the Operation of Building Energy Systems
vorgelegt von
M. Sc.
Saeed Sayadi
ORCID: 0000-0003-2917-9043
an der Fakultät III – Prozesswissenschaften
der Technischen Universität Berlin
zur Erlangung des akademischen Grades
Doktor der Ingenieurwissenschaften
Dr.Ing.
-genehmigte Dissertation
Promotionsausschuss:
Vorsitzender:
Prof. Dr.-Ing. Felix Ziegler
Gutachter*innen:
Prof. Dr.-Ing. George Tsatsaronis
Prof. Dr. Tetyana Morozyuk
Univ.-Prof. Dr.-Ing. Dirk Müller
This study has been developed during my work as a research associate at the Institute for Energy Engineering at the Berlin Institute of Technology.
Firstly, I would like to express my sincere gratitude to Prof. George Tsatsaronis for supervising this work and his invaluable guidance on my research. I am very grateful that I had an opportunity to participate in his lectures during my master’s study program and later work under his supervision at the Institute for Energy Engineering. Besides, I would like to thank my second supervisor, Prof. Tetyana Morozyuk, for her constructive feedback on this work and her unlimited support on both academic and personal levels. It really was the greatest pleasure to be supervised by both of these outstanding academics.
I am very thankful that Prof. Dirk Müller from the RWTH Aachen University and Prof. Ana María Blanco-Marigorta from the University of Las Palmas de Gran Canaria showed interest in my work and agreed to review it. Furthermore, I would like to thank Prof. Felix Ziegler for his kind willingness to chair my defense session.
Moreover, I am very grateful to all my colleagues at the Institute for Energy Engineering for the excel-lent friendly work atmosphere in both teaching and research activities, the exchange of knowledge and experiences, the fruitful scientific discussions, and their willingness to help at any time. I truly enjoyed the time we spent together, especially at the conferences.
Finally, I would like to thank my wonderful wife, Taravat Saeb Gilani, for her endless support and constant encouragement; and our beloved son, Adrian, who has brought great joy to our lives and made me more fulfilled to conduct this work.
Berlin, December 2020 Saeed Sayadi
The objective of the present research is the operational assessment of building energy systems through the application of conventional and advanced exergy-based methods. Two different case studies are considered here. The first one is a theoretical single-office building equipped with heating and ventilation systems, located in a typical cold-climate region. The second case study is a real-life system, including a large building and its complex heating and cooling systems, located in Aachen, Germany.
Since the first case study is theoretical, mathematical heat and mass transfer models representing the system’s dynamic behavior were first formulated. Then all dynamic exergy-based methods were developed and applied to this model. Unlike the first case study, the second one is a real existing building with an extensive data collection and monitoring system, which offers the opportunity to apply all methodologies developed for the first case study to a “real” system. Consequently, the input data for the exergy-based operational assessment of the second case study comes from the various temperature, pressure, and volume flow rate sensors installed in different parts of the considered building.
The present study provides a step-by-step framework for conducting dynamic exergy-based methods for building energy systems using a small and a large-scale case study. Two novel approaches for performing dynamic advanced exergetic analysis are proposed in this study for the first time. The first method, called the method of Serial F/P Arrangement, splits the dynamic exergy destruction into endogenous and exogenous parts, and the second one, called the Design-Based Approach, obtains the avoidable and unavoidable exergy destruction in dynamic systems. Moreover, the dynamic formulation of exergoeconomic analysis, taking into account the stored/discharged costs in different system components at different times, is also presented for the first time in the current research study. As a general rule of thumb, only between 15 % to 25 % of the total exergy destruction caused in the operation of building energy systems can be avoided. Approximately one-quarter of this avoidable exergy destruction is endogenous, and the rest is exogenous. This means improving the intercon-nections among system components through the implementation of better control systems has a stronger effect on the performance of the overall system than improving single components of the system.
Based on the results of dynamic advanced exergetic and exergoeconomic analyses, four new dimen-sionless performance indicators for evaluating the “operation” of dynamic systems are introduced. Results obtained from both case studies confirm that these new exergy-based parameters provide
Die vorliegende Arbeit verfolgt das Ziel, den Betrieb dynamischer Systeme durch Anwendung konven-tioneller und erweiterter exergiebasierter Methoden zu bewerten. Dabei wurden zwei verschiedenen Fallstudien untersucht. Bei der ersten handelt es sich um ein theoretisches Einzelbürogebäude, ausgestattet mit Heizungs- und Lüftungssystemen, in einer Region mit typisch kaltem Klima. Die zweite Fallstudie ist ein reales System, bestehend aus einem großen Mehrzweckgebäude und dessen komplexen Heiz- und Kühlsystemen, in Aachen, Deutschland.
Anhand der ersten Fallstudie wurden zunächst mathematische Wärme- und Stoffübertragungsmod-elle formuliert, die das dynamische Verhalten des Systems darstStoffübertragungsmod-ellen. Zur thermodynamischen Bewertung des Systems wurden dynamische exergiebasierte Methoden entwickelt und auf die for-mulierten Modelle angewandt. In der zweiten Fallstudie wurden die entwickelten dynamischen ex-ergiebasierten Methoden auf ein reales, bestehendes Mehrzweckgebäude mit einem umfangreichen Datenerfassungs- und Überwachungssystem angewandt. Die Eingangsdaten für die exergiebasierte Betriebsbewertung dieser Fallstudie wurden von Temperatur-, Druck- und Durchflusssensoren, die in verschiedenen Teilen des betrachteten Gebäudes eingebaut wurden, erfasst.
Die vorliegende Arbeit entwickelt einen schrittweise aufgebauten Rahmen für die Durchführung “dynamischer” exergetischer Methoden für Gebäudeenergiesysteme. Es werden zwei neue Ansätze für die Durchführung dynamischer erweiterter Exergieanalysen vorgestellt: Die erste Methode, die als Methode der Reihenanordnung von Aufwand/Produkt bezeichnet wird, teilt die dynamische Exergievernichtung in endogene und exogene Anteile auf; die zweite Methode, die als designbasierter Ansatz bezeichnet wird, befasst sich mit der Berechnung von vermeidbaren und unvermeidbaren Exergievernichtungen in dynamischen Systemen. Darüber hinaus stellt diese Forschungsarbeit zum ersten Mal die dynamische Formulierung einer exergoökonomischen Analyse vor, die die be- und entladenen Kosten in den einzelnen Komponenten zu unterschiedlichen Zeitpunkten berücksichtigt. Aus den Ergebnissen dieser Studie kann festgestellt werden, dass nur zwischen 15 % und 25 % der gesamten Exergievernichtung, die bei dem Betrieb von Gebäudeenergiesystemen verursacht wird, vermieden werden können. Davon ist etwa ein Viertel endogen und drei Viertel exogen. Daraus folgt, dass die Optimierung der gegenseitigen Beeinflussung der Systemkomponenten durch die Imple-mentierung besserer Regelungssysteme einen stärkeren Einfluss auf die Effizienz des Gesamtsystems hat als die Verbesserung einzelner Komponenten des Systems.
wer-mit den höchsten Optimierungsprioritäten identifizieren können.
Acknowledgement iii
Abstract v
Zusammenfassung vii
Contents ix
List of Figures xiii
List of Tables xvii
Nomenclature xix 1 Introduction 1 1.1 Background . . . 1 1.2 Contribution . . . 3 1.3 Thesis Layout . . . 4 2 Literature Review 5 3 Exergy Analysis for Dynamic Systems 33 3.1 Exergy of Material Streams . . . 34
3.1.1 Physical Exergy . . . 34
3.1.2 Chemical Exergy . . . 36
3.1.3 Exergy of Humid Air . . . 37
3.2 Exergy of Energy Streams . . . 37
3.3 Time Rate of Change in the Exergy of a System . . . 38
3.4 The Reference State for Exergy Analysis . . . 39
3.5 Exergy Balance . . . 41
3.5.1 Input-Output Method . . . 41
3.5.2 Fuel-Product Method . . . 43
3.6 Dissipative Components / Operation . . . 44
3.7 Exergetic Variables . . . 44
3.8 Case Study . . . 45
4.1 Splitting Exergy Destruction into Endogenous and Exogenous Parts . . . 56
4.1.1 Endogenous Exergy Destruction . . . 57
4.1.2 Exogenous Exergy Destruction . . . 57
4.1.3 Binary Interactions . . . 58
4.1.4 Interactions in Groups of More than Two Components . . . 58
4.1.5 Assumptions . . . 59
4.2 Splitting Exergy Destruction into Unavoidable and Avoidable Parts . . . 60
4.2.1 Unavoidable Exergy Destruction . . . 60
4.2.2 Avoidable Exergy Destruction . . . 62
4.3 Combination of Avoidable/Unavoidable and Endogenous/Exogenous Exergy Destructions 62 4.4 Developing New Parameters for Operational Assessment . . . 62
4.5 Case Study . . . 63
4.5.1 Final Products of the Entire System . . . 65
4.5.2 Endogenous Exergy Destruction . . . 65
4.5.3 Exogenous Exergy Destruction . . . 68
4.5.4 Binary Interactions . . . 68
4.5.5 Interactions in Groups of More than Two Blocks . . . 70
4.5.6 Unavoidable and Avoidable Exergy Destructions . . . 72
4.5.7 Endogenous/Exogenous and Avoidable/Unavoidable Exergy Destructions for All Blocks . . . 73
5 Exergoeconomic Analysis for Dynamic Systems 79 5.1 Exergy Costing . . . 80
5.2 Cost Balance . . . 81
5.2.1 Auxiliary Equations . . . 81
5.2.2 Dissipative Component . . . 82
5.3 System of Linear Equations . . . 82
5.4 Exergoeconomic Variables . . . 83
5.5 Advanced Exergoeconomic Analysis . . . 84
5.6 Case Study . . . 85
5.6.1 Conventional Exergoeconomic Results . . . 86
5.6.2 Advanced Exergoeconomic Results . . . 90
6 Real-Life Application of Dynamic Exergy-Based Methods 93 6.1 Case Study . . . 93
6.1.1 Energy Concept of the E.ON ERC Building . . . 94
6.1.2 Monitoring System of the E.ON ERC Building . . . 99
6.2 Dynamic Exergetic Assessment . . . 99
6.2.1 The Reference Environment . . . 99
6.2.2 Definition of Fuel and Product Exergies . . . 99
6.2.3 Dynamic Results for One Year . . . 100
6.2.4 Duration Curve for the Exergetic Efficiency . . . 103
6.2.5 Dynamic Results for One Day . . . 104
6.2.6 Average Results for Different Stages of the Building Energy System . . . 106
6.2.7 Energy and Exergy Flow Diagrams . . . 109
6.3 Dynamic Advanced Exergy Analysis . . . 110
6.3.1 Endogenous and Exogenous Exergy Destructions . . . 110
6.3.2 Avoidable and Unavoidable Exergy Destructions . . . 115
6.4 Dynamic Exergoeconomic Analysis . . . 124
6.4.1 Exergoeconomic Equations . . . 124
6.4.2 Modification of the Product Exergy . . . 126
6.4.3 Exergoeconomic Results for Streams . . . 127
6.4.4 Cost Rates Associated with Dissipative Operations . . . 127
6.4.5 The Final Cost of the Products . . . 130
6.4.6 Exergoeconomic Results for Component . . . 131
6.4.7 Energy Costing vs. Exergy Costing . . . 132
6.5 Dynamic Advanced Exergoeconomic Results . . . 133
7 Conclusions and Future Work 137 7.1 Conclusions . . . 137
7.2 Future Work . . . 139
References 139 Appendices 153 A Dynamic Model for the First Case Study 155 A.1 Boiler . . . 155
A.2 Pumps and Fans . . . 159
A.3 Warm Water Storage Tank . . . 160
A.4 Mixers . . . 163
A.5 Splitters . . . 164
A.6 Heating Coil . . . 164
A.7 Heat Recuperator . . . 167
A.8 Room Air . . . 169
A.9 Building Envelope . . . 170
A.10 Windows . . . 172
B Dynamic Exergoeconomic Analysis for the First Case Study 173 B.1 Cost Balance and Auxiliary Equations for Exergoeconomic Analysis . . . 173
B.2 Exergoeconomic Results for Streams . . . 179
C Exergy-Based Methods for the Second Case Study 183 C.1 Fuel and Product Exergies for Exergy Analysis . . . 183
C.2 Real and Ideal Exergies for Advanced Exergy Analysis . . . 186
C.3 Cost Balance and Auxiliary Equations for Exergoeconomic Analysis . . . 189
List of Figures
1.1 Changes in direct and indirect CO2 emissions and the total energy demand of the
building and construction sector in the world from 1990 to 2017. . . 1
2.1 The ambient temperature and the quality factor of the energy demand in buildings. . . 6
3.1 Specific physical exergy for a stream of matter consisting of (a) air as an ideal gas, and (b) liquid water as an incompressible fluid. . . 35 3.2 The ratio of exergy to heat for (a) convective heat flux at the boundary temperature Tb,
and (b) radiation heat flux from the surface temperature Ts. . . 38
3.3 The storage and discharge of the exergy content of the kth component of a system for different combinations of the component temperature at time t and t − 1. . . 39 3.4 Graphical representation of the exergy balance. . . 41 3.5 System boundaries and all incoming and outgoing streams for a typical building and its
heating system. . . 42 3.6 Schematic of a single-zone office building with its heating and ventilation systems. . . . 46 3.7 The sequence diagram of the reference control system for the theoretical case study. . . 47 3.8 The hourly-average ambient temperature and the solar radiation flux on horizontal and
tilted surfaces for one typical winter day. . . 48 3.9 Dynamic energetic results for the theoretical case study. . . 49 3.10 Dynamic exergetic results for the theoretical case study. . . 51 3.11 Energy and exergy flows for the overall energy supply chain of the theoretical case study. 54
4.1 Schematic of the method of Serial F/P Arrangement for splitting exergy destruction into endogenous and exogenous parts. . . 57 4.2 Creating new blocks for performing dynamic advanced exergy analysis using the method
of Serial F/P Arrangement. . . 64 4.3 The block diagram based on the method of Serial F/P Arrangement for the first case study. 64 4.4 The block diagram for calculating the endogenous exergy destruction within the boiler. 68 4.5 The block diagram for calculating the binary interactions between the boiler and the
heating coil. . . 70 4.6 The block diagram for calculating the exogenous exergy destruction within the boiler,
when the boiler, the storage tank, and the heating coil are in real operations and the rest of the system is ideal. . . 71 4.7 The percentage of endogenous/exogenous and avoidable/unavoidable exergy
destruc-tions within blocks of the first case study. . . 73 4.8 The ratio of the exogenous exergy destruction caused by block j in block k to the total
exergy destruction within block k. . . 75 4.9 The matrix of interactions among different stages of the energy system of the theoretical
case study. . . 76 4.10 Dynamic results of the advanced exergy analysis for the operation of the theoretical
case study in January. . . 78
compo-case study in one day. . . 89
5.4 Specific cost of fuel and product exergies for the overall energy supply chain of the theoretical case study. . . 90
6.1 The energy concept of the heating and cooling systems in the E.ON ERC building. . . . 95
6.2 The process flow diagram of the heating and cooling systems in the E.ON ERC building. 98 6.3 Ambient conditions, heating and cooling demands, total supplied energy, exergetic demand, exergy destruction, and exergetic efficiency for the energy systems of the E.ON ERC building in the year 2015. . . 102
6.4 Duration curve for the exergetic efficiency of the energy systems of the considered building in the year 2015. . . 103
6.5 Ambient conditions, heating and cooling demands, total supplied energy, exergy de-mand, exergy destruction, and exergetic efficiency for the energy system of the E.ON ERC building for (a) a summer day, and (b) a winter day. . . 105
6.6 Exergy destruction ratio in different components of the energy system in the E.ON ERC building in the year 2015. . . 108
6.7 Specific nergy and exergy flows for the overall energy supply chain of the E.ON ERC building in the year 2015. . . 109
6.8 Schematic of the heating and cooling systems in the main building of the E.ON ERC and specification of sub-systems for splitting exergy destruction into endogenous and exogenous parts. . . 112
6.9 The block diagram used for the application of a dynamic advanced exergy analysis to the energy system of the E.ON ERC building based on the operation of the system in the year 2015. . . 113
6.10 The effect of the reference temperature on the supplied and the gained exergy in a cooling consumer. . . 115
6.11 Percentage of endogenous/exogenous and avoidable/unavoidable exergy destruction within components of the E.ON ERC building. . . 118
6.12 The ratio of exogenous exergy destruction caused by component j within component k to the total exergy destruction within component k. . . 122
6.13 The matrix of interactions among different stages of the heating and cooling systems in the E.ON ERC building based on the operation of the system in the year 2015. . . 123
6.14 Adjusted process flow diagram of the heating and cooling systems in the E.ON ERC building for the exergoeconomic analysis. . . 125
6.15 Modification of the product exergy for exergoeconomic analysis when the reference temperature crosses the operating temperatures. . . 126
6.16 The contribution of different heating and cooling consumers to the total operating costs using energy-based and exergoeconomic-based cost allocation approaches. . . 133
A.1 Schematic of the boiler. . . 156
A.2 System boundaries for the pumps and fans. . . 159
A.3 System boundaries for the warm water storage tank. . . 160
A.4 System boundaries for mixing units. . . 163
A.5 System boundaries for splitting units. . . 164
A.6 System boundaries for the heating coil. . . 165
A.7 The ratio between convective heat transfer coefficient in the part-load and the nominal operations of the heating coil for (a) water and (b) air. . . 167
A.8 System boundaries for the heat recuperator. . . 168
A.9 The equivalent thermal resistance network of the room air, the building envelope and windows. . . 169
List of Tables
1.1 Layout of the thesis. . . 4
2.1 Summary of the main studies on the application of exergy-based methods to buildings and their HVAC systems. . . 8
3.1 Standard molar chemical exergy for selected substances at T0= 25 °C and p0= 1 atm. . 37
3.2 Results of the dynamic exergetic analysis for different stages of the energy system of the theoretical case study. . . 52
4.1 Calculation of avoidable/unavoidable exergy destruction using exergetic parameters obtained from the Design-Based Approach. . . 72 4.2 Splitting exergy destruction into endogenous, exogenous, avoidable, and unavoidable
parts for different blocks of the first case study. . . 74 4.3 A breakdown of the exogenous exergy destructions for all blocks of the system. . . 74 4.4 Comparison of exergetic performance indicators obtained from conventional and
ad-vanced exergy analysis. . . 77
5.1 Average exergoeconomic results for the stored exergy and cost in different components of the first case study. . . 86 5.2 Average results of the dynamic exergoeconomic analysis for different layers of the first
case study. . . 87 5.3 Splitting the cost of exergy destruction into endogenous, exogenous, avoidable, and
unavoidable parts for different blocks of the first case study. . . 90 5.4 Comparison of the performance indicators obtained from different exergy-based
meth-ods for the first case study. . . 91
6.1 Exergetic results for different layers of the energy system in the E.ON ERC building in the year 2015. . . 106 6.2 Calculation of avoidable/unavoidable exergy destruction using exergetic parameters
of each block of the energy system of the E.ON ERC building in their design operating conditions. . . 116 6.3 Splitting exergy destruction into endogenous, exogenous, avoidable and unavoidable
parts for the E.ON ERC building in the year 2015. . . 119 6.4 Comparison between the results obtained from the application of advanced exergy
analysis to different case studies. . . 120 6.5 Specific and total cost of the final products in the energy system of the E.ON ERC
building based on the operation of the system in the year 2015. . . 131 6.6 Exergoeconomic results for different layers of the energy system in the E.ON ERC
build-ing in the year 2015. . . 132 6.7 Splitting the cost of exergy destruction into endogenous, exogenous, avoidable and
unavoidable parts for the E.ON ERC building in the year 2015. . . 134 6.8 Comparison between the dimensionless performance indicators obtained from the
application of all presented exergy-based methods to the energy system of the E.ON ERC building. . . 135
A.4 Exergetic results for different zones of the boiler for the nominal operation. . . 159
B.1 Cost balances and auxiliary equations for all components of the theoretical case study in all operation modes. . . 174 B.2 Average exergoeconomic results for material streams of the first case study. . . 180 B.3 Average exergoeconomic results for energy streams of the first case study. . . 181
C.1 Fuel and product exergies for the main components of the E.ON ERC building at dy-namic operation and for different operation conditions regarding the reference temper-ature. . . 183 C.2 Calculation of real and ideal exergies for streams shown in Fig. 6.9. . . 187 C.3 Auxiliary parameters used for calculating ideal exergies. . . 188 C.4 Cost balances and auxiliary equations for all components of the E.ON ERC building and
in all operation modes. . . 190 C.5 Average material and electricity stream results obtained from the exergoeconomic
analysis applied to the E.ON ERC building based on the operation of the system in the year 2015. . . 202
Roman Symbols
e¯ molar exergy kJ/kmol
R¯ universal gas constant = 8.314 kJ/kmol · K
Ċ cost rate e/h
Ċ∗ cost rate associated with dissipative operations e/h
Ė exergy rate kW
ṁ mass flow rate kg/s
Q̇ heat rate kW
Ẇ power kW
Ż investment costs e
A interconnection matrix (in exergoeconomic analysis) –
B known parameters (in exergoeconomic analysis) e/h
X unknown vector (in exergoeconomic analysis) e/h
A surface area m2
C costs e
C thermal capacity MJ/K
c specific cost per unit of exergy e/GJex
C∗ capacity rate ratio –
c∗ concentration of CO2 ppm
c′ CO2generation rate by humans as a result of respiration cm3/h
cp specific heat capacity at constant pressure kJ/kg · K
cv specific heat capacity at constant volume kJ/kg · K
d diameter m
E cumulative exergy rate W · h
E exergy kJ
e specific exergy kJ/kg
F auxiliary parameter for calculating ideal exergies –
f exergoeconomic factor –
h convective heat transfer coefficient W/m2· K
m mass kg
n mole mol
p pressure kPa
q heat flux W/m2
R thermal resistance mK/W
r relative cost difference –
s specific entropy kJ/kg · K
T temperature K
t time s
U overall heat transfer coefficient W/m2· K
V volume m3
v specific volume m3/kg
w mass fraction kg/kg
x mole fraction kmol/kmol
y exergy destruction ratio –
z ratio between exergy destruction and product exergy in ideal operation –
DEP dependency (of a component) on the system –
INF influence (of a component) on the system –
M-POI monetary potential for operational improvement (of a component) –
M-DEP monetary dependency (of a component) on the system –
M-INF monetary influence (of a component) on the system –
POI potential for operational improvement (of a component) –
Greek Symbols
α absorbtion coefficient (of the building envelope) –
∆ difference – ϵ effectiveness % η energetic efficiency % κ thermal conductivity W/m · K λ air ratio – ω humidity ratio kg/kg ρ density kg/m3 σ Stefan-Boltzmann constant = 5.67 × 10−8 W/m2· K4
τ glass transmissivity for windows –
ε exergetic efficiency %
ε′ emissivity (of the building envelope) –
Subscripts
0 reference state (for exergy analysis)
A air amb ambient ave average B Boiler b boundary BE Building Envelope c cold cs cross section D (exergy) destruction diss dissipative elec. electricity F fuel (exergy) H heating h horizontal h hot HC Heating Coil HR Heat Recuperator hum humid i inside
i subscript for system components in inlet (stream)
in inner surface
int.G internal heat gain
j subscript for streams of matter j subscript for system components k subscript for system components
L (exergy) loss
l subscript for elements of a mixture
LL lower limit
m subscript for number of outlet cost streams N number of gases in an ideal mixture N number of nodes in storage tank
n subscript for number of inlet cost streams n subscript for streams of work
nom nominal
o outside
out outer surface out outlet (stream)
s surface ST Storage Tank t tilted tot total UL upper limit vap vapor W wall W water win Windows Z zone Superscripts
+ superscript indicating the increase of stored exergy - superscript indicating the decrease of stored exergy AV avoidable (exergy destruction)
CH chemical (exergy)
EN endogenous (exergy destruction) EX exdogenous (exergy destruction) in incoming (radiation heat transfer) init. initial
M mechanical (exergy)
MEX mexdogenous (exergy destruction)
mod. modified
nom nominal
out outgoing (radiation heat transfer) PH physical (exergy)
ref reference (for calculating enthalpy and entropy)
T thermal (exergy)
UN unavoidable (exergy destruction)
Abbreviations
ACB Active Chilled Beams AHU Air Handling Unit CCA Concrete Core Activation CFD Computational Fluid Dynamic
CHP Combined Heat and Power
CP Combustion Products
CWS Cold Water Storage
DHW Domestic Hot Water
EA Exhaust Air
EG Exhaust Gas
ERC Energy Research Center
ExMPC Exergy-based Model Predictive Control
FA Fresh Air
FC Free Cooler
FVU Façade Ventilation Unit
GTF Geothermal Field
HHV Higher Heating Value
HP Heat Pump
HS Hydraulic Separator
HTC High-Temperature Cooling HTH High-Temperature Heating
HVAC Heating, Ventilation, and Air Conditioning
HX Heat eXchanger
IAQ Indoor Air Quality ID Ideal (operation)
IEA International Energy Agency
LAB Laboratory
LCI Life Cycle Integrated LowEx Low Exergy
LTC Low-Temperature Cooling LTH Low-Temperature Heating M&S Mixer and Splitter
MPC Model Predictive Control NEG Negative (exergy demand) NTU Number of Transfer Units O&M Operation and Maintenance
OA Outdoor Air
POS Positive (exergy demand) RE Real (operation)
RW Return Water
SA Serial F/P Arrangement
SA Supply Air
SQL Structured Query Language
SR Server Rooms
SW Supply Water
1
Introduction
1.1 Background
Global warming and climate change pose a severe threat to human civilization’s future and draw attention to the need to use resources more efficiently. The building sector is a primary target for reducing greenhouse gas emissions, as it accounts for 36 % of final energy use and 39 % of energy and process-related CO2emissions in the year 2018. Among the total CO2emissions from the building
sector in 2018, only one-quarter results directly from manufacturing building materials such as steel, cement, and glass, while the rest is indirectly caused by the operation of buildings’ energy systems. The variation of CO2emission and energy demand of the global building sector from 1990 to 2017 are
depicted in Fig. 1.1[1]. According to these diagrams, the indirect CO2emission and the total energy
demand of the buildings have been substantially increased by approximately 2.4 % and 1.4 % per year since 1990, respectively. direct emission indirect emission 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 20 13 20 14 20 15 20 16 20 17 2 3 4 5 6 7 C O2 emissio n [︁ Gt ]︁ 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 20 13 20 14 20 15 20 16 20 17 80 90 100 110 120 ener gy deman d [︁ EJ ]︁
in prosperity and expanding middle class in the developing countries, spending more time indoors, and higher standards for indoor air quality[2]. Based on the data from Ref.[1], space heating, water heating, and cooking are the primary end-use energy demands in global building sector accounting for 34 %, 20 %, and 19 % of the overall energy supplied to buildings, respectively. Although space cooling uses only around 7 % of the total inlet energy to buildings, it is the fastest-growing end-use with an increase rate more than 33 % since 2010.
In the year 2018, from the global energy efficiency investments around USD 4.5 trillion, the building sector received the most substantial portion with USD 139 billion, 52 %, and 26 % of which were spent for improving building envelope and HVAC systems, respectively. This investment resulted in an approximately 16 % reduction in the final energy demand of the building sector. Although efficiency improvements continued to be made, they were not sufficient to outpace the total energy demand growth in buildings, and therefore, higher investments are required to limit demand and reduce energy intensity.
A global transformation towards an energy-efficient building sector is essential to achieve global ambitions to keep the increase of the average global temperature below 2 °C comparing with pre-industrial levels by 2030. The critical window of opportunity to improve the building sector is in the coming decade. The key priorities for realizing this goal are outlined in the global roadmap published by Ref.[3]focusing on the following eight areas to create a sustainable built environment for the future:
1. Developing urban planning policies that support efficient and low-carbon solutions.
2. Implementing new building standards to ensure higher uptake of net-zero-operating-emissions or net-zero-energy buildings in the future.
3. Increasing the rate and the depth of the energy retrofitting to reduce energy demand of the existing building.
4. Improving the operation of buildings through the implementation of smart control systems and analyzing the data from building automation systems.
5. Optimizing components of building energy systems such as the boilers, heat pumps, radiators, etc.
6. Using low-energy and low-emissions materials in the construction of buildings.
7. Improving the resilience of buildings in the design stage to reduce risks related to climate change, such as natural disasters and extreme weather conditions.
8. Integrating onsite renewable energy and replacing fossil-fuel-burning equipment with the ones using clean energy.
The present study deals with the operational improvement of building energy systems (topic number 4 in the above list) and proposes exergy-based methods as a novel approach for evaluating and optimizing the operation of these systems.
1.2 Contribution
Most energy-saving solutions for buildings are related to the design of a new building and its energy system or the renovation of an existing building. It means that for new buildings, optimal components are used in the construction of the building, and for existing buildings, the old and inefficient devices are replaced with better ones, or the building’s thermal insulation is improved. While delivering efficient new or renovated buildings is essential, it is equally essential to manage existing buildings efficiently. The present study aims to evaluate and improve the operation of building energy systems without changing the system structure or replacing the components of the system. Since energy analysis does not provide any insight into the real thermodynamic inefficiencies in a system, exergy analysis that combines the first and the second law of thermodynamics is used.
The present research develops conventional and advanced exergy-based methods on a dynamic basis, and addresses the related issues such as the proper selection of the reference environment, splitting of physical exergy into thermal and mechanical parts, proper definition of the fuel and the product for components of building energy systems regarding the exergy/cost storage in dynamic formulation of exergy-based methods, etc. Unlike the majority of studies on the application of exergy analysis to the built environment that follows a steady-state or a quasi-steady-state approach, the present study provides a step-by-step framework for conducting dynamic exergetic analysis in building energy systems. Furthermore, two novel approaches for performing dynamic advanced exergy analysis for buildings are proposed for the first time in the present work. First, the method of Serial F/P Arrangement for splitting dynamic exergy destruction into endogenous and exogenous parts is introduced, and then the Design-Based Approach for calculating avoidable and unavoidable exergy destruction is proposed. Moreover, this research study presents the dynamic formulation of exergoeconomic analysis for the first time. One of the main challenges related to this part is dealing with stored/discharged costs in each component of the system at different time, which can affect the definition of auxiliary equations. Another problem is associated with “negative” exergy demand, and “exergy gain” mainly in cooling processes that are both addressed in the present study.
Based on the advanced exergetic and exergoeconomic results, four new dimensionless parameters are introduced that can compare the performance of different components building energy systems with each other, and can suggest more rational optimization priorities in a system. The advantage of these parameters over the dimensionless exergetic/exergoeconomic variables obtained from conventional exergy-based analyses is that the new ones not only indicate intrinsic inefficiencies in each component, but also consider the external sources of inefficiencies caused by interconnection among system components.
The developed methodologies in this study are applied to two different case studies. The first one is a small-scale system including a single-office building with heating and ventilation systems; the second one is a real-life system consisting of a large building and its complex heating and cooling systems.
This thesis is structured in seven chapters. The motivation, contribution and novelty of the research are briefly introduced in Chapter 1, followed by a comprehensive literature review on the application of exergy-based methods to the built environment in Chapter 2. Two case studies are considered in the present study: the first one is a theoretical system that includes a building with its heating and ventilation system, and the second case study is a real system that includes a large building and its complex energy system. A novel approach for applying exergy-based methods on a dynamic basis is developed in Chapters 3-5, where the first case study is investigates, and then the proposed method is applied to the second case study in Chapter 6. Conclusions of the present research, together with the suggestions for future research work, are presented in Chapter 7.
Table 1.1: Layout of the thesis.
INTRODUCTION ANDLITERATUREREVIEW
Chapter 1 Research background, motivation for this study, contribution of the present work, and structure of the thesis.
Chapter 2 Literature review on the application of exergy-based methods to buildings and their energy systems.
THEORETICALCASESTUDY
Chapter 3
Dynamic modeling of a simple case study representing a building and its heating and ventilation systems, dynamic formulation of exergy analysis, and interpreta-tion/visualization of the results.
Chapter 4
Proposing the method of Serial F/P Arrangement for splitting dynamic exergy de-struction into endogenous and exogenous parts; and the Design-Based Approach for obtaining avoidable and unavoidable dynamic exergy destruction.
Chapter 5 Dynamic formulations of conventional and advanced exergoeconomic analyses.
REAL-LIFECASESTUDY
Chapter 6 Real-life application of dynamic exergy-based methods proposed in previous chap-ters.
CONCLUSIONS ANDOUTLOOK
Chapter 7 Conclusions of the current study and suggestions for future work.
2
Literature Review
The first law of thermodynamics is essential to obtain heating and cooling loads, temperature, pres-sure, and relative humidity distributions, airflow, heat transfer, and contaminant transportation in and around buildings. However, it does not always provide information on the real inefficiencies in a system and its improvement potentials. The second law of thermodynamics overcomes these limitations by considering the irreversibilities in energy conversion processes in terms of entropy generation that is always positive and decreases the useful work that can be obtained by a process. Exergy analysis merges the first and the second laws of thermodynamics and enhances the energy analysis by calculating the quantity and the quality of energy.
Exergy analysis has been widely applied to a variety of systems to gain knowledge about the real thermodynamic inefficiencies within system components, and to find solutions for reducing these inefficiencies. In buildings, the use of the exergy concept aims to improve the quality mismatch between energy supply and demand, and consequently, to increase the sustainability of building energy systems. A significant part of the building’s energy demand is to provide thermal comfort for the building occupants, which means to keep the indoor air temperature between 20 °C and 28 °C[4]. Since these temperatures are close to the ambient temperature, and because exergy is a measure of the departure of the state of a system from that of the environment[5], the building’s energy demand is considered a low-exergy demand, which can be covered by low-exergy sources. Fig 2.1 shows the hourly values of ambient temperature in Germany based on a Typical Meteorological Year (TMY)[6]1
together with the corresponding quality factor2of the energy demand in buildings.
As seen in Fig 2.1, the maximum value of the quality factor for the buildings’ energy demand is about 13 %. It must be noted that this maximum value corresponds only to a short period of time, when the difference between TZ and T0becomes large. According to Fig 2.1, the average quality
factor for heating and cooling modes are 5.65 % and 1.55 %, respectively. In most of the buildings, however, heating and cooling systems operate at relatively high temperatures, and use high-quality energy sources such as fossil fuels and electricity to satisfy this low-quality demand. It would be
−15 −10 −5 0 5 10 15 20 25 Jan Feb Ma r A pr M a y Ju n Ju l A ug Sep Oct N o v D ec amb ien t temper atur e [°C] C O O L IN G H E A T IN G 0.98 1.00 1.02 1.04 1.06 1.08 1.10 1.12 1.14 0 2 4 6 8 10 TZ/︁T0[K/K] qu ality fac tor [% ]
Figure 2.1: The ambient temperature and the quality factor of energy demand in buildings. TZ is the zone temperature which is between 20 °C and 28 °C, and T0represents the ambient temperature in a Typical Meteorological Year (TMY) in Germany[6], shown on the left diagram.
more sustainable to use such high-quality resources for processes with higher energy quality demand and get maximum benefit from low-quality energy sources by using them for heating and cooling purposes in buildings. For this reason, low-exergy (LowEx) systems for buildings, such as waste heat from power plants or heat from seasonal thermal storage systems have been grown in recent years. For new buildings, the combination of low-temperature heating and high-temperature cooling sys-tems, such as radiant ceilings and floors, with low-exergy sources, such as solar thermal energy, geothermal heat, or waste heat offers a suitable strategy for the rational use of energy in buildings. In existing buildings, even though the buildings are not equipped with LowEx systems, the implementa-tion of an exergy-based control algorithm can reduce exergy destrucimplementa-tion within the building. In this way, the existing heating, ventilation, and air conditioning (HVAC) systems of the buildings can be operated in low exergy fashion, and as a result, system operation will be more sustainable.
Table 2.1 provides a thorough literature review about the application of exergy-based methods to different types of buildings and their HVAC systems. The reviewed papers are the “main studies” in this field, published in highly-ranked scientific journals, and cited by many other researchers. Various research groups and universities from Turkey, China, Germany, and the USA have conducted almost half of the reviewed papers with contributions of 21 %, 11 %, 9 %, and 7 %, respectively.
Among the whole research articles shown in Table 2.1, 86 % followed a steady-state or a quasi-steady-state approach, and only 14 % dealt with a dynamic exergy analysis. Conventional and advanced exergoeconomic and exergoenvironmental analyses, were always applied to building energy systems on a steady-state basis (in 16 studies3). To the best of the author’s knowledge, no research has been carried out on the dynamic formulation of exergoeconomic analysis; and only two recent articles published by the author of the present research, addressed the dynamic formulation of advanced exergy analysis. The current study’s novelties are the dynamic formulation of exergoeconomic analysis
3Searching for TITLE:(exergo* OR adv* exerg*) AND TITLE:(“building”) in Web of Science[7]resulted in the total number of 16 studies.
In the reviewed articles, heating systems appeared in 81 % of the case studies, followed by cooling and domestic hot water (DHW) systems, with contributions of 50 % and 23 %, respectively. Ventilation systems were studied only in 13 % of the case studies. The present thesis includes two case studies: in the first one, heating and ventilation systems are investigated, and in the second one, heating and cooling systems of a large building are studied. So, domestic hot water is not included in the present study.
As seen in Table 2.1 and listed below, various software was used in the reviewed papers for simulation, optimization, sensitivity analysis, life cycle assessment, and for obtaining substance properties.
– Simulation: MATLAB4, Python5, TRNSYS6(transient system simulation tool), Dymola7 (dy-namic modeling laboratory), EnergyPlus8, Carrier-HAP9(hourly analysis program), EES10 (en-gineering equation solver), IES-VE11(integrated environmental solution - virtual environment), IDA-ICE12(indoor climate and energy), EPB (Energy Performance Legislation for Buildings), CAMEL-Pro13(modular elemental calculation), DeST14(designer’s simulation toolkit), Excel, PVGIS15(photovoltaic geographical information system), TOST program, and Ansys Fluent16 (for CFD modeling).
– Optimization: MATLAB, YALMIP Toolbox17(a modeling language for solving advanced op-timization problems in MATLAB), jEPlus18(an EnergyPlus simulation manager for paramet-ric studies), jEPlus+EA (jEPlus coupled with evolutionary algorithms for optimization), and GAMS19(general algebraic modeling language).
– Sensitivity analysis: R20(a programming language for statistical computing) and SimLab21(for performing global sensitivity analysis).
– Life cycle assessment: SimaPro22.
– Substance properties: CoolPack23and SOLKANE24. 4https://www.mathworks.com 5https://www.python.org 6http://www.trnsys.com 7https://www.3ds.com/de/produkte-und-services/catia/produkte/dymola/ 8https://energyplus.net 9https://www.carrier.com/commercial/en/us/software/hvac-system-design/hourly-analysis-program/ 10http://fchartsoftware.com/ees/ 11https://www.iesve.com/software/virtual-environment 12https://www.equa.se/de/ida-ice 13https://dergipark.org.tr/tr/download/article-file/65836 14http://elearning-southzeb.eu/mod/page/view.php?id=106 15https://ec.europa.eu/jrc/en/pvgis 16https://www.ansys.com/products/fluids/ansys-fluent 17https://yalmip.github.io 18http://www.jeplus.org/wiki/doku.php?id=start 19https://www.gams.com 20https://www.r-project.org 21https://ec.europa.eu/jrc/en/samo/simlab
Li ter at u re R ev iew
[8] Liu & Lin
2020
China
– exergy analysis
– heating & ventilation systems – steady-state model
– CFD simulation in Fluent
Floor & ceiling heating, wall radiator, and stratum ventilation heating sys-tems for a sleeping environment with a net floor area of 12 m2.
The stratum ventilation heating system required lower supply and return water temperatures (40 °C and 29 °C, respectively), and therefore, its exergy demand, compared with other con-ventional heating systems, was significantly lower.
[9] Li et al. 2020 China – exergy analysis – exergy-costing – cooling system – steady-state model
Radiant cooling and air-based cooling (fan coil) systems for ultra-low energy buildings in five typical cities in the hot summer and cold winter region of China.
– The exergetic efficiency of the radiant cooling system was about 30 percentage points higher than that of the fan coil system, because the former utilized radiation and convection, but the latter extracted heat only via convection.
– From an economic point of view, the radiant cooling system was better than the fan coil system throughout its life cycle. [10] Reddy et al. 2020 USA – exergy-based MPC design – heating system – dynamic model – simulation in MATLAB
– optimization in YALMIP Toolbox – Monte-Carlo simulation
A micro-scale concentrated solar power system for HVAC applications in a three-story office building at Michigan Technological University with a net floor area of 5 700 m2.
– The designed exergy-based model predictive controller (MPC) can reduce the exergy destruction within the MicroSCP and the HVAC systems by 17 %.
– A probability analysis showed that the system’s exergy savings varied from 16 % to 18 % in the presence of prediction uncer-tainties and from 13 % to 30 % by considering the seasonal variations of the solar irradiation and the ambient tempera-ture. [11] Dai et al. 2020 China – exergy analysis – exergoeconomic analysis – heating & cooling systems – steady-state model
A transcritical CO2 reversible heat pump-air conditioning system inte-grated with dedicated mechanical sub-cooling (DMS) for an ordinary residen-tial building with a net floor area of 100 m2in China.
– The increase in the annual exergetic efficiency was between 7.3 % and 24.8 % when the DMS system is used.
– Implementation of the DMS system reduced the total costs by 17 % for heating and by 4.5 % for cooling operations.
[12] Martínez et al. 2020 Spain – exergy analysis – heating system – steady-state model – simulation in TRNSYS – sensitivity analysis in R – optimization in jEPlus+EA
A natural gas-fired micro-cogeneration unit in an experimental test facility sup-plying the equivalent thermal demand of three single-family dwellings with a net floor area of 274 m2.
– A multi-objective optimization was solved using Non-Sorting Genetic Algorithm-II (NSGA-II) with two objective functions: (1) cost of electricity and natural gas, and (2) exergetic effi-ciency of the cogeneration plant.
– The optimized system reduced costs by 24.5 %, increased the exergetic efficiency from 24 % to 29.4 %, and decreased the overall natural gas consumption by 15 %.
[13] Sayadi et al.
2020
Germany
– new methodology – advanced exergy analysis – heating & cooling systems – dynamic model
– working with real measured data – simulation in MATLAB
The energy system of the main building of the E.ON Energy Research Center in Aachen, Germany, with a net floor area of 7 222 m2.
– A novel approach, called the method of Serial F/P Arrange-ment, for splitting the dynamic exergy destruction into en-dogenous and exogenous parts was proposed for the first time. – The existing methodology in the literature deals with design optimization, but the objective of the developed methodology in this paper was the operational optimization.
[14] Chowdhury et al.
2020
Bangladesh Saudi Arabia Malaysia
United Arab Emirates
– exergy analysis – steady-state model
The residential sector of Bangladesh from 2000 to 2015.
– The energy efficiency of Bangladesh’s residential sector was between 25.5 % and 37.8 %, while the exergy efficiency varied from 6.4 % to 9 %.
– Biofuel and natural gas-based cooking stoves were the major causes of poor sustainability in Bangladesh.
Li ter at u re R ev iew [15] Picallo-Perez et al. 2020 Spain Germany
– advanced exergy analysis – heating & DHW systems – dynamic model – simulation in TRNSYS
– system identification in MATLAB
An experimental heating and domes-tic hot water (DHW) facility supplied by a Stirling engine and a condensing boiler.
– Endogenous and exogenous exergy destructions were ob-tained from the Decomposition method, but instead of con-stant exergetic efficiencies for components, the real charac-teristic curves corresponding to varying exergetic efficiencies were used.
– In the considered case study, only 24 % of the exergy destruc-tion was avoidable, 8 % of it was endogenous that can be re-duced by using the best components in the market, and the rest was exogenous that can be avoided by improving the in-terrelations among components of the system by improving the control system.
[16] Sayadi et al.
2019
Germany
– exergy-based MPC design – heating, cooling and ventilation – dynamic model
– simulation in Dymola Modelica – optimization in MATLAB – MILP in GAMS
(1) Heating, cooling, and ventilation systems in one office building. (2) Six decentralized ventilation units connected to a circulation pump and a three-way mixing valve.
(3) A generic model representing the working principle of the energy sys-tems of the E.ON Energy Research Cen-ter in Aachen, Germany.
– Exergy is a universally applicable indicator that can pave the way for designing plug & play controllers.
– Implementing exergy-based optimal control strategies can re-duce the overall energy demand and the total operating costs of the building energy systems by up to 23 % and 13 %, respec-tively. [17] Yari et al. 2019 Iran Vietnam – exergy analysis – cooling system – steady-state model – simulation in EES
Air handling unit (AHU) in cooling mode for both dry and humid weather conditions.
After the implementation of the air-to-air heat exchanger for heat recovery, the exergetic efficiency of the AHU increased by 1.6 % and 2.8 % in dry and humid conditions, respectively. It means the contribution of heat recovery in AHUs was more noticeable in humid conditions than in dry conditions.
[18] Guo et al. 2019 USA Italy China – exergy analysis – human body – steady-state model
Human body exergy consumption model.
– The suggested metabolic rate by ASHRAE standards (58.2 W/m2) was over-estimated by up to 20 %.
– A new expression for human body radiant exergy that differ-entiates the skin on hands and head with the rest of clothing surfaces was proposed, and the mean radiant temperature was suggested as the reference temperature when quantifying radiant exergy.
– The selection of an inappropriate reference temperature may result in constantly positive exergy losses regardless of whether the body is emitting or absorbing heat.
[19] Habibi & Hakkaki-Fard
2019
Iran
– exergy analysis
– heating & cooling systems – quasi-steady-state model – simulation in MATLAB
(1) A common Air Source Heat Pump (ASHP).
(2) An ASHP with Ground Air Heat Ex-changer (GAHE).
(3) A Ground Source Heat Pump (GSHP) with Horizontal Ground Water Heat Exchanger (HGWHE).
(4) A GSHP with Vertical Ground Water Heat Exchanger (VGWHE).
– The seasonal exergetic efficiency of the GSHPs in heating mode was significantly higher than in cooling mode. – The annual exergetic efficiency of the GSHP with VGWHE, the
GSHP with HGWHE, the ASHP with GAHE, and the common ASHP were 42.4 %, 40.5 %, 31.5 % and 29.4 % over thirty years, respectively. [20] Rijs & Mróz 2019 Poland – exergy analysis – heating systems – steady-state model
– substance data from SOLKANE
A vertical-bore, ground source heat pump system installed in an educa-tional building located on the cam-pus of Poznan University of
Technol-Three solutions for increasing the exergetic efficiency of the heating system are proposed:
(1) Integration of renewable energy sources.
Li ter at u re R ev iew [21] Sayadi et al. 2019 Germany – exergy analysis
– heating & cooling systems – dynamic model
– working with real measured data – simulation in MATLAB
The energy system of the main building of the E.ON Energy Research Center in Aachen, Germany, with a net floor area of 7 222 m2.
– Although the considered building had an excellent energetic efficiency(︁122.6 %)︁, from the exergy point of view, the differ-ence between demand and supply was enormous, which made the exergetic efficiency of the building extremely low(︁3.7 %)︁. – Based on exergy analysis, the main causes of thermodynamic
inefficiencies in the considered building were chemical reac-tions, heat transfer, friction, mixing, and thermal losses. How-ever, only the last one can be detected by an energy analysis. [22] Byrne & Ghoubali
2019
France
– exergy analysis
– heating & cooling systems – steady-state model
– substance data from CoolPack – simulation in EES
Air-source heat pump for simultane-ous heating and cooling (HPS).
– Two prototypes of heat pump for simultaneous heating and cooling were built, tested, and exergetically evaluated. – The exergy destruction occurred mainly in the compressors,
where the heat losses were high.
– An undersized evaporator reduced the performance of the first HPS, and the second HPS presented a lower exergy destruction thanks to a better design of the components.
– The impact of the choice of the refrigerant was considered relatively low. [23] Stanek et al. 2019 Poland – exergy analysis – heating system – steady-state model – real meteorological data
A heat pump driven by electricity, and photovoltaic panels or wind turbines in a typical family house in Katowice, Poland, with a net floor area of 163 m2
– To avoid misleading conclusions, when a mix of renewable and non-renewable energy sources is analyzed, a global bal-ance boundary instead of a direct exergy analysis should be applied.
– The methodology presented here could be used to allocate subsidies in an incentive system for citizens to change their heat source to a more environmentally friendly one.
[24] Yin et al.
2019
China
– advanced exergy analysis – cooling system
– steady-state model
– substance data from SOLKANE
Air-conditioning system in a subway station consisting of a chiller, chilled water pump, cooling tower, cooling wa-ter pump, air handling unit (AHU), and reheater.
– According to the conventional exergy analysis results, AHU had the highest exergy destruction, but from an advanced exergy analysis, the compressor had the highest improvement priority for the largest avoidable exergy destruction.
– Avoidable exergy destruction within the system increased significantly as the compressor’s efficiency decreased. [25] Evola et al.
2018
Italy
– review paper – exergy analysis
– exergy of convective and radiant heat flows
– exergy of solar radiation – definition of dead state
– dynamic vs. steady state analysis – exergetic metrics
– (1) Buildings.
– (2) Heat pump systems. – (3) Solar systems in buildings. – (4) District or city scale.
– The majority of technologies in building energy systems shows very poor exergetic performance.
– Since exergy destruction associated with heat transfer through radiation is lower than convection, radiant ceilings and floors should be preferred in the design of new buildings’ energy systems.
– Low-exergy sources such as solar thermal energy, geothermal heat, or low-to-medium temperature waste heat should be used for space heating and cooling applications in buildings. – Combustion processes must be avoided, even condensing
boilers. [26] Bandera et al. 2018 Spain UK – exergy analysis
– heating & cooling systems – quasi-steady-state model – IEA Annex 49 for exergy demand – simulation in EnergyPlus – optimization in jEPlus
The School of Architecture building, lo-cated on the campus of the University of Navarra in Pamplona, Spain, with a net floor area of 4 291 m2including a gas-fired boiler, an electric chiller, and different air handling units.
– Exergy is proposed as a sustainable index for choosing optimal building retrofitting strategies.
– Exergy-based retrofitting solutions, unlike energy-based ones, consider the building and the reference environment together and aims to maximize the intake of energy resources from the reference environment.
Li ter at u re R ev iew
[27] Ekinci & Bilgili
2018 Turkey – exergy analysis – cooling system – human body – steady-state model
– Seven climate regions in the provinces of Turkey.
– Exergy consumption rates of people living in different climate regions were estimated.
– The maximum and minimum exergy consumption rates by the human body were obtained 2.33 W/m2and 0.91 W/m2for the cold and semi-dry climate region (Erzurum province in the eastern Anatolia, Turkey) and for the hot and semi-dry climate region (¸Sanlıurfa province in southeastern Turkey), respectively. [28] Picallo-Perez et al. 2018 Spain – exergy analysis – symbolic thermoeconomic – heating & DHW systems – dynamic model – simulation in TRNSYS
– weather data obtained from ME-TEONORM
The old and the retrofitted system sup-plying heat and domestic hot water (DHW) to four buildings in northern Spain, Bilbao.
– As a result of retrofitting, the system’s yearly average exergetic efficiency was increased from 2.6 % to 4.0 %.
– After the system’s refurbishment, the costs per exergy unit was reduced from 4.09e/kWh to 2.17e/kWh for supplied heat, and from 1.94e/kWh to 0.99e/kWh for supplied DHW.
[29] Açıkkalp et al.
2018
Turkey
– advanced exergy analysis – LCI advanced exergoeconomic
analysis
– heating & DHW systems – steady-state model – definition of depletion ratio
A simple heating system in Izmir, Turkey.
– An advanced life cycle integrated (LCI) exergoeconomic anal-ysis for a building heating system was proposed.
– The advanced LCI exergoeconomic depletion ratios were found 0.187, 0.599, 0.414, and 0.371 for the endogenous, ex-ogenous, unavoidable and avoidable parts, respectively.
[30] García Kerdan et al. 2017 UK Mexico – software development – exergy analysis – exergoeconomic analysis – multi-objective optimization – heating, cooling and ventilation – dynamic model
– simulation in EnergyPlus – sensitivity analysis in SimLab – parametric studies in jEPlus – exergy analysis in Python – optimization in jEPlus+EA
A primary school building (1 900 m2), located in London, UK. The floor area consists of classrooms, staff offices, laboratories, the main hall, corridors, bathrooms, and other common ar-eas. Heating is generated in a con-ventional gas-fired boiler and is sup-plied through high-temperature radi-ators (80 °C/60 °C) without heat recov-ery systems. Natural ventilation is con-sidered during the summer months.
– A retrofit-oriented simulation framework (ExRET-Opt) was presented, which identified the sources of inefficiencies within the energy supply chain of the building, and gave the possibil-ity to perform a comprehensive multi-objective optimization, so that indicators such as exergy destruction, investment costs, exergoeconomic variables, and carbon emissions combined with occupants’ thermal comfort, can be used as constraints or objective functions in the optimization procedure. – To compare the optimized case study with the pre-retrofitted
one, the utopian point, representing the minimum value for each of the three objectives (net present value, exergy destruc-tion, and thermal comfort) optimized individually, was se-lected. According to the optimization results, only 45 % of the maximum available budget for retrofitting was required to re-duce the cost of exergy destruction by 49 %, the uncomfortable hours by 51 %, and the building’s energy demand by 37 %. [31] García Kerdan et al.
2017
UK Mexico
– exergy analysis
– heating, cooling and ventilation – steady-state model
– building stock model – simulation in EnergyPlus – sensitivity analysis in SimLab – parametric studies jEPlus
A variety of retrofit measures typically applied to non-domestic buildings in the UK.
– The development of an exergy-based building stock model was presented.
– By 2050, current regulations can reduce carbon emissions by up to 52 % and increase the exergetic efficiency from 10.7 % to 13.7 %. However, a low-exergy scenario has the potential to re-duce carbon emissions by 91 %, achieving a sectoral exergetic efficiency of 19.8 %.
Li ter at u re R ev iew
[32] Sartor & Dewallef
2017
Belgium
– exergy analysis
– heating & DHW systems – steady-state model – simulation in EPB-software
Three types of buildings including ter-raced houses, semi-detached houses, and free-standing houses, represent-ing about 82 % of the Belgian buildrepresent-ing stock.
The best heating systems are district heating networks fol-lowed by heat pumps because, in the former one, industrial waste heat is recovered, which improves the fuel utilization, and in the latter one the low-grade energy is used.
[33] Ozgener et al. 2017 Turkey USA – exergy analysis – heating system – steady-state model
– working with real measured data
The PV assisted earth-to-air heat ex-changer system installed at the So-lar Energy Institute of Ege University, Izmir, Turkey.
The mean exergetic efficiency for heating seasons in 7 years was obtained 65 %. The maximum yearly-average exergetic efficiency was recorded in 2015 and was equal to 70 %.
[34] Han et al.
2017
China USA
– exergy analysis
– heating, cooling and ventilation – quasi-steady-state model – simulation in IES-VE
One office building in five different cli-mate zones of China: severely cold (Harbin), cold (Beijing), hot summer and cold winter (Changsha), mild (Kun-ming), and hot summer and warm win-ter (Guangzhou).
– In all cases, the exergy demand was small, but always was covered by high-quality energy sources, indicating the great potential for improving the quality mismatch between the energy demand and supply.
– The latent exergy demand should not be ignored, especially in humid climates. [35] Zhang et al. 2016 China – exergy vs. entransy – cooling system – steady-state model
A typical air-conditioning system con-sisting of a chiller, fan coil units, fresh air handling units, and a cooling tower.
– Exergy or entransy analysis takes into account the quantity and the quality of energy.
– It is recommended to choose different theoretical parame-ters for different purposes: entransy is more appropriate for a transfer process while exergy is more recommended for the heat-work conversion process.
[36] Liao & Chuah
2016 Taiwan – exergy analysis – thermoeconomic analysis – multi-objective optimization – cooling system – quasi-steady-state model – working with real measured data
An underground train station located in Taipei, Taiwan, with a net floor area of 7 920 m2, consisting of a chiller, air handling units, fan coils, and cooling towers.
Multi-objective optimization (costs and exergy destruction as objectives) increased the construction costs by 1.9 % but reduced the total exergy destruction and CO2emissions by 3.2 % and 2.7 %, respectively.
[37] Sangi & Müller 2016 Germany – new methodology – renewable vs. non-renewable – exergy analysis – heating system – steady-state model
– simulation in Dymola Modelica
A family house (80 m2) with two differ-ent energy systems:
(1) a conventional boiler system. (2) a solar-assisted boiler system.
Three approaches were proposed for a fair comparison of re-newable and non- rere-newable building energy systems from an exergetic viewpoint:
(1) The inner boundary of the system, which changes the sys-tem boundary so that exergy input is independent of the type of energy supplied.
(2) Comparative exergy efficiency, which assumes the supplied exergy to the non-renewable energy system is equal to its orig-inal one plus the renewable exergy source supplied to another system under consideration, but the product exergy of the sys-tem remains unchanged.
(3) Potential solar exergy efficiency, which assumes a whole building has been covered by solar collectors, but these imagi-nary solar collectors waste all exergy received from the sun. [38] Ashouri et al.
2016
Iran France
– exergy analysis – life cycle assessment – heating system – steady-state model – optimization in MATLAB
A building wall consisting of an in-ner plaster (1 cm), a brick wall (20 cm), insulation materials (Glasswool and Rockwool), and an outer plaster (2 cm).
– The optimal thicknesses regarding the environmental impacts were 21.9 cm and 9.8 cm, and regarding the exergetic life cycle assessment were 1.8 cm and 1.2 cm for Glasswool and Rock-wool, respectively.
– The reason for the remarkable difference between two opti-mum points is that the environmental impacts are dependent on fuel consumption, which makes thick insulation more in-teresting. However, the cost analysis reduces the material used while minimizing the exergy destruction within the wall.
Li ter at u re R ev iew [40] Ozbeck 2016 Turkey – exergy analysis – cooling system – steady-state model
– substance data from CoolPack – simulation in Carrier-HAP
A ceiling-type residential air condition-ing system for a buildcondition-ing with a net floor area of 37 m2in seven different provinces of Turkey (Adana, Gaziantep, Izmir, Trabzon, Erzurum, Ankara and Istanbul).
The total exergy destruction and exergetic efficiency decreased with decreasing atmospheric temperature. For instance, changing the province from Adana (36.8 °C) to Istanbul (28 °C) resulted in a reduction in the overall exergetic efficiency of the system from 41.3 % to 38.7 %. [41] Du et al. 2015 China – new methodology – exergy analysis
– exergy-based control evaluation – cooling system
– working with real measured data
The HVAC system of an airport with a net floor area of 67 950 m2, located at Hainan province in China. The water side is a variable water volume system including cooling towers, cooling wa-ter pumps, chillers, primary and sec-ondary chilled water pumps. The air side includes constant and variable air volume systems providing the required cooling capacity to the terminals.
– To evaluate the existing control strategy, first the ideal opera-tion of the HVAC system was obtained using the data envelop-ment analysis approach; then, the control-perfect index (CPI) was defined as the ratio between exergy destruction within the system under ideal operation and that of the real operation. – Six control strategies for the considered HVAC system of the
airport were evaluated. The best control strategy that opti-mized the set-points of supply cooling water and chilled water temperatures and the outdoor air flow rate simultaneously had the highest CPI value of 0.89, and the original control strategy had the lowest CPI value of 0.77.
[42] Razmara et al. 2015 USA – exergy-based MPC design – heating system – dynamic model – simulation in MATLAB
– optimization in YALMIP Toolbox
The Lakeshore Center at Michigan Technological University, which is a three-story building and equipped with ground-source heat pumps.
Compared to the rule-based controller, the designed exergy-based model predictive controller (MPC) reduced the exergy destruction within the system and the electricity consumption by 22 % and 36 %, respectively.
