050 presents the A2 scenario,
and thus the global radiation data is according to that and the changes in other scenarios
might be different.
51
4.4 Building specificities
4.4.1 Today
The Decree of the Ministry of the Environment on the energy efficiency of the new
building (1010/20172) includes reference values for the U-values for new buildings.
These reference values together with the ventilation losses and air-leakages are used to
define the total heat loss through the building envelope, so that building’s heat loss is
equal or smaller than the heat losses with the reference values from the decree. The overall
thermal transmittance reference values from the decree are gathered to the Table 6 below.
Table 6. The overall thermal transmittance values for different new building types
(Decree of the Ministry of the Environment on the energy efficiency of a new building
1010/2017 2017).
The building structure
U-value for
warm and
cooled spaces
[W/m2K]
U-value for
transferable and
partially warm
spaces [W/m2K]
U-value for
vacation use
–buildings
[W/m2K]
Wall 0.17 0.26 0.24
Solid wood with thickness
of at least 180 mm
0.40 0.60 0.80
Ceiling and floor
connected to the exterior
air
0.09 0.14 0.15
Floor with crawling space 0.17 0.26 0.19
Floor against ground 0.16 0.24 0.24
Window, skylight, door,
roof dome, smoke hatch
and exit hatch
1.00 1.4 1.4
Buildings from the past have different U-values as the regulations were different. The
thermal insulation requirements from past building codes are presented on Table 7 below
by the year when the regulation became in force.
2 Ympäristöministeriön asetus uuden rakennuksen energiatehokkuudesta (1010/2017)
52
Table 7. The U-values, air-leakage numbers n50 (q50 for 2012 and 2018) and heat
recovery rates from past building regulations in Finland. (The National Building Code
of Finland C3 1978, 1985, 2003, 2007, 2010, The National Building Code of Finland
D3 2012, Ministry of the Environment 2013, Decree of the Ministry of the Environment
on the energy efficiency of new buildings 1010/2017)
The structure
of the building 1978 1985 2003 2007 2010/2012/2018
U-values [W/m2K]
Wall 0.29/0.35
(under/over
100kg/m2)
0.28 0.25 0.24 0.17 (0.4 for
building timber)
Ceiling and
floor against
exterior air
0.23/0.29
(under/over
100 kg/m2)
0.22 0.16 0.15 0.09
Floor with
crawling space
- - 0.2 0.19 0.17
Floor against
ground
0.4 0.36 0.25 0.24 0.16
Windows and
doors
2.1/0.7
(Window/
solid part of
the door)
2.1/0.7
(Window
/solid
part of
the door)
1.4/1.5
(Window
and
door/sky
light)
1.4/1.5
(Window
and
door/sky
light)
1
Others
Building’s air-
leakage
number n50
[1/h]
(2012 and
2018 values
are for q50
[m3/hm2])
6.0 6.0 4.0 4.0 4.0
Heat recovery
rate [%]
0 0 30 30 45 (55 for 2018)
In addition to the legislative values, low-energy and passive buildings are considered as
their own building types. They differ from each other by the heating energy demand. RIL
(2009, pp. 12 & 28) on the other hand describes low energy buildings by their annual
combined heating and cooling energy consumptions corrected to Jyväskylä’s climate.
There the low energy building’s corrected annual consumption is between 26 and 50
53
kWh/m2. Conversely, Siikanen (2015, p. 63) had slightly higher limits for the low energy
building, that was described with heating need of less than 60 kWh/brm2 per heated
building area in South-Finland, and less than 90 kWh/brm2 per heated building area in
North-Finland.
The next step from a low energy building is a passive house, which is a construction
concept providing comfort and affordability to the occupants by being energy efficient
and ecological building. It is an improved low energy building consuming less energy to
heat up the building than conventional or low energy building. Yet, as the climates vary
in different regions, the passive house concept should adapt accordingly still fulfilling the
same principles. (Passive House Institute 2015) The Finnish definition for a passive
energy building is between 20 and 30 kWh/brm2 in South and North-Finland respectively
(Siikanen 2015 p. 63). RIL (2009, pp. 12 & 28) then defines passive house as one which
has less than 25 kWh/m2 heating and cooling need annually. The RIL (2009, pp. 12 & 28)
definitions for low energy and passive buildings are used and their characteristics are
presented on Table 8.
54
Table 8. The over-all thermal transmittance U [W/m2K], the building envelope’s air-
leak number n50 [1/h] and heat recovery [%] values from the Decree of Ministry of the
Environment on the energy efficiency of the new buildings (1010/2017) and RIL (2009
p. 34) values for low and passive energy buildings.
The National
Building Code
2018
Low energy
building
Passive energy
building
U-values [W/m2K]
Wall 0.17 0.12 0.08-0.10
Ceiling 0.09 0.08 0.07
Floor to exterior air 0.09 0.08 0.08
Floor to crawling
space
0.17 0.10 0.08
Floor against
ground
0.16 0.12 0.10
Windows 1.0 0.8 0.7-0.8
Doors 1.0 0.6 0.5
Others
Air-leakage
number n50 [1/h]
2.0 0.8 0.6
Heat recovery 55 % 70 % 80 %
Thermal mass on the other hand is not related to the thermal insulation level of the
building. This means that buildings with the same thermal insulation levels may have
different thermal time constants. (Karlsson 2012) Table 9 below presents the different
heat capacity values estimated for the buildings by The National Building Code of Finland
D5 (2017). The total heat capacity Cs [Wh/m2K] of the building is then achieved by
multiplying the heated floor area by the thermal mass value.
55
Table 9. The values and construction materials for different thermal masses estimated
by The National Building Code of Finland D5 (2017).
Thermal mass
description
Heat capacity for the
building envelope Cs
[Wh/m2K]
Construction materials
Light 40 Log
Medium light 70 Floor is concrete, and others are log
Medium heavy 110 Exterior walls are walling blocks or
solid timber, floor is concrete, and
roof and partition walls are log
Heavy 200 Exterior walls are concrete or bricks,
partition walls are walling blocks or
bricks, and roof and floor are concrete
4.4.2 In 2050
The buildings can be assumed to be more energy efficient in 2050, as EPBD
(2010/31/EU) and its amendment (EU 2018/844), define that all new buildings need to
be nearly zero energy buildings by the end of 2020, and that the member states need to
create a renovation strategy in order to transform current buildings into nearly zero energy
buildings and decarbonize the building stock. (The European Parliament and The Council
of the European Union 2010, 2018) Thus, it can be assumed that the level of insulation in
future increases, reducing the energy consumption and demand of buildings. In Lehtilä et
al. (2014) the combined space heating and hot water demand of new residential buildings
in 2010 was 123 kWh/m2, and for the existing residential building stock it was 181
kWh/m2. Similarly, they assumed the combined space and water heating energy demand
of 50 kWh/m2 and 94 kWh/m2 for new and existing buildings respectively in Growth
scenario. In Save scenario the same assumptions for new and existing buildings were 55
kWh/m2 and 116 kWh/m2. Using assumptions and calculations on hot water consumption
and its heat demand from Motiva Oy (2017b) the hot water demand is assumed to be
approximately 35 kWh/m2. This means that the new buildings in 2050 would be passive
houses and the existing building stock would have space heating demands between
current new houses and low-energy houses.
Zero and Net Zero (or Plus) Energy Buildings can also present future buildings. Currently,
there are several definitions on zero-energy building (ZEB), varying in its energy balance
and use, supply of renewable energy, connection to grid, emissions and costs (Torcellini
56
et al. 2006, Marszal et al. 2011). One example definition then is from the US Department
of Energy (DOE) (2015) who considers that the ZEB needs to be energy efficient and that
the annual exported energy from the on-site renewable sources is at least equal to
delivered one on source energy basis. Similarly, Siikanen (2015, p. 63) considers ZEB to
generate equal amount of energy to the building’s consumption including both heating
and electricity consumptions. This definition is used in the simulations to present ZEB.
Net Zero Energy Building (NZEB), or Plus Energy Building in some other references, is
another term used in the literature among with the zero-energy building to describe
potential future buildings. NZEB concept was discussed and reviewed by Wells et al.
(2018) where the issue of the lack of common definition was presented. Majority of the
definitions resembled ZEB ones showing the interconnection of the concepts and terms
to the extent of having similar measured metrics like energy demand, costs, emissions,
and primary energy. In Siikanen (2015, p. 63) and Laustsen (2008) Plus Energy Buildings
were considered as the buildings which should generate more energy in a year than what
they consume. Thus, it seems that NZEBs are buildings with similar characteristics as
ZEBs, but that their electricity generation is higher than of ZEB’s.
57
5 OWN MODEL
This section describes the work conducted to create the used thermal model and its
simulation rules. The chapter starts with discussing the general matters on thermal models
and reasoning in selecting the used model type. This is followed by presenting the existing
smart house model and the basics used in the thermal model. After that calculation
methods for the heat demand and other modelling blocks are defined, as well as the rules
used in the simulations.
5.1 General on thermal models
A thermal model for the building is created to test out the different DSM methodologies
for electric space heating. The first step was to consider different thermal models for
buildings in order to determine the most suitable one for this application. Li and Wen
(2014) considers the building energy modelling by the length of the simulation and
forecasting time: long-term forecasts are for system-planning, medium for system
maintenance and short-term for daily operation. The short-term thermal models can
further be roughly divided in to three main categories by their characteristics: to “white-
box”, “gray-box” and “black-box” models. In “white-box” models, the modelling is
purely utilizing the physical behavior of the building, while in the “black-box” models,
the entire system is based on statistical analysis modelling and it does not take any
physical properties or systems into account. “Gray-box” models are the third category of
thermal models and they combine “white” and “black-box” models as they include
physical systems but use statistical analysis to solve them. (Foucquier et al. 2013, Li and
Wen 2014)
The “white-box” models can be further divided in to three sub-categories: computational
fluid dynamics (CFD), zonal and nodal approaches. In CFD approach the modelling is
done in 3D and it gives the most precise results while requiring a lot of computing power.
Zonal approach divides each building zone in to smaller cells, in which the thermal
calculations and transitions are calculated. The calculations give 2D results and are not
as accurate as with the CFD approach but are computed faster. The final “white-box”
model is a one-dimensional nodal approach where each nodal area is a homogeneous area
having its own variables and the thermal transfer is happening between the nodes. The
nodal model can utilize either transfer functions or finite difference method, which can
58
also be described as the “RC” or “thermal-network” model as it mimics Ohm’s law from
the electricity systems. (Foucquier et al. 2013) Also majority of the building modelling
software, like TrnSYS, EnergyPlus and IDA-ICE, utilize the nodal approach (Foucquier
et al. 2013, Li and Wen 2014, Equa 2018, Thermal Energy System Specialists 2018, US
Department of Energy (DOE) et al. 2018). Yet, one of the downsides of “white-box”
models is that they are not able to measure and provide thermal comfort properly. Other
drawbacks are the requirements of physical formulation and parameters, which may be
uncertain or difficult to model sometimes. (Foucquier et al. 2013)
As the aim of the model is to be universal and able to simulate houses with different
characteristics without any actual measured data, a creation of “white-box” model is
chosen for the simulation. This way the model is bounded to the physical characteristics
of the building. At the same time, to create a computationally efficient model, a nodal
approach was selected as it is simplified enough description. The thermal house model
was created with Matlab simulation program on top of an existing smart house model,
which is described in sector 5.2 below (MathWorks 2018). The equations, assumptions
and the functioning of the different modeling blocks are defined in their respective
subsections of this chapter.
5.2 Current smart house model
Thermal model is built on top of an existing smart house model, which was created to
assess HEMS on reducing CO2 emissions in residential sector. (Louis et al. 2014a, Louis
et al. 2016) The current model includes 4 main sections: it creates electricity consumption
profiles of appliances, generates events, includes control by HEMS and considers the
effects of user responsiveness based on given input data. This consists different
parameters like energy efficiency of appliances, consumption profile types, electricity
contracts and building characteristics. It has 4 options for HEMS which are handled
through a controller and vary in providing information of consumption and billing to load
shifting of appliances. The model steps include different modeling blocks e.g. pricing,
environmental impacts, and occupancy estimation. In addition there is a simple thermal
house model, which is developed further in this work. (Louis 2016) It also has models for
electricity production with PV panels, wind power and fuel cells, making it possible to
study the integration of local generation. (Louis et al. 2016) In addition to these, the smart
59
house model includes various databases like dynamic hourly emission profile of the
electricity generation in Finland (Louis et al. 2014b).
5.3 Assumptions
There are some assumptions that are needed in the model for simplifying it to the degree
that the simulation is sensible. The assumptions used while creating this model are listed
down below.
- The simulated building is a detached house.
- The detached house is modelled as a single zone having only one room and one
floor. It only has a roof and not a separate ceiling. The building does not have a
balcony, which would impact the heat demand calculations.
- The building does not have separate thermal bridges.
- The system is considered to have hourly steady-state conditions.
- The building does not have any external shadings.
- Only dry heat is delivered. Thus, energy attached to the moisture is not considered
and neither is humidifying the ventilation air.
- There is no sauna in the building, as it would affect the heating calculations.
- The exhaust and supply air flows are equal.
5.4 General information and inputs to the simulation
The aim of the model is to study DSM methods and technologies on electric heating.
Thus, thermal house model is created to simulate performance and behavior of a building.
Firstly, the model includes separate sections for calculating heat demand and indoor
temperature with the simulated input values. Heat demand calculations are based on
energy balance method using assumptions of steady-state conditions. It follows the
heating demand calculations from The National Building Code of Finland D5 (2017) so
that the energy losses and internal heat gains are calculated on hourly-basis instead of
monthly-basis. Indoor temperature calculation follows standard SFS-EN ISO 52016-1
(2017), which utilizes a “thermal-network” model for dynamic calculation of indoor,
radiative and operational temperatures of the building on hourly-basis. Thus, assumptions
used to calculate the indoor temperature are defined in the standard.
60
The thermal house model needs heating power to keep the indoor climate thermally
comfortable and the lost heat is compensated by supplying heating energy to the building.
Heating power calculations in the model mainly comply with the heating power
calculations in The National Building Code of Finland D5 (2017) by calculating the
heating power required to match the heat demand on hourly steady-state conditions.
Therefore, power can be converted to energy delivered during an hour of constant
operation.
Other sections of the model include thermal comfort estimations and calculations,
forecasts on weather, heat demand and local electricity generation, determining
occupancy of the residents, calculating the local generation from PV panels, and
modelling the usage of a battery system. The functions and simulation blocks of these
sections are described in their respective chapters, and not all of them are needed on every
simulation scenario. A general description of the model is shown below on Figure 12 and
the inputs for the simulation are presented in Table 10.
61
Figure 12. The general description of the simulation. Each of the individual parts are
defined and discussed in their own sections shown in the chart. Not all looping parts are
needed in all simulations. The scenarios are depending on heating technologies and are
defined in section 5.11.
62
Figure 12 shows the general progress of the model. It starts by calculating heat demand
from heat losses and internal heat gains. Then it makes day-ahead forecasts for local
generation and heat demand for the future as they are needed in some of the simulation
scenarios. Then it simulates the action of ventilation and estimates suitable indoor
temperatures of thermally comfortable indoor climate. Then heating power is supplied,
or storages operated under their given limits. Finally, the model calculates CO2 emissions,
costs, savings, and monetary gains from the simulated hour.
Table 10. The input variables of the simulation, which are up to the selection of the user.
User may select to apply a database, which includes values for buildings which are
defined in section 4.7. or add their own values. Inputs for appliances, inhabitants and PV
generation are applied to Louis et al. (2016) as they are part of the original model.
Dimensions of the
building
Length of building in south and west sides, height, pitch
of the roof, areas of windows on each of the main
orientations and area of door.
Thermal inertia of the
building
Thermal Mass of building
Thermal insulation
values
U values for walls, floor, roof, windows, and door
Convective loss values Air leak of building (n50/q50), ventilation air change rate
(1/h), heat recovery rate
Internal heat gain values Number of inhabitants, appliances, g value of the window
Scenario selection Heating technology and scenario selection. Type of
ventilation technology. Selection of predefined database
values (by building construction year)
Local generation
selection
Usage, number, size, power, and connection types of PV
panels. Number of batteries.
Simulation period Start and end dates, timestep
Temperature limits Selection of the PMV range for thermal comfort.
Temperature set up for constant heating scenario
Type of electricity
contract
Selection of the electricity contract type (fixed, TOU or
RTP)
The inputs of the model are then presented in Table 10. It shows all the possible variables
which may be added and changed in the model. On the other hand, the selected input
values are presented in Table 11. These values will stay constant for all the scenarios and
some of them are used to create value vectors from the original smart house model. These
63
vectors will be used in all scenarios to ensure the consistency of the simulations with
different scenarios.
Table 11. The default input values used in the simulation.
Variable Input
Length of South wall 10 m
Length of West wall 12 m
Height 2.5 m
Pitch of the roof 8 °
Area of South Window 2 m2 (3.75 m2 for 15 %)3
Area of North Window 2 m2 (3.75 m2 for 15 %)
Area of West Window 2 m2 (4.5 m2 for 15 %)
Area of East Window 2 m2 (4.5 m2 for 15 %)
Area of Door 2 m2
Type of electricity contract Real Time Pricing, no price limits
Profile type 1
Metering type 2, Billing only
Appliances No Electric heater or sauna. Rest of the
appliances 1, type A or B, Low
consumption lightbulbs.
User type Orange
Number of inhabitants 1
With the input values from Table 11, the original smart house model creates appliance
consumption profiles and calculates both solar irradiance to vertical surfaces for the 4
main orientations, and electricity generation from PV panels. These are used to calculate
heat gains in the building and estimate the occupancy of the residents. Local electricity
generation is used in the scenarios either as a condition for actions or to calculate the
savings in electricity invoices and CO2 emissions.
3 15 % from the area of the wall for window is a default value from (The National Building Code of Finland D5 2017)
64
5.5 Heat Demand
Heat demand describes the state of the building from heat energy balance point-of-view,
as it describes whether a building is losing or gaining energy. Generally, in a heating
season, heat demand is the amount of energy needed to keep the system in a steady-state,
which means having a constant indoor temperature. Heat demand is therefore, affected
by the heat losses and gains of the system. The heat demand calculation procedure is
based on equations from The National Building Code of Finland D5 (2017) and they are
applied in the model with dynamic hourly values. On Figure 13 below the general concept
of heat demand by energy balance method is depicted.
Figure 13. A representative picture of main heat losses and gains in the building. Arrows
going through the building envelope to outdoor air present heat losses and arrows inside
or to the indoor air present heat gains. (Modified from the principles from Hagentoft 2003,
Siikanen 2015, The National Building Code of Finland D5 2017)
Heat demand is influenced by heat gains and losses in the building. Therefore, heat
demand is calculated by their difference as presented in equation (4) below:
QHeat demand
= QHeat losses
- QHeat gains
(4)
Where QHeat demand is heat demand of the building [Wh], QHeat losses is the heat losses from
the building [Wh] and QHeat gains is the heat gains in the building [Wh]. (The National
65
Building Code of Finland D5 2017) When heat losses are higher than gains, system has
positive heat demand, which needs to be fulfilled with heating energy to keep the system
balanced and indoor temperature constant. Vice versa, if heat gains are higher than heat
losses, heat demand will be negative, and system will gain energy, raising indoor
temperature.
Heat losses and gains can be further divided into smaller sections describing the sources
of them. First, heat losses are calculated with equation (5)
QHeat losses
= QConductive
+ QAir-Leak
+ QVentilation
(5)
Where QConductive, QAir-Leak and QVentilation are heat losses from conduction, air-leakage
through the building envelope and ventilation [Wh]. (Modified from The National
Building Code of Finland D5 2017) This shows that there are 3 main heat loss sources in
a building, including conductive heat transfer through building structures, and convective
heat transfers from building envelope air-leakages, and ventilation. Eventually,
conductive heat transfer can be divided further into conductive heat transfers through each
building element as presented in equation (6)
Qconductive
= Qew
+ Qc + Q
f + Q
w + Q
d + Q
other + Q
TB (6)
where 𝑄𝑒𝑤, 𝑄𝑐, 𝑄𝑓, 𝑄𝑤, 𝑄𝑑, 𝑄𝑜𝑡ℎ𝑒𝑟 and 𝑄𝑇𝐵 are the conductive heat losses of exterior
walls, ceiling, floor, windows, door, unconditioned places and thermal bridges
respectively [Wh]. (The National Building Code of Finland D5 2017) Conductive heat
loss through a single structure is then defined with equation (7)
Q = A × U × ∆T × t (7)
where Q is the heat flow through the material [Wh], A is the surface area of the structure
[m2], U is the overall thermal transmittance of the structure [W/m2K], ΔT is temperature
difference on indoor and outdoor [°C] and t is time [h]. (Siikanen 2015, p. 56, The
National Building Code of Finland D5 2017) Here the U-value describes the overall
conductive heat flow rate that surpasses the structure. Basically, this shows the amount
of heat conducted through 1 m2 area of the structure when temperature difference is 1°C.
Hence, it presents the thermal insulation level and quality of the structure. (Siikanen 2015,
p. 56)
66
Generally, the conductive heat transfer depends on the temperature difference between
indoor and outdoor, like with a wall structure, while heat conduction through floor
depends on the temperature difference of ground and inside air. The ground temperature
is calculated on monthly-basis according to the calculation method presented in The
National Building Code of Finland D5 (2017). This is expected to give high enough
precision, as according to the building code, the average ground temperature variation
during a year is 6°C. The needed annual average temperature in Oulu is taken from
Pirinen et al. (2012).
Other heat loss type is convective heat transfer, which includes building envelope air-
leakage and ventilation. Their convective heat transfer is calculated with equation (8):
Qconvective
= ca × ρa × q
v in × (Tsup - Tint) × t (8)
where Q𝑐𝑜𝑛𝑣𝑒𝑐𝑡𝑖𝑣𝑒 is the convection heat transfer [Wh], qv in is the air flow of supply air
[m3/s], ρ𝑎 is the density of air [kg/m3], c𝑎 is the specific heat capacity of air [J/kgK], T𝑠𝑢𝑝
and T𝑖𝑛𝑡 are the supply and indoor air temperatures [°C]. The supply air temperature
depends on the type of the convective heat transfer; in case it is building envelope air-
leakage or a non-heated ventilation system, supply air temperature is equal to the outdoor
air temperature. If the ventilation system includes ventilation heater, then supply air
temperature is higher. (SFS-EN ISO 52016-1 2017, The National Building Code of
Finland D5 2017) The air flow rate is also different between the air-leakage and
ventilation. In building envelope air-leakage, the supply air flow is equal to the air flow
through untight building structures, which is calculated in accordance with The National
Building Code of Finland D5 (2017) calculation methods for building air-leakage value
q50. Ventilation system on the other hand can adjust its supply air flow.
The heat demand is also impacted by the heat gains, which are either waste heat emitted
from appliances or people, or radiative short-wave heat transfer through transparent
materials. Thus, they can be calculated by using equation (9).
QHeat gains
= QPeople
+ QAppliances
+ QSolar Heat Gain
+ QHot water
(9)
Where QPeople, QAppliances, QSolar Heat Gain, and QHot water are internal heat gains from people,
appliances, solar radiation, and hot water respectively [Wh]. Here appliances include heat
67
gains from lighting sources, and as the model does not consider hot water consumption,
its effect is neglected. (SFS-EN ISO 52016-1 2017, The National Building Code of
Finland D5 2017)
5.5.1 Heat gains from appliances
Appliances generate waste heat during their operation, which may be utilized as a source
of heat. This reduces the heat demand inside the building. The heat generation from an
appliance depends on its characteristics and parameters. For example, dishwasher uses
electricity to heat up the supply water and then discharges it to the sewage decreasing its
heat gain. (Wilson et al. 2014) Heat gains from appliances are divided into sensible and
latent heats. Sensible heat includes radiant and convective heat transfers, and thus, its
share from the electricity consumption is used to determine the heat gains from
appliances. The used fractions of the appliances are presented below on Table 12.
Table 12. The sensible heat fraction of appliances from their electricity consumption.
Values for “Other appliances” and “Lighting” are from (The National Building Code of
Finland D5 2017) and the rest are from (Wilson et al. 2014).
Appliance Sensible heat fraction
Refrigerator 1.00
Washing Machine 0.80
Dishwasher 0.60
Range (Hobs & Oven) 0.40
Television 1.00
Microwave 1.00
Freezer 1.00
Other Appliances 1.00
Lighting 1.00
After knowing the sensible heat gain fractions of the appliances, their internal heat gain
profiles are created. First, the electricity consumption profiles of the appliances are
created with the original model, after which each appliances’ electricity consumption is
differentiated from each other. Then, each appliance’s internal heat gains are calculated
on hourly-basis by multiplying their electricity consumption by their sensible heat
fraction. After that, the internal heat gain loads are aggregated to one hourly heat gain
load from appliances.
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5.5.2 Heat gains from people
The presence of people creates heat loads according to their occupancy and actions in the
building. The activity level of a person varies with their metabolism rate, which changes
their heat generation amount as well. According to SFS-EN ISO 7730 (2005) standard,
the metabolic rate of a person in a seated position is considered to be 1.0 met, which is
equal to 58 W/m2. As the standard body surface area of an adult is 1.80 m2, which is
calculated with the Du Bois and Du Bois (1916) method presented in Ahmed et al. (2017),
the heat generation of a person is 104.4 W. Assuming the body surface area as a constant
from Du Bois and Du Bois (1916) and with the metabolic rate estimates from the SFS-
EN ISO 7730 (2005) standard, the heat gains from certain activity levels can be
calculated. These values and activity levels are presented below on Table 13.
Table 13. The metabolic rates for different activities and their internal heat gains for
surface body area of 1.80 m2. (SFS-EN ISO 7730 2005, Ahmed et al. 2017)
Activity Metabolic Rate
(W/m2)
Metabolic Rate
(met)
Heat generation
(W)
Recline 46 0.8 82.8
Seating 58 1.0 104.4
Sedentary activity 70 1.2 126
Domestic Work 116 2.0 208.8
Next the occupancy and actions of the residents need to be determined to calculate the
heat generation from the people, for which the electricity consumption profiles of
appliances are used. The occupancy and action detections are done differently for single
and multiple inhabitant houses. The current model already includes an occupancy
detection method, also utilizing electricity consumption of appliances to determine
whether lighting is on or not. This principle is now developed to bring out the actions of
people to provide dynamic information about their heat generation. This information is
also used in the thermal comfort functions. The flow chart on determining the occupancy
and actions of a single person household are presented on appendix A and on multiple
person households on appendix B. The principle used in them is described below.
The detection of sleeping, sedentary activity, seating, and domestic work comes mainly
from time and action of few appliances. When time is between 22 and 8, people are
sleeping. In other times, when vacuum or oven is on, at least one person is doing domestic
69
work, as these appliances are the ones with which people are needed to be involved with
all the time, unlike with dishwasher or washing machine. Otherwise, when a computer or
TV is on, people are most likely using them, meaning that they are seating. After that, if
any other occupancy requiring appliances from Louis et al. (2016) has power
consumption higher than their stand-by power, people are doing sedentary activities.
Metabolic rates of the actions are described in appendices and the resulting heat gain is
equal to values in Table 13.
5.5.3 Heat gain from solar radiation on transparent materials
The third considered heat gain source in the model is short-wave radiation through
transparent material. The main principle for estimating the solar heat gain, is calculating
heat transfer through the transparent material. This requires calculations of global
irradiances on vertical surfaces facing the four main orientations. These global irradiance
values are achieved by using the solar radiation function from Louis et al. (2016) with
their respective values. The solar heat gain calculation in the model is done in accordance
with the The National Building Code of Finland D5 (2017) by following their calculation
method and using their default values, except for the total solar radiation transmittance
value g, which is calculated by using equation (10).
g = FW × gdirect
(10)
Where FW is a non-scattering glazing correction factor, for which 0.9 can be used as a
default value, and gdirect is total solar energy transmittance value on direct solar incident.
(SFS-EN ISO 52016-1 2017) The total solar energy transmittance g values for different
windows can be calculated by utilizing their total solar energy transmittance values. For
this purpose, databases from Ikkunawiki (2018a, 2018b) are used in order to find
corresponding window types to U-values from the legislation. With the selected window
types, the total solar energy transmittance value g is calculated with the equation (10)
with the matching gdirect value from The National Building Code of Finland D5 (2017)
and are added to the Table 14 below.
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Table 14. Different glazing types selected from the database and their total solar energy
transmittance values. g value is calculated with equation (10) by taking gdirect values
from The National Building Code of Finland D5 (2017) and matching their U-values to
their legislative limits from Table 7. (RIL 2009, p. 34, Ikkunawiki 2018a, 2018b)
Year Glazing type from
database
Suitable glazing type
from (Ikkunawiki
2018a, 2018b)
U-values g-values
1978 Three glazes and
two frames
Three glazes and one
frame
1.8 0.9 × 0.7 =
0.63
1985 Three glazes and
two frames
Three glazes and one
frame
1.8 0.9 × 0.7 =
0.63
2003 Three glazes with
selective layer
and two frames
Three glazes and one
frame
1.3 0.9 × 0.7 =
0.63
2007 Three glazes with
selective layer
and two frames
Three glazes and one
frame
1.3 0.9 × 0.7 =
0.63
2010 From RIL From RIL 1.1 0.56
2018 From RIL From RIL 1.0 0.56
Low energy
building
From RIL From RIL 0.8 0.56
Passive
house
From RIL From RIL 0.7 0.46
5.5.4 Solar Shading
Solar irradiation combined with high outdoor temperatures may raise the indoor
temperature during the summer, and thus, an internal solar shading device is applied to
prevent the indoor temperatures from increasing too high. The selected solar shading
device is internal white horizontal venetian blinds, which are operated manually. The
impact of the shade is calculated by utilizing a reduction factor for the blinds, for which
a value of 0.6 is used from the The National Building Code of Finland D5 (2017) and the
total solar energy transmittance value g is then corrected by multiplying it by the
reduction factor. This results in a new total solar energy transmittance value, which is
used when the venetian blinds are used.
The manual operation of the blinds includes determining the occupancy of the residents
and measuring both indoor temperature and global irradiation value. The first requirement
is that there needs to be someone inside the building to turn the blinds on or off. Then,
71
the blinds are operated to prevent higher indoor temperatures, so they are applied when
the indoor temperature rises over 25 °C. Third clause for the operation is that the amount
of global irradiation to the surface of the window is higher than 0. The blinds are then
turned off once either the indoor temperature drops under 25 °C or the global irradiance
value is 0. This action also requires someone to be occupant in the building to turn them
off.
5.6 Ventilation
Building ventilation can be divided to mechanical and natural ventilations. Mechanical
systems include three sub-categories which are exhaust, supply, and balanced
ventilations. On mechanical exhaust and supply ventilations, only the respective air flow
is forced, while the other flows naturally. On balanced ventilation both supply and exhaust
air flows are controlled at the same time, and they are usually attached to a heat exchanger
or a heat recovery heat pump. The most common residential ventilation types are natural,
mechanical exhaust air and balanced ventilations, out of which balanced ventilation has
heat recovery system. (Concannon 2002)
Common practices in balanced ventilation is assumed to have either an electric ventilation
heating system for the supply air or preheater prior to the heat exchanger and they both
reduce the heating need inside the building. Both balanced and exhaust ventilations use
electricity to run; exhaust ventilation only for the extraction of the exhaust air and
balanced for supplying and extracting air. Natural ventilation does not use electricity nor
have a heat recovery system. It also does not have a good control over the air circulation
in and out of the building, and thus here it is considered to have only fixed air change rate
in the building. As natural ventilation is not considered to provide control over the indoor
air flow, it is not considered in the simulations. The selection of the used technology is
based on the legislative limits given to the building at the time of the construction, which
are presented in section 4.7, so that if building needs a heat recovery system, then it has
a balanced ventilation system. Otherwise building is expected to have mechanical exhaust
air ventilation. A flow chart showing the characteristics of the ventilation systems and the
simulation procedure is presented in Figure 14 below.
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Figure 14. Flow chart presenting the ventilation systems included in the model and their
specificities.
As Figure 14 shows, the electricity consumption differs by the characteristics of the
ventilation system. Now the maximum values for electricity consumption of the air
circulation of the ventilation system are described in Table 15 by the construction year of
the building.
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Table 15. The electricity consumption of ventilation system by the type and construction
year or building type. This does not include the electric heating of the supply air. (RIL
2009 p. 34, Ministry of the Environment 2013, Decree of the Ministry of the Environment
on the energy efficiency of a new building 1010/2017 2017)
Type of
ventilation
Electricity consumption of the ventilation system (kWh/m3/s)
Before
2012
2012-
2018
2018 Low Energy Passive
Natural
Ventilation
0 0 0 - -
Exhaust
Ventilation
1.5 1.0 0.9 - -
Mechanical
Ventilation
2.5 2.0 1.8 < 2.0 < 1.5
Table 15 shows how the ventilation system’s electricity consumption is dependable on
the ventilation technology and construction year. Newer buildings have lower limits for
the electricity consumption than the older ones, and these values are used in the model
according to the construction year. Low energy and passive house consumptions are equal
to the maximum allowed values and they are only allowed to have mechanical ventilation.
Ventilation flow values may differ with the occupancy and usage of the building to
optimize consumption and good indoor air-quality. The values and clauses for the
ventilation flow are presented in Table 16. These values are used in the simulations for
balanced and mechanical exhaust ventilations.
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Table 16. Different settings for the electrically adjustable ventilation according to the
actions or temperature of the building. (RIL 2009, p. 114) Summer time operation is used
when inside temperature rises over 25°C in the building.
Clause Ventilation flow rate (1/h)
Absence/ no occupancy 0.2
Normal operation 0.5
Normal operation during summer 0.7
Increased flow rate while cooking or vacuuming 1.0
Increased flow rate for summer time cooling during
night time
1.5
5.6.1 Heat flow and heating calculations
The heat transfer of the ventilation system in general is calculated with the equation (11).
In the equation it is considered that the ventilation system utilizes outdoor air either as
supply air or as replacement air, which needs to be heated to room temperature or to
supply air temperature depending on whether the heating occurs in the room or in the
ventilation system. Heat recovery can recover energy from the warm indoor air, and thus
reduce the heating need, or vice versa cool down the warmer outdoor air before the
supply. Balanced ventilation has a heating system, which heats the air to supply air
temperature from either the outdoor or recovered heat temperature. Heating energy
QVentilation need for the supply air can be calculated with equation (11) below:
Qventilation
= ρa × ca × q
s × (Tinlet + ΔTHU - THr) × tci (11)
Where qs is the supply air flow [m3/s], Tinlet is the inlet air temperature [°C], ΔTHU is the
temperature change in the humidifier [°C], THr is the heat recovery temperature [°C] and
tci is the timestep [h]. This equation is achieved by making assumption that the
recirculation of air is off, making all the supply air come from the heat exchanger, and
that the ventilation system does not have a cooling coil in it. The ventilation heater is
assumed to heat the supply air to the temperature of 18°C, which is the supply air
temperature assumed in the building code. On the other hand, as humidifier is not
considered in the model and the ventilation system efficiency should be in accordance
with the standards, the humidifier temperature change is neglected. Heat recovery
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temperature is limited to supply air temperature, and if outdoor temperature is higher than
that, heat recovery system can cool the inlet temperature in the heat exchanger. (Modified
from SFS-EN 16798-5-1 2017, The National Building Code of Finland D5 2017)
Next the heat recovery temperature THr can be calculated by using equation (12) below
THr = Te + QHr
ρa × cpa × f × qs × tci (12)
Where QHr is the amount of energy recovered [Wh] and f is the fraction of outdoor air.
Here the outdoor air fraction is assumed to be 1, as there is no recirculation of air.
(Modified from SFS-EN 16798-5-1 2017)
Using standard SFS-EN 13053 + A1 2011, the temperature efficiency ŋt is calculated with
equation (13) if the ventilation system mass flows are equal and only dry conditions are
studied:
ηt =
TSupp - Te
Ti - Te (13)
Where TSupp is the supply air temperature from the heat recovery system [°C], which is
equal to THr. Now, by solving heat recovery temperature from equation (12), combining
it with equation (13) and rearranging terms, a following equation (14) for the energy
recovered from heat recovery QHr [Wh] can be achieved.
QHr
= ηt × ρ
a × cpa × q
s × tci × (Ti - Te) (14)
Where ŋt is the temperature efficiency, which is considered to be equal to the annual heat
recovery efficiency. (The National Building Code of Finland D5 2017)
In heat recovery system, the frost prevention needs to be accounted during the winter in
Finland, as outdoor temperatures may be under the frost prevention temperature. The
National Building Code of Finland D5 (2017) considers 5°C as the lowest temperature
from the heat exchanger in residential buildings. Now to maximize the efficiency of the
heat exchanger, there is a preheater added to the inlet of the outdoor air. With the
preheater, the air arriving to the heat exchanger is heated up to the temperature where the
annual efficiency is achieved. The required temperature to the heat exchanger is
calculated by modifying equation (13) to heat exchange efficiency on the exhaust side,
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and assigning the outlet air temperature from the heat exchanger to the defrost limit of
5°C. This gives outdoor temperature limit for the preheating, which is calculated by using
equation (11) with the temperature difference of outdoor air and the temperature to the
heat exchanger. Otherwise, the outdoor air is used in the heat exchanger without any
preheating. Low energy and passive buildings are assumed to have rotary heat
exchangers, which have high enough annual heat recovery efficiencies (RIL 2009, p.
113). As rotary heat exchanger uses electricity in its operations, the electricity
consumption used in running the motor is added to the model, with constant power
consumption and motor power of 60 W (HOVAL 2015).
5.7 Forecasting of model variables
The model includes forecasting of some of the input values in the model. These values
include forecasting of the next day’s outdoor temperature, PV generation, and heat
demand. The outdoor temperature forecast is needed for the heat demand estimations, so
that the model can calculate the heat needed in the next day and estimate indoor
temperatures. The forecasted outdoor temperatures are used in underfloor heating
scenarios, as its control requires estimating the heat demand for the next day. Similarly,
it is used in “Cost optimized heating” -scenario, as it needs to calculate the indoor
temperature of every hour of the next day to keep the indoor temperature within the
thermal comfort threshold values. The outdoor temperature forecasting is done artificially
as there is no historical data of the forecasted temperatures freely available. Finnish
Meteorological Institute estimates that their 24 hours forecasts are within 2.5°C from the
measured value 90 % of the time (Hyrkkänen 2014, Finnish Meteorological Institute
2018b). Therefore, artificial weather forecasts are created by assuming they are in a
normal distribution where mean value is the actual measured value for each hour and
standard deviation is 1.5. The vector is created with “normrnd” function in Matlab and
output values are within the 2.5°C limit 90 % of the time.
The second forecasted variable is the forecasted heat demand, where the calculation is
based on the forecasted temperatures and internal and solar heat gains from the The
National Building Code of Finland D5 (2017). As the forecast is for the daily heat
demand, the internal and solar heat gains are fixed values used in the monthly calculation
method. They are sized for an hour and multiplied for a full day to provide an estimation
on the aggregated heat they generate, as the forecasting is done for the full day, and not
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for a specific hour. This way the electric underfloor heater can estimate the magnitudes
of the heat gains and prevent overcharging itself. In case of undercharging, an extra heater
is available to provide direct space heating.
PV generation forecasts are also created for charging the underfloor heater with local
generation. This way the management system can estimate what is the approximate
amount of generation that is going to be charged in to the storage during the day. This
prevents underfloor heater from overcharging itself with electricity from the grid if the
charging happens during a time it may expect some local generation. Yet, the system will
charge itself with grid electricity in case there is need for the heat before the time of the
local generation or if the generation levels are lower than expected. The forecast is a
simple simulation result, where the inputs to the system are the information of the PV
system and weather information is from TRY2012 (Jylhä et al. 2012). It calculates an
average generation value for each month with the installed PV system and uses the
average monthly value to estimate what is the magnitude of the generation the system
might expect from the PV panels in the on-going month.
5.8 Thermal comfort
One way to consider the occupants’ satisfaction to the indoor climate is to consider their
thermal comfort indoor. This is added to the model so that the temperature is let to vary
between the thermal comfort limits of the residents. Thermal comfort is measured in the
model by a method called Predicted Mean Vote (PMV) and Predicted percentage
dissatisfied (PPD) presented in standard SFS-EN ISO 7730 (2005). Since the simulation
is handled on hourly-basis it could be assumed that the conditions inside the building have
reached steady-state conditions, and thus the standard can be used. On the other hand, the
standard is chosen because of its suitability to fast computing.
Thermal comfort calculations are utilized twice in the model: firstly, to determine the air
temperature limit values, and secondly to check whether thermal comfort was achieved.
The calculation procedure follows standard SFS-EN ISO 7730 (2005) and the equations
and iterations used in the calculations are presented there. The calculation of the thermal
comfort temperature limit is done between 10 and 30°C and the met-rate comes from the
simulation. The actual indoor and radiative temperatures are then used in determining the
thermal comfort achievement.
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People dress out differently by the season, even inside, as the outdoor temperature
changes. This has been found to affect the clothing by Morgan and de Dear (2003), where
the clothing of office workers showed correlation with outdoor temperature when they
were able to wear casual clothes. The mean outdoor temperature from the previous day is
used as a decision parameter for the clothing insulation value. Table 17 provides
temperature ranges and their respective clothing insulation values. Indoor air velocity is
set at 0.20 m/s, which is described as the maximum air velocity in winter for a residential
building from the ventilation by The National Building Code of Finland D2 (2012). The
air velocity value is likely smaller, but due to lack of presented data, the maximum value
is used. Relative humidity inside is constant 50 % all the time, since only dry heat is
considered in the model. For calculating the indoor temperature limits, the radiative and
indoor air temperatures are set to equal, to ensure effective computing and to provide easy
limit values for indoor temperature in the model. This should not provide too large errors
in the estimation, as radiative temperatures will likely follow the pattern of the indoor
temperature and should not differ too much from it. Only exception is underfloor heater
where the floor’s higher surface temperature can impact the radiative temperature, but
due to the consistency of the model, the temperatures are considered as equal on that
scenario as well. The temperature limits come from calculating PMV values with them
and accepting only temperatures that are inside the given PMV limits. In the first phase
the strictest PMV limits (- 0.2 < PMV < 0.2) from the standard are used so that the system
aims at maximizing thermal comfort of the residents. When checking the thermal comfort
values, radiative temperature from the calculations is used and a wider PMV limit (-0.7 <
PMV < 0.7) is used to assess the accepted thermal comfort limits. This generates more
dissatisfied people, but the limit was chosen to prevent the heater from overheating the
building, if temperature limits vary a lot. (SFS-EN ISO 7730 2005) Stricter PMV limits
might require the model to cool itself down after heating and vice versa to meet the limit
values. In the end, an estimation on how many times the operative temperature inside was
able to provide thermal comfort is calculated for the times of occupancy.
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Table 17. Clothing insulation values based on season and mean outdoor temperature
from the previous day. Seasonal temperature limits are from (Finnish Meteorological
Institute 2018c)
Season or Action Mean temperature
range
Clothing insulation
value (m2 °C/W) Reference
Winter < 0 °C 0.155 (1.0 clo) (Ala-Juusela and
Shukuya 2014)
Spring &
Autumn 4
0 – 10 °C 0.8 × 0.155 (0.8 clo) (Stoops 2000)
Summer > 10 °C 0.6 × 0.155 (0.6 clo) (Ala-Juusela and
Shukuya 2014)
Sleeping 5 - 0.96 × 0.155 (0.96 clo) (ASHRAE 2004)
5.9 Photovoltaic generation
Local generation is considered in the scenarios with PV panels, and it can utilize load
shifting on heating power or storage systems. To compare the potential of load shifting
and battery systems, net metering is also added in the model. PV generation is calculated
on hourly-basis by using existing model from Louis et al. (2016) with the inputs described
in Table 18. For the 2050 scenario, two test scenarios are created with the PV capacities
of 6 and 8 kWp. These scenarios are able to generate 6.6 and 9 MWh of electricity with
the TRY2050 solar radiation and created temperature values (Finnish Meteorological
Institute 2012). This is enough to match heat demands of 55 kWh/m2/a and 75 kWh/m2/a
respectively, for the example building used. The input values for them are presented in
Table 18 below. Other input values are the default values of the model. The generation of
electricity with PV panels is considered carbon neutral, as the panel uses solar irradiation
to generate electricity and does not emit CO2 during its operation.
4 The values in Stoops (1999) for Swedish office workers are close to the values for winter and summer
clothing, so the spring and autumn values are taken from there as an average value as an approximation. 5 A sleeping person is considered with its own clothing insulation value.
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Table 18. The installed PV peak capacities for the tested scenarios. One panel has a peak
power capacity of 200 Wp (Louis et al. 2016).
Installed PV peak capacity for the scenarios
Today
- 1 kWp (5 panels, all connected in series)
- 2 kWp (10 panels, 2 parallel connected modules, which both have 5 panels
connected in series)
- 4 kWp (20 panels, 2 parallel connected modules, which both have 10 panels
connected in series)
2050
- 6 kWp (30 panels, 3 parallel connected modules, which all have 10 panels
connected in series)
- 8 kWp (40 panels, 4 parallel connected modules, which all have 10 panels
connected in series)
5.10 Battery system
Battery systems are included in scenarios either with PV generation or with charging from
the grid. They are utilized as electricity storages at the times of extra generation from the
PV panels or when there is cheap electricity available from the grid. Battery system in the
model is a Lithium-ion battery, which was found to be the most likely succeeding
residential battery technology in van de Kaa et al. (2018). Tesla Powerwall is an example
of Lithium-ion battery designed for residential use, and thus it was used to provide
technical information for the model (Tesla 2018a). This technical information is presented
in Table 19 below.
Table 19. Technical information of Tesla Powerwall used as an example in the model.
(Tesla 2018a, 2018b)
Technical information
Usable Capacity 13.5 kWh
Depth-of-Discharge 100 %
Efficiency of full cycle 90 %
Maximum input 5 kW
Maximum output 5 kW
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With the technical information from the Table 19, State-of-Charge of the battery in
charging and discharging modes can be calculated with the equations (15) and (16) below:
SOCt = η × Input
Battery Capacity+ SOCt-1 (15)
SOCt = SOCt-1-Output
Battery Capacity (16)
Where SOCt and SOCt-1 is the State-of-Charge at times t and t-1 [%], ŋ is the cycle
efficiency, Input is the amount of electricity charged in 1 hour [Wh], Output is the amount
of electricity discharged for 1 hour and Battery capacity is the total usable capacity of the
battery [Wh]. Full cycle efficiency is used as the efficiency of the battery system, and the
battery capacity is equal to usable capacity from Table 19. Maximum input and output
values give the limits on charging and discharging of battery. The operation of the battery
system is then presented on Figure 15 below.
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Figure 15. The decision-making tree for the battery system. Charging and discharging are
done separately and only one can applied at a time.
The inputs to the battery are defined in heating power chapter 5.11 in Figures 20 and 21.
The operation of the battery is considered to include charging and discharging cycles as
the battery cannot charge and discharge at the same time. Therefore, it is considered that
battery will be charged every time to at least 80 % SOC and discharged to 30 %, which
is similar to charging cycle constrains used for Li-ion battery in Tran and Khambadkone
83
(2013). This operation is assumed to give 5000-7000 cycles for Li-ion battery (Rydh and
Sandén 2005). A middle ground value of 6000 cycles for cycle life is used in the
simulations. Wearing down of the battery is not considered in the model currently nor is
adding a battery system considered add emissions itself. The actual emissions from the
utilization of the battery system are only related to the electricity used in its charging.
The battery system related scenarios include different number of batteries, so that the
feasibility and effect of the batteries is studied, and the optimal solution is tried to be
found. The scenarios include having 1, 2 or 3 example batteries in the system for both
local generation and grid-based electricity. Similarly, electricity cost for charging the
battery from the grid is studied with percentile values of 5, 10, 15 and 20 for trying to
find feasible solution and to study the impacts of them.
5.11 Heat production technologies
The following sections describe the electric space heating calculations and rules on
simulations of the heating scenarios. The calculation methods and heater capacity limits
are described first, and then the simulation rules on the scenarios are presented.
5.11.1 Heating Calculations
First, the heat consumption of the building is calculated with equation (17):
QH
= QHS + QHv + QHW - QSHW - QOthers
ηgeneration
(17)
where QHS, QHV and QHW, describe the heating needs of the different heating components
of space, ventilation, and hot water heating [kWh/a], QSHW and QOthers are hot water
produced with either solar heat or all the other heat generation methods for space,
ventilation or hot water heating [kWh/a] respectively, and ŋgeneration is heat generation
efficiency in the heat production of total heating. The heat generation efficiency ŋgeneration
for direct electric heating is 1.00. (Shemeikka et al. 2011, The National Building Code of
Finland D5 2017)
Now, the amount of space heating QHS can be calculated by using equation (18) described
below.
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QHS
= QNHS
ηHS
+ QDL
+ QSL
(18)
Where QNHS is the space heating need of the heating system [kWh/a], ŋHS is the total space
heating efficiency of the system, QDL is the distribution loss of the system which cannot
be used in space heating [kWh/a] and QSL is the losses occurring during storage [kWh/a].
The total space heating efficiency considers the losses in the system due to temperature
stratification, heater’s controller, heat distribution and from heat emission, and its annual
value for direct electric heating is 0.95. (The National Building Code of Finland D5 2017)
Distribution and storage losses are neglected for direct electric space heating. This
equation is used for ventilation heating as well, where heating need of the supply air is
first calculated with equation (11) if applicable. The total ventilation heating efficiency
value for direct electric ventilation heating is 1.00 (The National Building Code of
Finland D5 2017).
The heat need of a thermal storage heater can be calculated similarly to any other heating
system using equation (18). Yet, storage heaters are different from direct electric heaters,
as they include charging and discharging of the system, dividing electricity consumption
from the heat dissipation. The charging power of the storage heater is calculated with
equation (19) below:
PSH = QHD 24 + QSL 24
tr (19)
Where PSH is the input rating of the storage heater [W], QHD 24 and QSL 24 are heat demand
of the building and heat losses of the storage for the next 24 hours respectively [Wh] and
tr is the recharge time or the number charging hours of the storage heater [h]. (Oughton
and Hodkinson 2008, p. 163) Heat storage losses are calculated by utilizing the annual
efficiency value of the heater.
The charge of the storage heater is then calculated with an energy balance equation (20)
below:
m × cp × dTs
dt= Q
input- Q
output- Ustorage × Astorage × (Tf - Ts) (20)
Where QInput and QOutput are inputs and outputs from the storage [Wh] and Ts and Tf are
start and final temperatures of the timestep [°C]. (Sarbu and Sebarchievici 2018)
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Underfloor heater does not have a separate storage output value as it only emits energy
from the storage, but input value is modified to include the heat losses occurring in the
storage system to differentiate the actual input to the storage and electricity consumption.
Thus, input to the storage is the electricity consumption multiplied by the annual
efficiency of the storage system, for which a value of 0.85 is used for the underfloor heater
(The National Building Code of Finland D5 2017).
5.11.2 Design Heat Power Calculations
The design heating capacity for the direct electric heater and ventilation heater is
calculated in accordance with the The National Building Code of Finland D5 (2017) by
calculating the heating power need in design conditions, which are defined in Table 20.
For the storage heater, the design input value to the system is calculated with equation
(19) by assuming daily heat demand equal to the design conditions for a space heating
scenario. The maximum input capacity is then design input power multiplied by 1.3 to
provide high enough response power to changes (DEVI 2018). The design values for the
heat demand calculations are defined in Table 20 below.
Table 20. Conditions for the design heating power calculations in Oulu, for which the
outside temperature is taken from area III from the Decree of the Ministry of the
Environment on the energy efficiency of a new building (1010/2017). (The National
Building Code of Finland D5 2017)
The design conditions for heating capacity calculation
Outdoor Temperature -32 °C
Ground Temperature 5.4 °C
Indoor Temperature 21 °C
Supply air Temperature 18 °C
Air exchange rate 0.5 1/h
Outlet temperature from heat recovery 5 °C
5.11.3 General Description of the heating model
Heating model is divided into space heating and ventilation. Total heating in the model is
calculated with equation (17) while space heating uses equation (18), ventilation heating
equation (11) and storage heating equation (20) with the losses included in the input. The
calculation heating procedure of space and ventilation heating follow heating power
capacity calculations from The National Building Code of Finland D5 (2017) on
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delivering energy by using the heating power for an hour. On Figure 16, a principle
diagram of the heating system and its losses are presented, while different space heating
technologies and operation strategies are described below on Table 21.
Figure 16. Flow chart describing the total electricity consumption and all the different
losses of the electric heating system. Modified from Shemeikka et al. (2011)
Table 21. The different space heating technologies and scenarios available in the model.
Direct electric heating from the grid
• Manually Controlled
• Constant Temperature Setting
• Time Setting Temperatures
• Cost Optimized with RTP
PV using
• PV with load shifting
• PV + battery system
• (Net metering scheme)
Only Battery
• Battery from Grid
Thermal Storages
• Underfloor heating, fixed charging hours
• Underfloor heating, cheapest charging hours
• Underfloor heating, PV charging
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There are four main technologies available in the model, differentiating in characteristics.
Direct electric heating from the grid includes only systems that use grid electricity in the
radiator. Their main differences are variations in the times and powers of the heating
schedules. “Manually controlled” -scenario considers only manually adjustable
thermostat, while in “Constant temperature setting” -scenario the heating power matches
with the heat demand and a constant temperature setting indoor. “Time setting
temperatures” -scenario uses a programmable thermostat to create hourly temperature
settings and “Cost optimized” -scenario utilizes optimization mechanisms with forecasted
temperature and real-time price. PV scenarios include local generation with the solar
panels, with or without the electricity storage possibility. Net metering scheme is
considered here as a reference scenario to see the difference in the potential of the other
PV scenarios compared to net metering. The third main technology includes a battery
system which will be charged with the grid-based electricity during cheap prices and used
when the electricity in the storage is cheaper to use than the grid-based electricity. The
final main technology includes the thermal storage as in underfloor heating. Here
underfloor heating includes three different charging scenarios with either fixed or
cheapest hours, or with utilizing the local generation to charge the underfloor heater.
Basically, “fixed charging hours” is conducted by having a programmable thermostat,
while in “cheapest charging hours” charging requires some sort of smart device, which is
applicable of receiving cost information and adjusting the charging accordingly. “PV
charging” on the other hand, needs smart functions and will likely require a HEMS to
operate, as well as to utilize the forecasted data on the estimated PV generation.
In almost all cases, the heating power is used to match the heat demand from equation
(4), with which the system tries to calculate the needed amount of heat and supply it
accordingly. The aim of heating is to provide thermally comfortable conditions inside the
building, and thus temperature limits are considered from estimating thermal comfort. It
is assumed that every hour either residents or controller would shift the temperature
setting of the thermostat to thermally comfortable temperatures according to the actions
and met-rate of the residents inside. Here the temperature limits mean that there is an
acceptable range, in which temperature can float before the resident or controller would
re-adjust the temperature setting.
Heating is limited by the heater’s capacity restrictions as well as high indoor
temperatures. The simulation runs on hourly-basis and it can be assumed that if the system
88
has temperature measuring devices, the system may measure the actual temperature in
the building within a shorter time-frame and adjust the heating power during that hour.
This function is added to the model by utilizing an “extra heater”, with which the model
can calculate the effect of adjusting the heat supply from the originally estimated. On the
other hand, when indoor temperature is changing according to the temperature setting,
the supply of heating power is calculated by considering the system as a thermal storage,
where the sum of air and building structures is the heat capacity of the storage. Therefore,
equation (21) below is used to calculate the amount of energy needed to increase the
indoor temperature to the wanted one.
Q = m × c × ΔT (21)
Where Q is the amount of energy stored [J], ΔT is the temperature change in the material
[°C], c is specific heat capacity of the medium [J/kgK] and m is the mass of the storage
material [kg]. (Siikanen 2015, p. 58)
The actual operative and indoor temperatures are then calculated by a more precise
method by using a standard SFS-EN ISO 52016-1 (2017). The controller of the thermostat
is assumed to be perfect as it supplies exactly the amount of heat needed and the effect of
the unperfect control is included in the heat losses of the space heating system. With
equation (18) the actual electricity consumption of the space heater is calculated and
added in to equation (17) for the total heating electricity consumption calculation.
Now the direct electric space heating model always contains the described building blocks
making it follow the functions in flow chart on Figure 17 below:
89
Figure 17. Flow chart presenting the pathway of the model for the direct electric space
heating.
90
5.11.4 Direct Electric Space Heating from Grid-based Electricity
This section includes the four different heating scenarios, which all utilize direct electric
space heating and grid-based electricity. First, “Constant temperature setting” -scenario
always has a constant temperature setting of 21°C, which is used in the calculations in
The National Building Code of Finland D5 (2017). For the “Time setting temperatures”
-scenario, a regular programmable thermostat is used, where the temperature setting is
changing according to the time. Each time slot has their own preset temperatures, and
heat is supplied to keep the indoor temperature in the temperature setup.
Manually controlled heating differentiates from the “Constant temperature setting” and
“Time setting temperature” heaters, as it does not have any temperature measuring
thermostat or device in it and it only uses manual control. The output powers form the
heaters were determined by utilizing examples from K-Rauta (2018) and Clas Ohlson
(2018), which both offer three possible heat outputs for a single heater: 750W, 1250W
and 2000W. Cases having more than a single manually controlled heater, all possible
output combination options are calculated in the model. The simulation is done according
to rule-based actions from the user, which are presented on Figure 18 below. Here the
occupant is considered to have good enough knowledge to set up the heater close to the
heat demand value with the respective outdoor temperature. Also, if indoor temperature
rises over 1°C from the upper thermal comfort temperature limit, the user will shut down
heating until the temperature drops to the thermal comfort limits.
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Figure 18. Rule-based actions of the user of the manual heater.
The fourth direct electric space heating scenario is the “Cost optimized” heating, which
creates and utilizes heating schedules. These schedules are optimized with RTP, and it
uses weather forecasts and indoor temperature limits. “Cost optimized” -scenario was
created with a linear programming optimization method from Matlab, so that the heating
pattern is predefined from the output of the optimization result. The aim of the linear
programming tool is to minimize the cost of electricity and the restrictions are set by
default lower and upper indoor temperature limits, which are defined as inputs, and by
the heater capacity. It utilizes equation (21) to calculate the indoor temperature with the
supplied heating power to check if it stays between the temperature limits. The output
from the linear programming optimization tool is a heating load profile for the next 24
hours. After that the system runs the created heating pattern accordingly. Thermal comfort
estimation is done on hourly-basis also in “Cost optimized” heating, and the system will
react similarly than other systems by using the “extra heater” in case the current indoor
temperature does not meet the thermal comfort limits. Adjustments are also needed in
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case the forecasted heating need is lower than actual, resulting in dropping the indoor
temperature under the predefined values. In “Cost optimized” -scenario, the system
knows the RTP for the next 24 hours and creates a scenario every midnight for the next
day.
5.11.5 Heating with photovoltaic electricity generation
The second main section in the heating scenarios is for the PV generation of a building.
Here local generation can be in the system either alone or with a battery system providing
electric storage capacity. The reference scenario is to have a PV system without any load
shifting or battery systems and use its generation only when the heating in the constant
heating scenario matches the PV panel generation. The second scenario is to include DSM
mechanisms to shift heating loads to the times when panels are generating electricity. A
flow chart describing the rules of the model is presented in Figure 19. The third option is
to compare battery system as an electricity storage to both reference and load shifting
scenarios. In “PV with battery storage” -scenario, the usage of the local generation is still
prioritized over charging the battery like in the heating load shifting option. Battery
cannot be charged and discharged at the same timestep, and thus it has charging and
discharging modes to prevent too short cycles and waring down of the battery. A flow
chart of the actions of the controller is shown in Figure 20, while the description of the
battery comes later. The final PV generation scenario is for net metering the PV
generation and it is used as a comparison to check the potential of heating load shifting
and battery systems when they are compared to net metering scheme, which is generally
used to promote PV panels. Net metering scheme operates the same way as the battery
system, but net metering does not have any capacity limits on how much it can be
“charged”. Yet, the “charge” is immediately used when there is need for electricity. This
is “free electricity” price-wise.
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Figure 19. The rule-based actions for the PV with load shifting DSM method. Aim is to
maximize local generation.
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Figure 20. Rule based actions for a PV system with battery. Heating clauses are found in
Figure 17. Shifting heating load is done by matching generation with heating as much as
possible within temperature and capacity limits.
5.11.6 Heating with battery from grid
The third main section utilizes battery system without local generation. Here, the battery
system is charged with cheap grid-based electricity and used when the RTP price for the
grid electricity is higher than electricity in the battery when including the battery costs.
The electricity value in the battery is calculated with equation (22) below:
pb =
pbt-1 × (Battery charge - Input × ηbattery) + pe × Input
Battery charge
(22)
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Where pb and pbt-1 are electricity values in battery currently and on time t-1 [€cent/kWh]
and pe is the electricity price in the current hour [€cent/kWh]. Thus, the value of electricity
in the battery is firstly calculated for the previous hour, then the value of current input is
added and finally this value is normalized to the current battery charge. Now the price
limit for discharging the battery is defined by equation (23).
pd
= pc +
IB
BC × Dmin + p
p (23)
Where pd and pc are price limits of discharging and charging of batteries [€/kWh], IB is
the investment of cost of the battery [€], BC is number of estimated life cycles, Dmin is the
minimum amount of electricity discharged from the battery in one full cycle [kWh] and
pp is the wanted profit per usage [€/kWh]. The discharge price is attached to the charging
price, so that there is fixed price limit for discharing the battery and to ensure that each
time the battery is used, it will at least cover all the costs of the system. Next the minimum
discharge cycle is calculated with the technical information and operation conditions from
section 5.10. with equation (24):
Dmin = NB × Battery capacity × (SOCupper - SOClower) (24)
Where SOCupper and SOClower are the state-of-charge operation limits of the battery
defined in the section 5.10. and NB is the number of batteries on the system.
The used battery system is the same as in PV generation scenario with the same inputs
and outputs, as well as with the same full cycle efficiency. The charging of the battery is
done when RTP is lower than the nth percentile value from the previous year’s real time
price. Savings here are the amount of money saved when using cheaper electricity from
battery instead of more expensive grid electricity. The rule-based actions are described in
Figure 21.
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Figure 21. Rule-based actions in the battery from grid scenario. Charging battery from
the grid with cheap electricity. Heating clauses are defined in Figure 17.
5.11.7 Thermal Storage Heaters
The fourth main section is the utilization of thermal storage heaters. This includes electric
underfloor heater, which is charged with electricity and discharged by dissipating heat.
The input to the storage is calculated with equation (19) and the input is directly applied
to the indoor temperature calculation function, so that all the input is supplied directly to
the first floor-node with electricity value equal to electricity flow per floor area and
considering fully radiative dissipation. The thermal mass of the floor is calculated
separately by having 15 cm deep concrete slab in the floor with specific heat capacity of
840 [J/kgK]. (Siikanen 2015 p. 58, DEVI 2018)
There are three charging scenarios of the underfloor heater: fixed charging hours,
cheapest charging hours, or with PV generation. Using fixed hours means that the system
uses programmable thermostat to always charge itself during the same time in the night,
from midnight to 8 in the morning. “Cheapest charging hours” -scenario then uses the
cheapest hours for the charging. The rule-based actions of these two charging scenarios
are depicted in Figure 22. For PV charging, the aim is to maximize charging of the thermal
97
storage with local generation as much as possible. The rule-based actions for the PV
generation charging are described in Figure 23. Here the heat demand estimation from
the forecast is used to estimate the amount of heat that needs to be stored to the system,
and PV generation forecast is used to estimate the amount of local generation, so that the
amount of electricity taken from the grid can be calculated. Input here is more variable
than in the others, due to the characteristics of solar radiation. It is mainly equal to the
local generation, as its usage is emphasized much as possible.
Figure 22. Charging underfloor heater. For fixed hours, the hours are predefined and
checking of cheaper hours is not included. For cheapest hours, the hours may be
predefined for the next day if RTP is known, or the controller may adjust the heating
hours every hour, if pricing is more dynamic.
98
Figure 23. Decision-making tree for PV charging of underfloor heater. Yesterday’s mean
temperature clause is used to prevent overheating and is modified from the Heating
Degree Days concept (Finnish Meteorological Institute 2018d) with the assumption that
here heat gains are able to keep the building warm without the need of extra heating when
outdoor temperature is over 15°C.
5.12 Operative Temperature
The main objective of heating is to provide thermal comfort to the people living inside
the building. As radiative and indoor temperatures are one of the main thermal comfort
99
parameters, they need to be calculated in the model. The temperature calculation is based
on SFS-EN ISO 52016-1 (2017) standard, which provides a dynamic hourly inside
calculation method. The standard uses “thermal network” -model which divides opaque
building structures into 4 layers and 5 nodes for opaque building structures, and into 1
layer and 2 nodes for windows and doors. It calculates the temperatures of each of the
nodes and inside the building with internal and solar heat gains and delivered heater
power. The default values from the standard are used in the model to calculate the surface
temperatures, which are required for the calculation of radiative temperature, and for the
calculation of the indoor temperature. Some of the methods used in the standard require
calculations and values from other standards, and they are done accordingly. Only
assumption is the calculation of the heat transfer for the virtual ground layer, which is
calculated with 2018 legislation values, and the transfer value is same for all buildings.
Hence, the change in overall transmittance value Ufloor for the floor against ground is
considered to come from the changes in the thermal resistance value of the floor. The heat
mass in the calculations is concentrated in the internal side.
5.13 Feasibility Test
One part of the research is to estimate the economic feasibility of the suggested scenarios.
For this reason, a simple payback time calculation is used to estimate how long would it
take to pay back the investment. Similarly, the model includes simplified calculation for
the payback time of PV panels and battery systems based on their expected investment
costs, but not taking operation and maintenance costs into account. The calculation
follows the equation (25) presented below.
Payback time = Investment costs
Savings and gains (25)
where payback time is presented in years, investment costs as [€] (or €/kW(h) of installed
capacity), and savings and gains are [€/year]. The savings calculations depend on the
utilized technology. For PV panels and batteries, the savings are considered as costs that
would have had to be paid unless of the local generation or storage, and for DSM
technologies, the savings are mirrored to the reference scenario with either constant
temperature thermostat, or manual heating. Gains are then calculated by the sold
electricity to the grid, which is considered as the real-time price. Table 22 below shows
the investment costs for PV panels, batteries and for an example HEMS system.
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Table 22. Investment costs for PV panels, Battery system and for HEMS for managing
electric heating for direct and underfloor heating.
Cost of investment Reference
PV panels (< 10 kWp) 1300 - 2000 €/kWp (Auvinen and Jalas
2017)
Battery system (Tesla
Powerwall)
650 – 800 €/kWh (Tesla 2018a)
HEMS for electric heating
(OptiWatti)
1450 € + monthly fee
(minimum amount of rooms)
(OptiWatti 2018)
5.14 Selected Simulation Scenarios
The simulations scenarios are aimed to bring information of the current and future status
and the potential of DSM technologies in different heating schemes, under different
technologies included in the house and by the characteristics of the house. The model will
bring information on the costs, CO2 emissions and thermal comfort on the building for
each of the scenario and calculates feasibility of systems having at least one of the
following: PV panels, battery system or HEMS for direct electric heating. Temperature,
solar radiation, emissions, electricity price and future buildings are defined on their
respective sectors for today and 2050. Table 23 below shows the selected simulation
scenarios and the associated results are presented in Section 6.
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Table 23. Tested simulation scenarios are presented here. Thermal insulation and mass
changes are tested in each of the scenarios to test if there is a different optimal solution.
Today
Thermal
insulation and
mass
- The selected buildings are by the construction year of 1985,
2003 and 2018, and low energy and passive houses.
- Buildings have medium heavy thermal mass by default
Change in heating
schemes for direct
electric heating
- Manual heating and constant heating of 21 °C are
considered as reference cases.
- Time set temperatures are 19 °C for night (22-8) and 21 °C
for day (8-22).
- Cost optimized RTP scenario
Potential of DSM
with PV
generation
- Four PV scenarios: only PV, PV with load shifting, PV with
battery system, and PV with underfloor heating.
- Three PV sizes: 1 kWp, 2 kWp and 4 kWp. All are tested
with each of the scenarios.
- Scenarios are compared to net metering scenario
Potential of
Battery
- Number of batteries is either 1, 2 or 3.
- Potential of battery system in reducing electricity costs and
emissions with RTP scheme
- Changes in the charging price limit
Potential of
Thermal Storage
- Underfloor and convective storage heaters are tested
- Underfloor heating is compared in three different charging
scenarios: fixed, cheapest and PV
2050
Change in
building types
- The change in building type is simulated. Insulation in
buildings in 2050 are 2018 value, low energy, and passive
buildings.
- Zero energy buildings are simulated with low energy
building insulation and 8 kWp PV system, and with passive
house insulation and 6 kWp PV system.
- Net zero energy building is simulated with passive house
insulation and with 8 kWp PV system
Change in climate - Three temperature change scenarios: B1, A1B and A2
- Solar radiation from TRY2050
Change in energy
system
- Two energy system options are tested: Growth and Save
- Testing of whether future energy system has different
solutions from single house’s point-of-view
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6 RESULTS
This section presents the annual results from the simulation. Firstly, the results from the
today scenario are presented by the technologies to conclude their costs, CO2 emissions
and thermal comfort achievement. After that, their feasibility test is conducted to see the
differences of the optimal solutions and their economic feasibility. The same procedure
is then presented for the 2050 scenario with only difference being the inclusion of ZEB
and NZEB.
6.1 Today scenario
First the results from the today scenario are presented. The results follow the technology
scenarios by starting with the reference scenario. Then results from the “Direct electric
space heating from the grid” -scenario are presented that is followed by PV electric space
heating scenarios. After that, the “Battery from the grid” -scenario results are provided,
and then underfloor storage heating is studied. The today results are then concluded with
overall results and economic feasibility test.
6.1.1 Reference scenario
Reference scenarios are “Constant temperature setting” with 21°C temperature setting
indoor and manually controlled heaters. The reference results are presented on Table 24.
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Table 24. Results from the reference scenario simulations.
1985 2003 2018 Low
Energy Passive
Constant
Total Costs (€) 1830 1600 1050 700 560
Total CO2 emissions
(kg)
2291 1994 1325 803 640
Thermal Comfort
achievement (%)
22.7 24.3 35.3 40.1 40.5
Total Heating Energy
(MWh)
18.4 15.7 10.3 6.0 4.8
Average indoor
temperature (°C)
21.1 21.1 21.1 21.4 21.4
Manual
Total Costs (€) 2580 2000 1450 1020 890
Total CO2 emissions
(kg)
3040 2330 1690 1110 960
Thermal Comfort
achievement (%)
77.9 64.4 68.6 69.8 69.2
Total Heating Energy
(MWh)
26.1 19.6 14.4 9.6 8.3
Average indoor
temperature (°C)
24.9 23.4 24.2 24.6 24.7
The reference scenario results confirm the impact of thermal insulation as newer buildings
have lower heating energy consumption and thus, lower costs and emissions. Low energy
building meets its definition limit as it has an average heat demand of 50 kWh/m2 a year,
while passive building’s heat demand is slightly higher than in the RIL (2009, pp. 12 &
28) definition at 40 kWh/m2 a year. Therefore, this building would still belong in the low
energy building class. The impacts on thermal comfort likely come from the variations in
the indoor temperature, as more insulated buildings have higher indoor temperatures in
the summer. The manual heaters on the other hand, consume from 25 to 73 % more
energy, but generate 29-55 %-points higher thermal comfort achievement values as they
have higher indoor temperatures. The highest thermal comfort achievement value in the
lowest insulated building is due to the highest indoor temperature achieved.
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6.1.2 Heating from the grid
The results for electricity grid-based heating technologies show that manual heating
delivers 25-73 % more heating power, which results in increase in all the variables. “Time
setting temperature” -scenario reduces average indoor temperature by 14-20 % from
“Manually controlled” and 1-3 % from “Constant temperature setting” -scenarios, and
thermal comfort by 2-11 and 34-58 %-points, respectively. At the same time, “Time
setting temperature” has basically the same CO2 emissions and total costs than constant
heating. The “Cost optimized” -scenario has 1-15 % higher costs and 2-17 % higher CO2
emissions than “Constant temperature setting” and “Time setting temperature” -scenarios.
This relates to higher heating energy delivery and indoor temperature, resulting in 12-51
%-points higher thermal comfort rate. Overall, the biggest absolute influence of the cost
optimization on all parameters was detected with the least insulated buildings. Comparing
the “Cost optimized” -scenario to manually controlled heating, thermal comfort is
reduced by only 7-17 %-points while costs and CO2 emissions are reduced by 18-55 %
and 11-47 % respectively. The impacts compared to manually controlled heating are
higher in more insulated buildings. Also, average indoor temperature in “Cost optimized”
-scenario are lower with more insulated buildings, which would suggest that with better
thermal insulation the optimization is closer to the given lower temperature limit. The
simulation results are presented on Figure 24.
Figure 24. The results from the “direct electric space heating from the grid” -scenarios.
105
6.1.3 PV panels
The generation from the PV panels depends on the size of the panels and the level of the
solar irradiance. Generally, the simulation results show that the self-consumption rate is
higher with smaller panel powers, and with less production. The mismatch between the
heat demand and the PV production is visible from the decreasing self-consumption rate
during summer months, when the production increases. This means that a large share of
PV power cannot be used only in heating. The PV production from the panels and the
monthly self-consumption rate of 2018 building with heating load shifting is depicted on
Figure 25.
Figure 25. The monthly self-consumption rate and PV electricity generation with different
panel powers in 2018 building.
The electricity costs in the load shifting scenarios with local generation are 2-14 % higher
than in “Cost optimized” -scenario, 9-31 % lower than in manually controlled heating and
4-18 % higher than in constant 21°C temperature setting. This is partially due to the
applied rules, which increase the indoor temperature higher than in “Constant temperature
106
setting” -scenario even without PV generation. Utilizing local generation with battery
system, the electricity costs are closer to the constant 21°C temperature setting, as with 1
kWp panel capacity the costs increase from 4-16 %, with 2 kWp capacity the cost are 1-
11 % higher and with 4 kWp capacity the costs are 5 % higher in 1985 building but
decrease 4-5 % in other building types when compared to constant temperature setting.
PV generation with battery system then decreases the costs by 14-39 % when compared
to manually controlled heating. It seems that costs reduce when the PV power increases,
and the costs could be reduced 4-19 % more by using a battery system instead of just load
shifting. The reductions to CO2 emissions seem to be linear; when the panel power is
higher, the CO2 emissions decrease more. The decrease in CO2 emissions in battery
systems is 4-20 % from the load shifting scenario. Conversely, the battery system has 2-
8 %-points lower thermal comfort achievement rate than the load shifting scenario. The
highest thermal comfort values are achieved with the least insulated building type and
with load shifting, higher indoor temperatures are achieved. Also, utilizing battery
system, the self-consumption rate can be increased by 12-26 %-points when compared to
load shifting. Similarly, more insulated buildings have smaller potential in using the PV
generation to direct electric space heating. Yet, the self-consumption change does not
seem to impact the costs and CO2 emissions much, and it even reduces the thermal
comfort sensation. Part of this may be caused by emphasizing similar load shifting
principle both scenarios. The self-consumption seems to decrease together with heat
demand, and therefore, the higher demand relates to higher utilization. Battery systems
can utilize more the local generation than just load shifting in all house types due to its
ability to storage electricity. Also, increasing the number of batteries in the system has
basically no impact to any direction. Therefore, the PV capacity is more impactful on the
results. The results from the PV scenarios are presented on Figure 26.
107
Figure 26. The comparison of PV load shifting and battery system scenarios.
6.1.4 Battery from grid
In general, the costs from the “Battery from the grid” -scenario are close to the PV
scenarios with the changes in costs from 14 % reduction to 18 % increase, whereas CO2
emissions are in between an increase of 19 % and decrease of 18 %. The passive buildings
have the least positive results from the “Battery from grid” -scenarios, whereas the 1985
building type can benefit more. Thermal comfort achievement levels are then 6-20 %-
points lower than in the PV generation scenarios with the biggest differences from poorly
insulated buildings. Equivalent results are achieved by comparing “Battery from grid”
and “Cost optimized” -scenarios; the changes in costs are in the range of increase by 8 to
decrease by 15 %. The costs are increased in passive building type and decreased in 1985
building type, and with the rest, the results are in between of the two depending on the
technology and charging rule. Comparing thermal comfort achievement to “Cost
optimized” -scenario thermal comfort values range from 10 %-points higher to 6 %-points
lower values, with the lower values coming from 2018, low energy and passive building
types. Between the scenario itself, the highest impact seems to be related to the charging
rule, as higher charging limit reduces costs and direct CO2 emissions by 4-11 %. The
thermal comfort achievement is more stable, and results are not as clear, with some
building types the higher percentile for charging limit increases the thermal sensation
108
within a year while in some buildings it does not impact or might even reduce it. As
heating energy delivered and indoor temperatures do not change within the scenarios all
the impacts found here are related to the charging and discharging of the battery. The
results are presented below on Figure 27 by the number and charging rule of the batteries.
Figure 27. The comparisons of battery from grid technologies by the different amount of
batteries and their charging rule.
6.1.5 Underfloor heating
With underfloor heating, “Set Time” -scenario has the highest costs, CO2 emissions,
thermal comfort, and total electricity used. With “Set Time” -scenario the thermal comfort
achievement rate is 2-8 %-points higher than with the second highest value. In the
“Cheapest charging hours” -scenario, the costs are 8 and 1 % less than in “Cost
optimized” -scenario in 1985 and 2003 building types respectively, whereas the rest had
1 to 10 % higher costs than in “Cost optimized” -scenario. With 1985, 2003 and 2018
buildings the cheapest charging hours decreased the CO2 emissions by 1-13 % from “Cost
optimized” -scenario, while in low and passive buildings CO2 emissions were increased
by 1 and 10 %, respectively. Thermal comfort achievement was 6-16 %-points higher in
“Cheapest charging hours” -scenario than in “Cost optimized” -scenario with increase of
difference with the increase of insulation in the building. Utilizing PV generation in
underfloor heating decreases the annual costs by 38-80 % from “Set Time” -scenario and
by 17-40 % from the “Cheapest charging hours” -scenario, and CO2 emissions by 62-127
% and 33-74 %, respectively. These are partially related to the lower electricity
109
consumption and average indoor temperature. Yet, “PV charging” -scenarios have higher
indoor temperatures than “Constant temperature setting” and “Time setting temperature”
-scenarios, even though underfloor heating with PV charging has 8-31 % lower costs and
25-69 % lower direct CO2 emissions than “Constant temperature setting” -scenario.
Thermal comfort achievement is affected by the lower indoor temperature when
compared to other underfloor heating scenarios. The results from the underfloor heating
scenario are presented below on Figure 28.
Figure 28. Comparison of the different charging methods in underfloor heating.
6.1.6 Overall
Generally, the impact on the absolute values between the scenarios is higher with less
insulated buildings. This means that less insulated buildings have more potential in
reducing electricity costs and CO2 emissions by utilizing DSM methods like load shifting.
This finding is in line with the results from Ippolito et al. (2014) who discovered that the
impact of building automation is higher to buildings with higher energy consumption.
Yet, the biggest impact comes from increasing the energy efficiency of the building by
improving its insulation level. By renovating the least insulated building to passive
building level, costs are reduced on average by 235 % and CO2 emissions by 263 %.
110
Some of the differences between the scenarios are also related to the applied rules; time
and constant temperature sets are operated on lower temperature limits which clearly
decreases the costs and CO2 emissions as they use less energy, while in “Cost optimized”
-scenario the costs are higher due to the higher energy delivery. PV generation with load
shifting creates 2-14 % higher costs and 2-16 % higher CO2 emissions than PV generation
with battery systems, but load shifting creates 2-8 %-points higher thermal comfort
achievement. In “Battery from the grid” -scenario the costs and CO2 emissions are around
the scenarios with PV generation while the charging of the battery decreases the thermal
comfort indoors. This may be related to the charging rule, as the system first emphasizes
the battery charging and only heats up the building after that. Underfloor heaters charged
with grid electricity can deliver good thermal comfort, but suffer from lower heating
efficiency, even though charging with the cheapest hours has costs close to the “Cost
optimized” -scenario. Charging underfloor heater with local generation on the other hand
decreases costs and CO2 emissions, but is not able to provide equal thermal comfort than
charging with grid-based electricity.
The optimization of the electric space heating is estimated by ranking all the scenarios by
the three categories: total costs, CO2 emissions and thermal comfort. Then an overall rank
is given to all scenarios by the average value of the three and by assuming that the smaller
the number, the better it is. The three lowest number technologies are described under on
Table 25.
111
Table 25. Ranking of the technologies by the average rank from the cost, CO2 emissions
and thermal comfort parameters.
Ovarall
Rank 1985 2003 2018 Low Energy Passive
1st Underfloor
heating with
PV charging
Underfloor
heating with
PV charging
Underfloor
heating with
PV charging
Underfloor
heating with
PV
charging
Underfloor
heating
with PV
charging
2nd Battery from
grid with 3
batteries and
20th
percentile
rule
PV 2 and 4
kWp with
battery system
PV 2 and 4
kWp with
battery
systems
PV 2 and 4
kWp with
battery
systems
PV 2 and
4 kWp
with
battery
systems
3rd Cheapest
hours
underfloor
heating
Battery from
grid with 3
batteries and
20th percentile
rule
Battery from
grid with 3
batteries and
20th percentile
rule
Battery
from grid
with 3 and 2
batteries
and 20th
percentile
rule
Constant
The ranking of technologies shows the high potential of utilizing PV charging with
underfloor heating as it was ranked first in every building type. Similarly, local generation
combined with battery system, as well as, battery from the grid show potential in electric
space heating. Therefore, storing the local generation shows more potential than load
shifting in heating systems. Yet, as the ranking system does not consider the magnitudes
of the differences between technologies it may be biased towards a technology that has a
good rank in two categories with small difference, and a worse rank in the third with
larger difference to the others. Still, this is considered to provide a good estimate on the
potential of the technologies as they need to be good in several categories to do well.
6.1.7 Feasibility test
Another important parameter in comparing the technologies, is their economic feasibility
analysis, as it is possible that an otherwise optimal solution might not be profitable on
present prices. Currently, there does not seem to be any economically viable option for
battery systems. This is in line with the findings of McKenna et al. (2013), Uddin et al.
(2017) and Cerino Abdin and Noussan (2018) that current PV system combined with
batteries in residential sector are not economically feasible. The lowest payback times
112
were achieved with local generation on poorly insulated buildings, and the payback time
seemed to increase with the higher insulation level of the building. However, load shifting
can slightly decrease the payback time on more insulated buildings. Conversely, the
number of panels does not seem to have high impact on the payback, which is likely
caused by the sale of electricity to the grid. The lower payback times with load shifting
than with battery systems complies with the findings of O’Shaughnessy et al. (2018).
Underfloor heating with PV generation charging has the shortest payback time out of the
tested scenarios. These are almost comparable to net metering payback times on poorly
insulated buildings. The results on the payback times of PV panels and batteries are
presented on Figure 29.
Figure 29. The payback year estimations from the simulations.
The second economic feasibility analysis is related to the HEMS and its usage. HEMS is
likely used in “Cost optimized”, “PV load shifting”, “PV battery”, “Battery from the grid”
and “Underfloor heating” -scenarios to control their consumption and charging. Their
payback times are presented below on Table 26. Calculation of the payback time of “Cost
optimized” -scenario is done against manually controlled heater, while in reality the
system might require also other changes to utilize HEMS.
113
Table 26. The total payback time of different technologies requiring HEMS by savings
from the simulation. PV systems are 1 kWp and only one battery is considered. Battery
from grid uses 20th percentile charging rule.
Technology Payback time [years]
Cost Optimized 3 – 4.7
PV load shifting 31.7 – 44.1
PV battery system 140 – 192
Battery from grid 800 – 1240
Cheapest charging underfloor 2.5 - 13.3
PV charging underfloor 30.8 - 40
Now it seems evident that only electricity from grid technologies are feasible in their
current investment costs. The total payback time in load shifting PV systems almost
doubles even without considering the possible monthly fees for HEMS system and O&M
costs for the PV panels. Battery systems are unfeasible at their current costs and will not
likely ever return the investment back due to shorter lifetimes.
6.2 2050 Scenario
Here the 2050 scenario results are presented by first starting with the different scenarios
and by checking how they are related to each other. Then results from the technology
simulations are presented and they are followed by looking into the ZEB and NZEB
buildings. Finally, overall results and feasibility test conclude the 2050 scenario results.
6.2.1 Effects of the scenarios
The simulations were run on 3 different climate scenarios all resulting a different increase
in average temperature by 2050. The impact of the temperature on the results is shown in
Figure 30 below where the results from the simulation scenarios are scatter plotted against
the results of the same technology on different temperature scenario.
114
Figure 30. The relations of the temperature scenarios on the costs, CO2 emissions and
thermal comfort of different technologies in 2050.
115
There seems to be clear relation with all temperature scenarios to each other on the costs,
CO2 emissions and thermal comfort achievement. Therefore, it can be concluded that the
temperature scenario in the future does not impact the DSM potential of different
technologies against each other. Yet, all the climate scenarios decrease the heat demand
from today’s values. The A1B scenario decreases heat demand the most by 4-12 %, and
B1 the least by 1-8 %. Furthermore, heat demand is decreased more in the highly insulated
buildings. Comparing these values to Jylhä et al. (2015) where the change in space heating
and ventilation need was -15-18 %, it seems that the impact found here is smaller than in
earlier research. This may be caused by a higher reference year temperature, which
reduces the change in heat demand. The impact of the temperature scenarios on heating
energy is shown on Table 27 below.
Table 27. The effect of the temperature scenario on the heating energy delivery when
compared to 2016 scenario.
Comparison 2018 Building Low Energy Passive building
B1 Constant
heating
-2.6 % -3.6 % -3.9 %
B1 other scenarios -0.8 – 5.8 % -1.6 – 7.4 % -1.6 – 7.7 %
A1B Constant
heating
-6 % -7.6 % -8.5 %
A1B other
scenarios
-4.0 – 9.4 % -3.8 – 11.2 % -4.9 – 11.8 %
A2 Constant
heating
-5.7 % -7.3 % -8.2 %
A2 other scenarios -3.5 – 9.0 % -3.8 – 10.9 % -4.5 – 11.4 %
Next, the results from the two energy scenarios are compared on Figure 31 below.
116
Figure 31. The comparison of energy system scenarios on the results of the DSM
technologies in A1B temperature scenario.
Similarly, to the temperature scenarios, differences in the energy systems do not seem to
impact the DSM potential of the technologies when they are compared against each other.
Therefore, testing a single scenario should give relative results to other scenarios as well.
The effect of the energy scenario to the results is presented below on Table 28. The results
indicate that electricity costs would increase by 37-128 %, and direct CO2 emissions
decrease by 95-98 %. The energy scenario does not seem to impact the environmental
parameter, but costs are higher in Save than Growth scenario.
Table 28. The changes in electricity costs and CO2 emissions from today to 2050 by the
building types and energy scenarios.
2018 Low Energy Passive
Growth Cost
changes
63 – 110 % 40 – 86 % 37 – 84 %
Growth CO2
emission changes
-95-98 % -95-98 % -95-98 %
Save Cost changes 75 – 128 % 54 – 99 % 50 – 97 %
Save CO2 emission
changes
-94-98 % -95-98 % -95-98 %
117
6.2.2 Technology comparison
A technology comparison is conducted to test out the potential of the DSM technologies
in 2050. The simulation results here are from “Growth” and “A1B” -scenarios and are
expected to show representative results for all the scenarios. Generally, the trendline of
the future scenarios show that electricity prices are higher and direct CO2 emissions are
lower than today. Yet, the energy system related direct CO2 emissions show that the
energy system has not been fully decarbonized in the scenario. However, similar pattern
than in today’s scenario is visible; the “Constant temperature setting” and “Time setting
temperature” -scenarios have smaller costs and CO2 emissions, but are unable to provide
thermal comfort. Results from “Cost optimal” -scenario show 8-13 % higher costs and 2-
6 % higher direct CO2 emissions than “Constant temperature setting” -scenario, but “Cost
optimized” -scenario has 8-28 %-points higher thermal comfort achievement rate.
Manually controlled heating provides the most thermal comfort, but it also has the highest
costs and CO2 emissions. Like today, the insulation level has high impact on electricity
costs and CO2 emissions; passive building has 63-100 % lower costs and 80-114 % lower
CO2 emissions than 2018 building type. The results from the grid electricity-based heating
technologies are compared on Figure 32.
Figure 32. The comparison of electricity from the grid technologies in 2050.
118
Next the different “PV generation” -scenarios are compared on Figure 33. The
comparison is done on 1 kWp capacity as the impact of the capacity is studied separately
on ZEB and NZEB results. The biggest differences between the results come from the
thermal comfort achievement rate, as load shifting and battery system can achieve 21-26
and 17-24 %-points higher thermal comfort rates than heating with a constant temperature
setting, respectively. Similarly, load shifting, and battery system can create 6-13 %-points
higher thermal comfort than “Cost optimized” heating, while PV with load shifting and 1
kWp panel has 0-8 % higher costs and 2-8 % higher CO2 emissions than “Cost optimized”
-scenario. PV with battery system can decrease the costs by 2-5 % and direct CO2
emission by 1-4 % when compared to the “Cost optimized” -scenario. Therefore, it seems
that load shifting, and battery system have higher potential than cost optimized heating in
2050. The lower costs and CO2 emissions in constant temperature setting relate to lower
total heating and simulation rules.
Figure 33. The comparison of different PV technologies.
The third main simulation technology is “Battery from grid” -scenario, which results are
presented on Figure 34. The results are for the most promising scenario from 2016
simulation, considering charging rule of 20th percentile. The results indicate that the
number of batteries in use has no impact on the results in 2050. Generally, the costs in
the scenario are 1-13 % higher than in PV scenarios, and direct CO2 emissions increase
119
1-13 %. Thermal comfort on the other hand is 4-8 %-points lower than in PV and “Cost
optimized” -scenarios, which shows comparable results than the 2016 scenario. Thus, the
“Battery from the grid” -scenario is less potential than PV generation scenarios.
Figure 34. Comparison of battery only technology with different number of batteries and
20th percentile rule.
The fourth main technology is the underfloor heating with several charging options and
their results are presented on Figure 35. The underfloor heater shows similar patterns in
2050 scenarios than in 2016; “PV charging” -scenarios are the cheapest and generate the
least CO2 emissions. Yet, they do not generate as much thermal comfort as “Set times”
or “Cheapest charging hours”, which both have higher costs than other scenarios except
of manually controlled heating. Similarly increasing the local generation capacity reduces
costs and CO2 emissions, but their impact is less than improving the insulation of the
building.
120
Figure 35. Underfloor heating comparison.
6.2.3 Zero and net zero building
ZEB and NZEB are included in the 2050 scenario as specific house types. The annual
generation amounts were estimated with 2016 heat demand for low energy and passive
buildings so that their heat demands, and annual PV generation would be equal or that
there would be more PV generation than what the building’s heat demand is. The results
from the simulations for ZEB and NZEB are presented on Figure 36. ZEB with 6 kWp
capacity and NZEB are simulated with passive energy building insulation, while ZEB
with 8 kWp capacity is simulated with low energy building characteristics. There is a
slight mismatch with the annual generations and heat demands as ZEBs generate 2.2-3.4
MWh more electricity than what their heat demands are. In the results there does not seem
to be any differences on the same insulation level buildings in any parameters; only a
slight increase in self-consumption with lower PV capacity. The technology used on the
other hand, has more influence on the results, as utilizing either electrical or thermal
storage, costs and CO2 emissions can be reduced from the load shifting scenario. The only
difference in thermal comfort achievement rate comes from “Constant temperature
setting” -scenario, to which PV panels are added. The storage scenarios also increase the
self-consumption rate of technologies.
121
Figure 36. The ZEB and NZEB comparisons by the technology.
6.2.4 Overall
Generally, the 2050 scenario results follow the same pattern as today’s scenarios; same
technologies provide the same benefits and have the same downsides. Big differences
come from the higher electricity costs and lower CO2 emissions from the new energy
system. Temperature rise decreases the heat demand of buildings, but it is not enough to
compensate the increase in electricity costs. The results show that underfloor heating with
PV electricity supply shows good potential like today, whereas it has lower thermal
comfort achievement rate than other technologies. PV load shifting, manually controlled
heating and grid electricity with underfloor heating provide the highest thermal comfort
rates but are closely followed by PV with battery system and “Cost optimized” heating.
The lowest costs are associated with lower heat delivery, except for “Cost optimized”
heating, which has almost as low costs as “Constant temperature setting” and “Time
setting temperature” -scenarios. CO2 emissions are low in “PV generation”, “Constant
temperature setting” and “Time setting temperature” -scenarios and in “Cost optimized”
heating. Still, the biggest impact comes from increasing energy efficiency of the system,
e.g. improving the insulation of the building. The ranking of the technologies by the
average rank from the parameters is presented in Table 29 below.
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Table 29. The overall ranks of the technologies by the average rank of costs, CO2
emissions and thermal comfort.
Overall Rank 2018 Low Energy Passive
1st PV 8, 6 and 4 kWp
with battery system
Underfloor heating
with PV charging
PV with battery
system
2nd Underfloor heating
with PV charging
PV with battery
system
Underfloor heating
with PV charging
3rd PV load shifting PV with load
shifting
PV load shifting
The results from Table 29 show that PV systems are potential in electric space heating in
the future. The high electricity price level reduces the potential of electricity from grid
technologies as they have higher costs than local generation. Furthermore, the sale of
electricity to the grid seems more profitable in the future with higher electricity price
levels and this likely increases the potential of the PV systems. As the 2050 price includes
todays distribution costs and taxes, the electricity market price has higher impact than
today’s total electricity price and may influence the result.
6.2.5 Feasibility test
The feasibility of the solutions in 2050 are tested to consider the usability of the
technologies. The feasibility test is conducted with the investment cost estimations from
2016, so that the direction of the economic feasibility of the technologies can be assessed.
The results from PV and battery system payback time calculations are presented on Figure
37. All scenarios having only PV systems seem to become more economically feasible in
the future as the payback times reduce from the range of 17-25 years to 7-12 years. This
can practically cut the payback time by half and would make the PV only systems more
attractive. The PV combined with battery systems also have lower payback times than
today, but they can still be considered economically unfeasible. Yet, the payback time
decreases as the panel capacity increases, which would indicate that the batteries are
economically more suitable on those scenarios. The lowest payback time of PV combined
with battery system is achieved with 8 kWp panel and 1 battery with 19 years of payback
time. Comparing the payback time of PV net metering to PV with load shifting, the
payback time for low capacities is still lower in PV net metering but becomes higher when
the panel capacity increases. This would indicate that the stored electricity in the meter
cannot be used fast enough to gain more economic benefits, and likely a sale price should
123
be added to PV net metering in case the total generation cannot be utilized. The battery
from grid system on the other hand, seems to have extremely high payback times, which
is likely related to the created electricity price scenario, which would not have enough
volatility for the battery system’s proper operation. This means that the battery system
does not charge and discharge itself properly as the results from the scenario indicate that
there was only 1 full cycle during the year, which generated a profit of less than 1 €.
Figure 37. The feasibility test of 2050 technologies in today’s prices.
Finally, the impact of the HEMS on the payback times is presented on Table 30 below.
The payback times are calculated for 1 kWp PV capacity and for 1 battery. Battery from
grid is neglected by its high payback time even without HEMS.
124
Table 30. The total payback times of technologies including HEMS.
Technology Payback time [years]
Cost Optimized 2.4 – 3.2
PV load shifting 17 – 19
PV battery system 77.5 – 147
Cheapest charging underfloor 3.7 – 11.1
PV charging underfloor 16 – 17.4
Now the payback times for just load shifting are all under 20 years. The electricity from
the grid technologies seem more economically viable than systems including PV panels.
This is caused by higher investments related to PV panels, whereas load shifting with
grid-based technology only includes the HEMS. Yet, as these payback times are
calculated with 2016 costs, the changes in the costs of the system affect the feasibility of
the technology. Still, this shows a direction of the future feasibility of the technologies,
making them likely more feasible in 2050 than today.
125
7 DISCUSSION
To test out the different DSM methodologies, a thermal model was created basing it
partially on energy method from The National Building Code of Finland D5 (2017),
which is used in monthly calculation methods, but can be applied to dynamic calculations
with dynamic approach. The model also includes another dynamic calculation method for
estimating hourly indoor temperatures with SFS-EN ISO 52016-1 (2017), which takes
more phenomena into account. Thus, the used models do not both consider the same
parameters, which may cause some discrepancies on the simulations. Similarly, some
simplifications have been made in the creation of the model that may influence the
dynamic behavior on hourly-basis. The created electric space heating model also includes
simplifications like perfect thermostat and hence the results may be more ideal than in
reality. Dead band in the thermostat setting is not considered even though it exists and
can impact all the parameters neglecting some of the consumption variation. Furthermore,
thermal comfort is measured using PMV method with the mean vote limits. This means
that the thermal comfort achievement may not be a thermally comfortable climate to
everyone. Also neglecting thermal discomfort from the surfaces impacts results,
especially with heat delivery from underfloor heater. Similarly, it may give higher thermal
comfort achievement values from the radiative floor structure. Yet, the calculations on
the energy demand of a building in The National Building Code of Finland D5 (2017) are
conducted with constant 21°C indoor temperature setting, which did not seem to provide
much thermal comfort in the simulations. Therefore, it should be considered whether this
is the temperature with which the calculations should be done, as the indoor temperature
is likely higher in the building.
The directive on the Energy Performance of Buildings (2010/31/EU) and its amendment
directive (EU) 2018/844 are requiring that building stock moves towards more insulated
and energy efficient buildings which can integrate local generation and DSM methods
through smart functions. The amending directive (EU) 2018/844 proposes a creation of
Smart Readiness Indicators (SRI), which would evaluate the buildings technological
capability to interact with occupants and the energy grid by utilizing ICT while having
energy efficient operations. (The European Parliament and The Council of the European
Union 2010, 2018, Verbeke et al. 2018) Some of the proposed and tested heating
functions can comply with the changing legislation and SRI as they would integrate local
generation and DSM methods to the building and provide smart heating functions. Yet,
126
these methods were not found to be economically feasible in electric space heating
currently, which would indicate that some of the indicators contradict with the economic
benefits to customer. Naturally, the issue is more complex as local generation can be used
to fulfill other consumptions as well, and in those cases, the suitability and potential of
the local generation in electric heating should be studied as aggregated loads from the
appliances and electric heating. This also impacts the control of the whole building as all
loads have different DSM potentials. Therefore, the creation of the heating and heat
demand models and attaching them to the existing smart building model allow studying
the impact of aggregated loads and hence should benefit the SEN2050 project. On the
other hand, the proposed dynamic thermal comfort model, which would adapt to people’s
actions, improves the quality-of-life of the occupants, but would require smart control
system or active human control of the system.
The studies on DSM were made on considering methods that can be applied solely on
building by the occupant without any load control from the utility or aggregator. Taking
these aggregated load control methods into account and studying their impacts, the results
would provide more thorough effects of all the DSM methods. As the local and global
optimums may be different, as described in Muratori et al. (2014), the importance of
thinking the global and local effects together arises.
For testing electric space heating in the future, scenarios for 2050 were applied and
created based on information from literature. The future scenarios always include
embedded uncertainties related to the nature of the future and testing out different
scenarios was conducted to minimize the level of uncertainty. The hourly temperature
scenario was created by interpolating the monthly average temperatures and creating an
hourly temperature scenario that is based on the changes and TRY2012. Currently, a new
European project Copernicus Climate Change Service (C3S) (2018) provides daily,
monthly, seasonal and annual temperature values for the future climate scenarios, and
they could provide temperature values for 2050 without the need of interpolation,
providing possibly more accurate values for the simulation. Creation of the hourly profiles
for electricity generation and real-time electricity prices then are approximations based
on current information of the generation distribution and LCOE. Uncertainties rise from
the actual operation of the 2050 electricity system as balancing technologies like hydro
power could be operated differently. Therefore, the energy system in 2050 may be
different as well as the prices in the liberalized electricity market. Similarly, the conducted
127
feasibility test was mainly based on investment prices of today, and depending on the
maturity of the technology, the prices will decrease in the future at some point. Today’s
feasibility of the technologies is also related to estimations of the starting level, as some
space heating technologies may require investments on the radiator itself for example,
and not just the investment on HEMS, to ensure the proper functioning of the control
system.
This work provides an electric heating and heat demand models for the SEN2050 project
to be used in further evaluating and optimizing the electricity demand of smart buildings.
Similarly, it gives results on the DSM programs that can be used in decarbonizing the
building stock and evaluates their profitability. Future research on electric space heating
should include confirming the positive results on PV charging underfloor heating and
testing out various control mechanisms on it to optimize the parameters better. This could
include utilization of weather or production forecasts to provide more dynamic
estimations on the production to be applied to approximate the grid electricity need.
Similarly, the model should be further developed to optimize the heating in presence of
PV and control systems with HEMS, to see if the heating can be optimized with several
parameters, e.g. by firstly emphasizing PV generation, then real-time costs, CO2
emissions and thermal comfort. The optimization by the CO2 emissions should be
included as well as studying the adaptive control on direct electric heating when the
emissions levels or outdoor parameters are different than forecasted. The model should
include the possibility to test out the impacts on other parameters, if one is normalized
between the technologies. Similarly, assessing the optimal solution should consider the
differences in the parameters as the results on some parameters may be closer to each
other than others. The potential of the flexibility of the electric space heating from the
utility point-of-view with these technologies should be investigated as well. The model
should also be further developed to allow testing of the local generation and load shifting
of aggregated loads to obtain more thorough energy management system. On the other
hand, reducing the future uncertainties should be conducted by testing out more 2050
scenarios and by trying to improve the current hourly profiles. All these could be included
in optimizing the electricity demand and in improving the model and could therefore help
in achieving the project targets in decarbonizing the building stock.
128
8 CONCLUSIONS
The work is part of an Academy of Finland funded SEN2050 project and aims in creating
a thermal model for calculating the heat demand and simulating the effects of electric
space heating in a building. This model will be integrated to an existing smart house
model, with which electricity consumption of appliances, user behavior and DSM
programs can be simulated. Therefore, the created model allows further studying of the
demand of electricity in buildings and multi-microgrids and hence helps in achieving the
project objectives. Furthermore, the model is used to optimize the heating related costs,
CO2 emissions and thermal comfort with the help of DSM technologies on electric space
heating today and in 2050. The created thermal model utilizes energy method for
building’s heat demand calculation while the indoor temperature is calculated with the
help of standard SFS-EN ISO 52016-1. Different electric heating technologies are studied
from cost optimized direct electric heating to load shifting from PV panels generation.
Electric and thermal storage technologies are also studied for their potential in electric
heating. Different building types are modelled to test out their characteristics and
potential, as well as to study whether they have different optimal solutions. Thermal
comfort of the occupants is tested using standard predicted mean vote (PMV) method
from SFS-EN ISO 7730. Feasibility test on the solutions is also conducted.
To test out the 2050 scenarios, the changes in climate, energy system and electricity price
were modelled by selecting two energy scenarios from the literature. The selected energy
scenarios are “Growth” and “Save”, originally created with VTT TIMES model. The
climate scenarios showed different increases in monthly average temperatures in Oulu
and energy scenarios different energy mixes for 2050. Utilizing the electricity generation
profile from 2016, an approximated but detailed hourly electricity generation profiles
were created for the 2050 energy mixes. Similarly, an hourly real-time price scenario was
created from LCOE estimations to model the costs and impact of the energy mix to DSM
potential. Similarly, ZEB and NZEBs were simulated in 2050 network for testing their
characteristics and potential of having electric space heating. The uncertainties related to
the scenarios are discussed. The research questions are answered below on their separate
paragraphs.
129
1. Which demand side management technologies are useful for optimizing the
electric space heating between price, environmental parameters, and thermal
comfort?
The results from both 2016 and 2050 simulations show the difficulty of the optimization
as the technologies may create low costs and CO2 emissions, but at the same time cannot
provide proper thermal comfort. On the other hand, some technologies can provide
thermal comfort to occupants while having high costs and CO2 emissions at the same
time. In the 2016 simulations and in direct electric space heating from the grid, the “Cost
optimized” -scenario provided 12-51 %-points better thermal comfort than “Constant
temperature setting” and “time setting temperature” -scenarios, but it has 1-15 % higher
costs and 2-17 % higher direct CO2 emissions as well, since it delivers more heat to the
building. Yet, it decreases electricity costs by 18-55 % and direct CO2 emissions by 11-
47 % from the “manually controlled heating” -scenario, with 7-17 %-points lower thermal
comfort rate. Therefore, the “Cost optimized” -scenario shows the best operation from
the direct electric space heating technologies in 2016. PV generation with battery system
has 4-20 % lower costs and direct CO2 emissions than with only load shifting but also 8-
12 %-points lower thermal comfort rate. “Battery from the grid” -scenario in 2016 shows
results within a 20 % range in electricity costs and direct CO2 emissions from PV
generation scenarios but has 6-20 %-points less thermal comfort achieved. The biggest
impact in “Battery from the grid” -scenario comes from the charging rule as utilizing
higher price limit for charging reduces both costs and direct CO2 emissions by 4-11 %.
Underfloor heating with the PV charging can reduce the costs and CO2 emissions the
most, with decreases of 8-31 % and 25-69 % from the “Constant temperature setting” -
scenario, respectively. Generally, storage technologies, especially underfloor heating
with PV charging, show promising results on the optimization problem with local
generation today.
In the 2050 scenario, the heat demand decreases 0.8-11.8 % from 2016 values depending
on the technology and the climate change scenario. The electricity costs increase 37-128
% from today, with Save scenario having higher costs than Growth. Conversely, the direct
CO2 emissions decrease 94-98 % from today, showing the direction towards
decarbonization, with both scenarios having equal emission reductions. Yet, the results
from the 2050 scenario show that the technologies are related to each other in all
scenarios, and therefore they show similar relative characteristics. Thus, it can be assumed
130
that the optimal solution for one scenario is optimal solution for them all. Considering the
overall rank of the technologies, it seems that PV generation is becoming more optimal
solution in 2050.
2. What DSM technologies are profitable?
The economic profitability of the technologies is estimated with calculating payback time
for the technologies in 2016 and 2050. In 2016, all the PV generation scenarios show
payback times of over 30 years when HEMS is included in the calculations. The payback
times for “Battery from the grid” are even higher with all showing over 800 years as
payback time. The only economically feasible options in 2016 are “Cost optimized” and
“Cheapest charging hours” -scenarios with payback times from 2.5 to 13.3 years when
compared “Manually controlled” and “Set times” -scenarios, respectively. In 2050 the
scenarios with only PV panels become more profitable as their payback times reduce to
16-19 years. Still, “Cost optimized” and “Cheapest charging hours” -scenarios stay
economically more feasible with payback times of 2.4-11.1 years. The “Cost optimized”
-scenario has the lowest payback times in both 2016 and 2050. Therefore, the optimal
solutions do not seem to be the economically the most feasible options.
3. How does heat demand and thermal insulation affect the heating of buildings and
DSM programs?
The thermal insulation level impacts electricity costs and direct CO2 emissions the most
as on average it decreases the costs by 235 % and direct CO2 emissions by 263 % when
a 1985 building type is renovated to passive building today. In 2050 scenario renovating
2018 building to passive building insulation levels, the electricity costs decrease 63-100
% and the direct CO2 emissions decrease 80-114 %. Therefore, the increase in insulation
level seems to be the most effective decarbonization method out of the tested in both 2016
and 2050 scenarios. From the technology point-of-view it seems like there are slight
changes in the most prominent technologies but mainly the PV system scenarios are the
best. Hence, it seems that the optimal solutions do not change too much between different
thermal insulation levels. Similarly, the optimal solutions between the heat demands with
2016 and 2050 scenarios both include PV generation with storage technologies in the
highest ranked technologies.
131
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APPENDIX A – OCCUPANCY DETECTION OF A SINGLE
PERSON HOUSEHOLD
Appendix A. Occupancy detection of a single person household by using electricity
consumption values of appliances.
150
APPENDIX B – OCCUPANCY DETECTION OF A
MULTIPLE PERSONS HOUSEHOLD
Appendix B. Occupancy detection of multiple person households by the electricity
consumption information on appliances.