numerical simulation and analysis of a heat pump …

TU-Berlin

Technische Universität Berlin

FAKULTÄT III INSTITUT FÜR ENERGIETECHNIK

GEBÄUDE-ENERGIE-SYSTEME, HERMANN-RIETSCHEL-INSTITUT

UB & UPC

Universitat de Barcelona Universitat Politècnica de Catalunya EUETIB MASTER OFICIAL INTERUNIVERSITARI EN ENGINYERIA EN ENERGIA

NUMERICAL SIMULATION AND ANALYSIS OF A

HEAT PUMP SYSTEM COMBINED WITH THERMAL STORAGE TANK FOR DHW SUPPLY, CONTROLLED

THROUGH A DEMAND SIDE MANAGEMENT STRATEGY

Rubén Sánchez Muñoz Author

Supervised by:

Prof. Dr.-Ing. Martin Kriegel MSc. Hamidreza Esfehani

Berlin, June 2014

I

ABSTRACT As general conception, this project is aimed to develop a software simulation model based on TRNSYS simulation platform which should help in the analysis and implementation of domestic Demand Side Management (DSM) strategies, based on load shifting techniques, as management tool for residual load and surplus of energy associated to renewable not dispatchable power sources like wind power or photovoltaics. The reference framework of the study, which sets real boundaries and limitations of the present work, is to develop and analyze a novel domestic descentralized energy storage system formed by a heat pump system combined with thermal storage tank that applies a DSM strategy of load shifting between valley and peak periods. The DSM control strategy implemented by the system is what, in fact, makes novel this usual configuration. The work analyzes the simulated behaviour of a DHW supply system for a single-family house located in Berlin, considering its weather conditions and its particular energy demand. Six different days (corresponding to the colder season of the year) and two basic systems configurations (according to normal commercial solutions) have been introduced in the model. After the simulation process the obtained results have been in depth analyzed in order to obtain a better understanding of the system and its potential. Additionally new study paths that could be later explored have been proposed. The choice of the scenario has not been a random decision and obeys to a special motivation; according to German government’s energy and climate policy, during next decades renewable power source, in general, and renewable electricity, in particular, will increase dramatically it contribution share to final german electricity feed-in. The expected development of higher proportion of electricity generated from uncontrollable sources will require facing some still not solved challenges to assure correct grid integration of these sources. Smart grids development and residual load and surplus of energy management tools will play a fundamental role in the final success of such an imposing challenge.

III

TABLE OF CONTENTS

ABSTRACT ...................................................................................................... I

TABLE OF CONTENTS ........................................................................................ III

LIST OF FIGURES .............................................................................................. VII

LIST OF TABLES ................................................................................................ IX

LIST OF EQUATIONS

1. INTRODUCTION & OBJECTIVES .......................................................................................... 1

1.1.- INTRODUCTION ............................................................................................................... 1 1.2.- OBJETIVES ....................................................................................................................... 1 1.3.- CASE STUDY DESCRIPTION & JUSTIFICATION .................................................................... 2

2. LOAD SHIFTING METHODOLOGY AND SYSTEM DESCRIPTION ............................................ 3

2.1.- STATE OF THE ART ........................................................................................................... 3 2.1.1.- German energy aspects overview ............................................................................. 3

2.1.1.a.- Use of energy in Germany ................................................................................. 3 2.1.1.a.1 Primary energy sources ............................................................................. 3 2.1.1.a.2 Final uses of energy .................................................................................. 5 2.1.1.a.3 Electricity generation ................................................................................ 6

2.1.1.a.3.1 Renewable electricity generation ....................................................... 8 2.1.1.a.4 Housing/Residential use of energy .......................................................... 10

2.1.1.b.- Energiewende ................................................................................................. 11 2.1.1.b.1 Tools for the change ............................................................................... 13

2.1.1.b.1.1 Renewable Energy Sources Act (EEG) ............................................... 13 2.1.1.b.1.2 German Energy Saving Ordinance (EnEV) ......................................... 13 2.1.1.b.1.3 Act on the Promotion of Renewable Energies in the Heat Sector (EEWärmeG) 14

2.1.1.c.- Heat pump market in Germany ....................................................................... 15 2.1.1.c.1 Current development of heat pump market ............................................ 15

2.1.1.c.1.1 Characterization of the installed utilities .......................................... 16 2.1.1.c.2 Future development of market................................................................ 17

2.1.1.c.2.1 Characterization of the future scenarios ........................................... 18 2.1.2.- Demand side management (DSM) and residual load .............................................. 19

2.1.2.a.- Demand Side Management (DSM) .................................................................. 20 2.1.2.a.1 Load shape changes ................................................................................ 20

2.1.2.a.1.1 Peak Clipping ................................................................................... 21 2.1.2.a.1.2 Valley Filling ..................................................................................... 21 2.1.2.a.1.3 Load Shifting .................................................................................... 21 2.1.2.a.1.4 Strategic Conservation ..................................................................... 22 2.1.2.a.1.5 Strategic Load Growth ..................................................................... 22 2.1.2.a.1.6 Flexible Load Shape.......................................................................... 23

2.1.2.a.2 Relevance of the energy storage to implement load shift strategies: the case of electricity. .................................................................................................... 23 2.1.2.a.3 Practical approach of DSM at user’s level. ............................................... 24

.......................................................................................... IX

IV

2.1.2.b.- Residual load .................................................................................................. 25 2.1.3.- Storage of energy................................................................................................... 26

2.1.3.a.- Thermal Energy Storage (TES) ......................................................................... 26 2.1.3.a.1 TES and DSM ........................................................................................... 27 2.1.3.a.2 Key points in thermal energy storage (TES) ............................................. 27

2.1.3.a.2.1 Process and Technology Status ........................................................ 27 2.1.3.a.2.2 Performance and Costs .................................................................... 28 2.1.3.a.2.3 Potential and Barriers ...................................................................... 28

2.1.3.a.3 TES Technologies .................................................................................... 28 2.1.3.a.3.1 Sensible Thermal Energy Storage ..................................................... 29 2.1.3.a.3.2 Phase Change Materials (PCM’s) for TES .......................................... 29 2.1.3.a.3.3 Underground Thermal Energy Storage (UTES) .................................. 30 2.1.3.a.3.4 TES via Chemical Reactions .............................................................. 30

2.1.3.a.4 Application.............................................................................................. 30 2.2.- CONCEPTUAL DESCRIPTION OF THE SYSTEM .................................................................. 32

2.2.1.- Main system components ...................................................................................... 32 2.2.1.a.- Heat Pump ...................................................................................................... 32

2.2.1.a.1 Heat pump technology ............................................................................ 32 2.2.1.a.1.1 Heat pump working principles.......................................................... 32 2.2.1.a.1.2 Vapour compression cycle ............................................................... 33

2.2.1.a.2 Heat pump performance (COP) ............................................................... 34 2.2.1.a.2.1 Seasonal performance factor (SPF)................................................... 35

2.2.1.a.3 Heat sources ........................................................................................... 35 2.2.1.a.4 Types of HP ............................................................................................. 35 2.2.1.a.5 Working fluids ......................................................................................... 36 2.2.1.a.6 Heat pump applications .......................................................................... 36

2.2.1.b.- TES Devices ..................................................................................................... 37 2.2.1.b.1 Requirements and characteristics ........................................................... 37

2.2.1.b.1.1 Temperature stratification ............................................................... 38 2.2.1.b.1.2 Isolation........................................................................................... 38

2.2.1.b.2 Storage tank classification ....................................................................... 38 2.2.1.b.2.1 Classification according to its layout ................................................ 38 2.2.1.b.2.2 Vertical storage tanks ...................................................................... 38 2.2.1.b.2.3 Horizontal storage tanks .................................................................. 39 2.2.1.b.2.4 Classification according to its material ............................................. 39 2.2.1.b.2.5 Carbon steel .................................................................................... 39 2.2.1.b.2.6 Stainless steel .................................................................................. 40 2.2.1.b.2.7 Classification according to its heat exchanger system ...................... 40

3. SIMULATION MODEL ....................................................................................................... 43

3.1.1.- Software environment ........................................................................................... 43 3.1.1.a.- Building Energy Simulation software ............................................................... 43 3.1.1.b.- Dynamic methods ........................................................................................... 43 3.1.1.c.- TRNSYS ........................................................................................................... 44

3.1.1.c.1 Description of the tool ............................................................................ 45 3.1.2.- Simulation process ................................................................................................. 46

3.1.2.a.- Conceptual description of the model .............................................................. 46 3.1.2.a.1 Heat pump circuit (A) .............................................................................. 48 3.1.2.a.2 Heat pump circuit control (C) .................................................................. 48 3.1.2.a.3 Demand side supply hydraulic circuit (B) ................................................. 49 3.1.2.a.4 Demand side Supply circuit control (D) .................................................... 49 3.1.2.a.5 Complementary groups ........................................................................... 50

V

3.1.2.b.- Complementary data ...................................................................................... 51 3.1.2.b.1 Weather data.......................................................................................... 51 3.1.2.b.2 Basic system configuration...................................................................... 52 3.1.2.b.3 Energy demand ....................................................................................... 52

3.1.2.b.3.1 Detailed description of demand ....................................................... 53 3.1.2.c.- Description of the components used in simulation .......................................... 54

3.1.2.c.1 Heat pump .............................................................................................. 54 3.1.2.c.2 Storage tank ............................................................................................ 55 3.1.2.c.3 Hydraulic pumps ..................................................................................... 57 3.1.2.c.4 Data readers ........................................................................................... 58

3.1.2.c.4.1 Weather Data Reader ....................................................................... 58 3.1.2.c.4.2 Generic Data Reader ........................................................................ 58

3.1.2.c.5 Controllers .............................................................................................. 59 3.1.2.c.5.1 Differential Controllers ..................................................................... 59

3.1.2.c.6 Complementary component .................................................................... 60 3.1.2.d.- Base cases description .................................................................................... 60

3.1.2.d.1 General features ..................................................................................... 61 3.1.2.d.1.1 Time features .................................................................................. 61 3.1.2.d.1.2 Temperature features ...................................................................... 61

3.1.2.d.2 Specific features ..................................................................................... 61 3.1.2.d.2.1 On-Peak criteria ............................................................................... 61 3.1.2.d.2.2 Death band definition ...................................................................... 62 3.1.2.d.2.3 Case definition ................................................................................. 62

3.1.2.d.3 Determination of final number case studies ............................................ 63

4. SIMULATION OF THE COMBINED HEAT PUMP SYSTEM AND RESUTS .............................. 64

4.1.- GRAPHIC RESULTS ......................................................................................................... 64 4.1.1.- Influence of the month of study (ambient temperature) ......................................... 64

4.1.1.a.- Final consideration.......................................................................................... 65 4.1.2.- Contribution of PID controller ................................................................................. 66 4.1.3.- Simulation for working day .................................................................................... 67

4.1.3.a.- Set-up A .......................................................................................................... 67 4.1.3.b.- Set-up B .......................................................................................................... 68 4.1.3.c.- Comparison between Set-up A y and Set-up B ................................................. 70

4.1.4.- Simulation for weekend ......................................................................................... 71 4.1.4.a.- Effect of considering different death bands of temperature in controller ........ 71 4.1.4.b.- Set-up A .......................................................................................................... 72 4.1.4.c.- Set-up B .......................................................................................................... 73

4.2.- NUMERICAL RESULTS .................................................................................................... 75 4.2.1.- Simulation for working day .................................................................................... 76 4.2.2.- Simulation for weekend ......................................................................................... 77 4.2.3.- Final summary ....................................................................................................... 78

5. CONCLUSIONS .................................................................................................................. 80

5.1.- CONCLUSIONS ............................................................................................................... 80 5.2.- FURTHER DEVELOPMENTS ............................................................................................ 80

6. BIBLIOGRAPHY ................................................................................................................. 83

6.1.- BASIC BIBLIOGRAPHY .................................................................................................... 83 6.2.- OTHER SOURCES ........................................................................................................... 85

7. APPENDIX ........................................................................................................................ 86

VI

MAIN CHARACTERISTICS OF USED DEVICES ........................................................................... 86 HEAT PUMP DEVICES ............................................................................................................ 86 STORAGE TANKS ................................................................................................................... 89

VII

LIST OF FIGURES Figure 2.1—Macroeconomic energy efficiency in Germany, 1990-2013 (relative to 1990). [AGEB)

........................................................................................................................................... 3 Figure 2.2—Primary energy consumption in Germany, 2013. [BDWI] .......................................... 4 Figure 2.3—Evolution of energy imports (%) and primary energy utilization in Germany, 1990-

2011. .................................................................................................................................. 4 Figure 2.4—Evolution primary energy of consumption in Germany, 1990-2013. [AGEB] .............. 4 Figure 2.5—Use of energy structure in Germany, 1990-2012. [BMWI]......................................... 5 Figure 2.6—Energy flow diagram of energy utilization (PJ) in Germany, 2012 .............................. 5 Figure 2.7—Specific use of the energy in Germany, 2012. [BMWI] .............................................. 6 Figure 2.8—Final energy consumption by energy sources in Germany, 1990-2012. [BMWI] ........ 6 Figure 2.9—Gross electricity generation structure in Germany, 2013. [BDEW] ............................ 7 Figure 2.10—Shares of energy sources on the net electricity generation, comparison 2002-2012.

[BDEW] ............................................................................................................................... 7 Figure 2.11—Development of installed capacity (Total& Renewable) in Germany, 2008-2012.

[EIA] ................................................................................................................................... 8 Figure 2.12—Annual full load hour in Germany according to Power plant type, 2008 and 2012.

[BDEW] ............................................................................................................................... 8 Figure 2.13—Use of energy power generation in Germany, 1991-2012. [BDEW] ......................... 9 Figure 2.14—Renewable electricity generation by energy sources (TWh), 2002-2012. [BDEW].... 9 Figure 2.15—Schematic development of current EnEv .............................................................. 14 Figure 2.16—Heat pump sales in Germany, 1990-2008 ............................................................. 15 Figure 2.17—Heat pump sales in Germany, 2007-2013 ............................................................. 15 Figure 2.18—DHW Heat pump sales in Germany, 2007-2013 .................................................... 16 Figure 2.19—Heat pump expected sales (annual and accumulated) in Germany, 2013-2030 ..... 17 Figure 2.20—DHW Heat pump sales in Germany, 2007-2013 .................................................... 18 Figure 2.21—Expected seasonal performance factor (SPF) for heat pumps systems, 2008-2030 19 Figure 2.22—Schemathic representation of power demand (winter/summer) .......................... 19 Figure 2.23—Peak clipping ........................................................................................................ 21 Figure 2.24—Valley filling.......................................................................................................... 21 Figure 2.25—Load shifting ........................................................................................................ 22 Figure 2.26—Strategic conservation .......................................................................................... 22 Figure 2.27—Strategic load growth ........................................................................................... 22 Figure 2.28—Flexible load shape ............................................................................................... 23 Figure 2.29—Weekly load curve for a typical power utility ........................................................ 24 Figure 2.30—Typical weekly electrical load demand, renewable power supply and residual Load

(as result of their difference) ............................................................................................ 25 Figure 2.31—Thermal machine scheme .................................................................................... 32 Figure 2.32—Operating and P-h qualitative diagrams for simple (Ideal/Real) vapour compression

cycle ................................................................................................................................. 33 Figure 2.33—Main component scheme of a heat pump ............................................................ 34 Figure 2.34—Vertical and horizontal configuration and water thermal stratification ................. 39 Figure 2.35—Hot water storage tank without heat exchanger ................................................... 40 Figure 2.36—Double-walled exchanger ..................................................................................... 41 Figure 2.37—Single coil exchanger ............................................................................................ 41 Figure 2.38—Double coil exchanger .......................................................................................... 42 Figure 2.39—“Tank in Tank” storage tank ................................................................................. 42 Figure 3.1—Display TRNSYS Simulation Studio .......................................................................... 45 Figure 3.2—Display TRNBuild .................................................................................................... 46 Figure 3.3 —Schematic representation of simulated system ..................................................... 46

VIII

Figure 3.4—General view of developed model and main groups ............................................... 47 Figure 3.5— Heat pump circuit part of developed model .......................................................... 48 Figure 3.6—Heat pump circuit control part of developed model ............................................... 48 Figure 3.7—Supply circuit part of developed model .................................................................. 49 Figure 3.8—Supply circuit control of the model......................................................................... 49 Figure 3.9—Power flow information plotting that model also supplies ...................................... 50 Figure 3.10—Time operation period plotting that model also supplies ...................................... 50 Figure 3.11—Hydraulic flow information plotting that model also supplies ............................... 51 Figure 3.12—TRY Weather-data Regions ................................................................................... 51 Figure 3.13—Ambiental and water temperature of Berlin, according to TRY 2010. .................... 52 Figure 3.14—Daily unitary DHW load demand (WWB and WSH day)-according to VDI- (1 h time

step) ................................................................................................................................. 54 Figure 3.15—Schematic representation of stratified water storage tank modelled by TRNSYS... 56 Figure 3.16—Type2 (generic) Controller Function ..................................................................... 59 Figure 3.17—On-Peak criteria for WWB day .............................................................................. 62 Figure 3.18—On-Peak criteria for WSH day ............................................................................... 62 Figure 4.1—Energy output of “set-up A” in October .................................................................. 64 Figure 4.2—Energy output of “set-up A” in December and February ......................................... 64 Figure 4.3—Energy plot of “set-up B” in October (WWB) .......................................................... 65 Figure 4.4—Energy plots of “set-up B” in December and February (WWB) ................................ 65 Figure 4.5—Power load demanded and supplied through PID controller (WWB-February-)....... 66 Figure 4.6—Power load demanded and supplied - without PID controller- (WWB-February-) .... 66 Figure 4.7—Energy plot of “set-up A” in October (WWB) .......................................................... 67 Figure 4.8—Energy plot of “set-up A” in February (WWB) ......................................................... 67 Figure 4.9—Temperature plot of “set-up A” in October (WWB) ................................................ 68 Figure 4.10—Temperature plot of “set-up A” in February (WWB) ............................................. 68 Figure 4.11—Energy plot of “set-up B” in October (WWB) ........................................................ 69 Figure 4.12—Energy plot of “set-up B” in February (WWB) ....................................................... 69 Figure 4.13—Temperature plot of “set-up B” in October (WWB)............................................... 70 Figure 4.14—Temeprature plot of “set-up B” in February (WWB) ............................................. 70 Figure 4.15—Energy outlet plots of “set-up A” in February with different death band

temperatures (WSH) ......................................................................................................... 71 Figure 4.16—Temperature of “set-up A” in February with different death band temperatures

(WSH) ............................................................................................................................... 71 Figure 4.17—Energy outlet plot of “set-up A” in October (WSH) ............................................... 72 Figure 4.18—Energy outlet plot of “set-up A” in February (WSH) .............................................. 72 Figure 4.19—Temperature plot of “set-up A” in October (WSH) ................................................ 73 Figure 4.20—Temperature plot of “set-up A” in February (WSH) ............................................... 73 Figure 4.21—Energy outlet plot of “set-up B” in October (WSH)................................................ 74 Figure 4.22—Energy outlet plot of “set-up B” in February (WSH) .............................................. 74 Figure 4.23—Temperature plot of “set-up B” in October (WSH) ................................................ 75 Figure 4.24—Temperature plot of “set-up B” in February (WSH) ............................................... 75

IX

LIST OF TABLES Table 2.1-Gross electricity generation in Germany, 2010-2013. [BMWI] ...................................... 7 Table 2.2—a) Energy consumed by german households and b) final purpose of that

consumption, 2008 ........................................................................................................... 10 Table 2.3—Energy heating demand of german households supplied by natural gas, 2008 ......... 10 Table 2.4—Energy heating demand of german households supplied by electricity, 2008 ........... 11 Table 2.5—Summarized Energiewende qualitative targets ........................................................ 12 Table 2.6—Average heating capacity (current/expected) of german heat pump systems, 2010-

2030 ................................................................................................................................. 18 Table 2.7—Typical Parameters of Thermal Storage System (TES) ............................................... 29 Table 2.8—TES-relevant Applications ........................................................................................ 31 Table 2.9—Cooling thermodynamic cycles ................................................................................ 33 Table 3.1—List of building simulation software validated by IEA ............................................... 44 Table 3.2—Main characteristics of implemented set-ups in this simulation ............................... 52 Table 3.3—Typical-day categories ............................................................................................. 53 Table 3.4—Distribution of winter days for each typical-day category ........................................ 53 Table 3.5—Maximum values of energy demand in winter days for each typical-day category ... 54 Table 3.6—Main characteristics of storage tanks ...................................................................... 57 Table 3.7—Basic common features of simulation processes ...................................................... 61 Table 3.8—Case study days ....................................................................................................... 62 Table 3.9—Number of case simulation to be ran in current study ............................................. 63 Table 4.1—Working day ............................................................................................................ 76 Table 4.2—Working day energy balance overview .................................................................... 76 Table 4.3—Weekend day (death band 10ºC) ............................................................................. 77 Table 4.4— Weekend day (death band 05ºC) ............................................................................ 77 Table 4.5—Weekend day Energy balance overview ................................................................... 78 Table 4.6—% On-Peak Period Time Coverage ............................................................................ 78 Table 4.7—% On-Peak Energy Demand Supply .......................................................................... 79 Table 4.8—Overall efficiency of the system ............................................................................... 79

LIST OF EQUATIONS Equation 2.1—Theoretical Carnot limit (for Work and Cold production) .................................... 33 Equation 2.2—Coefficient of performance ................................................................................ 34 Equation 2.3—Ideal COP limit (reverse Carnot’s cycle) .............................................................. 34 Equation 2.4—Heat pump COP limit (according to Second Law of Thermodynamics) ................ 35 Equation 3.1—Energy absorbed from fluid stream .................................................................... 55 Equation 3.2—Source outlet temperature ................................................................................ 55 Equation 3.3—Load outlet temperature .................................................................................... 55 Equation 3.4—Heat losses from tank to environment ............................................................... 56 Equation 3.5—Energy removed from tank to load supply .......................................................... 56 Equation 3.6—Energy supplied to tank from source .................................................................. 57 Equation 3.7—Mass flow rate for pump .................................................................................... 58

NUMERICAL SIMULATION AND ANALYSIS OF A HEAT PUMP SYSTEM COMBINED WITH THERMAL STORAGE TANK FOR DHW SUPPLY, CONTROLLED THROUGH A DEMAND SIDE MANAGEMENT STRATEGY

1

1. INTRODUCTION & OBJECTIVES

1.1.- INTRODUCTION

Considerable amount of electrical energy loss occurs in grids due to the voltage fluctuations caused by fluctuations of consumption loads. In germany, during nights and in summer time where demand is in its lower levels, there is always extra electricity generation, this period is called off-peak hours while during peak hours where the demand is in highest rate, generating capacity have to meet peak demands. Therefore it has been always concerns for power system planners to manage this variable daily and seasonally energy demand and define a strategy which enables peak shave by shifting energy demand from one time to another time and thus reduce the consumption hours, there are relatively many investigations and attempts accordingly looking to find a solution to prevent or recover this significant energy loss in power grids by flexible charge and discharge hours. Electrical Energy Storage (EES) is seen to be one of yhe most reliable and inevitable solution to resolve this mismatch between power supply and demand, in fact EES allows energy production to be decoupled from its supply, self generated or purchased. Different EES technologies have been so far introduced and developed, including Fuel Cell, Flow Battery, Compressed Air Energy Storage (CAES), Pumped Hydroelectric Storage (PHS) and Electrical Thermal Storage (ETS) tending to provide sufficient energy storage capacity to resolve the mentioned problem. The main benefit of EES to shift the loads from peak to off-peak hours which lets the large utility generation systems to meet average electrical demand rather than peak demands. Besides all these technologies which are seen as direct ways of electricity storage, there is other method called Electrical Thermal Storage (ETS), which is based on conventional thermal energy storage(TES), to store electricity in form of thermal energy or heat; Basically the idea of ETS technology is to convert low cost off-peak power to thermal energy by means of an energy conversion system and store this energy in reservoir which can feed this relatively low cost heat for household and business demands 24 hours a day. Within this context of increasing need of storage capacity for electrical power and according to German government’s energy and climate policy, by 2020 renewable energies in Germany are to have a share of at least 35% in gross electricity consumption, a 50% share by 2030, 65% by 2040 and 80% by 2050. This means that it will require a even much higher proportion of electricity to be generated from uncontrollable sources such as wind and solar, so one of the challenges ahead will be providing sufficient electricity storage capacity to deal with intermittency of renewable sources in order to achieve the complete grid integration of this valuable resource.

1.2.- OBJETIVES

Present work developes and analyzes a domestic decentralized energy storage system formed by a heat pump system combined with thermal storage tank that applies a DSM strategy of load shifting between valley and peak periods. The novel Demand Side Management (DSM) control strategy that model implements controlling both heat source (heat pump) and heat supply (tank outlet) in a coordinated way allows to study different configurations of the system following a load shifting scheme. By analyzing different parameters and their influence on system behaviour overall system efficiency can be evaluated not only in energy terms, but also in demand supply terms. Since present work represents a new approximation to DSM and storage of energy some final advices in order to consider new study paths are given.


2

1.3.- CASE STUDY DESCRIPTION & JUSTIFICATION

The work analyzes the simulated behaviour of a DHW supply system for a german single-family house and tries to make an initial evaluation of its energy storage capacity. During next years grid development and storage of electricity will play a key role in Germany. The main reason for that is German government’s energy and climate policy. According to that, during next decades renewable electricity will increase dramatically its contribution share to final german electricity feed-in. Any feasible Storage solution that could help to manage residual load and surplus of energy from non dispatchable power sources would be an useful and necessary tool.


3

2. LOAD SHIFTING METHODOLOGY AND SYSTEM DESCRIPTION

2.1.- STATE OF THE ART

2.1.1.- German energy aspects overview

2.1.1.a.- Use of energy in Germany

According with its gross domestic product (GDP) Germany is the fourth world economy and the main one of Europe [United Nations, 2012]. With more than 80 million inhabitants and 469.4 MTCE of energy consumed in 2012 Germany is the seventh largest energy market in the world, behind China, the USA, Russia, India, Japan and Canada [EO]. It is also the Europe's largest electricity consumer with a total amount of 623.9 TWh of electric power in 2013 [BMWI].

Figure 2.1—Macroeconomic energy efficiency in Germany, 1990-2013 (relative to 1990). [AGEB)

Due to the large effort In between 1990 and 2012, macroeconomic energy efficiency (measured in terms of primary energy consumption per unit of real gross domestic product) improved at an annual average rate around 1.9 %, reaching that year figures around 177 KgCE per €1,000 of gross domestic product. It is half the comparative figure in the USA but is still twice the global average for this measure of energy consumption.

2.1.1.a.1 Primary energy sources

Germany’s own energy reserves are limited largely to coal. Its share of global reserves of oil and natural gas is marginal, making Germany heavily dependent on imports of these energy sources. In 2011 around 30 % of energy its consumption was covered by domestic energy (which don’t includes nuclear energy) while the remaining of energy consumption (about 70 % of total) was covered by means of imported energy sources. This energy imports are spread over a diverse range of energy sources and countries of origin, but it is well known that Germany’s most important foreign energy supplier is the Russian Federation. Last year (2013), oil contributed 4,637 PJ (or 33 %) of total energy production of 14,005 PJ. The contribution of coal added 3,404 PJ (or 24 %) to the total production. This was comprised of both lignite (1,625 PJ) and hard coal (1,779 PJ). The third main contributor was natural gas with 3,152 PJ (23% of total). Gas was followed by renewable energy with 1,654 PJ, nuclear energy with 1,058 PJ, natural gas/associated gas with 1654 PJ (or 12%) , oil with 3.8 MTCE, and energy from other sources with 8.7 MTCE.


4

Figure 2.2—Primary energy consumption in Germany, 2013. [BDWI]

During the last years Germany has made considerable effort to improve its energy efficiency and to reduce its consumption of primary energy. As result the country has reduced the total amount of energy imports, but, in percentage terms, has slightly increased its energetic dependence of foreign resources.

Figure 2.3—Evolution of energy imports (%) and primary energy utilization in Germany, 1990-2011.

(1)

The use of fossil fuels remains as main primary energy source but natural gas increases its importance in front of oil and coal, considered more pollutant technologies. Renewable energy sources also show an important development in opposition to nuclear power, which decreases constantly year by year.

Figure 2.4—Evolution primary energy of consumption in Germany, 1990-2013. [AGEB]


5

2.1.1.a.2 Final uses of energy

Regarding to the final use of the energy, the structure of utilization has remained pretty stable during the last 20 years, although the amount of energy involved, as seen before, has been constantly decreasing [Figure 2.5].

Figure 2.5—Use of energy structure in Germany, 1990-2012. [BMWI]

Germany, as one of the most developed and industrialized countries, allocates the greatest amount of its useful energetic resources to the industrial production. In 2011 [Figure 2.6] spent 30% (2,599 PJ) of its total useful energy consumption (8,998 PJ) on this purpose. After it followed mobility purposes, which reached a figure of 29.4% (2,571 PJ). In third place stayed domestic use of energy with a 25.5% (2,431 PJ) and, finally and less important, a percentage of energy fixed in 15.5% (1,397 PJ) which was delivered to services.

Figure 2.6—Energy flow diagram of energy utilization (PJ) in Germany, 2012

(2)


6

Concerning to the specific use of energy but regardless of this four main previous groups exposed, the energy consumption by applications in Germany is predominantly aimed to different ways of heat production. As shows Figure 2.7, in year 2012 the 55% of the whole useful energy that Germany spent (4950 PJ of 8998 PJ) was allocated for heating purposes. Among this general heating purposes space and water heating consumed more than a third (34%) of the total amount of German energy target reaching that year a global quantity of 3060 PJ.

Figure 2.7—Specific use of the energy in Germany, 2012. [BMWI]

The evolution of the final energy consumption by energy sources [Figure 2.8] shows that the final end-users in Germany are changing their consumption habits, avoiding gradually the use of “dirty” or pollutant sources (i.e. coal) in favour to more clean and environmental-friendly alternatives such natural gas or renewable power sources. In that context it has to be noted that the use of electricity as final energy source took about the 20% of the total supply market, showing a slightly but constant increasing importance in the last decades.

Figure 2.8—Final energy consumption by energy sources in Germany, 1990-2012. [BMWI]

2.1.1.a.3 Electricity generation

The current gross output of electrity in Germany in year 2013 reached a total amount of 634 TWh, which is almost the same as the previous year and shares the same stable behaviour showed since the beginning of the decade Table 2.1.


7

Gross electricity

generation

2010 2011 2012 2013

TWh in % TWh in % TWh in % TWh in %

Total 633,0 100 613,1 100 629,8 100 633,6 100

Renewable 104,7 16,6 123,8 20,2 143,5 22,8 151,7 23,4

Table 2.1-Gross electricity generation in Germany, 2010-2013. [BMWI]

The current generation structure of electricity in Germany can be described by analyzing Figure 2.9. There is clearly shown that, among the whole electric generation, the share of that which was delivered by conventional power sources (such as nuclear, coal, lignite or natural gas) reached a percentage at about 76%, while renewable energy sources were responsible for completing the electricity supply with a figure close to 24 %.

Figure 2.9—Gross electricity generation structure in Germany, 2013. [BDEW]

These percentages have varied significantly in the last decade as shows Figure 2.10. According to that it can be seen that renewable power sources have increased their relative weight at the expense of conventional sources. This aspect should not be surprising as it is consistent with the development policies for renewable energy sources, which drives the country since recent years.

Figure 2.10—Shares of energy sources on the net electricity generation, comparison 2002-2012. [BDEW]


8

In relation to the installed capacity, according to U.S. Energy Information Administration (EIA) and referred to 2012, the share of renewable power capacity installed in Germany in the last five years has increased from about 25% until overcoming 35% (Figure 2.11). The reason of the difference in the consideration of electrical work and installed capacity is the different capacity utilization of the different types of power plants, shown in Figure 2.12 .

Figure 2.11—Development of installed capacity (Total& Renewable) in Germany, 2008-2012. [EIA]

In the last five years nuclear power and lignite have constituted the typical base load capacity, working in the range of 7,000 full load hours per year. In the case of dispatchable renewable energy (hydraulic and biomass), it also assigned their contribution to this segment running about 6,000 full load hours. Hard coal range of use has been settled in the mid load segment with around 4,000 full load hours along these years, while pumped storage, heating oil and natural gas have been considered typical in peak load utilization. Finally wind and photovoltaic achieved limited annual full load hours because of its natural offer variability.

Figure 2.12—Annual full load hour in Germany according to Power plant type, 2008 and 2012. [BDEW]

2.1.1.a.3.1 Renewable electricity generation

During the last 20 years electricity supply from renewable sources in Germany has shown a constant increment of its figures in front of conventional power sources with a remarkable growing trend, shown even before to the Energiekonzept implementation. The use of renewable electricity has allowed a decreasing in use of controverted conventional sources power for electricity generation, such as nuclear and coal. According to Figure 2.13, nuclear

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Flow water

Wind onshore

Oil

Photovoltaic

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2008

http://www.eia.gov/


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and coal power, starting from predominant figures at the beginning of 90's, have lowered their absolute input to the final energy mix, losing relative share in favour to renewable. These constant trends have leaded these three different sources to reach a similar level of importance in the final supply. In each case their constant trends have lead these three so different sources to reach similar importance in the final present supply of electricity. In the near future, according to the expressed trends and considering the future energetic policy of the Federal government [2.1.1.b.-], it is easy to state that renewable sources will rapidly overcome their figures; just in the opposite way that it is expected for coal and nuclear. As detailed in next chapters, in year 2022 Germany will phase out nuclear electricity supply and in year 2050 it is expected that renewable electricity will be able to cover 80% of total electricity supply that the country will demand.

Figure 2.13—Use of energy power generation in Germany, 1991-2012. [BDEW]

In regard to power supply sources implemented as renewable [Figure 2.14], wind energy and biomass have supplied more than 50% of final amount of generated electricity during the last decade. Nevertheless during the last years photovoltaic share has been constantly increasing showing important development strength and, therefore, becoming the third renewable electricity source of Germany. It is also important to state that the most important part of electricity from renewable sources that Germany uses come from fluctuating sources and this trend seems that is not going to change.

Figure 2.14—Renewable electricity generation by energy sources (TWh), 2002-2012. [BDEW]


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2.1.1.a.4 Housing/Residential use of energy

According to BDEW (3), households are a particularly important customer group. Just right following industry, this group posses the second largest energy consumption share with about 30% of both net electricity and natural gas of the country. As further description of this statement and based on official data supplied by BDEW (3) in the year 2008 [Table 2.2] a total amount of 2,559 PJ of energy were consumed by german households. This huge quantity of energy, that didn’t take into account transportation purposes, was supplied by five different sources. The main energetic source, which supplied 38% of the needs (972 PJ), was natural gas, followed by oil with 25% (641 PJ). Electric power was responsible for supplying 20% (512 PJ). Finally, the rest of solid fuels (fossil or not) and district-heating facilities completed the final figures with the remaining 19% (486 PJ).

Energy Source Amount of energy (PJ)

% of TOTAL supply

Energetic purpose

Amount of energy (PJ)

% of TOTAL supply

Natural gas 972 38 Heating 1869 73

Oil 641 25 Hot water (DHW) 307 12

Electricity 512 20 Process heat

(without DHW) 128 5

Solid fuels 282 11 Mechanical

energy 153 6

District-Heating 152 6 IT&T 51 2

TOTAL 2559 100 Lighting 51 2

Table 2.2—a) Energy consumed by german households and b) final purpose of that consumption, 2008

(3)

As regards to its energetic purposes, more than the 80% of the energy supply that the average german home received in 2008 was spent as heating loads not only for heating water, but fundamentally for heating facilities. The rest of uses as lighting or feeding mechanical systems (mainly electrical devices such as motors and compressors for refrigerators and freezers) completed almost the rest of consumption with a low figure of 10%.

Energetic purpose


% of TOTAL supply

Heating 820 84

Hot water (DHW) 138 14

Process heat (without DHW)

16 2

TOTAL 972 100

Table 2.3—Energy heating demand of german households supplied by natural gas, 2008

(3)

As easily supposed, due to the total amount of energy spent and to the difference of price between the different sources, heating demand of german households was normally supplied by fossil fuels. In the case of the most common source -natural gas- [Table 2.3] the 84% of the


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972 PJ consumed in 2008 were spent on heating purposes and DHW facilities required from 136 PJ (14%).

Energetic purpose


% of TOTAL supply

Cooling / freezing 159 31

Process heating 124 20

Hot water 87 17

Space heating 67 13

IT&T 56 11

Lighting 19 8

TOTAL 512 100

Table 2.4—Energy heating demand of german households supplied by electricity, 2008

(3)

In the case of electric power [Table 2.4], and due to its greater versatility and higher relative price, was broadly implemented to cover many other energetic purposes apart from space heating and DHW, being its utilization in these fields much less spread. according to that, in 2008 electricity contributed with an amount of just 154 PJ for conventional domestic heating in Germany (far from the 958 PJ supplied by natural gas) and just implying 30% of the domestic electric supply. While almost 30 % of DHW demand of average german house is covered by electric power, the use of electricity as heat supplier for households has not been widely introduced in Germany yet. As example, just in 2008 it roughly reached a total figure of 7% (154 PJ of 2176 PJ). Different factors could be mentioned as cause of it, but maybe its the price would be important. In this context the introduction of high efficient heating devices which use electric power should be taken into account in order to explore opportunities in this gap. Additionally, depending on the origin of the power and its rewarding, the introduction not just of heat pumps but also of support devices offering additional storage capacity, could not just enable but also boost the introduction of this heating alternative as a feasible complement of the conventional supply.

2.1.1.b.- Energiewende

The Energiewende is a highly ambitious energy policy concept (or energetic reconversion Plan) for Germany which promotes the complete transformation of the current model and the transition to a sustainable economy through renewable energy, energy efficiency and sustainable development the ultimate goal of what is the abolition of coal, nuclear energy and other non-renewable resources and the drastic reduction of the carbon emissions. This new energy policy wants to provide a safe, affordable and environmentally compatible energy supply, setting the basis for the future growth and competitiveness of the nation and decoupling economic growth from energy consumption. Based on this energetic strategy the German Federal Government introduced in 2011 the fundamental transformation of the Germany's energy supply model for the next decades which, since that moment, aims its prioritary development efforts towards renewable energies and the improvement of its energy efficiency.


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Table 2.5—Summarized Energiewende qualitative targets

(4)

According to this strategy by 2022 Germany will have phased out gradually the use of nuclear power. In parallel and as replacement to this source, the use of renewable power sources as final energy supply will grow progressively to 60%, reaching by 2050 at least the 80% of the total amount of electricity produced (this share was about 23% in 2012). In addition, that year the primary energy consumption (based on fossil resources) shall decrease up to 50% compared to 2008 and total greenhouse gas emissions shall be reduced by 80% compared to 1990 (Table 2.5). To achieve these goals the German Government shall affront considerable technological, social and legal challenges which will require also from considerable efforts. The energy transition is considered as the Germany’s largest post-war infrastructure project. The switch to a highly efficient renewable energy economy will require large-scale investments of up to 200 billion euro. Nevertheless it is also assumed that it will strengthen its economy and will create new job opportunities. It has to be taken into account that transition to this new energetic scenario is broader than just switching from nuclear and coal to renewable in the electricity sector; electricity makes up roughly 20 percent of German energy demand, with roughly 40 percent devoted to heat and 40 percent to transportation. So, Energiewende not only includes renewable electricity, but also involves deep changes to energy use in the transportation and housing sectors. For a reliable future energy supply and in addition to renewable sources, flexible, modern power plants (some of which will be still fossil) and alternative storage utilities will be needed, mainly because energy from renewable sources is not always available. In this deep transformation, matters such expansion and modernization of the grid also will be crucial because renewable power sources will be often produced far from the consumption. Currently, it is already remarkable for wind energy, which is mainly produced in northern Germany and has to be transported to the south and west of the country.


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Although expansion of the transmission networks will represent a very efficient option for the large-scale balancing of fluctuations in power production from renewable energy sources, complete balancing throughout the network will not be either financially viable or technologically possible. Therefore, storage systems could enable flexible compensation of these fluctuations at local, regional and trans-regional levels and can also relief grid congestions. In the next forty years the interaction and integration of renewable power with whole energy system, especially with grid and power plants must be improved in order to fit it special requirements and singularities. Moreover, the cost of production and transportation for renewable power must be limited till a competitive and acceptable level. According with the new policy strategy, it will be also necessary reach new development in the field of energy efficiency, aimed to reduce the consumption of resources and to save power and heat, not only in generation utilities, but also in buildings due to important amount of energy that consume. Additionally research may help to develop new technologies that better integrate the not dispatchable and fluctuating energy sources into the grid and could contribute to solve the management problem for residual load and surplus of electricity (excessive power production), thus contributing to security of supply.

2.1.1.b.1 Tools for the change

To achieve its ambitious goals the German government has settled and readapted several legislative instruments that affect different fields or sectors involved in the change process. Among the variety of available tools, there are three main statutes involving renewable power, domestic energy utilization and heating technologies that must be highlighted; they are, respectively, the “Erneuerbare-Energien-Gesetz” (or EEG ), the “Energieeinsparverordnung” (EnEV) and the “Erneuerbare-Energien-Wärme-Gesetz“ (EEWärmeG).

2.1.1.b.1.1 Renewable Energy Sources Act (EEG)

The “Act on granting priority to renewable energy sources” (for short: Renewable Energy Sources Act) or, in German “Erneuerbare-Energien-Gesetz” (EEG) is a federal law which dates from 2000. The Act regulates the grid operators’ priority obligation to purchase electricity from renewable energies, the (declining) feed-in tariffs for the individual generation methods, and the procedure for allocating the resulting additional costs among all electricity customers. Different amendments to the act entered into force in 2004, 2009, on 1 January 2012 and – most recently – with retroactive effect to 1 April 2012.

2.1.1.b.1.2 German Energy Saving Ordinance (EnEV)

As just previously mentioned, the “Energieeinsparverordnung” (EnEV) or, in English, “German Energy Saving Ordinance” is another important part of the energy- and climate politic of the German Government. The aim of this Ordinance is to regulate the energy performance both for new buildings and building stock (either residential or non-residential buildings) as well as to determinate the energy certification process of buildings. For new buildings, EnEV primarily poses requirements to the primary energy demand as well as taking into account issues related with the heat insulation of the building envelope as well as the energy efficiency of the building appliances used (heating, domestic hot water, ventilation, cooling, and - for - lighting).


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Figure 2.15—Schematic development of current EnEv

[http://www.bbsr-energieeinsparung.de]

The Energy Saving Ordinance was firstly released in 2002, when inside its content merged two previous Ordinances which regulated respectively thermal insulation of buildings and heating appliances. It was amended for the first time in 2004 in Order to implement the first European Directive on Energy Performance of Buildings (2002/91/EC). In 2007 was reissued improving several aspects that involved, for example, non-residential buildings and, in 2009, was once more amended following the government’s decision "Integriertes Energie- und Klimaprogramm (IEKP)" and introducing aspects like the obligatory renovation of night heat storage tanks older than 30 years. On 1st May 2014 the latest version of the ordinance came in to force as consequence of a further amendment: “Ordinance amending the Energy Saving Ordinance of 18th November 2013" (EnEV 2013).

2.1.1.b.1.3 Act on the Promotion of Renewable Energies in the Heat Sector (EEWärmeG)

The Erneuerbare-Energien-Wärme-Gesetz (normally referred as EEWärmeG) or, in English, “Renewable Energies Heat Act” is an Act specifically established to promote the introduction and expansion of renewable power within the energy supply for heating and cooling of buildings, which entered into force on 1 January 2009. The law set that, as latest, in 2020 not less than 14% of the total amount of heat used in buildings must come from renewable sources and promotes the prioritary use of green energy to supply that demand. One example of such a strong will is the compulsory utilization of renewable energy sources together for heating or cooling purposes in case of new residential building whose surface will be greater than 50 m2 (proportionally according to each the case). Since the way of covering that obligation is not totally fixed and the final decision is up to owner the implementation of heat pump facilities in new residential buildings usually fulfils without complications the requirements that the law sets. Besides of specific quality certifications (EHPA-Gütesiegen or equivalent) the EEWärmeG law fixes the minimum SHP values for different facilities. In case of DHW heat pump facilities the minimum number must be between 3.3 and 3.8, depending of the case). In case of air-water and air-to-air heat pumps the stipulated SPF must reach, at least 3.5, while for the rest of devices (ground-water heat pump) must reach not less than 4.0.


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2.1.1.c.- Heat pump market in Germany

2.1.1.c.1 Current development of heat pump market

In the last few years, the rising costs of fossil fuels as well as the favourable political conditions (EEWärmeG and EnEV) have increased in Germany the demand of efficient and climate-friendly heating systems like heat pumps. According to the information from the federal association heat pump [BWP) the development of the German heat pump market in the last 25 years has been remarkable. Since 1990 and until 2008 sales of heat pump facilities increased strongly year by year. Since then and till nowadays heat pump market has reached considerable and stable sales rates.

Figure 2.16—Heat pump sales in Germany, 1990-2008

(5)

Analyzing in detail the market development shown in Figure 2.16, from the beginning of 1990’s until 2005 the number of sales grew gradually reaching a number of utility sales about 20,000/year. However, in the year 2006 sales doubled in comparison with the previous period, reaching about 48,000 sold plants. In the year 2008 about 62,500 heating heat pumps were sold, with an increasing rate about one third in comparison with 2007.

Figure 2.17—Heat pump sales in Germany, 2007-2013

(5)(6)

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During the last 6 years and despite of a light decrease of sales in 2010, the heat pump sales has remained in values similar to those reached in 2008 , reaching annual values of about 60,000 facilities/year, which shows the real health and strength of the current german heat pump market [Figure 2.17]. Specifically related with heat pump facilities aimed to DHW water production, the market development shows a pretty close proportional behaviour with the previous facilities, which with share technologic principles. From 1990 heat pump facilities for DHW production sales grew up year by year, reaching their top in 2008, with more than 15,000 sold units [Figure 2.16]. After that year, between 2009-2011, markets relaxed and the sales rates fell down. Anyway, during the last two years (with available data) sales have once again improved, reaching in 2013 about 12,500 sales [Figure 2.18].

Figure 2.18—DHW Heat pump sales in Germany, 2007-2013

(5)(6)

According to BWP, in 2008 around 350,000 heat pump (for heating and DHW) were operating in Germany. Due to the strength of the market, in the last six years this number has almost multiplied by two, reaching in 2013 a total amount of about 690,000 facilities, 52,000 of which are aimed to produce DHW.

2.1.1.c.1.1 Characterization of the installed utilities

As shown in Table 2.6, in the year 2010, the average heating capacity of the installed systems was about 12 kW for ground-water heat pump systems. In case of air-water heat pump facilities reached 13 kW and for water-water heat pump was about 17 kW (6). Due to the increasing demands of building insulation implemented by every time more demanding standards, the average heating capacity of the german facilities has reduced on average by about 5 kW since 1990. With regards to their performance [2.2.1.a.2] according to EN 14511 (special conditions) (5) [GZB], in 2009 ground-water heat pump (also denominated as B0/W35-) were characterized by a performance level of 4.5 (+0.5/-0.5) while the second group (air-water devices -A2/W35-) reached, as expected, a lower performance levels of 3.4 (+0.5/-0.4). Taking into account real working conditions ground-water heat pumps reached in 2009 average seasonal performance factors of 3.8 and 3.3 (for new buildings and existing buildings respectively). For air-water heat pumps the average seasonal performance factor was 3.0 for new buildings and 2.6 for existing buildings (5).

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17

2.1.1.c.2 Future development of market

Last 2013 the BWP presented a report (6) where the development trends of the water heat pump market in German between 2013 and 2030 was examined (it has to be noted that, although having a quite important potential, the study doesn't analyze air-air heat pump devices). Anyway, returning to the study, it assumed two different scenarios. The first situation, called "Scenario 1", represented a conservative trend prediction which assumed an almost unchanged modernization backlog. On the other hand optimistic assumptions were taken to describe the second possible situation, called "Scenario 2", where appropriate policy measures to modernize the existing heating park were implemented. The increasing promotion of renewable energy sources will lead to a higher installation of heat pumps facilities. Around 2020 and depending on the Scenario, is expected that market share for heat pump will reach between 12% (Scenario 1) and 18% (Scenario 2) of the whole domestic heating systems market.

Figure 2.19—Heat pump expected sales (annual and accumulated) in Germany, 2013-2030

(6)

The base point for the BWP assumptions expects that in 2014 a slight increase in annual sales of approximately 61,000 heat pump heating facilities. From that point both scenarios share steadily rising sales figures but with different growth figures depending on the set-up framework [Figure 2.19]. From the settled starting point of 61,000 devices, and according to Scenario 1, 86,000 heat pumps would be sold annually in Germany by 2020, while in Scenario 2 this number could reach around 160,000. By 2025, Scenario 1 (conservative) assumes that annual heat pump sales may rise to 96,000, consolidating subsequently that amount around 93,000 heat pumps by 2030, while Scenario 2 (optimistic) predicts steadily increasing sales figures, which in 2030 would reach heat pump sales up to 235,000. At the end of the study period, that is, in 2030, the BWP estimates that in the whole Germany between 1,750,000 and 3,000,000 heat pump devices will be installed as domestic heating systems. The previous heat pump sales figures have also taken DHW heating device sales into account. So that to know their expected specific development is necessary to give some additional figures. Due to their considerable future potential (especially in conjunction with photovoltaic devices) BWP expects that DHW heat pump devices will show a very positive market development until 2030 [Figure 2.20]. According to Scenario 1, by 2020 they would be supposed to achieve annual sales of 15,600, which would grow up to 17,600 sold devices in 2030. In the most ambitious situation (Scenario 2) BWP holds that the previous sale figures could even rise to 23,500 in 2020, reaching figures of 30,000 in 2030.


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Figure 2.20—DHW Heat pump sales in Germany, 2007-2013

(6)

2.1.1.c.2.1 Characterization of the future scenarios

In the next years the average heating capacity expected for installed systems is assumed to continue its decline gradually. So that in the next 15 years the average heating capacity of the german facilities will reduce between 1 or 2 kW , depending on the technology [Table 2.6]. Analyzing by groups, heating capacity of ground-water heat pump systems will be reduced from 13 kW to 10 kW. For air-water heat pump facilities this reduction could shift its value from 12 kW to 13kW and also reduce from 17 kW to 16kW the figures for water-water heat pumps.

Table 2.6—Average heating capacity (current/expected) of german heat pump systems, 2010-2030

(6)

With regards to average performance levels (SPF) currents and expected [Figure 2.21], water-water heat pumps showed between 2012 and 2013 a considerable improvement of efficiency (due to a technological developments) and after this period will be characterized by a constant rate of improvement which, in 2030, would lead the average performance level (SPF) up to 4.5, that could reach up to 4.7 in the case of new building. Ground-water heat pumps would reach annual improvement rates close to those reached by the previous group, which would leave their average performance levels about 4.4, achieving 4.5 for new buildings.


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It is also expected that, around year 2020, technological and efficiency improvements in air-water heat pump devices might allow a sudden increase of their performance, reaching levels of 3.8 for new buildings. This value would apply as average annual coefficient both in old and new buildings by 2030. Finally, as summary, it is expected that the average seasonal performance factor of all electric heat pumps devices will increase from 3.0 in 2008 to 3.6 or 3.7 in 2030, depending on the assumed scenario [Figure 2.21].

Figure 2.21—Expected seasonal performance factor (SPF) for heat pumps systems, 2008-2030

(6)

2.1.2.- Demand side management (DSM) and residual load

The pattern of electricity consumption varies in the course of a day, typically reflecting the patterns of human activity; high during the day and low at night. This fact has fundamental implications on the electric utilities. Adequate generating capacity has to be available to serve the demand during the peak periods, even though much of this capacity is idle during the periods of low demand (off-peak). In order to cost-effectively serve this varying demand, with both diurnal and seasonal variations, the utilities use three types of generating facilities: base load, intermediate load, and peak load plants [Figure 2.22 and 2.1.2.b.-].

Figure 2.22—Schemathic representation of power demand (winter/summer)

[Own]

While this apportionment of the load among different types of generators represents the most cost-effective means of meeting the expected load pattern, it also results in electricity costs that vary depending on the magnitude of the demand. As the load increases, additional plants are brought on-line according to economic dispatch procedures, which aim to keep the cost of


20

delivered electricity as low as possible. This is accomplished by "stacking" the plants in the order of their operating costs -- starting with those that are least expensive to operate and adding successively more expensive generators as needed. For these reasons it is generally more expensive to provide customers' electricity needs during the day (peak period) than at night (off-peak period). Although some "valleys” in the annual load may be needed to provide time for plant maintenance, the power can be supplied most efficiently if the variability in the load is relatively small, allowing the more efficient plants to carry a greater portion of the load. One of the measures used to describe the variability of the electrical load is the load factor, a ratio of the average demand to the maximum demand during a given time period. A load factor close to one (100') describes a load that varies only minimally with time. Conversely, a low load factor implies a demand pattern characterized by sharp peaks and valleys. The close relationship between costs and load patterns was recognized essentially at the very beginning of the utility industry's existence. It was then only a small step from this recognition to initiation of activities designed to influence customers' use of electricity so as to create a better match between the demand and supply.

2.1.2.a.- Demand Side Management (DSM)

Demand-side management (DSM), also known as “Energy Demand Management”, is a set of energy demand control activities aimed to bring the demand and supply closer to a perceived optimum developed through the use of diverse methods such system or infrastructure improvement, financial incentives or education and which involve the modification of the consumer demand for energy. DSM generally involves reducing electricity use through activities or programs that promote electric energy efficiency or conservation, or more efficient management of electric energy loads. Usually, the goal of demand side management is to encourage the consumer to use less energy during peak hours, or to move the time of energy use to off-peak times such as night-time and weekends. However DSM does not necessarily decrease total energy consumption, but could be expected to reduce the need for investments in networks and/or power plants for meeting peak demands. An example is the use of energy storage units to store energy during off-peak hours and discharge them during peak hours, encouraging customers to shift non-critical usage of electricity from high-use periods or even providing limited utility control of customer equipment such as air conditioners or heating devices. The concept of demand-side management was initially gestated during the period of time between the two oil crisis of the 1970s (1973-1979). As result, in the latest 1970s, electric utilities in the Unites States of America first began to implement programs aimed at changing the level and timing of electricity demand among their customers. Finally, in the 1980s DSM was introduced publicly by Electric Power Research Institute (EPRI). Nowadays, DSM technologies are key matters to implement management strategies for the next decades, based on information and communication, efficient use of energy and grid integration of renewable supply. Integration of information and communications technology and power system, will lead to a new infrastructure fact; the Smart Grid.

2.1.2.a.1 Load shape changes

The various changes able to being introduced in the load patterns could be classified into six generic categories shown in Figure 2.23 to Figure 2.28. The first three of these categories, peak clipping, valley filling, and load shifting, are the techniques used in appropriate combinations to achieve load management. When added to the second group of load shape changes, which emerged more recently, they comprise a set of options for demand-side management.


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2.1.2.a.1.1 Peak Clipping

Or reduction of the system peak loads embodies one of the classic forms of load management. It is generally considered as the reduction of peak load by using direct load control. Direct load control is most commonly practiced by direct utility control of either service to customer facilities or of customers' appliances. While many utilities consider this as means to reduce peaking capacity or capacity purchases and consider control only during the most probable days of system peak, direct load control can be used to reduce operating cost and dependence on critical fuels by economic dispatch.

Figure 2.23—Peak clipping

(7)

2.1.2.a.1.2 Valley Filling

Is the second classic form of load management and applies to both gas and electric systems. Valley filling encompasses building off-peak loads. This may be particularly desirable where the long-run incremental cost is less than the average price of energy. Adding properly priced off-peak load under those circumstances decreases the average price. This management form can be accomplished in several ways, one of the most popular of which is new thermal energy storage (water heating and/or space heating) that displaces loads serve by fossil fuels.

Figure 2.24—Valley filling

(7)

2.1.2.a.1.3 Load Shifting

Is the last classic form of load management and also applies to both gas and electric systems. This involves shifting load from on-peak to off-peak periods. Popular applications include use of storage water heating, storage space heating, coolness storage, and customer load shifts. The load shift from storage devices involves displacing what would have been conventional appliances.


22

Figure 2.25—Load shifting

(7)

2.1.2.a.1.4 Strategic Conservation

Is the load shape change that results from programs directed at end use consumption. Not normally considered load management, the change reflects a modification of the load shape involving a reduction in consumption as well as a change in the pattern of use. In employing energy conservation, the planner must consider what conservation actions would occur naturally and then evaluate the cost-effectiveness of possible intended programs to accelerate or stimulate those actions. Examples include weatherization and appliance efficiency improvement.

Figure 2.26—Strategic conservation

(7)

2.1.2.a.1.5 Strategic Load Growth

Is the load shape change that refers to a general increase in sales beyond the valley filling described previously. Load growth may involve increased market share of loads that are or can be, served by competing fuels, as well as economic development. Load growth may include electrification. Electrification is the term being employed to describe the new emerging electric technologies surrounding electric vehicles, industrial process heating, and automation. These have a potential for increasing the electric energy intensity of the industrial sector. This rise in intensity may be motivated by reduction in the use of fossil fuels and raw materials resulting in improved overall productivity.

Figure 2.27—Strategic load growth

(7)


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2.1.2.a.1.6 Flexible Load Shape

Is a concept related to electric system reliability, a planning constraint. Once the anticipated load shape, including demand-side activities, is forecast over some horizon, the power supply planner studies the final optimum supply-side options. Among the many criteria he uses is reliability. Load shape can be flexible –if customers are presented with options as to the variations in quality of service that they are willing to allow in exchange for various incentives. The program involved can be variations of interruptible or curtailable load; concepts of pooled, integrated energy management systems; or individual customer load control devices offering service constraints.

Figure 2.28—Flexible load shape

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2.1.2.a.2 Relevance of the energy storage to implement load shift strategies: the case of electricity.

In a conventional electricity power system scenario based on thermal, nuclear, hydro and renewable generation, storage will find a wide field of application and may perform various duties, which must be taken into consideration in order to gain the largest possible advantage in the supply side optimization. In a general way, power utilities have to meet increasing demand year by year. This means that additional plants must be installed to meet the peak demand on the system. In such a context, it would be clear that an installation capable of taking electricity from the grid at night (low demand periods) and returning it during peak periods will reduce the need for generation capacity in the system. Hence the capital cost of a storage unit may be compensated by savings on the conventional power stations, which will be superseded. Contrary to popular belief, the variation of demand on electricity utilities does not constitute a need for storage, but provides an opportunity for storage methods to compete with mid-merit and peaking generating sources. The variation of load through the day stimulates a demand for storage especially when the increase in installed capacity of large coal or nuclear plants, designed to operate at maximum efficiency on their rated power output, exceeds base-load demand, and when an increase in utilisation of intermittent energy sources (such as solar, wind or tidal energy) exceeds the utilities’ reserve capacities.

Considering the typical weekly load curve of a utility (with and without energy storage) shown in Figure 2.29 which shows a qualitative load variation for any developed country utility where cheap off-peak electricity rates exist. We can easily describe and discuss these two following situations:

In the upper curve (“A” case, where no one storage facility is implemented) the intermediate and peaking power involves extensive generating capacity and highlights the fact that installed capacity is about double the yearly average load, which finally means that electric power supply runs at considerably low capacity factors. This situation becomes even worse when increased levels of renewable energy are


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introduced on the grid, leading to even lower capacity factors for conventional generation sources.

Figure 2.29—Weekly load curve for a typical power utility

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Nevertheless, if large-scale energy storage were available( as illustrates lower curve “B”), then the relatively efficient and economical base power plants could be used to charge the storage units during off-peak demand (lower shaded area). Complementary the discharge of the stored energy (upper shaded areas) during periods of peak load demand would then reduce or replace fuel-burning peaking plant capacity, thus conserving resources.

As shown previously, the use of energy storage to generate peak power in this manner is termed ‘peak saving’. The higher base-load level may replace part of the intermediate generation, thus performing load levelling and enabling the more extensive use of storage to eliminate most or all conventional intermediate cycling equipment. Assuming that new base-load plants use non-oil-based fuel, there are further savings of both cost and oil resources. In addition to previous concepts, it is also necessary to remark that one fundamental issue related with the load shift strategies is making power available when it is needed for matching load demand. This duty is common to all generating methods in the system but if storage methods are involved the main parameter -energy capacity- has to be taken into account. In order to cover the period of power demand, the storage systems have to have sufficient storage capacity, and since the amount and duration of load demand are typically stochastic, unconstrained storage capacity would theoretically be necessary to avoid the possibility of being unable to cover the load owing to exhaustion of the stored energy. Under these conditions it is necessary to remind that the capital cost of a storage system depends on its energy capacity, and therefore the choice of storage parameters should be a compromise between cost and the risk that they may not be able to cover the demand. In planning a power system it is necessary to evaluate this compromise quantitatively.

2.1.2.a.3 Practical approach of DSM at user’s level.

Residential, commercial and industrial sectors represent approximately 40% of an average EC country’s oil consumption and nearly 80% of its natural gas consumption, primarily to provide heat for water, buildings and industrial processes. With the help of storage methods it would be possible to use less valuable and more economic primary sources of energy to supply this demand. Although shifting much of it to coal by burning it at the site of energy consumption would be impractical (because of fuel handling and pollution problems), the burning of oil and gas to provide heat could be reduced by shifting domestic heating loads to current coal and uranium utilities in form of electricity, and also by turning to renewable energy sources, which, in addition, should expedite their grid integration. When such a strategy was effectively


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implemented, the importance of energy storage would increase and in some cases it would be essential. Another way to employ storage methods to shift energy consumption would be the storage of thermal energy close to its end consumer represents. According to this concept, heat or ‘cold’ could be produced by off-peak energy on the consumer’s premises and stored for consumption during peak load periods.

2.1.2.b.- Residual load

To ensure a secured power supply the amount of electricity fed into the grid must be exactly the same as the electricity demand. In a conventional electricity system electricity demand can be normally divided into three different segments called base, intermediate and peak load. Each of these load segments is covered by specific power plants which are particularly suited to that purpose [Figure 2.22]. Base load plants serve the portion of the demand which is present most of the time and, as such, they are designed to operate at a constant level for most of the year and use low-cost fuels, such as coal and nuclear, resulting in low operating cost. The capital cost of these plants is, however, high. On the other hand, peaking plants that serve the peak portion of the load and operate for only about 10% of the time, have low capital but high operating costs. They rely primarily on, hydraulic, oil or natural gas-powered units to provide quick start capabilities. Intermediate plants meet the portion of the load that varies daily and, as implied by the term "intermediate" their sizes and costs fall between those of base load and peaking units. In addition to these conventional segments, the final power supply that covers the demand also includes a considerable amount of power from renewable sources. This amount of energy increases year by year and nowadays begins to reach important figures in the final energy mix. Although being a very valuable power supply, renewable sources are more fluctuating, less reliable and practically undispatchable. The residual load is defined as the grid’s load minus the electricity generated by intermittent renewable energies [Figure 2.30]. Thus, it represents the remaining load, which has to be covered by conventional dispatchable power plants or non intermittent renewable energies, thus necessary to ensure a secure power supply. As result of its “renewable” origin, a main characteristic feature of residual load is that it can change significantly faster than demand and in a non predictable way. Even in the case that renewable energies represent a large part of the energy mix, residual load can become very small or even negative under certain circumstances what is then known as surplus of electricity.

Figure 2.30—Typical weekly electrical load demand, renewable power supply and residual Load (as result of their difference)

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A general goal is to balance this residual load to enable the increase of the renewable energy share and decrease the continuously provided capacity of conventional power plants. This situation will be especially remarkable in the case of Germany in the next 35 years, where, assuming the previously exposed and planned energetic scenario [2.1.1.b.-], 80% of the total electricity supply in year 2050 will be feed by renewable sources. According with some authors (10) and depending on the future scenarios (where the “flexibility” of the infrastructure and, particularly, the development of the power grid and will play a basic role). The estimated surplus of electricity in Germany will reach figures of some TWh in the next decades (10). In order to control residual load properly (and thus ensuring grid stability) and also to not to waste such a necessary amount of energy, any new management tool will be welcome. Within this framework some authors consider storage of energy as a feasible management tool, having regard to cost technology implementation of any reasonable option (10).

2.1.3.- Storage of energy

2.1.3.a.- Thermal Energy Storage (TES)

Thermal energy storage (TES) is a technology that stocks thermal energy by heating or cooling a storage medium so that the stored energy can be used at a later time for heating and cooling applications and power generation. TES systems are used particularly in buildings and industrial processes. In these applications, approximately half of the energy consumed is in the form of thermal energy, the demand for which may vary during any given day and from one day to next. Therefore, TES systems can help balance energy demand and supply on a daily, weekly and even seasonal basis. They can also reduce peak demand, energy consumption, CO2

emissions and costs, while increasing overall efficiency of energy systems. Furthermore, the conversion and storage of variable renewable energy in the form of thermal energy can also help increase the share of renewables in the energy mix. TES is becoming particularly important for electricity storage in combination with concentrating solar power (CSP) plants where solar heat can be stored for electricity production when sunlight is not available. There are three kinds of TES systems, namely:

Sensible heat storage that is based on storing thermal energy by heating or cooling a liquid or solid storage medium (e.g. water, sand, molten salts, rocks), with water being the cheapest option;

Latent heat storage using phase change materials or PCMs (e.g. from a solid state into a liquid state);

Thermo-chemical storage (TCS) using chemical reactions to store and release thermal energy.

Sensible heat storage is relatively inexpensive compared to PCM and TCS systems and is applicable to domestic systems, district heating and industrial needs. However, in general sensible heat storage requires large volumes because of its low energy density (i.e. three and eve times lower than that of PCM and TCS systems, respectively). Furthermore, sensible heat storage systems require proper design to discharge thermal energy at constant temperatures. Several developers in Germany, Slovenia, Japan, Russia and the Netherlands are working on new materials and techniques for all TES systems, including their integration into building walls (e.g. by encapsulating phase change materials into plaster or air vents) and transportation of thermal energy from one place to another. These new applications are just now being commercialised, and their cost, performance and reliability need to be verified.


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Thermal energy storage systems can be either centralised or distributed systems. Centralised applications can be used in district heating or cooling systems, large industrial plants, combined heat and power plants, or in renewable power plants (e.g. CSP plants). Distributed systems are mostly applied in domestic or commercial buildings to capture solar energy for water and space heating or cooling. In both cases, TES systems may reduce energy demand at peak times. A TES system’s economic performance depends substantially on its specific application and operational needs, including the number and frequency of storage cycles. In general, PCM and TCS systems are more expensive than sensible heat systems and are economically viable only for applications with a high number of cycles. In mature economies (e.g. OECD countries), a major constraint for TES deployment is the low construction rate of new buildings, while in emerging economies TES systems have a larger deployment potential. Support for research and development (R&D) of new storage materials, as well as policy measures and investment incentives for TES integration in buildings, industrial applications and variable renewable power generation is essential to foster its deployment. R&D efforts are particularly important with regards to PCM and TCS systems.

2.1.3.a.1 TES and DSM

Thermal energy storage (TES) systems are widely recognized as means for decoupling electricity production and demand. They are used mainly to integrate renewable energy in the electricity production mix, but in recent decades TES systems have also proved useful for shifting electrical loads from peak to off-peak hours, becoming a powerful DSM tool. TES applications on the customer’s side have numerous advantages: customers can have a more efficient system and save money if they take advantage of different electricity prices during peak and off-peak hours; and utilities can spread the demand over the whole day, enabling a more efficient use of existing generating capacity and reducing the need for new capacity. The basic principle behind a TES system is that energy is supplied to the TES (charging), where it is stored, then drawn from the TES (discharging) and used at a later time. A TES unit is a device that can store thermal energy by cooling, heating, melting, solidifying or vaporizing a material. Different substances can be used, e.g. oils, molten salts, water or rock for sensible heat storage, or ice, phase change materials (PCM), or salt hydrates for latent heat storage. The medium is chosen to suit the storage period required, working temperature, economic viability, and so on (11).

2.1.3.a.2 Key points in thermal energy storage (TES)

2.1.3.a.2.1 Process and Technology Status

Thermal energy storage (TES) includes a number of different technologies. Thermal energy can be stored at temperatures from -40°C to more than 400°C as sensible heat, latent heat and chemical energy (i.e. thermo-chemical energy storage) using chemical reactions. Thermal energy storage in the form of sensible heat is based on the specific heat of a storage medium, which is usually kept in storage tanks with high thermal insulation. The most popular and commercial heat storage medium is water, which has a number of residential and industrial applications. Underground storage of sensible heat in both liquid and solid media is also used for typically large-scale applications. However, TES systems based on sensible heat storage offer a storage capacity that is limited by the specific heat of the storage medium. Phase change materials (PCMs) can offer a higher storage capacity that is associated with the latent heat of the phase change. PCMs also enable a target-oriented discharging temperature that is set by the constant temperature of the phase change. Thermo-chemical storage (TCS) can offer


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even higher storage capacities. Thermo-chemical reactions (e.g. adsorption or the adhesion of a substance to the surface of another solid or liquid) can be used to accumulate and discharge heat and cold on demand (also regulating humidity) in a variety of applications using different chemical reactants. At present, TES systems based on sensible heat are commercially available while TCS and PCM-based storage systems are mostly under development and demonstration.

2.1.3.a.2.2 Performance and Costs

Thermal energy storage includes a number of different technologies, each one with its own specific performance, application and cost. TES systems based on sensible heat storage offer a storage capacity ranging from 10-50 kWh/t and storage efficiencies between 50-90%, depending on the specific heat of the storage medium and thermal insulation technologies. Phase change materials (PCMs) can offer higher storage capacity and storage efficiencies from 75-90%. In most cases, storage is based on a solid/liquid phase change with energy densities on the order of 100 kWh/m (e.g. ice). Thermo-chemical storage (TCS) systems can reach storage capacities of up to 250 kWh/t with operation temperatures of more than 300°C and efficiencies from 75% to nearly 100%. Regarding to cost, the cost of a complete system for sensible heat storage ranges between €0.1-10/kWh, depending on the size, application and thermal insulation technology. The costs for PCM and TCS systems are in general higher. In these systems, major costs are associated with the heat (and mass) transfer technology, which has to be installed to achieve a sufficient charging/discharging power. Costs of latent heat storage systems based on PCMs range between €10-50/kWh while TCS costs are estimated to range from €8-100/kWh. The economic viability of a TES depends heavily on application and operation needs, including the number and frequency of the storage cycles.

2.1.3.a.2.3 Potential and Barriers

The storage of thermal energy (typically from renewable energy sources, waste heat or surplus energy production) can emissions and lower the need for costly peak power and heat production capacity. In Europe, it has been estimated that around 1.4 million GWh per year could be saved— and 400 million tonnes of CO2 replace heat and cold production from fossil fuels, reduce CO2 Thermal Energy Storage | Technology Brief4 2 emissions avoided—in the building and industrial sectors by more extensive use of heat and cold storage. However, TES technologies face some barriers to market entry. In most cases, cost is a major issue.

2.1.3.a.3 TES Technologies

Energy storage systems are designed to accumulate energy when production exceeds demand and to make it available at the user’s request. They can help match energy supply and demand, exploit the variable production of renewable energy sources (e.g. solar and wind), increase the overall efficiency of the energy system and reduce CO3 emissions. This brief deals primarily with heat storage systems or thermal energy storage (TES). An energy storage system can be described in terms of the following properties:

Capacity: defines the energy stored in the system and depends on the storage process, the medium and the size of the system

Power: defines how fast the energy stored in the system can be discharged (and charged)


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Efficiency: is the ratio of the energy provided to the user to the energy needed to charge the storage system. It accounts for the energy loss during the storage period and the charging/discharging cycle

Storage period: defines how long the energy is stored and lasts hours to months (i.e. hours, days, weeks and months for seasonal storage)

Charge and discharge time: defines how much time is needed to charge/discharge the system

Cost: refers to either capacity (€/kWh) or power (€/kW) of the storage system and depends on the capital and operation costs of the storage equipment and its lifetime (i.e. the number of cycles)

Thermal energy (i.e. heat and cold) can be stored as sensible heat in heat storage media, as latent heat associated with phase change materials (PCMs) or as thermo-chemical energy associated with chemical reactions (i.e. thermo-chemical storage) at operation temperatures ranging from -40°C to above 400°C. Typical figures for TES systems are shown in Table 2.7, including capacity, power, efficiency, storage period and costs.

TES System Capacity (kWh/t)

Power (MW)

Storage Period (%)

Storage Period (h, d, m)

Cost (€/kWh)

Sensible (hot water) 10-50 0.001-10 50-90 d/m 0.1-10

PCM 50-150 0.001-1 75-90 h/m 10-50

Chemical reactions 120-250 0.01-1 75-100 h/d 8-100

Table 2.7—Typical Parameters of Thermal Storage System (TES)

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2.1.3.a.3.1 Sensible Thermal Energy Storage

The use of hot water tanks is a well-known technology for thermal energy storage (11). Hot water tanks serve the purpose of energy saving in water heating systems based on solar energy and in co-generation (i.e. heat and power) energy supply systems. Water tank storage is a cost-effective storage option and that its efficiency can be further improved by ensuring an optimal water stratification in the tank and highly effective thermal insulation. (See 2.2.1.b.-). This technology is also used in thermal installations for DHW (conventional or renewable) combined with building heating systems. Either small or Large hot water tanks are used both for daily or seasonal storage of thermal heat, alone in case of decentralized systems or, in case of larger volumes, in combination with systems such district heating. These systems can have a volume from few litres up to several thousand cubic meters (m3) and charging temperatures are in the range up to 80-90°C. The usable temperature difference can be enhanced by the use of heat pumps for discharging (down to temperatures around 10 °C). In case of residential decentralized installations supplementary heating is commonly provided by solar thermal system or, in the case that we study, by absorption heat pumps, using the seasonal storage as a low temperature heat reservoir. This allows for a wide operation temperature range of the storage.

2.1.3.a.3.2 Phase Change Materials (PCM’s) for TES

Sensible heat storage is relatively inexpensive, but its drawbacks are its low energy density and its variable discharging temperature. These issues can be overcome by phase change materials (PCM)-based TES, which enables higher storage capacities and target-oriented discharging temperatures. The change of phase could be either a solid/liquid or a solid/solid process.


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Phase change materials can be used for both short-term (daily) and long-term (seasonal) energy storage, using a variety of techniques and materials.

2.1.3.a.3.3 Underground Thermal Energy Storage (UTES)

UTES is also a widely used storage technology, which makes use of the underground as a storage medium for both heat and cold storage. UTES technologies include borehole storage, aquifer storage, cavern storage and pit storage. Which of these technologies is selected strongly depends on the local geological conditions.

Borehole storage Based on vertical heat exchangers installed underground, which ensure the transfer of thermal energy to and from the ground layers (clay, sand, rock). Ground heat exchangers are also frequently used in combination with heat pumps where the ground heat exchanger extracts low-temperature heat from the soil

Aquifer storage Uses a natural underground water-permeable layer as a storage medium. The transfer of thermal energy is achieved by mass transfer (i.e. extracting/re-injecting water from/into the underground layer).

Cavern storage and pit storage Based on large underground water reservoirs created in the subsoil to serve as thermal energy storage systems. These storage options are technically feasible, but applications are limited because of the high investment costs. It works reasonable well for medium-high-temperature (i.e. above 100 °C) sensible heat storage technologies based on the use of liquids like oil or molten salts. For very high temperatures, solid materials like ceramics, concrete are also taken into consideration. However, most of such high-temperature-sensible TES options are still under development or demonstration.

2.1.3.a.3.4 TES via Chemical Reactions

High energy density TES, up to 300 kWh/m, systems can be achieved using thermo-chemical reactions. That kind of reactions such as adsorption, which consists on the adhesion of a substance to the surface of another solid or liquid, can be used to store heat and cold, as well as to control humidity. Typical applications involve adsorption of water vapour to silica-gel or zeolites used in hot/humid climates or confined spaces with high humidity. There is also interesting fields of application that includes waste heat utilization Some of the adsorption systems and materials, currently under investigation, are based on lithium-chloride to cool water and on previously mentioned zeolites (micro-porous crystalline alumino-silicates). TCSs are able to store thermal energy with high efficiency and to convert heat into cold at the same time, which makes these systems very attractive. Their high storage capacity of adsorption processes also allows them to provide thermal energy transportation.

2.1.3.a.4 Application

Important fields of application for TES systems are in the building sector (e.g. domestic hot water, space heating, and air-conditioning) and in the industrial sector (e.g. process heat and cold). TES systems can be installed as either centralised plants or distributed devices. Centralised plants are designed to store waste heat from large industrial processes,


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conventional power plants, combined heat and power plants and from renewable power plants, such as concentrated solar power (CSP). Their power capacity ranges typically from hundreds of kW to several MW (i.e. thermal power). Distributed devices are usually buffer storage systems to accumulate solar heat to be used for domestic and commercial buildings (e.g. hot water, heating, appliances). Distributed systems are mostly in the range of a few to tens of kW.

Application Technology Central/Distrb Energy Effic./ Ren. Energy

Cold storage (buildings)

PCM (ice, passive cooling)

D EE + RE

Cold storage (industry, appliances),

PCM (slurries) Absorption storage (heat to cold)

D EE+RE

Domestic hot water (buffer storage)

Sensible storage (hot water)

D RE

Heating (buildings, seasonal

storage)

Sensible storage (UTES, large water tanks, district

heating) C RE

Process heat (industrial

heating/drying)

Thermo-chemical storage (sorption storage)

D EE+RE

Waste heat (cement & steel

industry)

Sensible storage (solids)

PCM, chemical reactions C+D EE

High temp. storage (>400°C) for CSP & CAES

Sensible storage (liquids, molten salt)

PCM, chemical reactions C RE

Table 2.8—TES-relevant Applications

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TES systems – either centralised or distributed - improve the energy efficiency of industrial processes, residential energy uses and power plants by storing waste or by-product heat or renewable heat when it is available and supplying it upon demand. Thermo-chemical storage systems can also convert waste heat into higher temperature heat or into cold. A number of energy-intensive industrial sectors and processes (e.g. cement, iron and steel, glass) benefit from TES systems. Manufacturing industry (e.g. automobile industry) can also benefit significantly from TES. Most importantly, TES can help integrate variable renewable solar heat into the energy system. This applies either to short-term storage based on daily heat buffers for domestic hot-water production or to long-term heat storage for residential and industrial heating purposes, based on large central storage systems and district heating networks. TES systems can also help integrate renewable electricity from PV and wind. For example, the efficiency of a (mechanical) compressed air energy storage (CAES) can be improved from about 50% to more than 70% by storing heat during compression and discharging it to support expansion. Charging a cold storage system using renewable electricity during high solar irradiation periods or wind peaks and delivering cold to consumers on demand is a further potential TES application. Table 2.8 lists applications for centralised and distributed TES technologies, along with their contribution to energy efficiency or to the integration of renewable energy.


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2.2.- CONCEPTUAL DESCRIPTION OF THE SYSTEM

2.2.1.- Main system components

2.2.1.a.- Heat Pump

Heat pumps are devices able to force heat flowing from a lower to a higher temperature, using a relatively small amount of high quality drive energy (electricity, fuel, or high temperature waste heat). Thus, heat pumps can transfer heat from natural heat sources in the surroundings (so called low-temperature sources normally unusable), such as air, ground or water, or from man-made heat sources, such as industrial or domestic waste, to a building or an industrial application. Because heat pumps consume less primary energy than conventional heating systems, they are an important technology for reducing gas emissions that harm the environment, such as CO2, SO2 and nitrogen oxides (NOX) and, at the same time , allow the use of renewable sources (both from ambient heat harvesting and electric power generation). However, the overall environmental impact of electric heat pumps depends very much on how the electricity is produced. Therefore, a heat pump can potentially save energy and money and reduce harmful emissions to the environment.

2.2.1.a.1 Heat pump technology

2.2.1.a.1.1 Heat pump working principles

A thermal machine is a cyclically operating device that transfers heat and work with its environment. The simplest devices just exchange heat with two heat reservoirs.

Figure 2.31—Thermal machine scheme

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Thermal machines can be classified as follows:

1- Heat engine: System that performs the conversion of heat or thermal energy to mechanical energy which can then be used to do mechanical work. That work, |W|, is obtained by the adsorption of heat (|QH|>|W|) from a hot reservoir at temperature, TH and giving part of this heat, |QC|, to a cold reservoir at temperature TC.

2- Heat pump: Thermal system that supplies heat, |QH|, adsorbing work, (|W| < |QH|) and heat, |QC|, from a cold reservoir at temperature, TC.


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3- Refrigerating machine: Thermal machine that extracts energy as heat, |QC| from a cold reservoir at temperature TC, adsorbing work, |W|, and giving heat,(< |QH|), to a hot reservoir at temperature, TH.

In case of cold production at a given set of temperature conditions, Carnot’s cycle is the most appropriate theoretical comparison process. The only difference between the operation of a heat engine and a heat pump cycle is that, in cold production, the working fluid completes that cycle in opposite direction that in the first one does.

1 CCarnot

H

T

T ,

CCarnotReverse

H C

T

T T

Equation 2.1—Theoretical Carnot limit (for Work and Cold production)

In a general way, the thermodynamic cycles for heat pump and refrigerators systems can be classified as shown in Table 2.9.

Cycle Type

Theoretical Carnot Real Vapour compression Real Adsorption

Table 2.9—Cooling thermodynamic cycles

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2.2.1.a.1.2 Vapour compression cycle

As shown before, all heat pumps currently in operation are either based on a vapour compression, or on an absorption cycle. The great majority of heat pumps, normally driven by an electrical compressor, work on the principle of the vapour compression cycle, which also is the reverse Rankine thermodynamic cycle. In a practical approach the differences between ideal and real cycles show the imperfections of the real (irreversible) refrigeration machine, associated to certain load and heat losses.

Figure 2.32—Operating and P-h qualitative diagrams for simple (Ideal/Real) vapour compression cycle

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In a real set-up the main components in such a heat pump system are two heat exchangers, namely the evaporator and the condenser, the compressor and the expansion valve, as shown


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in Figure 2.1 and Figure 2.33. The components are connected to form a closed circuit through a volatile liquid, known as the working fluid, circulates.

Figure 2.33—Main component scheme of a heat pump

[Own]

In the evaporator (A) the temperature of the liquid working fluid is kept lower than the temperature of the heat source, causing heat to flow from the heat source to the liquid, and the working fluid evaporates. Vapour from the evaporator is compressed (B) to a higher pressure and temperature. The hot vapour then enters the condenser (C), where it condenses and gives off useful heat. Finally, the high-pressure working fluid is expanded to the evaporator pressure and temperature in the expansion valve (D). The working fluid is returned to its original state and once again enters the evaporator. The compressor is usually driven by an electric motor, sometimes by a combustion engine and in some cases (as this case study) by renewable-driving energy. The main interest of the heat pump devices relies on that the amount of energy consumed to make the compressor run (normally electrical power) is generally low compared with the amount of energy that the condenser supplies, and consequently that is received by the heated environment.

2.2.1.a.2 Heat pump performance (COP)

The steady-state performance of an electric compression heat pump or refrigerating machine at a given set of temperature conditions is referred to as the coefficient of performance (COP). It is defined as the ratio of heat delivered by the heat pump and the electrical energy supplied to the compressor:

. .

.

C CHEATING

H C

Q QUseful Heat SuppliedCOP

Work Consumed W Q Q

Equation 2.2—Coefficient of performance

On the other hand Equation 2.3 shows the upper COP limit for a reverse Carnot’s cycle, which is also the hypothetical upper limit of the system.

.2 1

.

2 1

( )

( ) ( ) ( )

C C CHEATING IDEAL REF IDEAL CarnotReverse

H C H CH C

Q T s s TCOP COP

T T s s T TQ Q

Equation 2.3—Ideal COP limit (reverse Carnot’s cycle)


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Finally, according to the Second Law of Thermodynamics, it must be met that COP in real processes will be always lower than the theoretical limit.

.HEATING HEATING IDEALCOP COP

Equation 2.4—Heat pump COP limit (according to Second Law of Thermodynamics)

In the previous expressions is clearly shown that the COP of a heat pump device (working as a heater) is not a constant parameter, but depends both on the outside (TC) and inside (TH) temperature (Temperature of cold and heat reservoirs). The COP of a heat pump is closely related to the temperature lift, that is, temperature difference between the temperature of the heat source and the output temperature of the heat pump. Typical COP rage for a heat pump driven by an electrical compressor is between 2.5 and 5, i.e. 1 kWh of electrical energy consumed by the heat pump produces between 2.5 and 5 kWh of heat, depending on working conditions.

2.2.1.a.2.1 Seasonal performance factor (SPF)

The operating performance of an electric heat pump over the season is called the seasonal performance factor (SPF). It is defined as the ratio of the heat delivered and the total energy supplied over the season. It takes into account the variable heating and/or cooling demands, the variable heat source and sink temperatures over the year, and includes the energy demand, for example, for defrosting. The SPF can be used for comparing heat pumps with conventional heating systems (e.g. boilers), with regards to primary energy saving and reduced CO2 emissions. For evaluating electric heat pumps the power generation mix and the efficiencies of the power stations must be considered.

2.2.1.a.3 Heat sources

The technical and economic performance of a heat pump is closely related to the characteristics of the heat source. Ambient and exhaust air, soil and ground water are practical heat sources for small heat pump systems, while sea/lake/river water, rock (geothermal) and waste water are used for large heat pump systems. The heat source utilized by the heat pump considered in this work is ambient air. Ambient air is free and widely available, and it is the most common heat source for heat pumps. Air-source heat pumps, however, achieve lower efficiency performance than water-source heat pumps.

2.2.1.a.4 Types of HP

Heat pumps can be categorised on the basis of the cold reservoir and hot reservoir that they use. According to the fluid used for the transfer of heat from the cold reservoir to the heat pump, and from the heat pump to the hot reservoir, there may be four types:

Air-Water: Where the heat pump draws heat from the cold reservoir, which consists of air (external), and transfers it to the hot reservoir, which consists of a water circuit (for heating)

Air-Air: Similar to previous case, the heat pump draws heat from the cold reservoir, which consists of air (external), and transfers it to the hot reservoir, which likewise consists of air (that of the heated environment)


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Water-Water: In this third case the heat reservoir changes and heat pump draws heat from the cold reservoir, which consists of water (from lakes, rivers or the water table) and gives it off to the hot reservoir, which consists of a water circuit (for the heating)

Water–Air: In this last case the heat pump draws heat from the cold reservoir, which consists of water (from lakes, rivers or the water table) and gives it off to the hot reservoir, which consists of air (that of the heated environment)

Air as a cold reservoir possesses the advantage of being available everywhere; in any case, as the temperature of the cold reservoir falls, so does the output supplied by the heat pump. Nevertheless, if external air is used, a defrosting system, which uses the heat of the hot reservoir, is needed at temperatures of around 0ºC. That entails additional energy consumption in order to defrost the absorption battery. Water as a cold reservoir ensures optimal performance of the heat pump without it suffering from the effects of external climatic conditions; however it entails additional cost, due to the system of water adduction. Finally, ground as a cold reservoir possesses the advantage of undergoing less temperature changes than the air.

2.2.1.a.5 Working fluids

The working fluids must fulfil certain thermodynamic, physic, chemical and safety properties which allow them being used in different applications. In the case of heat pump systems it is necessary to choose those ones whose operational conditions fit with the range of temperatures fixed by the external design conditions; proper thermodynamic properties (specific frigorific production and specific frigorific power) allow the fluid to condense and to evaporate in the expected limits, while appropriate physical and chemical properties (evaporation and condensation pressure, latent heat of vaporization or corrosion potential) let the device work without mechanical or environmental concerns. Finally, working fluids must fulfil safety standards which involve issues related to their toxicity or leak detection. Working fluids can be classified according to several criteria. Nevertheless, the America Society of Refrigerating Engineers in accordance with “ASRE Standard 34—designation of Refrigerants” divides them into six main categories:

Halogenated hydrocarbons

Saturated hydrocarbons

Azeotropic mixtures

Zeotropic mixtures or non-azeotropic mixtures

Inorganic compounds (without carbon atoms)

Unsaturated organic compounds According with the initial mentioned requirements and included in the fifth group of the previous list, water is considered as a good working fluid both for high and low temperature heat pumps due to its favourable thermodynamic properties (even at temperatures higher than 150 °C) and because it is neither flammable nor toxic. Water has mainly been used as a working fluid in open and semi-open mechanical vapour compression systems, but there are also a few closed-cycle compression heat pumps with water as working fluid. The major disadvantage with water as a working fluid is that the low volumetric heat capacity that requires large and expensive compressors, especially at low temperatures.

2.2.1.a.6 Heat pump applications

Heat pumps for heating and cooling systems can be either used in industrial facilities or in residential and commercial purposes.


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Related with their industrial application the most interesting application areas for this type of heat pumps are space heating of greenhouses and industrial buildings or drying process. They can also be used in industrial dehumidification and drying processes (like drying of pulp and paper, food processing, but also for commercial cloth or car wash drier). Other important industrial heat pump application is water heating and cooling, providing cool, warm and hot process water for washing, sanitation and cleaning purposes. Evaporation and distillation processes, in chemical and food industries and steam production (up to 150ºC) can be also achieved by using these industrial devices. As referred to heat pump systems for heating and cooling in residential and commercial buildings, they can be divided into four main categories depending on their operational function:

Heating-only heat pumps, providing space heating and/or water heating

Heating and cooling heat pumps, providing both space heating and cooling

Integrated heat pump systems, providing space heating, cooling, water heating and sometimes exhaust air heat recovery

Heat pump water heaters fully dedicated to water heating Air is the most common distribution medium in the mature heat pump markets of Japan and the United States. The air is either passed directly into a room by the space-conditioning unit, or distributed through a forced-air ducted system. The output temperature of an air distribution system is usually in the range of 30-50°C. In residential applications room heat pumps can be reversible air-to-air heat pumps. Water distribution systems (hydronic systems) are predominantly used in Europe, Canada and the north-eastern part of the United States. Conventional radiator systems require high distribution temperatures, typically 60-90°C.

2.2.1.b.- TES Devices

2.2.1.b.1 Requirements and characteristics

In a decentralized (or also called decoupled) system the power demand not always fits in the time with the provided energy, being necessary to have an storage/accumulation system that faces the demand at moments of little or null power contribution, and that allows to store energy when the consumption is low or null. Heat accumulator is a decisive element in the correct operation of the installation, either of DHW or of heating. The storage tanks/cells are thermally-isolated deposits that can incorporate a heat exchanger. The period of time for accumulation is variable (hours, days and even months), depending mainly on the type of application (DHW, HEATING, swimming pool heating, etc.) and on the expected load shift fraction/amount. The requirements that a storage cell must fulfil are the following ones:

Durability, when being a key element in the life utility of the installation

Thermal losses as small as possible (small external interchange surface and good isolation)

Good capacity of stratification

Able to support predicted working temperatures and pressures

Availability of storage means.

Security of the storage means (non-toxic, non-flammable and respectful with environment)

Compatibility of the storage means with the rest of materials of the system


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Storage means with high specific heat Due to its availability, security and high heat capacity, water is generally chosen as storage mean. For DHW systems it is possible to use potable water, and in case that an inertial storage tank and a heat exchanger are used, water of the circuit of heating can be used.

2.2.1.b.1.1 Temperature stratification

If the storage tank is correctly designed it must allow a stratification of the temperatures inside the cell to take place, that is to say, that the temperature of the water is vertically distributed based on its density. Thereby the yield of installation improves, since warmer water remains on the top of the storage cell and can be directly send to consumption, whereas the water that returns towards the heat exchanger is the one which lower temperature has, which improves the yield of the heat source.

2.2.1.b.1.2 Isolation

In a thermal installation the losses take place mainly in periods with no heat supply and through the storage tank. That’s why good heat insulation is a crucial matter. In order to maintain heat losses heat within acceptable limits the following requirements must be taken into account:

The surface/volume ratio in the storage tank should be so small as possible

The storage tank must be completely isolated, including the upper and lower surfaces, keeping the isolation layer closely attached to the outer surface of the storage cell

All pipe connections must be properly isolated

All pipes must be leaded inside the storage cell through the bottom or side

Avoid heat losses within the connection pipes due to circulation by natural convection

2.2.1.b.2 Storage tank classification

Any heat storage tank can be classified regarding to three basic criteria:

According to its layout/setup

According to the material used in its manufacture

According to the configuration of the heat exchange system

2.2.1.b.2.1 Classification according to its layout

2.2.1.b.2.2 Vertical storage tanks

Vertical position/layout is the most habitual in conventional thermal facilities. This position favours the water stratification. It means that the hotter water is accumulated in the upper part of the tank, and the colder one, in the bottom part. This allows providing water to service temperature, even without having all the water of the storage at that temperature, whereas the cooler water can be sent towards the heat source, improving its yield. In addition, the vertical stratification allows the contribution of support heaters (which can use conventional power systems) in the upper part of the storage tank, without generating any interference between that source and the yield of the main heat source. Related to the accumulation capacity, the standard volumes for this type of storage tank oscillates between 150 and 500 litres, for single family houses they can get to 1,000 litres when


39

installation also contributes energy to the heating system. For collective facilities (hotels, hospitals, etc.) the volumes oscillates between 1,000 and 7,000 litres.

2.2.1.b.2.3 Horizontal storage tanks

In this configuration, the main dimension of the tank is parallel to the ground and predominates over the vertical dimension. It is normally used in the following cases:

Large storage tanks of more than 4,000 litres, due to the best distribution of loads on the grounding surface

Locations whose height is limited In this position, unlike the vertical storage tanks, the water cannot stratify by temperature. Therefore, water at service temperature will not be obtained until all the stored water would have not been warmed up. This fact must be taken into account before selecting the storage tank, and while it is being connected with the auxiliary systems.

Figure 2.34—Vertical and horizontal configuration and water thermal stratification

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2.2.1.b.2.4 Classification according to its material

The storage cells are normally made of carbon steel or stainless steel.

2.2.1.b.2.5 Carbon steel

In case of being aimed to sanitary purposes a protective inner coating must be spread in order to avoid the corrosion. The most habitual treatments are the following ones:

Hot galvanization by immersion It is the most economic and extended system used for deposits with large storage volume. The process consists on submerging the storage tank in an electrolytic bath of zinc salts, while the ions zinc deposit over the surface of the storage cell, forming a layer with thickness previously defined. This type of tanks behaves fine for standard temperatures of accumulation, between 45 and 60 ºC. From 70 ºC or higher, the salts precipitation and the acid character of water accelerate the corrosion processes.

Vitrified It is also a treatment applied to the inner surface of the storage tank. Firstly a nickel layer is applied over the steel tank surface. After that and through an immersion process, a liquid


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enamel is applied over the storage cell. The enamel is constituted by ceramic substances and contains anodic magnesium particles which protect against corrosion (cathodic protection). The storage tank is finally placed in a furnace and being warmed up to 800ºC, that's what vitrifies or crystallizes the enamel. This type of tanks also shows good behaviour for standard temperatures (45 and 60 ºC.) and from 70 ºC or higher have the same problems of salts precipitation and corrosion. In addition this type of storage tanks must be protected against shocks or strikes during the transport and the installation process in order to avoid enamel flaking.

Epoxy resins These coatings show good behaviour and they fit pretty well to large storage volumes due to its more elastic properties than the vitrified solutions.

2.2.1.b.2.6 Stainless steel

Stainless steel AISL 316L offers good behaviour against corrosion caused by water and that’s why is most commonly used in the manufacture of storage tanks for DHW. Nevertheless, it is more expensive than the previous treatments. They can be easily found with capacities from the 50 to the 1000 litres.

2.2.1.b.2.7 Classification according to its heat exchanger system

Hot water storage tank without heat exchanger: The storage tank is exclusively aimed to hot water storage [Figure 2.35]. The hot water production takes place outside the storage cell, in a previous upstream series connected tank, or by means of an external heat exchanger. Also they are called inertial accumulators.

Figure 2.35—Hot water storage tank without heat exchanger

(http://www.lapesa.es/)

Hot water storage tank with heat exchanger Also known as interaccumulator tanks, where the storage tank is aimed both to produce and to store hot water. The hot water production takes place inside the storage tank, through a heat exchanger which is built-in. According to the type of exchanger, they can be classified as follows:

http://www.lapesa.es/


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o Double-walled exchanger The storage tank is formed by two independent circuits [Figure 2.36]. The heat carrier fluid comes from the heat source circulating along the space that separates both walls. Hot water is accumulated inside the storage tank. The heat exchanger is formed by the own wall of the storage cell, which remains in contact with the heat carrier fluid. This type of storage tank is used mainly for small production systems of DHW.

Figure 2.36—Double-walled exchanger

(13)

o Single coil exchanger

In this type of storage tank, the heat exchanger by a spiral curved tube or coil [Figure 2.37]. The heat carrier fluid donates heat to water in the secondary circuit without being direct bonding between both.

Figure 2.37—Single coil exchanger

(13)

o Double coil exchanger

The difference between this and the previous one is the existence of a second coil within the same exchanger [Figure 2.38], which allows the use of two different heat sources (conventional or not). The main heat source is connected to the lower coil, which has a greater exchange surface, whereas the secondary power source is connected to the upper coil, with


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less exchange surface. The integration of both power sources in the same storage tank simplifies the installation and the assembly processes, but we should be aware in order to avoid the interferences (yield penalty) between both sources during the operation mode.

Figure 2.38—Double coil exchanger

(13)

o Double service storage tank “tank in tank”. Also know as combined accumulator

It has two independent volumes; in the first one is DHW stored, whereas the second one stores hot water for heating [Figure 2.39]. The bigger deposit contains the water for the heating, while the smaller one, water to provide the DHW circuit. EI “tank in tank” system and the DHW coil allow separating water for human consumption against heating water.

Figure 2.39—“Tank in Tank” storage tank

(13)


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3. SIMULATION MODEL

3.1.1.- Software environment

3.1.1.a.- Building Energy Simulation software

In general, to characterize the energy requirements, lifecycles and comfort conditions of any kind of building, they would be normally considered as complex systems in which their components are walls, windows, heating and cooling, etc. and which are characterized by their thermal and optical properties, yields, etc. The settings are the environment (climate) in which the building is located and the surrounding obstacles thereto (other buildings, trees, etc). The relationships between the different parts as well as the environment are ruled by the laws of heat transfer (conduction, convection, radiation and mass transfer). Once that the current system is finally reduced to an abstract model, which allows easily the manipulation of its components and properties and obtaining reliable results, it can take full potential of the simulation because once this has been done, any part of the model can be replaced by another different one, getting a quick and reliable information about what could happen under different indoor and outdoor boundary conditions, without the need of building it and without waiting until the conditions we are looking for were finally met. Moreover, the increasing use and application of these models are every greater day due to two main reasons. In one side, the increasing appearance and development of software codes and applications for thermal response simulation in buildings. On the other side, the continuous development of the computers market, which time after time offers better and cheaper products. That is why any interested user can get all the necessary information and infrastructure to design energy efficient buildings for a reasonable cost. In order to simulate the energetic behaviour of a building, several types of methods can be used:

Static methods

Correlation methods

Dynamic methods The most interesting are dynamic methods, since although they introduce a greater degree of complexity, they finally also offer the best results. Nevertheless the dynamic methods also require from an exhaustive definition process for the building and, in addition and a step-by-step solving process (usually one hour time steps) for the equation system where the real problem becomes idealized.

3.1.1.b.- Dynamic methods

There are many models based on dynamic methods, among the most known there could be pointed S3PAS, SUNCODE, APACHE, PASSPORT+, TRNSYS, M2M, DOE, EnergyPlus, etc., all of them posses its own advantages and drawbacks, but all of them also have something in common and that is all of them use to have an unwieldy handling, not only with inputs data but also with output results. It doesn't mean that these tools had been incorrectly developed, it just shows such the great amount of information that they involve. The operational scheme is pretty similar for any software program based on this configuration. Generally, all these software simulation models are made up of three great blocks, through which is necessary operate in order to obtain the results for a complete simulation process:

The geometric, constructive and operational definition of the building and its systems


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The simulation block, where the equations system that represents the building is considered and solved. This part is closely linked to the previous definition

Finally the analysis block for results, where different levels of information can be obtained, depending on the objectives to fulfil within the study

The main problem of these kinds of tools is the lack of a rigorous validation of the software code. That’s why the results obtained by their use in simulation models must be tested and verified to demonstrate their reliability.

Software Software development Org. Test developer

BLAST-3.0 level 193 v.1 (BLAST)

CERL (Civil Engineering Research Laboratory)

USA

NREL (National Renewable

Energy Laboratory) USA

OOE2.1D 14 (DOE2)

LANL/LBL (Los Alamos National Laboratory/

Lawrence Berkeley Laboratory)USA NREL, USA

ESP-RV8 (ESP)

Strathclyde University UK

Monfort University UK

SERIRES/SUNCODE 5.7 (SRES/SUN)

NREUEcotope USA

NREL, USA

SERIRES 1.2 (SERIRES)

NREL, USA / BRE (Building Research Establishment) UK

BRE, UK

S3PAS Universidad de Sevilla

Spain Universidad de Sevilla

Spain

TASE Tampere University

Finland Tampere University

Finland TRNSYS 16 (TRNSYS)

University of Wisconsin USA

BRE, UK Vrije Universiteit, Belgium

Table 3.1—List of building simulation software validated by IEA

(14)

Perhaps, the most important effort in this direction has been the task done by the IEA (International Energy Agency), where several simulation models has been considered in order to develope a systematic comparison among them, defining several building types and obtaining a wide range of results of diverse nature (demand loads, energy consumption, temperatures, etc.). The final result has been the attainment of a test called "BESTEST" with which it is possible to validate the quality of certain model to calculate a wide variety of casuistries. In Table 3.1 a list of software programs that have passed the BESTEST is shown.

3.1.1.c.- TRNSYS

TRNSYS is a complete and extensible software environment for transient systems simulation, which evolve over time, moving from one state to another, allowing the simulation of buildings consisting of several areas. It has been developed at the Solar Energy Laboratory of the University of Wisconsin-Madison and has been enriched by the contributions of TRANSOLAR Energietechnik GMBH, Centre Scientifique et Technique du Bâtiment (CSTB) and Thermal Energy Systems Specialists (TESS). Nowadays TRNSYS is commonly used by engineers and researchers worldwide to justify projects referring to their energetic behaviours. For example, a domestic hot water system designing and simulating the building with all their equipment, including control strategies, habitability, renewable energy systems (wind, solar, photovoltaic, hydrogen-based systems, etc.).


45

3.1.1.c.1 Description of the tool

One of the main reasons why TRNSYS has become a program with such a widespread acceptance is its modular and open structure, i.e., the code of the modules is open, which allows the end user to modify and adapt components model to their needs, making the program a versatile and intuitive tool. The structuration of the software, using different libraries, also allows users to create their own modules, using a large variety of programming languages (C, C +, Pascal, FORTRAN, etc.). Also during the TRNSYS simulation can call other applications like Microsoft Excel, Mat lab, COMIS, etc The TRNSYS is used to simulate systems as diverse as:

Energy saving systems in buildings and air conditioning systems with advanced features (natural ventilation, double facade, etc)

Renewable Energy Systems

Cogeneration, fuel cells

Another that requires a dynamic simulation TRNSYS is a set of software programs, whose common nexus is TRNSYS Simulation Studio [Figure 3.1]. This tool is the graphical interface of TRNSYS, where the structure of the simulation is usually created. TRNSYS Simulation Studio consists on a desktop where the elements that take part in the simulation (also known as TYPES) are joined together to create a complex structure (model). Once this structure has been elaborated the TRNSYS Simulation Studio creates the *.DCK file (which is simply a text file where the *.Txt extension is replaced by a new one called *.DCK) that is readable for TRNExe.exe. Once TRNExe.exe is capable to understand the description file, the project becomes defined and it is possible to run the simulation process for the system, obtaining the results. In fact, to perform any TRNSYS simulation is not compulsory to use the TRNSYS Simulation Studio, as long as it can be replaced by any text editor. Nevertheless using the TRNSYS Simulation makes the writing of the *.DCK file much easier than, for example, using the TRNEdit, which is the specific TRNSYS text editor.

Figure 3.1—Display TRNSYS Simulation Studio

In addition to the basic integrator of the whole system (TRNSYS Simulation Studio) and to simulate the energy performance of a building it is necessary to support this basic framework with complementary software tools. The most common used examples are TRNBuild, WINDOW X.Y SOMBRERO X.Y. The first one (TRNBuild ) is a friendly and flexible tool used to


46

define concrete aspects of the building such as the thermal areas, enclosures, thermal loads, material properties, etc [Figure 3.2]. The second tool SOMBRERO 3.0: Simulates the effect of shadows on the studied building. Finally WINDOW X.0 allows simulating the optical properties of glazing envelope.

Figure 3.2—Display TRNBuild

3.1.2.- Simulation process

3.1.2.a.- Conceptual description of the model

The present model simulates the combined heat pump system with heat storage tank to provide a single family house, located in Berlin, with Domestic Hot Water (DHW) [Figure 3.3]. The system operates based on DSM strategy which enables system to shift the DHW load depending on the consumption period. The control strategy determines working periods of the system which are restricted to the off-peak periods of the DHW load profile and when the heat pump uses residual load from grid to store it as hot water. When the load profile reaches its peak period the controller activates the second phase of the operational mode which allows the buffer tank to discharge in order to cover DHW demand of the building.

Figure 3.3 —Schematic representation of simulated system

These two phase operational mode is considered as the base of the load shifting strategy.


47

The main interest of that feed-in strategy remains is the possibility of using charge periods as residual load management tools that could also being used to store hypothetical surplus of renewable (not controllable) electricity sources, allowing their storage to further use rather than become a management problem for the system stability. As it is illustrated in Figure 3.4 the model is basically divided in five main parts:

Figure 3.4—General view of developed model and main groups

A. Heat pump circuit, where electric power and ambient heat are harvested and get

together as stored energy

B. DHW supply circuit, which is connected to the storage tank and covering the DHW demand based on load profile.

C. Heat pump circuit control, which implement the off-peak storage charge strategy based on load profile and controlling the inner temperature of supply side storage tank

D. Supply circuit control, which implements a complementary control strategy in response to the previous one (heat pump control), supplying hot water within the given temperature range.

E. Complementary and calculation groups, which implements the control strategies, the plotting diagrams and some other necessary functions.


48

Further description of the most important groups can be found in the following sections. Additional and more detailed description of each component can be found in 3.1.2.c.-.

3.1.2.a.1 Heat pump circuit (A)

Both heat pump and hydraulic pump gets control signal from control circuit, which operates this part of the model in an independent way from supply side During cycle operation heat pump generates heat and sends off the generated heat to the storage tank in form of hot water. Weather conditions affecting heat pump performance are considered by connecting weather the weather data reader and heat pump Type

Figure 3.5— Heat pump circuit part of developed model

3.1.2.a.2 Heat pump circuit control (C)

The heat pump control circuit output signal merges two different control signals, which come from demand load profile and from storage tank, respectively.

Figure 3.6—Heat pump circuit control part of developed model

The signal from load profile is based on certain power level that sets on-off peak criteria in flexible way (according to user's will).


49

The temperature signal from storage tank is monitored to force the output supply flow temperature to remain between an upper and a lower temperature limit. That allows supplying hot water at required quality; the importance of a good temperature control strategy can be seen in 4.1.4.a.-. Both previous signals are merged by a multiplication (intersection) controller, which combines off-peak and temperature requirements creating a time operating window, when heat pump is allowed to operate. During this time window only heat pump is allowed to run. The complementary time window (on-peak heat pump running not allowed) is also considered. It is calculated and sent to supply circuit control.

3.1.2.a.3 Demand side supply hydraulic circuit (B)

This hydraulic circuit takes (cold) water from water supply network and introduces it in the storage tank by a variable-flow hydraulic pump controlled in a certain way (see next point 3.1.2.a.4). Water remains inside the storage tank where elevates its temperature within a set limits by controllers. When it is allowed by supply control strategy water is supplied to cover the load demand.

Figure 3.7—Supply circuit part of developed model

3.1.2.a.4 Demand side Supply circuit control (D)

Energy stored in tank is removed in a precise way through a controlled strategy that merges three different control signals.

Figure 3.8—Supply circuit control of the model


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The first signal is the on-Peak time window supplied by heat pump control, which only allows removing water from tank in high demand periods set by user (setting peak point). The second one is a temperature controller which only allows removing quality hot water at a specific range of temperature (not lower than a given value). The third and more precise signal come from a PID controller and controls the flow rate of the supply circuit, tracking the synthetic load profile and adjusting the energy flow following the load demand curve. The importance of a good flow control strategy can be seen in 4.1.2.-. These three independent signals are merged together (intersection of signals) through the PIC controller. The final signal goes from this controller to the variable flow hydraulic pump.

3.1.2.a.5 Complementary groups

These groups include some complementary tools aimed to introduce parameters and calculate variables and also to plot necessary results for a better comprehension of the model behaviours.

Figure 3.9—Power flow information plotting that model also supplies

For example energy and temperature plots showed as in Results Chapter (section 4) or, additionally, hydraulic and energy flow information and operation time periods [Figure 3.9, Figure 3.10 and Figure 3.11] are available both as figures and as numerical data (as digital file).

Figure 3.10—Time operation period plotting that model also supplies


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Figure 3.11—Hydraulic flow information plotting that model also supplies

3.1.2.b.- Complementary data

3.1.2.b.1 Weather data

Weather data used in current simulation correspond to "Test Reference Years" ("Testreferenzjahre von Deutschland für mittlere und extreme Witterungsverhältnisse (TRY)“) and have been obtained from the official website of the German Federal Weather Service (“Deutsche Wetterdienst (DWD)”). The complete data collection, which describes weather characteristics of the whole country, is divided in 15 different “TRY-Regions” [Figure 3.12].

Figure 3.12—TRY Weather-data Regions

(http://www.dwd.de/TRY)


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In current case, simulation weather data correspond to Region 4, where Berlin is placed. Additionally, year 2010 has been chosen as reference data year. In Figure 3.13 temperature data of the year can be checked.

Figure 3.13—Ambiental and water temperature of Berlin, according to TRY 2010.

(http://www.dwd.de/TRY)

3.1.2.b.2 Basic system configuration

Based on TRNSYS potential, the developed model allows a wide range of configurations to be analyzed regarding to heat pump and storage tank models (power, volume, with or without stratification, etc), demand load profiles (fed through generic data readers) and modulation of control strategy (such as output temperatures or peak demand limits determination). Nevertheless for the current analysis purpose just two different set-ups (according to commercial and available in market devices) have been chosen. Both configurations correspond to normal set-ups that could be easily found in a real german case. The values of heat pump power and storage volume have been set in order to fit a wide range of implementations, coupling lower power with higher storage capacity and vice versa. The current implemented set-ups are briefly described in Table 3.2.

Set-Up Name

Heat pump model

HP power (kW) (electric/heating)

HP COP

Storage tank model

Storage tank volume (m

3)

Set-up A Steibel

WWP300 0.6/1.7

3,57 (+15ºC)

Viessman Vitocell 300-V

(EVI) 0.5

Set-up B Panasonic Aquarea ADC3GE5

1.0/3.0 3

(+2ºC)

Viessman Vitocell 300-V

(EVI) 0.3

Table 3.2—Main characteristics of implemented set-ups in this simulation

In case of further interest technical specifications of devices remains available in Chapter 7.

3.1.2.b.3 Energy demand

The energy demand of DHW used in this simulation has been obtained from a detailed report edited in year by VDI in year 2007 (15) and whose content is going to be briefly shown in following lines.


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The study develops a in depth statistical analysis of some real and representative cases of single family houses placed in different parts of Germany and focus its efforts on the procurement of synthetic 24-hours diagrams which may be used as reliable description in further study cases for yearly characterization of electrical energy, heating energy and DHW energy (mainly associated to the implementation of CHP technologies). As result of the common evaluation of all building groups, balanced average values of the demand of the three considered energy forms are given, according to their allocation to 10 typical-day categories that includes weather and seasonal information and discriminates between working day and holiday day. These data correspond - in accordance with the examined buildings - to a single family house with 169 m² heated floor space and 3 occupants that will be the case study for current simulation process. The most important feature of these data is that they are finally given as synthetic load demand profiles along a period of 24 hours time and in time steps of one minute. Such a high level of detail in time distribution of DHW load profile allows introducing variability factors in the simulation model which, in fact, improve the final quality of the results (Nevertheless it has to be noted that for case study original data have been transformed into hourly steps data).

3.1.2.b.3.1 Detailed description of demand

The result of the study divides the days of the year into 10 typical-day categories, according to the following nomenclature:

season Type of day Clouds in day

Ü transition

W work day

H = fine (no clouds)

S summer

S Sunday

B = cloudy

W Winter

X = any

Table 3.3—Typical-day categories

(15)

According to that a WWH day would be a winter working day with fine weather, while a SSX day would be a summer holiday day with fine or cloudy weather.

W-W-H W-W-B W-S-H W-S-B

Number of days for each typical-day category

32 37 6 10

Table 3.4—Distribution of winter days for each typical-day category

(15)

The VDI publication also considers that the typical german winter 85 days. German winter is also described according to day distribution of Table 2.3, where is clearly shown that winter cloudy days are more frequent than fine days. Finally, the average amount of energy which is demanded in a certain day of the year for the typical german single-family house is given in three different tables, according to the season and the demand level (classified as low, average and maximum). Among the whole number of results, the part of them which describe the winter energy maximum demand (less favourable cases) are shown in Table 3.5.


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W-W-H W-W-B W-S-H W-S-B

Electrical energy demand [kWh]

20,55 24.83 30.77 26.13

Heating energy demand [kWh]

125,24 114.19 147.97 116.31

DHW energy demand [kWh]

6,94 6.56 12.90 11.53

Table 3.5—Maximum values of energy demand in winter days for each typical-day category

(15)

The final step necessary to obtain DHW load profile is choose the average value of the day that will be analyzed and multiply it by the synthetic unitary load profile, that, in this case, is shown in Figure 3.14. For the current study two days have been chosen among the days of maximum demand, which, of course, are the less favourable and more demanding situation for the model:

The first study situation sets a WWB day (winter working day with cloudy weather) because is the most frequent situation during winter

The second study situation sets a WSH day ( winter holiday with fine weather) because in this case the highest demand during winter is reached

Figure 3.14—Daily unitary DHW load demand (WWB and WSH day)-according to VDI- (1 h time step)

(15)

3.1.2.c.- Description of the components used in simulation

3.1.2.c.1 Heat pump

"Type 941" of TRNSYS TESS library has been chosen in order to implement heat pump device. This component models a single‐stage air to water heat pump which conditions a water stream by absorbing energy from the air stream to operate only in heating mode (cooling mode has not been considered in this simulation).

0%

10%

20%

30%

40%

50%

60%

70%

80%

1:00 5:00 9:00 13:00 17:00 21:00

WWB

WSH


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The model is based on user-supplied catalogue data for the heating capacity (in kW) and power demand (in kW), based on the entering load and source temperatures. A special feature of Type 941 is that it can take either air relative humidity or absolute humidity ratio as an input and considers the effects of air humidity. In this case first option has been chosen. It is necessary to note that the model is able to interpolate data within the range of input values specified in the data files, but it is not able to extrapolate beyond the data range and will print a warning in TRNSYS if the conditions fall outside the data range. The model uses following equations to calculate the different outputs:

Amount of energy absorbed from the source fluid stream in heating

source load inputQ Q W

Equation 3.1—Energy absorbed from fluid stream

(16)

With following parameter description,

Qsource Energy absorbed from the source by the heat pump in heating mode

[kJ/h]

Qload Amount of useful energy delivered to the load [kJ/hr]

Winput Electric power input into the heat pump [kJ/h]

Outlet temperatures of the two liquid streams

, ,source

source out source in

source source

QT T

m c

, ,

loadload out load in

load load

QT T

m c

Equation 3.2—Source outlet temperature Equation 3.3—Load outlet temperature

(16)


Tsource,out Temperature of liquid exiting the source side of heat pump[ºC]

Tsource,in Temperature of liquid exiting the source side of heat pump [ºC]

msource Mass flow rate of liquid on the source side of the heat pump [kg/h]

csource Specific heat of the liquid on the source side of the heat pump [kJ/kg.K]

Tload,out Temperature of liquid exiting the load side of heat pump [ºC]

Tload,in Temperature of liquid exiting the load side of heat pump [ºC]

mload Mass flow rate of liquid on the load side of the heat pump [kg/h]

cload Specific heat of the liquid on the load side of the heat pump [kJ/kg.K]

3.1.2.c.2 Storage tank

The component models a stratified multi-node fluid storage tank (Type 4a). According to TRNSYS developers (16), this model provides very good accuracy and still keeping the parameter complexity and the computational effort reasonable. This is the most used tank model in the TRNSYS standard library. There are two outlets and two inlets. The two flow streams enter at fixed positions. The load flow from the water supply network enters at the bottom of the tank while the hot source flow from the heat pump enters at the top of it. Then the flow into the heat pump exits


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at the bottom and the flow that supplies the DHW load demand exits at the top, as shown in Figure 3.15.

Figure 3.15—Schematic representation of stratified water storage tank modelled by TRNSYS

(16)

The tank consists of N fully- mixed equal segments. The degree of stratification is determined by the value of N. No stratification effects are possible if N is equal to 1. The tank would then be modelled as a fully-mixed tank. In this simulation case N is chosen to be 3, to make use of the effects of stratification. Fluid streams flowing up and down from each segment are fully mixed before they enter next segment. It is assumed in the model that at the end of each time interval, any temperature inversions that exist are eliminated by total mixing of the appropriate adjacent segments (16). Following equations calculate the different energy rates of the water storage tank:

Rate of heat losses from the tank to the environment

1

( )N

tanklosses i i amb

i

Q U A T T

Equation 3.4—Heat losses from tank to environment

(16)


Qtank losses Rate of energy loss from the tank to the surroundings [kJ/h]

U Tank loss coefficient [kJ/h.m2.K]

Ai Surface area of the ith tank segment [m2]

Ti Temperature of the ith tank segment [°C]

Tamb Temperature of the environment surrounding the tank [°C]

Rate of energy removed from the tank to supply the load

1( )load L f LQ m c T T

Equation 3.5—Energy removed from tank to load supply

(16)



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Qload Rate at which sensible energy is removed from the tank to supply the load

[kJ/hr]

mL Fluid mass flow rate to the load [kg/h]

cf Specific heat of the tank fluid [kJ/kg.K]

T1 Temperature of the fluid exiting from the tank to supply the load [°C]

TL Temperature of the fluid entering the tank from the load [°C]

Rate of energy transfer from the hot fluid stream coming from the heat pump to the tank

( )source H f H NQ m c T T

Equation 3.6—Energy supplied to tank from source

(16)


Qsource Rate of energy input to the tank from the hot fluid stream [kJ/h]

mH Fluid mass flow rate from the heat source [kg/h]

cf Specific heat of the tank fluid [kJ/kg.K]

TH Temperature of the fluid entering the tank from the heat source [°C]

TN Temperature of the fluid exiting the tank to the heat source [°C]

Finally the main characteristics of the tank (which can also checked in detail in Chapter 7 -Appendix-) are given in Table 2.

Characteristics Value Comments Source (Heat pump flow) specific heat

(kJ/kg.K) 4.190 Cp for water

Load (storage loop flow) specific heat (kJ/kg.K)

4.190 Cp for water

Tank loss coefficient (kJ/h.m2.K)

2.5

Tank volume (m3)

0.3 and 0.5

Table 3.6—Main characteristics of storage tanks

3.1.2.c.3 Hydraulic pumps

The model includes two different hydraulic pumps: one conventional single speed pump (Type 3d) and one variable speed pump (Type110)

Type 3d It is a pump model that computes a mass flow rate using a variable control function, which must be 1 or 0, and a fixed (user-specified) maximum flow capacity. This component sets the flow rate for the rest of the components in the flow loop.


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Type110 This type models a variable speed pump that is able to maintain any outlet mass flow rate between zero and a rated value. The mass flow rate of the pump varies linearly with control signal setting. Type110 sets the downstream flow rate based on its rated flow rate parameter and the current value of its control signal input.

.o maxm m

Equation 3.7—Mass flow rate for pump

(16)


M0. Outlet mass flow rate [kg/h]

mmax. User-specified maximum flow capacity (when =1) [kg/hr]

Control function [kJ/kg.K] In the current model Type 3d feeds hydraulic heat pump circuit according to the signal given by heat pump control system type ON-OFF at different (but fixed) rates (depending on the Heat Pump case specifications) while Type 110 modulates its mass flow rate between 0 and 300 kg/h in the supply circuit according to the control signal given by a PID controller and adapting its response to energy load supply.

3.1.2.c.4 Data readers

3.1.2.c.4.1 Weather Data Reader

The component used in this simulation to provide the weather data is data reader Type 15-7, a specially configurated Type that read data according to German Test Reference Year (TRY 2010) format (see 3.1.2.b.1).

Type 15-7 This type serves to the purpose of reading data at regular time intervals from an external weather data file, interpolating the data at time steps of less than one hour, and making it available to other TRNSYS components. The model also calculates several useful terms including the mains water temperature, the effective sky temperature, and the heating and cooling season forcing functions.

3.1.2.c.4.2 Generic Data Reader

The model requires a tool which reads the exported DHW load demand profile, originally given as excel file (15) but here exported into a .dat file. The Data Reader chosen for that purpose has been a Type 9, which reads generic data files.

Type 9 It is a component that serves the purpose of reading data at regular time intervals from a logical unit number, converting it to a desired system of units, and making it available to other TRNSYS Units as time varying forcing functions.


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In this case Type 9 has been used in "free" mode to read the DHW load profile, what allows reading exported data from with not exactly identical format.

3.1.2.c.5 Controllers

3.1.2.c.5.1 Differential Controllers

To implement a correct control strategy is necessary to control some specific key parameters of the system. This so critical task wouldn't be correctly done without the contribution of both Differential Controllers (Type 2b and Type2-AquastatH) in Hydraulic heat pump circuit and a PID Controller (Type 23) in the DHW supply circuit.

Type 2b and Type2-AquastatH These two tools are both on/off differential controllers that generate a control

function (0) that can have values of 0 or 1. This value is chosen as a function of the difference between upper and lower variable limits (TH and

TL), compared with two dead band temperature differences (TH and

TL). The new values of control function (0) is dependent on whether i = 0 or 1. These controllers are normally used with the connected to the output control signal. However, control signals from different components may be used as the input control signal for this component providing a hysteresis effect. The

controller is normally used with the input control signal (0) connected

to the output control signal (i) giving a hysteresis effect. It is important to note that the most common variable control in TRNSYS simulations are temperatures ( Type2through-AquastatH) but it is not restricted to them and, if necessary, some other different variables can be controlled (through Type2b). Finally it has to be said that, for safety considerations, these controllers include a high limit cut-out and that, regardless of the dead band conditions, the control function will be set to zero if the high limit condition is exceeded.

Figure 3.16—Type2 (generic) Controller Function

(16)

Type 23 (PID Controller) PID controller calculates the control signal (u) required to maintain a controlled variable (y) at the set point (ySet). Its control signal is proportional to the tracking error, as well as to the integral and the derivative of that tracking error. It is based on state-of-the-art discrete algorithms for PID controllers and implements anti windup for the integrator. In this case PID controller


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operates in an iterative mode (mode 1) which implements an iterative control. A key point for this type is the right choice and tuning of its parameters. Optimal parameters depend on the algorithm used in the PID, for which different implementations are available.

In the developed model Type 2 allowed to implement the on-Off Peak strategy in the heat pump circuit, trough controlling synthetic DHW load profile. Also in both circuits of the model two Type 2 Aquastat controllers (in heating mode) allowed to supply quality DHW, setting a supply band with temperatures between 45ºC and 60º. Finally to fit energy supply to load demand requirements a PID controller (Type 23) tracked the DHW load demand profile and controlled the flow rate of hydraulic pump of supply circuit.

3.1.2.c.6 Complementary component

As complementary tools some edition, integration and plotting Types have been used to model the system. They are described in brief as follows:

Equation editor This type is an extremely flexible tool that allows creating and manipulating intermediate variables and using them within new expression and equations in order to obtain new necessary results for the working simulation.

Quantity Integrator (Type 24) This component model is analogous to a piece of equipment in a physical system that integrates a quantity over a period of time; for example, a kWh meter that continuously totals the amount of electrical energy consumed. Whenever a quantity in a system simulation requires integration over the period of simulation, the use of this component will perform the required function. Type 24 can integrate up to 250 variables and there is no specific limit on the number of Type 24 units that can be used in a simulation.

Online-file Plotter With File (Type 65c) The Online Plotter displays selected system variables at specified intervals of time while the simulation is progressing. This component is provides valuable variable information and allows users to immediately see if the system is not performing as desired. The selected variables will be displayed in a separate plot window on the screen. In addition to plotter outputs the tool also offers the results of calculations in a .dat file, which can be easily analyzed or edited.

3.1.2.d.- Base cases description

According to previous sections, two main heat pump heating system configurations (set-up A and Set-up B) have been proposed [Table 3.2] in order to be analyzed under three different weather conditions (cases) along a TRY year (2010 [3.1.2.b.1]) and under two different VDI energy load requirements ( WWB day and a WSH day) [ 3.1.2.b.3.1]. In addition to the two basic set-up system configurations, and thanks to the model flexibility, two more complementary set-ups have been implemented to modify certain features of the heat pump system and show or analyze special considerations (case of death band modification and PID control removal).


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It is necessary to remind that first configuration (set-up A) merges lower heating capacity with higher storage capacity, whereas the second one (set-up B) combines higher heating capacity and lower storage availability.

3.1.2.d.1 General features

All the simulation processes have been developed under a common analysis and simulation framework [Table 3.7]; along identical length simulation periods (48 hours) with same time step (30 seconds) and always using air to water heat pump devices to reach similar supply temperature quality requirements to cover final demand.

Description Numerical

value Unit

Time step 30 [s]

Length of simulation (Period)

48 [h]

Water (stock values)

Density 998 [kg/m3]

Specific heat Capacity 4.19 [kJ/kgK]

Air

Density 1.204 [kg/m3]

Specific heat Capacity 1.012 [kJ/kgK]

Table 3.7—Basic common features of simulation processes

3.1.2.d.1.1 Time features

The initial run length modelization period (originally set on 24 hours long) was finally extended up to 48 hours to promote system stabilization and to avoid the initial transient situation. Time step of 30 seconds offers detailed enough simulation results without demanding large computation time.

3.1.2.d.1.2 Temperature features

The starting temperature boundary conditions in storage tank are initially set on a temperature of 50ºC for the retained water. The temperature range of DHW supply remains between 45ºC and 60 ºC. The upper limit (60ºC) is aimed to eliminate legionella risk. The lower limit (45ºC) covers usual general supply standards. The rest of boundary conditions depend on the associated weather data, according to each case.

3.1.2.d.2 Specific features

3.1.2.d.2.1 On-Peak criteria

Due to such a different profile description offered by VDI for both WWB day and WSH day it is not possible to establish a common on-peak criterion. For that reason two different criteria, based in similar limit principles, have been chosen. The Peak criteria try to discriminate larger power demand periods but compensating length periods both for valleys and peaks.


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Following these precepts, selected WWB day On-Peak criterion covers 60% of the daily energy demand [Figure 3.17], while WSH day On-Peak criterion covers 85% of it [Figure 3.18].

3.1.2.d.2.2 Death band definition

The important issue in the simulation of control strategy is to ensure that the controllers and control setting have been chosen which are controlling the operation trend in the expected way. In case of the temperature controller, responsible for heat pump, the effect of the temperature death band in controller setting must be considered, a too narrow upper death band will cause a continuous and undesirable start-stop operation mode whilst a too wide death band is also having unwanted impact on operation trend. In current model and in case of low demanding situations (WWB day) a death bands of 10ªc (from an upper limit of 60ªc) would be effective enough. Nevertheless, for high demand profile (WSH day) it is required to be reduced to 5ºC.

3.1.2.d.2.3 Case definition

In order to obtain a representative number of case to study but, at the same time, trying to avoid spread of the analysis number, just 6 cases (days) have been finally chosen. Three of them correspond to working days whereas the rest three days are weekend days (holidays). According to that:

Working days correspond to the first working day in alternative months along the colder season of the year.

Weekend days follow similar criteria but, alternatively, have been chosen during the second week of the same month.

List of case study days are showed in Table 3.8.

Working day case study Weekend day case study

Friday, October 1, 2010 Sunday, October 17, 2010

Wednesday, December 1, 2010 Sunday, December 19, 2010

Monday, February 1, 2010 Sunday, February 14, 2010

Table 3.8—Case study days

Figure 3.17—On-Peak criteria for WWB day Figure 3.18—On-Peak criteria for WSH day


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3.1.2.d.3 Determination of final number case studies

12 is the basic final number of simulations to develop, which correspond to the numbers of days to study (6) multiplied by the set-ups numbers (2). In addition to that and in order to modify death band temperature of set-up A in weekend days, 3 complementary simulations must be taken into account. Adding figures the final number of case simulation to run in current study is 15, as shows following summary table.

Set-up A*

(death band 05ºC) Set-up A*

(death band 10ºC) Set-up B**

Oct Nov Dec Oct Nov Dec Oct Nov Dec

Working day (WWB)

0 0 0 1 1 1 1 1 1

Weekend day (WSH)

1 1 1 1 1 1 1 1 1

Set-up A*: (HP: Steibel WWP300+Tank: Viessman Vitocell 300-V (500 l)) Set-up B**: (HP:Panasonic Aquarea ADC3GE5+Tank: Viessman Vitocell 300-V (300 l))

Table 3.9—Number of case simulation to be ran in current study


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4. SIMULATION OF THE COMBINED HEAT PUMP SYSTEM AND RESUTS

4.1.- GRAPHIC RESULTS

4.1.1.- Influence of the month of study (ambient temperature)

The first necessary result, in order to justify the definitive number of cases to study, is to show the real influence of the temperature in the model behaviour and its effects in the result of the simulation process. This section shows the influence of the month of study, and thus the influence of ambient temperature in system response; in case of “set-up A”, the analysis of a working day shows that there is a significant difference between energy generation rates and thermal behaviour of the system between October [Figure 4.1] and the coldest months of winter [Figure 4.2]. That difference could also be noted in temperature plots (see appendix). The higher ambient temperature in October increases the amount of absorbed heat by heat pump which increases the output heat and reduces the operation hours of the system respondigly.

Figure 4.1—Energy output of “set-up A” in October

Nevertheless, there are no remarkable differences between the behaviour in November and February [Figure 4.2].

Figure 4.2—Energy output of “set-up A” in December and February


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In case of “set-up B” the same working day analysis shows also some differences of behaviour in the energy flows between October [Figure 4.3] and the rest of months of winter [Figure 4.4]. Nevertheless these differences a less remarkable than before and only have a small influence in the amount of energy stored in tank. “The effect of small changes of ambient tempetature is only considerable at high rates of heat pump power.

.

Figure 4.3—Energy plot of “set-up B” in October (WWB)

Figure 4.4—Energy plots of “set-up B” in December and February (WWB)

As happened previously, in case of “set-up B” there are neither remarkable differences between the behaviour in November and February [Figure 4.4]. As IT IS showed in appendix, the behaviour of the system during a not-working day follows the same trends as working day shows.

4.1.1.a.- Final consideration

According to the previous results it is easy to accept that introducing both December and February diagrams in this chapter doesn't offer many different qualitative results, but hinders the development of the study. For this reason it is reasonable to reduce from six to four the number of cases to expose in present chapter. The four case studies to expose in current


66

chapter will correspond to working and not-working days of October and February. Nevertheless the whole numerical simulation results are shown in summary tables of section 4.2.-.

4.1.2.- Contribution of PID controller

The PID controller on supply side plays a fundamental role for the correct operation of the model. Figure 4.5 shows how the controlled output supply (dark blue) fits the demand profile (light blue) removing just the required amount of energy from the tank. That strategy, based on controlling the output flow of the hydraulic pump of supply circuit, optimizes the operational period of the heat pump (grey) feeding demand properly and reducing the power consumption of the system.

Figure 4.5—Power load demanded and supplied through PID controller (WWB-February-)

When PID controller is removed from supply side [Figure 4.6] the signal that controls the output flow in supply side is inappropriate and doesn't track the load demand, thus supplying an excess of not required power, which constantly empties the storage. In addition, heat pump is forced to operate longer periods, consuming and harvesting energy that is neither stored nor modulated.

Figure 4.6—Power load demanded and supplied - without PID controller- (WWB-February-)


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4.1.3.- Simulation for working day

4.1.3.a.- Set-up A

Regarding to energy flow, in both cases the same amount of energy is supplied, nevertheless the period where heat pump works and the use of the storage tank are different. In October [Figure 4.7] heat pump supplies more energy, which reduce the time use of the heat pump and balances the use of the tank.

Figure 4.7—Energy plot of “set-up A” in October (WWB)

While in February [Figure 4.8] heat pump starts with a long working period which requires more amount of storage that is afterwards used.

Figure 4.8—Energy plot of “set-up A” in February (WWB)


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Regarding to the temperatures of the system and directly related to the heat pump utilization there is a clear difference between October and February. In the first case [Figure 4.9] the higher stability of the system is clearly shown in diagrams, where the supply remove the energy needed without disturbing the heat pump circuit.

Figure 4.9—Temperature plot of “set-up A” in October (WWB)

In February [Figure 4.10], a less constant supply of heat required from the heat pump induces higher variability in the temperatures of the system.

Figure 4.10—Temperature plot of “set-up A” in February (WWB)

4.1.3.b.- Set-up B

Both energy profiles show similar behaviour, supplying in both cases the required amount of energy when necessary.


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Figure 4.11—Energy plot of “set-up B” in October (WWB)

Even showing different slope in heat pump charging curve (higher in October [Figure 4.11] than in February [Figure 4.12]) the use of the heat pump and the storage tank is pretty similar, which can be directly conditioned with the storage capacity

Figure 4.12—Energy plot of “set-up B” in February (WWB)

Regarding to temperatures, both configurations reach similar values. During October [Figure 4.] the temperature changes are a bit more abrupt than in February [Figure 4.14] but this behaviour has nothing to do with what showed the previous set-up (“A”).


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Figure 4.13—Temperature plot of “set-up B” in October (WWB)

Comparing both diagrams [Figure 4. and Figure 4.14] it is also clear noted the ambient different temperature between October and February (red line) forces longer time operation periods in colder month (February). Contrary the higher slope of temperature diagrams of October shows a higher supply of energy during this month and a faster response of the system.

Figure 4.14—Temeprature plot of “set-up B” in February (WWB)

4.1.3.c.- Comparison between Set-up A y and Set-up B

Some interesting aspects can be obtained from the comparison of the two previous situations. The first one is the confirmation that all the configurations fit the demand, even the lower power heat pump coupled with larger storage tank (which implies higher thermal losses) seems to fit better in warmer periods (October). For colder periods the more powerful heat pump system seems to behave better.


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4.1.4.- Simulation for weekend

Not-working days are characterized by a higher energy consumption that, in addition, is mainly concentrated on some periods of the day. This demanding situation affects the operation of the system as follows.

4.1.4.a.- Effect of considering different death bands of temperature in controller

A correct configuration of the implemented controllers in model is a key issue to get the best operation of the system. This situation is clearly exposed as follows. An almost identical configuration (set-up A), where just a different death band temperature in temperature controller of heat pump circuit is introduced, can offer totally different results. Figure 4.15 shows the system behaviour in a February day. The left figure (higher death band temperature) doesn't supply enough energy to demand, as right figure does (Figure 4.15 –left: db=10 ºC/Figure 4.15 –right: db=5ºC).

Figure 4.15—Energy outlet plots of “set-up A” in February with different death band temperatures (WSH)

The reason of this different operation can be seen in Figure 4.16. where is clearly showed how thermal losses, after a long not working period and in addition to high energy demands, can reduce the “quality” of the energy stored in tank below a certain “critical level” of storage, necessary for the correct operation of the model.

Figure 4.16—Temperature of “set-up A” in February with different death band temperatures (WSH)

According to that, it is necessary to consider both thermal losses and peak demand in order to obtain the best operational response of the system in higher demanding working periods. From now to the end of the study and in order not to difficult the analysis, this parameter will be always correctly set in all study configurations.


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4.1.4.b.- Set-up A

Regarding to energy flow, In both cases the same amount of demanded energy is supplied, even in February [Figure 4.18],when a lower thermal energy flow supply forces longer working periods in this device.

Figure 4.17—Energy outlet plot of “set-up A” in October (WSH)

It has to be noted the remarkable similarities that both diagrams show [Figure 4.17 and Figure 4.18] in a not-working day, contrasting to previous equivalent result diagrams for a working day.

Figure 4.18—Energy outlet plot of “set-up A” in February (WSH)


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Figure 4.19—Temperature plot of “set-up A” in October (WSH)

The large storage capacity of the tank allows the system to reach quickly a pretty similar stable behaviour along the winter [Figure 4.19 and Figure 4.20]. Only in colder days more time operation system is required (part of curves with positive slope).

Figure 4.20—Temperature plot of “set-up A” in February (WSH)

4.1.4.c.- Set-up B

Regarding to energy flow, in both cases [Figure 4.21 and Figure 4.22], and even being affected by temperature difference, the same amount of demanded energy is supplied. The problem is that this amount of energy supplied is lower than the amount expected by demand.


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Figure 4.21—Energy outlet plot of “set-up B” in October (WSH)

The main cause of the shortage of energy supplied could be a low storage capacity of the tank, which is not enough in the required situation even having the system a powerful heat pump device.

Figure 4.22—Energy outlet plot of “set-up B” in February (WSH)


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Figure 4.23—Temperature plot of “set-up B” in October (WSH)

The higher power demand affects the quality of the water supply and the storage capacity of the tank becomes totally drained [Figure 4.23 and Figure 4.24].

Figure 4.24—Temperature plot of “set-up B” in February (WSH)

This situation is longer in colder days [Figure 4.24], when the heat supply from heat pump reduces.

4.2.- NUMERICAL RESULTS

In the following section, qualitative results described in previous section [4.1.-] become comparable numerical values.


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4.2.1.- Simulation for working day

As illustrated in Table 4.1, both Set-ups (A and B) supply almost 100% of on-Peak energy demand of a normal working day, which means that both configurations are feasible to implement. There is just a slightly difference between ideal and obtained values due to tracking errors associated to PID controller. In relation with overall efficiency of the system (in lower part of the table and calculated as useful amount of energy -supplied or stored- divided by electricity consumption), set-up B seems to make better use of electricity than set-Up A. Related to season, warm month show better efficiency figures than colder months.

Working day Set-up A Set-up B

Oct Dec Feb Oct Dec Feb

TOTAL Daily Energy Demand (kWh)

13,12 13,12 13,12 13,12 13,12 13,12

On-Peak Daily Energy Demand (kWh)

7,95 7,95 7,95 7,95 7,95 7,95

(A) Tank Energy Supply -On-Peak Period- (kWh)

7,77 7,80 7,81 7,76 7,79 7,80

% On-Peak Energy Demand Supplied 97,8

% 98,2

% 98,4

% 97,7

% 98,0

% 98,2

%

(B) Electricity Consumption HP

(kWh) 5,85 5,06 5,52 4,06 6,09 6,31

Energy Supply by HP (kWh)

16,09 9,87 9,97 12,06 12,34 12,37

(C) Stored Energy in Tank (kWh)

4,94 -1,73 -1,65 1,86 1,99 2,01

Thermal Losses in Tank (kWh)

3,30 3,75 3,75 2,44 2,41 2,40

Overall efficiency of the System (A+C)/B

2,17 1,20 1,12 2,37 1,61 1,55

Table 4.1—Working day

The energy balances after running simulation processes [Table 4.2] also show a reasonable level of accuracy in both simulation cases, which make results acceptable.

Working day Energy overview

Set-up A

Set-up B


Energy Inputs (kWh)

16,09 9,87 9,97 12,06 12,34 12,37

Energy Outputs (kWh)

11,07 11,55 11,56 10,20 10,20 10,21

Energy Stored (kWh)

4,94 -1,73 -1,65 1,86 1,99 2,01

Period Energy Balance (kWh)

0,08 0,05 0,06 0,00 0,15 0,16

Table 4.2—Working day energy balance overview


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4.2.2.- Simulation for weekend

In contrast to the previous situation, the supply of DHW during weekend becomes impossible neither for Set-up A nor Set-up B initial configurations [Table 4.3].

Weekend day (death band 10ºC)

Set-up A

Set-up B


TOTAL Daily Energy Demand (kWh) 25,80 25,80 25,80 25,80 25,80 25,80

On-Peak Daily Energy Demand (kWh)

22,54 22,54 22,54 22,54 22,54 22,54

(B) Tank Energy Supply -On-Peak Period- (kWh)

20,46 16,51 15,45 16,44 17,72 18,49

% On-Peak Energy Demand Supplied 90,8

% 73,2%

68,5%

72,9%

78,6% 82,0

%

(B) Electricity Consumption HP (kWh)

16,17 22,29 21,40 6,77 10,29 10,29


28,60 24,86 23,98 21,82 23,31 24,23


4,87 5,21 5,62 2,58 2,72 2,78


3,14 3,00 2,77 2,79 2,74 2,70


1,57 0,97 0,98 2,81 1,99 2,07

Table 4.3—Weekend day (death band 10ºC)

In case of set-up A the problem relies on an inappropriate death band choice in temperature controller of heat pump control circuit. Once the correct death band value is set the problem vanishes and the configuration is able to supply expected demand [Table 4.4].


Set-up A

Oct Dec Feb

TOTAL Daily Energy Demand (kWh)

25,80 25,80 25,80 On-Peak Daily Energy Demand

(kWh) 22,54 22,54 22,54

(C) Tank Energy Supply -On-Peak Period- (kWh)

22,01 22,10 22,15

% On-Peak Energy Demand Supplied

97,7% 98,0% 98,3%

(B) Electricity Consumption HP (kWh)

10,19 14,72 17,00


30,95 31,30 31,49


4,80 5,13 5,32


3,98 3,88 3,82


2,63 1,85 1,62

Table 4.4— Weekend day (death band 05ºC)


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In case of Set-up B the problem is totally different and relies not on death band, but on an inappropriate storage configuration (not enough storage capacity). The energy demand is , by far, higher than the tank capacity therefore and even using a more powerful heat pump device, system response is always inappropriate and insufficient [Table 4.3]. In relation with overall efficiency of the system, set-up B seems work better than set-Up A (both cases) but, in case of a death band of 05ºC, figures are pretty similar. Related to season, as showed for working days, warm month show better efficiency figures than colder months.

Weekend day Energy overview

Set-up A (db 05ºC)

Set-up A (db 10ºC)

Set-up B (db 10ºC)

Oct Dec Feb Oct Dec Feb Oct Dec Feb

Energy Inputs (kWh)

30,95 31,30 31,49 28,60 24,86 23,98 21,82 23,31 24,23

Energy Outputs (kWh)

26,00 25,98 25,97 23,60 19,51 18,22 19,23 20,46 21,20

Energy Stored (kWh)

4,80 5,13 5,32 4,87 5,21 5,62 2,58 2,72 2,78

Period Energy Balance (kWh)

0,15 0,20 0,20 0,13 0,15 0,15 0,00 0,13 0,25

Table 4.5—Weekend day Energy balance overview

Once again and as check procedure, the energy balances after running simulation processes [Table 4.5] show a reasonable level of accuracy in both simulation cases.

4.2.3.- Final summary

As final summary both time operational availability [Table 4.6] and supply quality [Table 4.7] of every case study are overall exposed.

% On-Peak Period Time Coverage Set-up A Set-up B


Working day 100,0% 100,0% 100,0% 100,0% 100,0% 100,0%


97,4% 90,4% 88,3% 90,4% 92,5% 93,8%


100,0% 100,0% 100,0% - - -

Table 4.6—% On-Peak Period Time Coverage

Time operational availability [Table 4.6] shows the reliability of each configuration (or capacity of response) to supply demand before storage tank runs out of energy. Under previous considerations Set-up A (correctly tuned) is characterized for a more reliable response.


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% On-Peak Energy Demand Supplied

Set-up A

Set-up B


Working day 97,8% 98,2% 98,4% 97,7% 98,0% 98,2%


90,8% 73,2% 68,5% 72,9% 78,6% 82,0%


97,7% 98,0% 98,3% - - -

Table 4.7—% On-Peak Energy Demand Supply

Supply quality of case studies [Table 4.7] of case studies depends on both system configuration and controller response. Once system is correctly configurated (according to time operation requirements previously mentioned) the final response tuning will depend just on the controller refining process.

Set-up A*

(death band 05ºC) Set-up A*

(death band 10ºC) Set-up B**

Oct Nov Dec Oct Nov Dec Oct Nov Dec

Working day (WWB)

0 0 0 2,17 1,20 1,12 2,37 1,61 1,55

Weekend day (WSH)

2,63 1,85 1,62 1,57 0,97 0,98 2,81 1,99 2,07

Table 4.8—Overall efficiency of the system Finally Table 4.8 notes that the more energy efficient behaviour of the systems doesn’t implies the better supply conditions to cover demand requirements.


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5. CONCLUSIONS

5.1.- CONCLUSIONS

This project was aimed to develop a numerical software model which could simulate the real behaviour of a decoupled DHW system for single-family house, formed by a heat pump system combined with thermal storage tank, controlled under different demand side management operation modes based on load shifting strategies to cover on-peak supply periods. As result of the developed work it has been clearly confirmed the remarkable and previously expected influence of the ambient temperature in the overall operation of the system. This issue is directly related to heat pump model and operation conditions. Anyway the support of buffer tank mitigates the final influence in output response of the system without affecting supply conditions and limiting its final influence to heat pump operation period, which, obviously, increases. Another exposed key issue for the successful operation of this load shifting system is the importance of implementing and tuning proper control strategies for both heat pump side and supply side controllers.

In case of the temperature controller responsible for heat pump operation a too narrow upper death band will cause a continuous and undesirable start-stop operation mode. Contrary, a too wide death band control temperature in conjunction to high demand conditions will be a risky configuration that, may be, won't cover demand expectations.

Regarding to demand side, the installation of a flexible control device responsible for regulating the energy flow supply (such PID controller in developed case studies) will avoid heat pump nonsense operation modes and thus energy waste, while ensuring comfortable expected supply.

It has been also clearly showed the limiting condition introduced in the system by the choice of the storage tank volume that, in fact, it is already implemented by control strategies originary conception. This limitation has nothing to do with heat pump heating power. According to that, low storage capacities will not be appropriate to high demand expectations, unless that it would be taken as a first step to analyze its ability to influence on demand side behaviour. Another important consideration is to note that the more energy efficient behaviour of the systems doesn’t implies the better supply conditions to cover demand requirements. Finally it has to be remarked that the model works exactly as initially expected in the approach developing steps of this work. The analyzed case studies develop real usual domestic demand conditions and get appropriate response from commercial configurations allowing evaluating them in terms of time operational availability (or capacity of response) and supplying quality. .

5.2.- FURTHER DEVELOPMENTS

This work sets a practical start point and creates an initial software tool that should be helpful in further study and development of TES systems applications within the conceptual framework of demand side management strategies and renewable power grid integration.


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According to the flexibility showed by the model and the powerful software possibilities provided by TRNSYs platform, it would be reasonable to recommend extending current single-family house analysis to larger scale case studies such as, for example, apartment blocks or even district heating utilities. It would be also appropriate to use further improved versions of the tool to analyze possible implementation of Demand Side Strategies not only I residential sector, but also in Industrial processes, whose heat demand requirements differs from residential loads. It would be also reasonable to suggest continuing developing more sophisticated control strategies both in heat pump and demand side. Heat pump control strategies will optimize either heat pump operation modes or residual load storage capacity, whereas demand side control will improve the quality of energy supply. Finally it would be necessary to concentrate part of the development efforts on getting a deeper understanding of those mechanisms which could be the better ones in the integration of residual load process.


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6. BIBLIOGRAPHY

6.1.- BASIC BIBLIOGRAPHY

1. (BMWi), Bundesministerium für Wirtschaft und Technologie. Energie in Deutschland -Trends und Hintergründe zur Energieversorgung-. Berlin : Bundesministerium für Wirtschaft und Technologie (BMWi), Februar 2013. 2. Dipl.-ing.(FH) Dieter Böhme, Dipl.-Volkswirt Joachim Nick-Leptin. Erneuerbare Energien in Zahlen-Nationale und internationale Entwicklung. Berlin : Bundesministerium für umwelt, Naturschutz und reaktorsicherheit (BMU), Juli 2013. 3. Bundesverband der Energie- und Wasserwirtschaft e.V. (BDEW). Energieverbrauch im Haushalt- Ausgabe 2010 (Energie-Info - BDEW-Datenkatalog). Berlin : (BDEW), 2010. 4. First Monitoring Report “Energy of the future”. Berlin : Federal Ministry of Economics and Technology (BMWi) & Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU), 2012. 5. Bracke, Dipl.-Ing. Michael Platt Prof. Dr. Rolf. Analyse des deutschen Wärmepumpenmarktes – Bestandsaufnahme und Trends. Bochum : GeothermieZentrum Bochum , Hochschule Bochum – Bochum University of Applied Sciences , 2010. 6. Gorris, Verena and Jacob, André. BWP-Branchenstudie 2013-Szenarien und politische Handlungsempfehlungen-. Berlin : Bundesverband Wärmepumpe e.V., 2013. 7. Frank Kreith and D Yogi Goswami. Energy Management and Conservation Handbook. s.l. : CRC Press, 2007. ISBN 13: 9781420044294. 8. Ter-Gazarian, Andre. Energy Storage for Power Systems. s.l. : Institution of Engineering & Technology Published, 2011. ISBN 978-1-84919-220-0. 9. Grünwald, Reinhard. Regenerative Energieträger zur Sicherung der Grundlast in der Stromversorgung. Endbericht zum Monitoring. s.l. : Büro für Technikfolgen-Abschätzung beim Deutschen Bundestag (TAB), 2012. 10. Schill, Wolf-Peter. Residual Load, Renewable Surplus Generation and Storage Requirements in Germany. 2013. 11. Andreas Hauer (ZAE Bayern). Thermal Energy Storage. Technology Brief. s.l. : IEA-ETSAP and IRENA, 2013. 12. Rey Martínez, F. Javier. Bombas de calor y energias renovables en edificios. Madrid : Thomson-Paraninfo, 2005. ISBN: 8497323955. 13. Martínez, Pedro Rufes. Energía Solar Térmica. Técnicas para su aprovechamiento. s.l. : Marcombo, 2010. ISBN-13: 978-8426715586. 14. Gómez Prada, Guillermo and Maellas Benito, Jesús. Estado Del Arte De La Modelización Energética De Edificios. 15. Dubielzig, Guido. Referenzlastprofile von Ein- und Mehrfamilienhäusern für den Einsatz von KWK-Anlagen. Fortschrittberichte VDI : Reihe 6, Energietechnik ; 560 . Düsseldorf : VDI-Verl., 2007. ISBN 978-3-18-356006-6. 16. Klein, S.A. TRNSYS 17-a TRaNsient SYstem Simulation program-Volume 4 - Mathematical Reference. Madison : The Solar Energy Laboratory, University of Wisconsin-Madison, 2012. 17. Ochsner, Karl. Wärmepumpen in der Heizungstechnik : Praxishandbuch für Installateure und Planer. Heidelberg : Müller , 2007. ISBN 978-3-7880-7806-5 . 18. Baumann, Michael. Wärmepumpen : Heizen mit Umweltenergie. Berlin : Solarpraxis , 2007. ISBN 978-3-934595-60-6 . 19. Oberzig, Klaus. Solare Wärme : vom Kollektor zur Hausanlage. Berlin : Solarpraxis-AG, 2008. ISBN: 978-3-410-17985-6.


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20. Klesse, Andreas. Modellierung und Bewertung unterschiedlichen Nutzerverhaltens in hochwärmegedämmten Einfamilienhäusern. s.l. : LIT Verlag Münster, 2012. ISBN : 978-3-643-11502-7. 21. Modeling of a residential house coupled with a dual source heat pump. Chargui, R. and Sammouda, H. 81 (2014) 384–399, Tunisia : Elsevier, February 2014, Vol. Energy Conversion and Management . 22. Domestic demand-side management (DSM): Role of heat pumps and thermal energy storage (TES) systems. Arteconi a, A, Hewitt b, N.J. and Polonara c, F. 51 (2013) 155e165, Ancona , Italy : Elsevier, 2012, Vol. Applied Thermal Engineering.


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6.2.- OTHER SOURCES

S.A. Klein, et al., TRNSYS Manual, University of Wisconsin, Madison, 2012

http://www.bbsr-energieeinsparung.de/ (Bundesinstitut für Bau-, Stadt- und Raumforschung (BBSR))

http://www.bdew.de/ (Bundesverband der Energie- und Wasserwirtschaft e.V.)

http://www.bmwi.de/ (Bundesministerium für Wirtschaft und Energie. (BMWI))

http://www.dwd.de/ (Deutscher Wetterdienst, Bundesministerium für Verkehr und digitale Infrastruktur)

https://www.entsoe.eu/news-events/former-associations/ucte/other-reports/ (European Network of Transmission System Operators for Electricity. (ENTSO-E))

http://www.energy.gov/ U.S. The Department of Energy (DOE)

http://www.erneuerbare-energien.de/ (Bundesministerium für Umwelt, Naturschutz, Bau und Reaktorsicherheit- Erneuerbare Energien)

http://www.iea.org/ (International Energy Agency)

http://www.waermepumpe.de/ (Bundesverband Wärmepumpe e.V. (BWP))

http://www.ag-energiebilanzen.de/

http://www.worldenergy.org/

http://www.stiebel-eltron.co.uk/

http://www.aircon.panasonic.eu/DE_de/

http://www.lapesa.es

http://www.saunier-duval.at/

http://www.bbsr-energieeinsparung.de/

http://www.bdew.de/

http://www.bmwi.de/

http://www.dwd.de/bvbw/appmanager/bvbw/dwdwwwDesktop?_nfpb=true&_pageLabel=_dwdwww_klima_umwelt_klimadaten_deutschland&activePage=&_nfls=false

https://www.entsoe.eu/news-events/former-associations/ucte/other-reports/

http://www.energy.gov/

http://www.erneuerbare-energien.de/

http://www.iea.org/

http://www.waermepumpe.de/

http://www.ag-energiebilanzen.de/

http://www.worldenergy.org/

http://www.stiebel-eltron.co.uk/

http://www.aircon.panasonic.eu/DE_de/

http://www.lapesa.es/

http://www.saunier-duval.at/


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7. APPENDIX

MAIN CHARACTERISTICS OF USED DEVICES

HEAT PUMP DEVICES


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STORAGE TANKS


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numerical simulation and analysis of a heat pump …

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