Parabolic Concentrated Solar Systems for Heating, Cooling, And

Parabolic Concentrated Solar Systems for Heating,

Cooling, and Power Generation

in Cold Climates and Remote Communities

Faezeh Mosallat

A Thesis submitted to the Faculty of Graduate Studies of

The University of Manitoba

in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

Department of Mechanical Engineering

University of Manitoba

Winnipeg, Manitoba, Canada

Abstract

To date, concentrated solar trough collectors have focused primarily on electricity generation in low latitudes using 400oC thermal oil temperatures. To adapt this technology to remote communities—that rely on diesel and heating oil and reach ambient temperatures of -40°C for extended periods—it is postulated that lowering the fluid temperature below

100oC is a preferred approach to reduce safety risks and operator qualification requirements. This approach mitigates higher heat losses in cold climates and still allows refrigeration and heating loads to be displaced by thermal energy; however, it significantly reduces thermal power generation efficiency. To regain electrical efficiency, the system is redesigned using concentrated photovoltaic cells secured to each receiver tube that can be cooled by glycol, an environmentally safer working fluid compared to thermal oil, operating at temperatures below 100oC. To investigate lower operating fluid temperatures and control issues related to cold climates, the methodology adopted is to design and build a 52-kW parabolic solar trough pilot plant in Winnipeg, as this location is chosen by some industries to perform cold weather testing. In addition, a transient model is developed to investigate how to integrate solar troughs in remote community applications. The model is validated using the pilot plant, predicting the fluid outlet temperature of the solar field with an average deviation of 1°C from measurements during thermal energy generation. A concentrated-photovoltaic-thermal configuration is then introduced to achieve attractive payback periods for remote communities in cold climates experiencing high energy costs and implementing renewable energy. Furthermore, to maximize revenues in these communities, a pump control strategy is implemented to reduce parasitic power by 80%; a

multi-objective optimization algorithm results demonstrate the need to adjust the solar field flow rate in cold climates during operations.

Acknowledgment

First and foremost, I would like to thank my advisor Dr. Eric Bibeau whose support, knowledge, joy, enthusiasm, and contributions of time, ideas and funding made my Ph.D. experience dynamic and encouraging. I am also thankful to my co-advisor Dr. Tarek

ElMekkawy for his helpful guidance and constructive advice. I wish to thank the members of my examining committee: Dr. Douglas W. Ruth and Dr. Kris Dick for generously offering their time, support, guidance, and good will throughout the preparation and review of this document.

The development of a parabolic concentrated solar trough research pilot plant at Red River

College (RRC) was supported by the Department of Emerging Energy Technologies at

Manitoba Hydro. I would like to express my sincere gratitude to Mr. Tom Molinski for his role in obtaining funding for RRC to build the solar pilot plant, and actively participate in setting its research focus to develop new approaches to generate renewable energy in remote communities. I also would like to acknowledge the project team of RRC for their collaboration to provide the required information at various stages of this research.

This research was funded by NSERC/Manitoba Hydro Industrial Research Chair to whom

I am deeply grateful. Additionally, I would like to thank the financial support of the

University of Manitoba Graduate Fellowship and Manitoba Graduate Student Fellowship.

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I would like to thank my parents for all their love and encouragement. For the heartwarming presence and support of my brother Farid in Winnipeg during the past 5 years. And most of all for my loving, encouraging and patient husband Foad who has been my rock in this long journey and his faithful support during all the stages of this Ph.D. is greatly appreciated. Last, but definitely not least, my gratitude goes to my friends who supported me during difficult moments. Thank you for your thoughts, well-wishes, emails, phone calls, visits and being there whenever I needed a friend.

Dedication

To my parents who offered unconditional love and support and my loving husband Foad who has always been there for me.

Table of Contents

Chapter 1 Introduction ...... 1

1.1 Earth’s energy outlook ...... 1

1.2 Energy use in Canada ...... 5

1.3 Canada’s solar potential ...... 6

1.4 Canada’s northern remote and aboriginal communities ...... 8

1.5 Applying concentrated solar technology in Canadian remote communities...... 12

1.6 Research objectives ...... 17

1.7 Methodology ...... 18

1.8 Contributions to the state of knowledge ...... 23

1.9 Thesis outline ...... 25

Chapter 2 Literature Review ...... 26

2.1 Concentrated solar trough plants in cold climates ...... 26

2.2 Transient numerical simulation ...... 29

2.3 Solar power generation ...... 35

2.3.1 Organic Rankine Cycle (ORC) ...... 35

2.3.2 Hybrid photovoltaic/thermal system ...... 37

Chapter 3 Parabolic Solar Trough Experimental Pilot Plant and Numerical Model ..... 42

3.1 Concentrated solar trough demonstration pilot plant ...... 43

3.1.1 Circulating pump ...... 48

3.1.2 Expansion tank ...... 49

3.1.3 Heat exchanger ...... 50

3.1.4 Weather station ...... 51

3.1.5 Pyrheliometer ...... 53

3.1.6 Instrumentation ...... 53

3.1.7 Sun-tracking sensors ...... 58

3.1.8 Controller hardware ...... 59

3.2 Solar trough pilot plant simulation model developed ...... 62

3.2.1 Convection heat transfer between the HTF and the absorber ...... 65

3.2.2 Convection heat transfer from the receiver to the glass envelope ...... 66

3.2.3 Convection heat transfer from the glass envelope to the atmosphere ...... 70

3.2.4 Radiation heat transfer between the receiver and glass envelope ...... 72

3.2.5 Radiation heat transfer between the glass envelope and sky ...... 73

3.2.6 Solar irradiation absorption in the glass envelope ...... 74

3.2.7 Solar irradiation absorption in the receiver ...... 76

3.3 Solar trough pilot plant transient numerical model implementation ...... 76

3.4 Solar trough pilot plant model validation ...... 79

Chapter 4 Thermal Storage, Heating, Cooling and Power Generation Models Using

Hardware-Based Simulation ...... 87

4.1 Latent heat thermal storage using phase change material ...... 87

4.2 Solar space heating model with thermal storage ...... 96

4.2.1 Results of the solar space heating model ...... 102

4.3 Solar absorption cooling/refrigeration with thermal storage ...... 105

4.3.1 Results of the absorption cooling model ...... 113

4.4 Solar power generation ...... 116

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4.4.1 ORC power generation model ...... 117

4.4.2 CPV power generation model ...... 118

4.4.3 Results of the ORC and CPV power generation models ...... 122

Chapter 5 Reliability and Controls for Parabolic Solar Trough System Applications in

Cold Climates ...... 126

5.1 Solar trough pilot plant technical issues ...... 127

5.1.1 Flow meter ...... 127

5.1.2 Stow-switch ...... 128

5.1.3 Pressure relief valve ...... 132

5.1.4 Summary ...... 133

5.2 Control schemes for the solar field circulating pump ...... 134

5.2.1 Parasitic power reduction ...... 134

5.2.2 Maximizing the energy revenue using an optimization algorithm ...... 141

Chapter 6 Concentrated Solar Heat and Power Preliminary Economic Assessment for

Remote Communities ...... 151

6.1 Economic assessment ...... 152

6.1.1 Annual displaced cost – Case 1 ...... 153

6.1.2 Simple payback period – Case 1 ...... 155

6.1.3 Annual displaced cost and simple payback period – Case 2...... 159

6.2 Concentrated solar thermal energy generation potential of Manitoba ...... 162

6.3 Concentrated PV power generation potential in Manitoba ...... 165

6.3.1 Simple payback period of the CPVT system in remote communities ...... 168

Chapter 7 Summary, Conclusions, and Recommendations for Future Work...... 171

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7.1 Summary of research work performed...... 171

7.2 Conclusions ...... 173

7.3 Recommendations ...... 176

References ...... 177

Appendix A ...... 191

Appendix B ...... 201

List of Tables

Table 1.1: World projected energy consumption till 2040 in trillion kWh adapted from

Reference [1] ...... 2

Table 1.2: Manitoba remote communities’ energy use adapted from Reference [18] ...... 10

Table 2.1: Operational parabolic trough plants in low-latitude geographical locations ... 27

Table 2.2: Summary of literature review presented in Sections 3.12.1 and 3.22.2 ...... 33

Table 2.3: Summary of literature review presented in Section 2.3.2 ...... 41

Table 3.1: Specification of the WeatherHawk weather station measurement sensors [70]

...... 52

Table 3.2: The specifications of the flow meter and differential pressure switch used in the solar pilot plant [73][74] ...... 57

Table 3.3: The CR1000 channels, the connected sensors, and the signal levels of each sensor [72] ...... 61

Table 3.4: Definition of the heat flux terms used in Figure 3.16 and Equations 3.4 and 3.5

...... 64

Table 3.5: Coefficients in Equations 3.11 to 3.13 for three annulus gases [77] ...... 68

Table 3.6: Coefficients in Equation 3.22 with respect to Reynolds number ...... 72

Table 3.7: Estimates of the optical efficiency terms in Equation 3.25 [77] ...... 75

Table 3.8: Simplification assumptions made in the transient model for the receiver simulation ...... 79

Table 3.9: Mean deviation between the modelled and measured outlet temperature of the solar field for high and low/no solar irradiance periods in September 2013 ...... 82

Table 3.10: Comparison of model running time and results for 30 and 70 nodes along the

50-m receiver tube ...... 85

Table 4.1: Typical storage densities of sensible and latent heat storage methods [83] .... 89

Table 4.2: Thermophysical properties of Erythritol [87] ...... 96

Table 4.3: Thermophysical properties of 50/50 glycol solution [88] ...... 97

Table 4.4: The greenhouse space heating demand provided by the solar pilot plant and the natural gas water heater on October 3rd and March 16th with and without thermal storage

...... 105

Table 4.5: Input values for the simulated absorption cooling system chosen to validate the model with results of Reference [94] ...... 110

Table 4.6: Comparison of the absorption cooling simulation results of the present work with Hosseini’s work ...... 110

Table 4.7: Constant parameter values in Equations 4.17 and 4.19 [98] ...... 121

Table 5.1: Optimization assumptions and input parameters for the multi-objective genetic algorithm optimization ...... 149

Table 5.2: Optimal HTF mass flow rate and the associated heat and power revenues for

January 1st, 2013 from 11 am to 2 pm. The meteorological data measured and recorded by the solar pilot plant weather station is used. The operation temperature of the solar plant is

100ºC. Thermal storage is disabled...... 149

Table 6.1: Assumptions for the annual displaced cost calculation in Case 1 ...... 154

Table 6.2: Assumptions for the simple payback period calculation in Case 1 ...... 156

Table 6.3: Residential natural gas rates in 2017 and projections to 2026 assuming a 45% increase. Transportation and distribution prices are assumed to stay constant and fix monthly charges are not applied...... 158

Table 6.4: Natural gas carbon price based on Alberta’s current carbon tax and the carbon tax calculated using the solar pilot plant model to reduce the payback period to 10 years.

Natural gas produces 0.056 tonne CO2/GJ...... 159

Table 6.5: Assumptions for the annual displaced cost and the simple payback period calculations in Case 2 ...... 161

Table 6.6: Total 2014 electricity generated and the CSP potential in Canadian provinces, considering land areas with slope of less than 4% [32]. Results are compared to total power generation in each province [114]...... 166

Table 6.7: Remote Regions assumed to calculate the simple payback period of the concentrated photovoltaic/thermal system in remote communities ...... 169

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List of Figures

(All the figures that are not referenced have been created by the author)

Figure 1.1: Renewable energy share in the EU28 member states adapted from

Reference [4]. The blue and orange bars show the renewable energy distribution in 2005 and 2013, respectively; 2020 renewable energy targets are shown by green circles...... 4

Figure 1.2: Levels of CO2 concentration in the atmosphere from 1960 to present, as reported by Mauna Loa Observatory from Reference [5]...... 4

Figure 1.3: The share of different energy resources in Canada’s electricity generation mix for years 2014 and 2040 projections adapted from Reference [11] ...... 6

Figure 1.4: Annual average (a) global horizontal irradiation, and (b) direct normal solar irradiation in North America from Reference [16] ...... 8

Figure 1.5: Canada’s off-grid communities from Reference [19] ...... 9

Figure 1.6: Surface air temperatures across Canada, observed in December to February from 1981 to 2010 from Reference [23] ...... 11

Figure 1.7: Annual global solar insolation resource comparison for Manitoba’s remote communities and Canadian major cities, for a south facing surface tilted at latitude adapted from Reference [25] ...... 12

Figure 1.8: Efficiency of solar collectors as a function of the difference between mean collector temperature, Tm, and ambient temperature, Ta, and mean hourly global solar radiation, Gt, from Reference [27] ...... 14

Figure 1.9: Schematic of different solar thermal technologies and their maximum operational temperatures (open source pictures) ...... 16

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Figure 1.10: Comparison of (a) a lower latitude urban environment, and (b) a remote diesel community at higher latitude, each having significant difference in solar irradiation shown in Figure 1.4, access to qualified operators, load profiles, ability to apply safety standards, and scale of technologies required (open source pictures) ...... 17

Figure 1.11: Schematic of research methodology to address the research objectives ...... 21

Figure 2.1: CPV cells attached to the focus line of parabolic mirrors from Reference [57]

...... 38

Figure 3.1: The satellite view of the solar field and the greenhouse locations, obtained from

Google Earth ...... 44

Figure 3.2: The satellite view of the solar trough field along with the dimensions, obtained from Google Earth ...... 45

Figure 3.3: Different elevations of the solar collectors and the control room to provide drain back effect, obtained from Google Earth ...... 46

Figure 3.4: Solar pilot plant construction site at Red River College. The pictures show the different stages of the project from August 2011 to July 2012...... 47

Figure 3.5: The piping sections insulated with 1 inch of Earthwool-1000° ...... 48

Figure 3.6: SIHI ZTND circulating pump located inside the control room at the solar field

...... 49

Figure 3.7: The solar system expansion tank with volume of 114 liters located inside the control room ...... 50

Figure 3.8: Air cooled heat exchanger located in the control room ...... 51

Figure 3.9: The Series 500 WeatherHawk weather station installed on the top of the control room ...... 52

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Figure 3.10: The EKO MS-56 pyrheliometer, located on top of the control room, measures the solar DNI at the solar field ...... 53

Figure 3.11: Solar trough pilot plant instrumentation and piping diagram adapted from

Reference [72]. The solid lines show the piping and the dashed lines show the electric wire connections...... 54

Figure 3.12: Solar start-up sensor and wind anemometer measure the solar intensity and wind velocity at the solar field ...... 56

Figure 3.13: T-type thermocouples used to measure the oil temperature at five different locations in the solar field, (a) thermocouple installed on the supply pipe, and (b) thermocouple being installed on the cross over of the two collector rows ...... 58

Figure 3.14: Sun-tracking sensors on the receiver tube. The first pair is on the upper side and the second pair is on the lower side of the tube...... 59

Figure 3.15: The Local Controller located at the solar field that communicates with the

Master Controller located inside the control room via CR1000 datalogger ...... 60

Figure 3.16: The schematic of the one-dimensional energy balance on the receiver tube developed for the solar pilot plant transient simulation ...... 62

Figure 3.17: The solar trough pilot plant transient model developed in

MATLAB®/Simulink® and Thermolib. The solar trough block consists of several sub- blocks which are described in Appendix A...... 78

Figure 3.18: Ambient temperature, wind velocity, and solar normal irradiance measured at the solar field from September 13th to17th, 2013, recorded every minute by the pilot plant weather station ...... 80

Figure 3.19: Comparison of the HTF outlet temperature obtained from solar trough pilot plant model and experimental measurements for September 13th to 17th, 2013 at a constant

HTF mass flow rate of 1.2 kg/s. A five-hour period from 11 am to 4 pm on September 13th is also shown...... 81

Figure 3.20: The HTF, the pipe wall and the glass envelope temperatures at the outlet of each trough collector obtained from the solar trough pilot plant model for one hour period of 12 pm to 1 pm, September 13th, 2013 ...... 84

Figure 4.1: Schematic of the thermal storage process as sensible and latent heat adapted from Reference [83] ...... 88

Figure 4.2: Schematic of the modelled latent heat thermal storage tank design adopted for the solar trough pilot plant ...... 90

Figure 4.3: The control volume element (j, k) used to discretize Equation 4.2 ...... 92

Figure 4.4: The greenhouse building located next to the solar field: (a) east view and (b) south view ...... 97

Figure 4.5: Proposed design of the greenhouse solar space heating system including latent heat thermal storage and auxiliary natural gas water heater as the backup. The HBS approach is applied to integrate the solar pilot plant and the greenhouse models with the add-on space heating and latent heat storage models. This layout was implemented in the simulation model...... 99

Figure 4.6: Simplified view of the greenhouse space heating model. System controls are embedded into the module components to operate the integrated HBS system as expected when fully built and installed...... 100

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Figure 4.7: The details of the greenhouse heating block which is highlighted in Figure 4.6

...... 101

Figure 4.8: The HTF outlet temperature from the 94-l storage tank and the average PCM temperature during the tank charging and discharging periods for March 16th, 2013, obtained from the solar storage and space heating model. The meteorological data measured and recorded by the solar pilot plant weather station is used. The HTF temperature fluctuations during the discharging period are too small to be clearly shown in the figure...... 102

Figure 4.9: The contributions of the solar pilot plant and the natural gas water heater in the greenhouse space heating demand supply (a) without storage, (b) with storage on

October 3rd 2013, obtained from the integrated solar space heating and latent heat storage model. The storage volume is 94 liters. The meteorological data measured and recorded by the solar pilot plant weather station is used...... 103

Figure 4.10: The contributions of the solar pilot plant and the natural gas water heater in the greenhouse space heating demand supply (a) without storage, (b) with storage on March

16th 2013, obtained from the integrated solar space heating and latent heat storage model.

The storage volume is 94 liters. The meteorological data measured and recorded by the solar pilot plant weather station is used...... 104

Figure 4.11: A typical solar assisted absorption cooling cycle used for the HBS absorption cooling model. Direct conversion of heat to cooling is the viable option. A LiBr-H2O solution is used as the cycle refrigerant...... 107

Figure 4.12: Proposed solar assisted absorption cooling system design for the solar pilot plant. The HBS approach is applied to combine the solar pilot plant with the added

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absorption cooling and latent heat storage models. A glycol solution is used to transfer heat from the HTF to the LiBr-H2O solution...... 108

Figure 4.13: The solar absorption cooling system model along with thermal storage ... 111

Figure 4.14: The details of the absorption cooling block highlighted in Figure 4.13. The guide chart on the top identifies the inlet and outlet of each absorption cycle component.

...... 112

Figure 4.15: The outlet oil temperature from the 170-l storage tank and the average PCM temperature during the tank charging and discharging periods for July 1st, 2013, obtained from the solar storage and absorption cooling model. The tank temperature reached a maximum of 117.5°C during charging period. The meteorological data measured and recorded by the solar pilot plant weather station is used...... 113

Figure 4.16: The cooling effect provided from thermal energy generated by the solar pilot plant (a) without storage, (b) with storage for the 40-kW absorption cooling on July 1st

2013, obtained from the integrated solar absorption cooling and latent heat storage model.

The storage volume is 170 liters. The auxiliary backup is not included. The meteorological data measured and recorded by the solar pilot plant weather station is used...... 114

Figure 4.17: Comparison of the cooling COP for solar absorption cooling and solar electrical cooling using ORC ...... 116

Figure 4.18: The model developed to simulate the solar power generation system using low temperature ORC. The confidential input parameters to the ORC model are obtained from the Swan River waste heat ORC unit. The Thermolib library components are used in the

ORC model...... 118

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Figure 4.19: The heat transfer rate calculation blocks of the CPV power generation model.

Heat transfer rates are estimated for an absorber tube without a glass envelope. The purpose for the glass envelope removal is to provide CPV cooling and improve optical efficiency.

...... 121

The ORC simulation can only be performed at operating temperature of 150°C. The meteorological data measured and recorded by the solar pilot plant weather station is used.

...... 123

Figure 4.21: Monthly power generation efficiency of solar CPV and ORC technologies, obtained from the solar power generation model based on the weather data of the year 2013 measured and recorded by the solar pilot plant weather station. The ORC simulation can only be performed at operating temperature of 150°C...... 123

Figure 5.1: Measurement results for (a) solar normal irradiance, and (b) outlet temperature of the collectors for February 1st to 11th, 2013. The pump did not start circulating the HTF as the oil high viscosity caused the vortex flow meter to provide inaccurate readings to the controller, so the collectors stayed in stow position...... 129

Figure 5.2: The metallic channel holding the screw jack driver and the stow switch, located between the collector rows ...... 130

Figure 5.3: The snow accumulated at the solar field during winter 2014 ...... 130

Figure 5.4: The frozen stow switch caused by the snow entering the channel due to an improper enclosure design ...... 131

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Figure 5.5: Bent receiver tube and oxidized coating as a result of overheating up to 370°C

...... 132

Figure 5.6: Condensed oil droplets formed in the control room due to the release of oil

...... 133

Figure 5.7: Solar direct normal irradiance (1 sample per minute) on a randomly selected

(a) clear, and (b) cloudy day from 11 am to 5 pm, obtained from the meteorological data recorded by the solar pilot plant weather station and selected to compare Method-1 and

Method-2 controls...... 136

Figure 5.8: The outlet temperature of the solar field obtained from the solar pilot plant model using the two control methods (a) without PI controller (Method-1), and (b) with PI controller (Method-2) on a randomly selected clear day from 11 am to 5 pm. The meteorological data measured and recorded by the solar pilot plant weather station is used.

...... 136

Figure 5.9: The outlet temperature of the solar field obtained from the solar pilot plant model using the two control methods (a) without PI controller (Method-1), and (b) with PI controller (Method-2) on a randomly selected cloudy day from 11 am to 5 pm. The meteorological data measured and recorded by the solar pilot plant weather station is used.

...... 137

Figure 5.10: The HTF mass flow rate obtained using the two control methods (a) without

PI controller, and (b) with PI controller on a randomly selected sunny day from 11 am to

5 pm. The meteorological data measured and recorded by the solar pilot plant weather station is used. The mass flow rate values in (a) are based on the pilot plant measurements

while the results in (b) are obtained from the solar pilot plant model. A 15-minute period is also shown for the variable mass flow rate from 1 pm to 1:15 pm...... 139

Figure 5.11: The HTF mass flow rate obtained using the two control methods (a) without

PI controller, and (b) with PI controller on a randomly selected cloudy day from 11 am to

5 pm. The meteorological data measured and recorded by the solar pilot plant weather station is used. The mass flow rate values in (a) are based on the pilot plant measurements while the results in (b) are obtained from the solar pilot plant model. A 15-minute period is also shown for the variable mass flow rate from 1 pm to 1:15 pm...... 140

Figure 5.12: The (a) solar field outlet temperature, and (b) HTF mass flow rate for variable demand during the day obtained from the solar pilot plant model. The variable pump speed approach is used to set the field temperature at 170°C from 10 am to 1 pm and at 120°C from 1 pm to 4 pm on a randomly selected day. The meteorological data measured and recorded by the solar pilot plant weather station is used...... 141

Figure 5.13: Three types of individuals for the next generation creation in the genetic algorithm ...... 144

Figure 5.14: Schematic of the multi-objective optimization problem to maximize heat and electricity revenues of the CPVT plant ...... 146

Figure 5.15: Schematic of the optimization procedure used for CPVT plant revenue optimization. The fitness functions are calculated by the solar trough pilot plant model and transferred to the MATLAB® Optimization Toolbox™ using an interface code...... 147

Figure 6.1: The annual displaced cost of natural gas and propane for heating in Winnipeg obtained from the solar pilot plant model. The HTF outlet temperatures are set to be 100°C

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and 150°C. The 2013 meteorological data measured and recorded by the solar pilot plant weather station is used...... 155

Figure 6.2: Simple payback period resulted from displacing natural gas and propane in

Winnipeg obtained from the solar pilot plant model. The HTF outlet temperatures are set to be 100°C and 150°C. The 2013 meteorological data measured and recorded by the solar pilot plant weather station is used...... 156

Figure 6.3: Cost reductions for parabolic trough and solar tower technologies from 2010 to

2015, adapted from Reference [111]...... 158

Figure 6.4: (a) The annual displaced cost, and (b) the simple payback period of the CPVT system obtained from the solar pilot plant model. The cost of heat and electricity in a typical remote community in Manitoba are used. The weather data is assumed to be similar to the

2013 meteorological data measured and recorded by the solar pilot plant weather station.

...... 161

Figure 6.5: Manitoba DNI map based on annual average data from 1998 to 2014, provided by Natural Resources Canada ...... 163

Figure 6.6: Monthly concentrated solar thermal energy generation for six cities across

Manitoba obtained from the solar trough pilot plant model. The HTF outlet temperature is

100°C. A 20-year averaged hourly meteorological data of the six cities is used...... 164

Figure 6.7: Monthly concentrated solar thermal energy generation efficiency for six cities across Manitoba obtained from the solar trough pilot plant model. The HTF outlet temperature is 100°C. A 20-year averaged hourly meteorological data of the six cities is used...... 165

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Figure 6.8: Monthly power generation potential for six cities across Manitoba obtained from the solar CPVT model. The HTF outlet temperature is 100°C. A 20-year averaged hourly meteorological data of the six cities are used...... 167

Figure 6.9: The simple payback period of the solar CPVT plant for six Remote Regions shown in Table 6.7. The meteorological data of the six regions are assumed to be similar to those of the six cities chosen across Manitoba. A 20-year averaged hourly meteorological data of the six cities is used...... 169

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Nomenclature

2 a1 Heat loss coefficient (W/(m K))

2 2 a2 Coefficient of the heat loss temperature dependence (W/(m K ))

2 Amod Module area (m )

ADC Annual displaced cost ($/m2/y) c pHTF Heat transfer fluid specific heat (kJ/kg K) c pg Glass envelope specific heat (kJ/kg K) c pp Absorber pipe specific heat (kJ/kg K) cp,s PCM specific heat of solid phase (kJ/kg K) cp,l PCM specific heat of liquid phase (kJ/kg K)

CC Concentration coefficient

COP Absorption chiller coefficient of performance

D Diameter (m) fk(x) Objective function

FFP Fossil fuel price ($/kWh) gj(x) Inequality constraint function

2 Gb,t Direct hourly solar radiation on a tilted surface (W/m )

2 Gd,t Diffuse hourly solar radiation on a tilted surface (W/m )

2 Gt Mean hourly global solar radiation on a tilted surface (W/m ) h Convection heat transfer coefficient (W/m2 K) hm(x) Equality constraint function

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I Irradiance (W/m2) k Thermal conductance (W/m K)

Kθb Direct incident angle modifier for the angle of incidence θ

Kθd Diffuse incident angle modifier

Li Lower limit mg Mass of glass envelope (kg/m) mHTF Mass of heat transfer fluid in unit length of pipe (kg/m) mp Mass of absorber tube (kg/m) ṁ HTF Heat transfer fluid flow rate (kg/s) ṁ max Maximum mass flow rate of pump (kg/s)

2 Pcoll Price of solar collector ($/m )

PDC DC power of a trough CPV (W)

q'12, conv Convection from inner absorber pipe surface to heat transfer fluid (kW/m)

Convection from outer absorber pipe surface to inner glass envelope q'34, conv surface (kW/m) q'56, conv Convection from outer glass envelope surface to ambient (kW/m) Radiation from outer absorber pipe surface to inner glass envelope surface q'34, rad (kW/m) q'57, rad Radiation from outer glass envelope surface to sky (kW/m) q'5, SolAbs Solar irradiation absorption of outer glass envelope surface (kW/m) q'3, SolAbs Solar irradiation absorption of outer absorber pipe surface (kW/m) q'net, g Net heat transfer rate on the glass envelope (kW/m) q'net, p Net heat transfer rate on the absorber pipe (kW/m)

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2 Qann,sol,th Annual solar thermal energy generation (kWh/m /y)

Qe Cooling effect produced in the evaporator (kWh)

Qg Input energy to the generator (kWh)

Qgen Generated thermal energy by solar system at 100ºC (kWh)

Qsol Total incident solar energy on the aperture area of collectors (kWh)

Re(T) Electricity revenue ($)

Rh(T) Heat revenue ($)

SPBP Simple payback period (y)

Ta Ambient temperature (°C)

Tg Glass envelope temperature (ºC)

THTF Heat transfer fluid temperature (ºC)

Tm Mean heating temperature (°C)

Tm1 Lower melting temperature of PCM (°C)

Tm2 Higher melting temperature of PCM (°C)

Tmod Module temperature (°C)

Tp Absorber pipe temperature (ºC)

TSTC Reference temperature (°C)

TC Temperature coefficient assumed as a constant value

Ui Upper limit xi Decision variable

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Greek Symbol

α Absorptance coefficient

β Thermal expansion coefficient (1/K)

δT Temperature efficiency of the system

ε Emissivity coefficient

ηenv Effective optical efficiency of the glass envelope

ηmod CPV module efficiency under standard test conditions

η0 Zero-loss collector efficiency

ηsol, th Solar thermal efficiency

θ Incident angle

τ Transmittance coefficient

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Abbreviation

AMP Alberta Market Price Center for Energy Advancement through Technological CEATI Innovation CHP Combined Heat and Power

COP Coefficient Of Performance

CPV Concentrated Photovoltaic

CPVT Concentrating Photovoltaic/Thermal

CSP Concentrated Solar Power

DNI Direct Normal Irradiance

EPRI Electric Power Research Institute

ETC Evacuated Tube Collector

FPC Flat Panel Collector

GHG Greenhouse Gas

HTF Heat Transfer Fluid

IAM Incident Angle Modifier

IRENA International Renewable Energy Agency

LC Local Controller

LCOE Levelized Cost Of Energy

MC Master Controller

MLR Multiple Linear Regression

NREL National Renewable Energy Laboratory

ORC Organic Rankine Cycle

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PCM Phase Change Material

PVT Photovoltaic/Thermal

RRC Red River College

SEGS Solar Energy Generating Systems

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Chapter 1

Introduction

1.1 Earth’s energy outlook

As living standards improve and population grow, energy demand increases [1]: from 2011 to 2040, the world primary energy demand is projected to increase by an average of 1.4% per year, resulting in a compound growth of 48% [1]. Table 1.1 shows the total world energy consumption projections till 2040. Electricity is projected to have the largest growth: 21.6 trillion kWh in 2012, 25.8 trillion kWh in 2020, and 36.5 trillion kWh in 2040 [1]. Moreover, projections show coal will still play an important role for electric power generation in 2040. However, the role of coal is expected to decrease compared to renewables. Coal-fired generation is projected to increase by 0.8% per year from 2012 to

2040; for the same time period, renewable generation will grow by 2.9% per year [1], leaving fossil fuels still the dominant energy source. Many other references arrive to a similar energy scenario that fossil fuels remain the dominant world energy supply in

2040 [2][3].

However, sparked by energy drivers such as climate change and peak oil issues, some countries are adopting a non-fossil fuel future by implementing policies that will lead them to rely mainly on renewable energy to address their energy needs for electricity,

Chapter 1 – Introduction

heat/cooling, and transportation. For example, to keep the global temperature rise below

2°C to 2.4°C, the European Union reached an agreement on climate and energy for the year

2030 [4]. The agreement includes 40% domestic reduction in greenhouse gas (GHG) emissions compared with 1990; 27% growth in the renewable energy share in gross final

European Union energy consumption; and 27% improvement in energy efficiency.

Figure 1.1 presents the renewable energy share in the gross final energy consumption of

EU28 members in 2005 and 2013 and the target for 2020. As shown in the figure, countries such as Sweden and Bulgaria have already surpassed their 2020 targets, in part, through appropriate renewable energy policies.

Table 1.1: World projected energy consumption till 2040 in trillion kWh adapted from Reference [1]

2011 2012 2020 2025 2030 2035 2040 2012-2040* Trillion kWh (%) Liquids 52.8 53.8 59.8 62.3 65.0 68.3 72.1 1.1 Natural gas 35.5 36.4 40.5 45.4 50.7 56.4 62.0 1.9 Coal 44.5 44.9 49.4 50.8 51.1 51.8 52.8 0.6 Nuclear 7.7 7.2 9.1 10.1 11.8 12.7 13.5 2.3 Other 17.7 18.7 25.5 29.0 31.7 35.0 38.5 2.6 Total** 158.4 161.0 184.3 197.5 210.3 224.4 238.9 1.4 *Average annual percent change. **Net imports of coke coal and electricity generated from biomass in the US is included in the energy totals.

Issues faced by continuing a fossil fuel future include price uncertainty and climate change.

The price of oil is projected to increase to $76 per barrel based on the Brent spot price and

Chapter 1 – Introduction

$141 per barrel in 2040 based on the International Energy Outlook [1]. As part of the planning process, supplying energy from an affordable and secure source is an important concern. In addition, climate change due to the accumulation of atmospheric greenhouse gases is an inevitable drawback of continued reliance on fossil fuels. In the beginning of industrial revolution around 1750, the CO2 concentration in the atmosphere was 278 ppm.

th In the middle of 19 century, the CO2 level increased slightly to 280 ppm and since then, due to the growth in fossil fuels consumption, the GHG emission has increased every year [5]. More alarming, 2015 was the fourth year in a row that the CO2 concentration in the atmosphere raised by more than 2 ppm/year. Current annual growth rates of GHG emissions are twice as large as in the 1990s [5].

Figure 1.2 shows the atmospheric CO2 levels recently achieving 400 ppm, as reported by the US government agency lab in Hawaii located on the Mauna Loa volcano. According to

Reference [6], the safe upper limit of CO2 concentration to stabilize the global average temperatures within the range of 0.6°C to 1.4°C above the pre-industrial values is 350 ppm.

The concentration of 441 ppm would result in 3.1°C of global warming by 2030 with the

Earth on its way to become ice-free [6]. It is apparent that the present generation will experience a 500-ppm world.

Chapter 1 – Introduction

40 2013 30 2005 2020 20

Renewable energy share (%) share energy Renewable 10

Italy

Spain

Malta

Latvia

France

Poland

Greece

Ireland

Cyprus

Austria Croatia

Estonia

Finland

Sweden

Portugal

Bulgaria Belgium

Hungary Slovenia Slovakia

Romania

Denmark Germany

Lithuania

Netherlands

Luxembourg Czech Republic Czech United Kingdom United

Figure 1.1: Renewable energy share in the EU28 member states adapted from Reference [4]. The blue and orange bars show the renewable energy distribution in 2005 and 2013, respectively; 2020 renewable energy targets are shown by green circles.

Figure 1.2: Levels of CO2 concentration in the atmosphere from 1960 to present, as reported by Mauna Loa Observatory from Reference [5]

Chapter 1 – Introduction

In contrast, a renewable energy future requires energy policy to focus on three areas, summarized as RED: Renewable generation increase, Efficiency improvement, and

Demand reduction [7]. Energy generated from renewable energy sources such as hydro, solar, wind, geothermal and biomass is a prime objective of the RED policy approach.

During the last decade, there has been substantial growth in interest in using renewable energy to reduce the reliance on fossil fuels and limit greenhouse gas emissions that lead to climate change. For example, the International Renewable Energy Agency (IRENA) estimated an increase of 5% in the global renewable energy employment in 2015, reaching

8.1 million jobs out of which 2.8 million was in solar photovoltaic (PV), and 1.1 million was in wind power sector [8]. This demonstrates the additional social benefits associated with distributed renewable energy applications.

1.2 Energy use in Canada

In Canada, 81.1% of the energy for electricity, heat/cooling, and transportation comes from fossil fuels [9]; for remote communities, energy needs are mainly supplied by fossil fuels.

For electricity generation, the most important renewable energy sources in Canada are hydroelectricity with 59.3%, wind with 3.5%, and biomass with 1.4% [9]. The fastest growing sources of renewable electricity are intermittent wind and solar [9], as their

Levelized Cost of Energy (LCOE) has decreased significantly during the past two decades [10]. Moreover, as presented in Figure 1.3, the share of natural gas is predicted to increase till 2040, while the share of coal, oil, and nuclear fuel is expected to decrease. As the share of hydro decreases from 55% to 51%, the proportion of other renewables grows from 10% to 16% [11]. For energy efficiency, an energy efficiency program in Canada

Chapter 1 – Introduction

from 2011 to 2016 resulted in 4 Mt of GHG emission reductions in 2016 [12]. Most

Canadian provinces have made commitments for GHG emission reductions: 17% in 2020, and 30% in 2030, compared to 2005 [13]. For energy demand, based on the moderate economic growth and energy price in the future, the energy consumption in Canada is projected to increase from 13,444 PJ in 2013 to 16,233 PJ in 2040 [11]. Unlike Sweden, as shown in Figure 1.1, renewable energy use in Canada is not expected to increase well beyond the present 18.9%. Significant gains would require an effective RED energy policy that would entail implementing, for example, renewable heat and transportation on a massive scale.

2014 2040 3% 3% 2% 5% 10% 6%

Hydro/Wave/Tidal 15% 22% 51% Coal and Coke 55% Wind Natural gas 7% Uranium Oil 7% 11% 3% Biomass/Solar/Geothermal

Figure 1.3: The share of different energy resources in Canada’s electricity generation mix for years 2014 and 2040 projections adapted from Reference [11]

1.3 Canada’s solar potential

Globally, the Earth’s surface receives 3,400,000 EJ of ground level solar radiation every year [14] which is four orders of magnitude larger than the 560 EJ global energy consumption [15]. Figure 1.4 maps the direct normal and the global horizontal solar

Chapter 1 – Introduction

irradiation averaged over a year in Canada, US, and Mexico. These maps depict a gradual decrease of irradiation with increasing latitude and show non-uniformity, with parts of the

Prairie Provinces having irradiation values similar to those in northern Florida. As seen in the figure, Canada has population centers in the Prairie region, such as Regina, Calgary, and Winnipeg, that have solar potential well above the average. However, Ontario has a lower level of solar irradiation but had the highest growth in solar capacity from 0.8 GW in 2012, to 1.2 GW in 2013, and reached 1.7 GW by the beginning of 2015. This is approximately 4.5% of total installed generation capacity of Ontario [11]. Similarly, the solar potential in Canada is generally higher than Germany, but Germany had the largest number of installed PV systems in the world in 2014 [11]. Such outcomes are attributed to deciding to move away from a fossil fuel future by adopting effective renewable energy policies.

Ground level solar energy can be used to address electricity, heating/cooling, and transportation needs. The use of solar energy is increasing not just for PV applications, but for hot water and heating as well, particularly in developed countries which have average daily temperatures below freezing point in the winter [17]. As residential and commercial buildings represent 26% of energy use in Canada [11], a pathway to a renewable energy future is to implement solar energy systems to address building heating and cooling loads.

Chapter 1 – Introduction

(a)(a) (b)(b)

Figure 1.4: Annual average (a) global horizontal irradiation, and (b) direct normal solar irradiation in North America from Reference [16]

1.4 Canada’s northern remote and aboriginal communities

Off-grid communities in Canada are mainly small, isolated and scattered, as shown in

Figure 1.5. According to 2006 Statistics Canada Census, there are 292 remote Canadian communities out of which 170 are recognized as Aboriginal communities with a population of approximately 127,000 [18]. The remaining 122 communities, with a combined population of nearly 68,000, are cities, villages and commercial outposts that are non-

Aboriginal, or under non-Aboriginal governments [18]. Alberta, Saskatchewan, and

Manitoba have a more limited number of remote communities, and hence, they are grouped as a single region called the Prairies. A total installed capacity of 9 MW supplies energy to almost 4,000 people in the Prairies remote communities [18]. Table 1.2 shows the remote

Chapter 1 – Introduction

communities of Manitoba along with their population, annual energy demand, and fossil fuel generation type and capacity.

Figure 1.5: Canada’s off-grid communities from Reference [19]

These communities use fuel oil to heat their homes and diesel generators supply a microgrid. In such communities, for example, electricity prices for government facilities can reach $2.50/kWh [20], allowing to subsidize electricity to homes. Furthermore, these communities can experience soil contamination from fuel spillage [21]. The Canadian government requires to repeatedly remediate such spills at relatively high costs. For example, the cost of assessment followed by remediation for a recently contaminated site in the Sayisi Dene First Nation, Manitoba, was $110,473 and $3,629,871, respectively [22].

The cost of operating diesel plants, including fuel costs, operation, maintenance, and capital

Chapter 1 – Introduction

expenditure improvements is $90 million per year for Ontario’s Aboriginal remote communities [22]. These facts indicate that continued reliance on fossil fuel is not sustainable for remote communities; a preferred approach is to shift towards renewable energy resources.

Table 1.2: Manitoba remote communities’ energy use adapted from Reference [18]

Fossil Annual Population Fossil fuel Waste Renewable fuel energy Community on census generation heat capacity capacity demand 2006 on type recovery (kW) (kW) (MWh) Diesel Brochet 306 1,175 Yes N/A 2,368 restricted 1 Granville 126 N/A N/A N/A N/A N/A Lake Diesel Lac Brochet 604 1,450 Yes N/A 2,505 restricted Red Sucker 585 Diesel 1,250 N/A N/A 1,842 Lake Diesel Shamattawa 920 1,325 Yes N/A 3,169 restricted Tadoule Diesel 330 1,450 Yes N/A 2,459 Lake restricted Thicket 192 Diesel 525 N/A N/A 464 Portage 1Diesel generation is restricted to a few hours during the day.

Northern Canada experiences a harsh climate where average temperature reaches below

-25°C during winter for extended periods of time, as shown in Figure 1.6. Moreover, these communities experience temperatures well below -40°C with short periods of high solar irradiation during the day. For these communities to be sustainable in the long term, energy

Chapter 1 – Introduction

conversion technologies need to provide local economic, technical, social, and environmental benefits [20].

Figure 1.6: Surface air temperatures across Canada, observed in December to February from 1981 to 2010 from Reference [23]

Due to the lack of specialized skills in remote communities, operation and maintenance of energy generation and distribution systems is more expensive. Moreover, technologies such as wind and solar need to be adapted to endure harsh winter conditions. As current electricity generation capacity is often limited compared to community demands, installing renewable technologies that are adapted to operate in cold climates can provide opportunities to more economically supply power and increase community development [20]. Furthermore, intermittent renewable energy systems can be coupled with diesel generation and energy storage to supply energy for the community, and

Chapter 1 – Introduction

potentially, meet double redundancy criteria [24]. Finally, Figure 1.7 shows the annual solar irradiation resource of Canadian major cities, along with Manitoba’s remote communities located at much higher latitudes. The figure shows that the solar resource in these remote communities is almost as attractive as that in major cities across Canada.

Thicket Portage Shamattawa Red Sucker Lake Lac Brochet Granville lake Major Brochet Canadian Vancouver cities Calgary Manitoba Regina remote communities Winnipeg Ottawa Montréal St. John's 0 1 2 3 4 5 6

2 Global solar insolation (kWh/m )

Figure 1.7: Annual global solar insolation resource comparison for Manitoba’s remote communities and Canadian major cities, for a south facing surface tilted at latitude adapted from Reference [25]

1.5 Applying concentrated solar technology in Canadian remote communities

Although flat panel collectors (FPC), and to some extent, evacuated tubes, are economical choices for low-temperature heating applications, they remain sensitive to ambient temperature because of relatively high heat losses. Therefore, FPCs are not practical for

Chapter 1 – Introduction

heat transfer fluid (HTF) applications with temperatures above 80°C [26]. To reach higher

HTF temperatures for heat, storage, absorption cooling, and power applications, heat losses need to be reduced in cold climates. Throughout locations like Manitoba, this can be achieved by using concentrated solar systems. The efficiency curves for several representative solar collectors are given in Figure 1.8. The figure shows four different types of solar collectors applicable in cold climates, with different heat loss coefficients and incident angle modifiers (IAM). As can be seen in the figure, the efficiency of parabolic trough concentrators is lower than that of flat panel collectors when the temperature difference between the collectors and ambient is small; however, they have the advantage of being significantly more efficient at larger temperature differences.

As Canadian remote communities are located predominately at higher latitudes, as shown in Figure 1.5, winter months provide both lower solar irradiation values due to reduced zenith angles and higher temperature differences. In such conditions, the term (Tm-Ta)/Gt increases, and at values that exceed 0.28 (see Figure 1.8), the parabolic solar trough is the only collector with non-zero efficiencies. This indicates that this technology is more applicable for generating thermal energy in conditions with low solar radiation and low ambient temperatures, characteristic of northern climates.

Chapter 1 – Introduction

From Reference [27], the mean overall efficiency for solar thermal collectors is given by:

2 (Kθb(θ)Gb,t + KθdGd,t) (Tm − Ta) (Tm − Ta) η = η0 − a1 − a2 (1.1) Gt Gt Gt

where

η0: Zero-loss collector efficiency

2 a1: Heat loss coefficient (W/(m K))

2 2 a2: Coefficient of the heat loss temperature dependence (W/(m K ))

2 Gt: Mean hourly global solar radiation on a tilted surface (W/m )

2 Gb,t: Mean direct hourly solar radiation on a tilted surface (W/m )

Chapter 1 – Introduction

2 Gd,t: Mean diffuse hourly solar radiation on a tilted surface (W/m )

Tm: Mean HTF temperature (°C)

Ta: Ambient temperature (°C)

Kθb(θ): Direct incident angle modifier for the angle of incidence θ

Kθd: Diffuse incident angle modifier

The absorber area is a determining factor of the heat losses. One method to reduce the area is to concentrate the incident solar radiation onto a smaller absorber. The diffuse component of the solar insolation cannot be concentrated. It becomes advantageous in such applications to track the sun and keep it within the acceptance angle of the concentrator to collect as much normal irradiance as possible during the day.

Figure 1.9 categorizes the solar thermal technologies based on the maximum operational temperature. Solar troughs operate at relatively low temperatures compared to solar dishes and towers. Given that remote communities have no prior experience with concentrated solar technologies and no qualified operators, the focus of this research is on the parabolic solar trough. This approach was put forward, in part, by Manitoba Hydro as part of their energy planning process to reduce diesel dependencies in Manitoba’s First Nation and remote communities. Manitoba Hydro provided support and recommendations throughout the various phases of this solar trough research: simulation software platform, experimental facility design, financial support, and facilitating discussions with utility industry associations such as Center for Energy Advancement through Technological Innovation

(CEATI) and Electric Power Research Institute (EPRI).

Chapter 1 – Introduction

However, solar troughs are normally optimized for operation in locations with relatively high solar irradiation levels, as highlighted in Figure 1.4. Adapting this technology to address electricity, heating/cooling and transportation needs in Canadian remote communities has not been investigated to date. Figure 1.10 shows the contrasting applications. As solar trough technologies are not widely implemented in high-latitude harsh environments, their ability to provide reliable heat and power with minimal operational and safety issues is not documented. Therefore, they have not been considered as renewable energy options by stakeholders to eliminate fossil fuels in remote communities.

Figure 1.9: Schematic of different solar thermal technologies and their maximum operational temperatures (open source pictures)

Chapter 1 – Introduction

1.6 Research objectives

The main objectives of this research are summarized as below:

1. Investigate how to adapt concentrated solar trough technology to Canadian remote

communities that:

• is designed for small-scale requirements;

• addresses community need for electricity, heating, and cooling; and

• reduces safety risks and operator qualification requirements by decreasing the

HTF temperature compared to low latitude applications.

2. Design, build, and operate an instrumented solar trough system representative of the

conditions experienced in Canadian remote communities that can provide:

• operational experience when subjected to low ambient temperatures, snow, ice,

and short solar winter days;

• solar irradiation, wind, and temperature measurements every 1 minute for input

to transient numerical simulations with integrated controls;

Chapter 1 – Introduction

• system performance data collected every 1 minute to validate transient

numerical simulations; and

• experience to understand codes, standards, and safety issues of these systems in

cold weather applications.

3. Develop a transient solar trough simulation model that:

• applies to remote community applications;

• is validated with a representative commercial solar trough pilot plant data rather

than a single experimental solar trough module;

• allows for change of system configurations to model different technologies to

achieve combined heat and power (CHP) economics in these communities;

• implements controls using solar pilot plant real-time data;

• can simulate performance data and includes the effect of system HTF, controls,

and system configuration, to contribute to the energy planning process; and

• can perform a simple payback period analysis.

1.7 Methodology

The methodology to achieve research objectives is summarized in Figure 1.11 and includes:

1. Design, develop, build and continuously operate a 52-kW parabolic solar trough pilot

plant. The pilot plant was developed in collaboration with Manitoba Hydro, Red River

College (RRC) and the University of Manitoba. It consists of eight tracking parabolic

trough modules, manufactured by research partner Abengoa Solar with relatively high

concentration ratio. The pilot plant is located at RRC, Winnipeg, Manitoba as winter

Chapter 1 – Introduction

days are characteristics of remote communities. It is made to be representative of a

solar field with multiple rows. The experimental solar facility provides continuous

performance and operational data 24 hours a day and has an autonomous controller

supplied by Abengoa Solar. It was deemed important, as a part of the methodology, not

to focus on testing a single trough, but to adopt an approach which is based on

continuously testing an array of collectors with minimal supervision that is

representative of a larger system in remote communities. Such a methodology can thus

provide operational feedback in cold temperatures and help to understand the

limitations of implementing such a system where accessing qualified operators is a

concern.

2. Develop a transient numerical model of the parabolic solar trough pilot plant to

investigate the required adaptation approaches of this technology to operate at lower

HTF temperatures in Canadian remote communities.

3. Validate the model using the experimental data collected from the solar pilot plant to

provide a reliable tool to:

• predict the HTF outlet temperature of the solar trough field;

• design an integrated solar pilot plant model that includes thermal storage,

heating, cooling, and power generation;

• develop a CHP design in cold climates using concentrated photovoltaic

(CPV) cells that can compete effectively against other high-temperature

power generation approaches;

• study the effect of different control strategies on the thermal energy

generation and parasitic power of the system;

Chapter 1 – Introduction

• perform a simplified economic assessment of the technology for cold

off-grid communities; and

• exchange data with an optimization algorithm to find an optimal HTF flow

rate for revenue maximization and demonstrate the need to control the pump

using a variable-frequency drive.

An integral part of the methodology is to develop and validate a transient numerical model of a parabolic solar trough system using three simulation platforms:

• MATLAB®

• Simulink®, which is a block diagram environment and used for simulating,

modelling and analyzing dynamic systems. It is integrated with MATLAB®

and enables the user to combine MATLAB® codes with models and export

the model results to MATLAB®.

• Thermolib, which is a toolbox designed by EUtech Scientific Engineering

GmbH, a German engineering company. This toolbox is made for

MATLAB®/Simulink® to model and simulate thermodynamic systems. It

includes Simulink® blocks for components such as compressors, pumps,

valves, heat exchangers, etc. It also provides a database for thermophysical

properties of various fluids.

Chapter 1 – Introduction

research objectives research

e e

: Schematic of research methodology to address to methodology research of Schematic :

Figure

Chapter 1 – Introduction

The transient numerical model predicts the HTF temperature along the receiver length using the finite difference method and is to be validated using the data obtained from the experimental solar pilot plant. The finite difference method is a numerical method to solve differential equations and approximates the derivatives by linear combinations of function values at the grid points.

The validated solar system model is then reconfigured to simulate storage, heating, absorption cooling, and power generation using a Hardware-Based Simulation (HBS) approach. In this thesis, the hardware-based simulation means that a real, physically installed component is included in the simulations. In the adopted methodology, this means the solar pilot plant, with other components virtually added as the simulation model. For example, in the thermal storage model, the storage tank does not exist physically but it is simulated as an add-on to the solar trough pilot plant model. Moreover, simulations contribute to designing components to be implemented later in hardware.

The model is extended to evaluate the simple payback period for CHP generation at lower temperatures in off-grid communities. Calculations are based on the simulations performed at 1-second intervals over 24 hours, for 365 days, and for each set of boundary conditions.

The 1-s simulation time step is chosen due to the following reasons:

• Simulink® transient calculations are performed for a time step of 1 second.

• The per second calculation leads to a smoother temperature profile which is

important in the model validation phase.

Chapter 1 – Introduction

• As the model becomes more integrated, by adding components such as controls,

heating, cooling, and storage, the numerical simulation accuracy and convergence

requires such a low time step.

An optimization algorithm is employed to estimate an optimized HTF flow rate at which the CHP revenue is maximized. The objective functions are computed by the transient model and data is exchanged with the linked multi-objective optimization algorithm during the simulation. By using this approach, there is no need to define a mathematical formula for the objective function as it is calculated by the solar pilot plant model.

In collaboration with Abeona Solar, a committee composed of various professionals from

Manitoba Hydro, RRC and the University of Manitoba met regularly to address safety, contracts, non-disclosure agreements, design, construction schedules, and controls to develop the pilot plant. As part of this thesis work, contributions were focused on developing and implementing the controls and instrumentation.

1.8 Contributions to the state of knowledge

This dissertation achieves five main contributions:

1. The real-time operational data of a solar trough pilot plant, built in Winnipeg, is

documented and analyzed to provide a database for model validation purposes and

study the impact of harsh, cold weather conditions on the system performance.

2. Due to temperatures as low as -40°C during winter in Winnipeg, the system studied is

subjected to cold weather issues. The technical operational issues and the possible

Chapter 1 – Introduction

causes of system malfunctions are addressed and documented through on-site

observations of the pilot plant performance during winter 2014. This can be referred to

as a valuable learning experience that can be taken into consideration during the design

and construction phases of projects in remote communities.

3. A transient numerical model of the solar trough pilot plant is developed using

Simulink®, Thermolib, and MATLAB®, which predicts the temperature of the receiver

tube, the glass envelope, and the HTF along the tube length using a 1-second time step.

The model is validated using the experimental data obtained from the temperature

measurements at the solar field. It is also reconfigurable and includes a passive mode

to simulate the solar plant whenever the troughs are in stow position, such as night time

and cloudy periods. The passive mode is also validated and is required to simulate solar

troughs in cold weather conditions to predict the heat loss when the HTF is stagnant.

4. The use of CPV cells attached to the receiver tube of the solar troughs is studied for

combined heat and electricity generation. This approach reduces the required operating

temperature of the solar system and, consequently, safety risks and operator

qualification requirements. This method also allows using more environmental friendly

HTFs such as glycol instead of flammable and toxic synthetic oils. The transient solar

pilot plant model is reconfigured to simulate the solar CHP system using two different

technologies: concentrated PV and organic Rankine cycle (ORC). The performance of

the two power generation approaches is compared using the results obtained from the

model.

5. The validated transient model is utilized to compute the concentrated heat and power

generation potential of six cities in Manitoba. A 20-year averaged weather data of the

Chapter 1 – Introduction

cities are used as input to the model. The cities and their latitudes are Brandon

(49.8° N), Winnipeg (49.9° N), Dauphin (51.1° N), The Pas (53.8° N), Thompson

(55.7° N), and Churchill (58.8° N).

As stakeholders investigate eliminating fossil fuels in remote communities in Canada, the contribution seeks to make solar trough integrated systems more practical for cold and remote community applications. To this effect, the solar pilot plant measurements, meteorological data, and simulation models are stored in an online database in Reference

[28] to further develop this technology for high latitudes applications.

1.9 Thesis outline

Chapter 1 is an introduction to the solar thermal technology. The motivation, objectives, and methodology of the research are also covered in this chapter. Chapter 2 presents the literature review which includes concentrated solar thermal systems in cold climates, numerical simulation of concentrated solar heat and power generation and hybrid photovoltaic and thermal systems. In Chapter 3, the solar trough pilot plant is described, and the modelling approach and results validation are presented. Chapter 4 describes the storage, heating, cooling, and power generation HBS models. Chapter 5 presents the control issues related to cold weather, compares two control strategies to operate the solar field pump, and describes the optimization problem to maximize heat and power generation revenue. The results of the numerical models are used in Chapter 6 to assess the solar CHP technology in cold climates from the economic perspective. The conclusions and recommendations for future work are presented in Chapter 7.

Chapter 2

Literature Review

2.1 Concentrated solar trough plants in cold climates

By the end of 2015, the worldwide operational capacity of concentrated solar power (CSP) reached nearly 4.8 GW, of which 2.3 GW was installed in Spain as the global leader in

CSP capacity. The second largest installed capacity is in the United States with

1.7 GW [29]. Table 2.1 presents some of the operating parabolic trough plants in low- latitude geographical locations. There are also records of operating solar trough plants in northern latitudes such as Sweden and Denmark. A solar district heating system using solar troughs combined with solar flat panels has been operating in Tars, Denmark since August

2015. The plant has an annual heat production of 6,082 MWh [30]. A CHP plant is also generating 80 kW of heat and 20 kW of electricity in Härnösand, Sweden since June 2011 using photovoltaic/thermal (PVT) technology [31]. The minimum recorded average temperature in Tars and Härnösand is between -5°C to -10°C. Compared to the extremely cold weather in Winnipeg with temperatures as low as -40°C during winter, the solar pilot plant in Winnipeg can be referred to as the only high latitude installation which is exposed to low ambient temperatures, characteristic of remote Canadian communities.

Chapter 2 – Literature Review

Table 2.1: Operational parabolic trough plants in low-latitude geographical locations

Capacity Plant name Country Location (MW) Solar energy generating systems Mojave desert, USA 354 (SEGS) California

Genesis Solar Energy Project USA Blythe, California 250

Solaben Solar Power Station Spain Logrosán 200

Solacor Solar Power Station Spain El Carpio 100

Abu Dhabi Shams solar power station UAE 100 Madinat Zayad

Godawari Green Energy Limited India Nokh 50

Yazd integrated solar combined Iran Yazd 17 cycle power station

Archimede solar power plant Italy Syracuse, Sicily 5

Most of the literature data on concentrated solar plants are focused on the applications in the sun-belt countries such as southern Europe (Spain), southwest USA, and North

Africa [32]. Therefore, the documented literature on the application of the concentrated solar plants in high-latitude geographical locations such as Canada is more limited.

At lower latitudes, Dudley et al. [33] performed tests at Sandia National Laboratories, New

Mexico to investigate the thermal efficiency of parabolic trough concentrators. They compared the thermal efficiency of the collectors with three different receiver tubes: air filled glass envelope, vacuumed glass envelope, and bare tube. The efficiency is a function

Chapter 2 – Literature Review

of temperature difference between the receiver tube and the ambient. By introducing air into the glass envelope, the thermal efficiency decreases due to increasing thermal losses in the air. Although the optical efficiency can be improved by removing the glass envelope, the total solar efficiency drops as the heat loss increases from the receiver tube surface.

Montes et al. [34] analyzed the contribution of solar thermal power in the improvement of the gas-fired combined cycle performance in hot and dry climates. They analyzed the annual power generation of an integrated solar combined cycle and a conventional gas turbine combined cycle for two warm locations; Almeria, Spain and Las Vegas, US. The heat gain of the collectors was calculated using a first-order polynomial function of solar normal irradiance and the outlet pressure of the solar field. In this study, the hours with high direct normal radiation and high ambient temperature were of importance since at high solar radiation, the solar thermal power is high and at high ambient temperatures, the gas turbine power is low. Zarza et al. [35], [36] used a test facility at the Plataforma Solar de Almeria, Spain to compare the performance of direct steam generation plant with oil systems. The total solar to electricity conversion efficiency for a direct steam generation plant was 22.6% while the efficiency of the traditional oil system was 21.3%. They performed the tests daily until sunset and in good weather conditions.

Zhai et al. [37] proposed and investigated a small-scale hybrid solar heating, cooling and power generation system for a building located in a remote off-grid region in northwestern

China. The remote region has a high solar mean irradiation of 5,400-8,400 MJ/m2 every year and the monthly average temperature of -5°C to 30°C. Calise [38] presented a

Chapter 2 – Literature Review

dynamic model of a solar heating and cooling system for 7 cities in the Mediterranean area.

The system uses parabolic solar troughs coupled with an absorption chiller. The average daily temperature in this study is between 5°C to 27°C for the selected cities.

Unlike the above studies that report numerical and experimental data in locations with a minimum ambient temperature range of -15°C to 12°C during winter, this thesis focuses on the numerical and experimental investigation of the parabolic trough collectors at much lower temperatures to enhance the portfolio of renewable energy technology options for remote Canadian communities.

2.2 Transient numerical simulation

Developing a transient numerical model of the parabolic solar collector system, which uses real-time meteorological data to calculate the HTF temperature 24 hours a day, is essential to study and analyze the system performance and integration of hardware components. The system exposure to low temperatures may cause technical issues leading to system malfunctions and unplanned outages. It is also necessary to include a passive mode in the simulation which models the system during zero/low solar radiation periods, such as night time or when the collectors are in the stow position; the HTF temperature can now reach

-40°C impacting start-up conditions.

Several researchers have studied modelling of the heat transfer process of parabolic solar trough systems. The majority of these studies neglected the non-uniform solar heat flux and considered the solar irradiance to be a constant value. Moreover, almost none simulated

Chapter 2 – Literature Review

the times when the solar troughs are stowed; an important consideration for system restart in the morning in cold winter days.

Al-Sulaiman et al. [39] modelled the performance of tri-generation parabolic solar trough plants. The input solar radiation data was considered to be two constant values for low and high radiation periods. To validate the model, they compared the effect of the average temperature above ambient on the heat loss, with the experimental data of Dudley et al. [33]. Dayem et al. [40] used TRNSYS to model the performance of the integrated combined cycle of Kuraymat plant, Cairo, Egypt. The hourly meteorological data was stochastically generated from the monthly data by Meteonorm. They compared the HTF temperature predicted by the model with the measured HTF temperature and found a significant difference, specifically during night time. Luo et al. [41] studied the effect of step increase and decrease in direct normal irradiance (DNI) on the outlet temperature of the collectors. They also presented the outlet temperature response to variations in flow rate and inlet temperature. In all the mentioned works, constant or hourly averaged solar radiation data was applied which does not take into account the effect of intermittency and its impact on the controls.

A detailed dynamic model of a parabolic solar trough test facility was made by

Xu et al. [42], [43] and Garcia et al. [44]. Xu et al. [42], [43] used the lumped capacitance method to calculate the HTF outlet temperature, therefore, they assumed the receiver tube to be at uniform temperature. They used Multiple Linear Regression (MLR) to identify the parameters in the outlet temperature equation using experimental data of a specific

Chapter 2 – Literature Review

parabolic trough collector design. These parameters could be used to characterize the thermal performance of that particular design for other conditions. They did not report results for HTF temperature during night time. Garcia et al. [44] compared the results of their dynamic model of a solar thermal power plant with the experimental data from

Andasol 2 plant in Spain. The temperature difference between the model and experiment was also found to be considerable during night time. They utilized empirical equations of a specific commercial receiver tube, obtained from SEGS plants in the USA, to calculate heat losses. For that reason, the results obtained by Xu et al. and Garcia et al. are applicable to a specific design.

Hachicha et al. [45] presented a detailed numerical simulation of solar heat flux distribution around the absorber tube of a parabolic solar concentrator. They used the finite volume method to discretize the heat collector element in both axial and azimuthal directions. The temperature variation was simulated along both directions and the influence of the fluid temperature above ambient on the heat loss was studied. Ghoneim et al. [46] and Padilla et al. [47] modelled the parabolic solar trough receiver tube and presented the efficiency and thermal loss profiles as a function of collector temperature. These studies investigated the effect of different design parameters on the thermal loss and efficiency of the parabolic concentrator and the real-time operation of the solar system during a day is not modelled. The literature review presented in Sections 2.1 and 2.2 is summarized in

Table 2.2.

Chapter 2 – Literature Review

The studies listed in Table 2.2 have not directly addressed the following cold weather issues, which are of particular interest for remote communities:

• Access to qualified personnel to operate the system.

• The impact of HTF high viscosities and system start up in the morning during

winter time.

• Ability to predict heat loss during night time to simulate weekly, monthly, and

yearly operation.

• Simulation of controls using real-time weather and solar plant performance data.

Chapter 2 – Literature Review

- - -

ime data data ime

time data data time t

27°C.

- -

was not not was not was

to 40°C. to

night time night

Significant Significant

considered. considered.

Comments

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ST ST

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ST/GT ST/GT

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method

2.2

generation

3.2

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No No No No No No No

2.1

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3.1

No No

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No No No No No No

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3 24

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N/A

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Temp. Temp.

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ties

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China

Almeria, Almeria, Almeria,

Las Vegas Las

Cairo, Egypt Cairo,

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al.

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Chapter 2 – Literature Review

e were were e

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al.

Research Luo Luo Xu [42] Garcia [44] Hachicha et Ghonei and Padilla [46] Study This

Chapter 2 – Literature Review

2.3 Solar power generation

Increasing concerns about environmental issues have encouraged researchers to study small-scale solar power plants. A common distributed scale solar power technology is a

CSP plant which generates electricity using ORC. The concentrated photovoltaic/thermal

(CPVT) solar plant is also an attractive technology since it is particularly suitable for CHP applications in which the heat is available and/or required at low temperatures [48].

2.3.1 Organic Rankine Cycle (ORC)

Power generation through a low-temperature ORC is a relatively well-known technology to harness energy from low quality heat sources such as solar, geothermal, biomass and waste heat energy. However, there are some drawbacks to solar ORC systems such as high investment cost, low efficiency, and large equipment footprint.

Baral et al. [49] studied the working fluid selection and economics of a solar based ORC system. They presented the effect of turbine inlet temperature on the solar power efficiency and the payback period of the system for various working fluids. For a turbine inlet temperature in the range of 100°C to 150°C, the ORC and solar power cycle efficiency ranged from 9% to 12%, and 14% to 17%, respectively. In contrast, Quoilin et al. [50] designed a higher temperature ORC unit for a rural clinic in Berea district of Lesotho in southern Africa. The overall electrical efficiency was lower at 8%, for SES36 working fluid at the evaporation temperature of 169°C. Decreasing the evaporation temperature to 85°C, resulted in an overall efficiency of 3.6% for R134a. Freeman et al. [51] presented a techno- economic model to study the performance and cost of a domestic combined solar heat and

Chapter 2 – Literature Review

power system. They showed that the electrical output from the system is a function of flow rate, temperatures, and working pressures in the ORC subsystem, and design and operation of the solar collector array. For a heat source temperature of 106°C, they found an average

ORC efficiency of 12.7%. In some of these studies, it may not be possible to select or fabricate a turbine that operates at the optimal specific speed and diameter required to attain these efficiencies which can partially explain large efficiency variations. Moreover, cycle efficiencies which are higher are sometimes reported instead of the overall system efficiency. In contrast, Tampier et al. [52] investigated biopower production of practical systems at flue gas temperatures exceeding 750oC. Efficiencies for seven technologies investigated ranged from 6% to 16% for distributed generation applications.

Delgado-Torres et al. [53] analyzed the low-temperature solar ORC using four different solar collector models: compound parabolic collector, two models of flat panel collectors and an evacuated tube collector (ETC). The maximum temperature for the cycle was

150°C. The objective was to obtain the overall efficiency of the solar ORC and its optimization for different collectors and working fluids. The maximum efficiency obtained was 8.5%, using Isopentane as the working fluid. The choice of working fluid affects the economics of ORC. Tchanche et al. [54] studied the selection of the most suitable working fluid for a low-temperature ORC. The heat source of power cycle was hot water with a maximum temperature of 90°C, which was provided by solar collectors. They compared and classified the fluids based on different criteria such as pressure ratio, mass and volume flow rates, efficiencies, cycle heat input, safety and environmental issues. None of the

Chapter 2 – Literature Review

fluids met all the desirable criteria. They found R134a as the most suitable working fluid for small scale solar applications when using a low-temperature heat source.

As discussed, several researchers have studied different aspects of ORC power generation systems, and they show that the efficiency of such systems increases at higher evaporation temperatures. In cold remote communities, the hybrid photovoltaic/thermal technology is an alternative for the ORC, as it provides the opportunity of generating combined heat and power at low temperatures with electrical efficiencies increasing with decreasing fluid temperature. Due to reported discrepancies in efficiencies, accessing low-temperature ORC data of an installed system in a cold climate is the adopted methodology.

2.3.2 Hybrid photovoltaic/thermal system

Hybrid photovoltaic/thermal systems offset more CO2 and can reduce the energy payback times compared to PV only systems [55]. The CPVT systems are popular because of their twofold purpose; cooling the PV module to improve its electrical performance and generating useful thermal energy. The capital investment of the PVT systems is higher compared to PV due to the additional required components related to thermal energy generation. However, long-term, effective financial incentives provided by governments and utilities can empower the renewable energy industries to overcome the high investment cost barrier [56]. Figure 2.1 shows how the CPV cells are mounted on the focus line of parabolic mirrors.

Chapter 2 – Literature Review

Figure 2.1: CPV cells attached to the focus line of parabolic mirrors from Reference [57]

The performance of a parabolic trough photovoltaic was described by Coventry [58]. He used a custom-built testing unit to measure the thermal and electrical performance of a

CHP system in Australia. The flux profile was measured along the receiver using a

0.04×0.05 m2 solar cell which was moved along the focal line of the solar trough. The thermal and electrical efficiency of the solar CHP system, with a concentration ratio of 37 suns, was found to be 58% and 11%, respectively. It was also shown by experiment that the efficiency drop with temperature is approximately 0.35%/°C. Calise et al. used

TRNSYS to develop a dynamic model of a solar PVT consisting sheet-and-tube collectors together with cooling [59] and a CPVT system with a concentration ratio of 10, along with absorption chiller and water desalination unit [60]. They used the weather data of Naples, south of Italy. To reduce the simple payback period, a parametric analysis was presented which resulted in suggestions including: the optimal ratio between the solar field area and the capacity of the absorption chiller should be around 5.9 m2/kW, and the outlet temperature of the solar field should be approximately 85°C in summer and 55°C in winter.

Chapter 2 – Literature Review

Calise et al. [48] also presented a detailed, one-dimensional, steady-state, finite volume model of a CPVT solar collector. The total length of the CPVT was 10 m and the concentration ratio was 10 suns. They showed that, as the collector temperature rose along the length of the CPVT, the electrical and thermal efficiency dropped. Moreover, increasing the HTF flow rate results in thermal and electrical efficiency improvement due to lower collector temperatures at higher flow rates.

Buonomano et al. [61] focused on design and simulation of CPVT using parabolic dish collectors. The goal was to include the dish CPVT collector model in TRNSYS for dynamic simulation of solar tri-generation systems. The effect of design parameters on the electrical and thermal efficiencies of the CPVT system was investigated. The model developed in this study can be used to design a system by detecting the optimal values of the main design parameters. Calise et al. [62] presented a dynamic model of parabolic dish CPVT integrated into a solar heating and cooling system. They found better overall performance than that of the system investigated by the authors in Reference [59], based on flat panel

PVT. As addressed by the authors in References [60] and [62], although the investigated system is expensive as it uses triple-junction PV cells, there is a considerable potential to reduce the system cost due to smaller required area of CPV cells.

Bernardo et al. [63] evaluated the performance of a CPVT system by comparing the simulation results with measured data. The test facility consists of a parabolic trough collector with a concentration ratio of 7.8 and PV cells with 16% efficiency. As the concentration ratio was low, the electrical efficiency obtained was 6.4%. They also

Chapter 2 – Literature Review

compared the performance of a hybrid PVT to the traditional side-by-side PV and thermal collector for Stockholm, Lisbon, and Lusaka. The ratio between the annual electric production of the hybrid and standard PV module was obtained to be 3.6 for Stockholm,

4.1 for Lisbon, and 4.4 for Lusaka. Moreover, it was shown that the required ground area for both systems to generate the same amount of electric and thermal output is similar.

As studied from the above literature, the use of CPV cells on the solar trough receiver tube to generate electricity has not been investigated in cold weather conditions. The traditional solar power generation method using ORC may not be an economic approach for such climates since high-temperature heat source is needed to increase the electrical efficiency.

In contrast, CPV has higher efficiencies at lower temperatures. This characteristic may enable CPVT technology to be a competing candidate for heat and electricity production in remote communities to eliminate fossil fuels. Lower temperatures can increase the workplace safety and lower operator qualifications, important, for example, in First Nation communities. The literature review presented in Section 2.3.2 is summarized in Table 2.3.

Chapter 2 – Literature Review

- - -

40°C.

efficiency. efficiency.

Comments

Studied the effect of of effect the Studied of effect the Studied

on the temperature and and temperature the on and temperature the on

temperatures as low as as aslow temperatures

different design parameters parameters design different parameters design different

Solar system is exposed to to exposed is system Solar

2.3.2

No No

Yes Yes

N/A N/A

mode

Passive Passive

Yes Yes Yes Yes Yes

Num. Num.

model

transient

No No No No

Yes Yes

Exp.

study

23 23

11 13 13

N/A

(°C)

1 to 23 to 1

Mean Mean

winter

Temp. Temp.

highest

11 to to 11

0 5 5

N/A

(°C)

5 to 9 to 5

Mean

winter

Temp. Temp.

lowest lowest

21 to to 21

: Summary of literature of Section in presented review Summary :

and and

N/A

Italy Italy

Place

Lusaka

Naples, Naples, Naples,

, Lisbon, Lisbon, ,

Australia

Canberra, Canberra,

Northern Northern

Winnipeg Winnipeg Manitoba

Stockholm

Table

TC/PVT

system

Parabolic Parabolic

dish/PVT

PTC/PVT PTC/PVT P

PTC/PVT

Solarpower

collector/PVT

Sheet and tube tube and Sheet

al.

59]

al.

[ [60]

[58]

al. al.

et et

Research Coventry Coventry Calise Calise Buonomano [61] Bernardo [63] Study This

Chapter 3

Parabolic Solar Trough Experimental Pilot Plant and

Numerical Model

Southern Manitoba receives an annual DNI of 1,800-2,400 kWh/m2 which represents one of the best solar resources in Canada [64]. For example, by taking advantage of Winnipeg’s

2,300 hours of sunshine per year, the Manitoba Hydro head office building is designed to use passive solar systems, such as natural daylighting, south-facing winter gardens, and solar chimney. The building uses 70% less energy compared to a similar office building of conventional design [65]. Despite the rich solar resources in Manitoba, most of the North

American CSP systems have been located in the southwestern United States. Manitoba

Hydro has identified the parabolic solar trough as an important technology for thermal energy generation, which requires research and applied analysis in Manitoba’s cold climate. To meet these requirements, RRC and the University of Manitoba partnered to develop a solar trough pilot plant in Winnipeg as it is chosen by many industries, such as the aerospace industry, as a location to investigate cold weather testing.

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

This project seeks to address the following questions which are of important concern in

Manitoba:

• Operational issues: Given Manitoba's diverse weather, especially during winter,

does the solar trough system encounter any major operation and maintenance

problems?

• Energy performance: Considering the solar resource and operating conditions in

Manitoba, how much actual energy can be captured by a solar trough system

operating at higher latitudes? Is there any opportunity for further research

objectives such as electricity generation?

• Cost effectiveness: Considering the system cost, maintenance requirements, and

energy production values, what are the opportunities for this technology in

Manitoba and remote diesel communities? Can the operational performance,

energy production, and economics be improved by optimizing the system?

3.1 Concentrated solar trough demonstration pilot plant

The 52-kW solar trough pilot plant was developed by a consortium involving the

University of Manitoba, Manitoba Hydro, RRC, and Abengoa Solar. The first phase of the project including the installation of eight parabolic solar trough units with non-evacuated receiver tubes was started in 2011. The system was commissioned in July 2012 with the on-site guidance and assistance of the Abengoa solar representative. The plant operation was stopped in winter 2014 due to a major cold weather-related control issue and may restart by 2018. The second phase of the project is to expand the solar system to be able to partially provide the thermal energy required for space heating of a greenhouse located in

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

the vicinity of the solar field. In summer, the thermal energy generated can be utilized to operate an absorption cooling unit to supply cooling demands and also continue to provide domestic hot water demands. The cooling and domestic hot water loads would not be as significant as the thermal energy generated during summer, therefore, the excess heat is required to be dumped. Figure 3.1 shows the location of the solar field and the greenhouse.

Figure 3.2 also shows the satellite view of the solar field along with the dimensions. The collectors face south and are installed in two rows oriented east-west. The field is surrounded by a 2-m tall chain link fence with slats to reduce the wind effect. The fence is installed far enough from the collector rows to prevent shading.

Figure 3.1: The satellite view of the solar field and the greenhouse locations, obtained from Google Earth

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

Earth

Google

obtained from from obtained

rough field along with the with along field dimensions rough

: The satellite view of the solar the t satellite The : of view

Figure

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

As shown in Figure 3.3, the trailer housing the equipment, referred to as the control room, is positioned at a 2-m lower elevation relative to the solar field to facilitate a future HTF drain back system. The purpose is to be able to discharge the thermal oil in case of any leakage incident, and drain back the hot HTF after sunset and store the remaining heat in the HTF inventory through night time. This can help the system start-up in the morning, especially during winter, since it is easier to pump warmer HTF.

Figure 3.3: Different elevations of the solar collectors and the control room to provide drain back effect, obtained from Google Earth

The trough collectors have an aluminum structure with silvered or aluminized polymer reflector film, bonded to the aluminum sheets. The system was designed, shipped, fabricated, and supervised during installation by Abengoa Solar based on the specifications provided to them. They supplied eight PT-1 collector modules, receiver tube assemblies,

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

support pylons, flex hoses, drive system, controls, and spare parts. The module design specifications such as area, efficiency curve, incident angle modifier equation, concentration ratio, and the diameter of the receiver tube can be obtained from RRC and

Abengoa Solar through confidentiality agreements.

The plant caisson foundations are designed assuming a frost depth of 1.5 m in Winnipeg with lengths and diameters varying between 1.5-2.4 m and 0.4-0.6 m, respectively.

Figure 3.4 shows the construction site at RRC.

Figure 3.4: Solar pilot plant construction site at Red River College. The pictures show the different stages of the project from August 2011 to July 2012.

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

Therminol 59 is chosen as the HTF which is a synthetic liquid phase heat transfer oil with an operating range of -40°C to 320°C. It has a boiling point of 289°C at the operating pressure of 101.3 kPa [66]. Heat transfer oils are mostly flammable and prone to leakage.

Therefore, the solar field piping is all welded with a minimum number of threaded fittings.

The pilot system total volume is approximately 166 liters. As presented in Figure 3.5, most of the piping sections are insulated with 1 inch of Earthwool-1000° which is a molded, heavy density, one-piece insulation made from inorganic glass fibers [67]. The motion of the receivers with respect to the fixed piping is facilitated by a flexible hose which is surrounded by an interlocked metal hose with insulation in between.

Figure 3.5: The piping sections insulated with 1 inch of Earthwool-1000°

3.1.1 Circulating pump

A variable speed pump circulates the HTF through the solar system piping. The circulating pump is a ZTND model 032200 made by SIHI Pumps, designed to circulate high- temperature heat transfer oils [68]. It is located inside the control room, which is 20 m away

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

from the inlet of the first collector. The design conditions are 4.5 m3/h at 24.4 m of head and at operating temperature of up to 350°C. The solar system pump is shown in Figure 3.6.

Figure 3.6: SIHI ZTND circulating pump located inside the control room at the solar field

3.1.2 Expansion tank

An expansion tank equipped with a nitrogen blanket is designed to maintain a positive pressure on the heat transfer oil through the full range of the operation. The volume of the tank, shown in Figure 3.7, is 114 liters.

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

Figure 3.7: The solar system expansion tank with volume of 114 liters located inside the control room

3.1.3 Heat exchanger

To simulate a heat load, an air-cooled heat exchanger is utilized to remove the heat and cool down the HTF whenever the temperature exceeds the maximum set value. The 50-kW unit is a horizontal air flow oil cooler made by Exact Exchanger Inc. with an air flow of

9,490 m3/hr [69]. The operation temperature of the solar collectors is set at 170°C. To keep the HTF temperature around the set value, it is necessary to actively dissipate heat using the oil cooler. Figure 3.8 shows the air flow oil cooler located inside the control room of the solar field, next to the expansion tank.

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

Figure 3.8: Air cooled heat exchanger located in the control room

3.1.4 Weather station

The on-site meteorological data collection is implemented using a Series 500

WeatherHawk weather station which is installed on the top of the control room as shown in Figure 3.9. The Series 500 weather stations measure air temperature and relative humidity, barometric pressure, rainfall, wind speed and direction, and solar radiation [70].

The specifications of the weather station sensors are listed in Table 3.1.

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

Table 3.1: Specification of the WeatherHawk weather station measurement sensors [70]

Sensor Sensor type Measurement range

Air temperature Ceramic, capacitive -52°C to +60°C

Relative humidity Polymer, capacitive 0% to 100%

Capacitive silicon Barometric pressure 60 to 120 kPa strain gauge

Stainless steel Rainfall 0 to 200 mm/hr piezometric surface

Ultrasonic Speed: 0 to 60 m/s Wind speed and direction transducers Direction: 0° to 360°

Solar radiation Silicon pyranometer 0 to 1000 W/m2

Figure 3.9: The Series 500 WeatherHawk weather station installed on the top of the control room

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

3.1.5 Pyrheliometer

Pyrheliometer is a device to measure the DNI emitted by the sun. An EKO MS-56 pyrheliometer by Ammonit is used at the solar field which is sensitive to solar irradiance through the spectral range of 200-4,000 nm, and measures the maximum irradiance of

2,000 W/m2 [71]. It can work under extreme conditions, having a temperature range of

-40°C to 80°C [71]. A built-in low power heater is devised inside the sensor to reduce the possibility of dew-deposition or condensation on the outside of the entrance optics.

Figure 3.10 shows the device on the top of the control room.

Figure 3.10: The EKO MS-56 pyrheliometer, located on top of the control room, measures the solar DNI at the solar field

3.1.6 Instrumentation

The solar system is instrumented to track the energy flow throughout the system. To maintain safe operating conditions, the system operating parameters such as pressure,

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

temperature, and flow rate are monitored by the controller. The controller can also record the system data for remote monitoring and download purposes. A schematic of the instrumentation and piping is presented in Figure 3.11 [72].

Figure 3.11: Solar trough pilot plant instrumentation and piping diagram adapted from Reference [72]. The solid lines show the piping and the dashed lines show the electric wire connections.

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

The solar trough pilot plant is designed to operate remotely with minimum operator intervention requirements. A master controller (MC) operates the pilot plant by monitoring operational parameters through the following sequence:

Operational window

The allowable time window for system operation is calculated by the MC once per 24 hours as the primary tracking authorization condition for the solar system. The local solar time must be within the calculated operational window.

Weather

A solar start-up sensor continuously measures the solar intensity and outputs a voltage to the controller. The voltage is compared to a minimum value by the controller and if it remains above this value for a specified period, the solar intensity is considered reliable for energy generation. Another important parameter at this stage is the wind speed measured by a wind anemometer. The wind speed must stay below a maximum allowable value to maintain equipment safety. Figure 3.12 shows the solar start-up sensor and the wind anemometer which are located between the two collector rows.

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

Figure 3.12: Solar start-up sensor and wind anemometer measure the solar intensity and wind velocity at the solar field

Static safety parameters

The system static condition is referred to as the condition at which the collectors are in stow position and not yet authorized to start tracking. All static safety parameters such as field pressure and temperature and the tank liquid level must be within operational limits before transferring the collectors to dynamic mode.

Dynamic safety parameters

After satisfying all the above prerequisites, the system moves to start-up mode of operation.

For the collectors to start tracking, the fluid flow in the solar pilot plant must be verified by two individual instruments: a DY025 vortex shedding flow meter manufactured by

Yokogawa Electric Corporation [73] and a C-10 type Wika pressure transmitter [74]. The pressure transmitter is installed at the top of the expansion tank which measures the pressure of the oil. After starting the pump, the readings of both devices must be within the

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

operational limits for a specified period to authorize the collectors to start tracking.

Table 3.2 presents the specifications of the flow meter and pressure transmitter.

Table 3.2: The specifications of the flow meter and differential pressure switch used in the solar pilot plant [73][74]

Operating/media Ambient Operating Device temperature temperature Accuracy pressure range range ±0.75% of DY025 vortex Process pressure -29°C-250°C -29°C-85°C reading flow meter limit: -0.1 MPa (liquids) C-10 type ≤0.5% of Wika pressure -30°C-100°C -30°C-85°C 0-103 MPa span transmitter

During the pilot plant operation, the pressure and temperature of the solar field are continuously measured and monitored. Five T-type stainless steel shielded and ungrounded thermocouples measure the temperature of the oil. As shown in Figure 3.11, these thermocouples measure the oil temperature at the supply and return pipes inside the trailer, at the center of each row of the collectors, and at the cross over location in the middle of the two rows. The temperature range of the thermocouples is -200°C to 350°C with the accuracy of ±1°C [75]. Figure 3.13 shows two thermocouples; one is installed on the supply pipe and the other is being installed on the cross over between two collector rows.

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

3.1.7 Sun-tracking sensors

A bracket holding two sun-tracking sensors is mounted on each side of the receiver tube of the northern row. The sensors generate a voltage proportional to the solar irradiance. As long as the voltage ratio of the upper side sensor to the lower side sensor is greater than

0.04, the tracking system resumes searching for the optimal positon. When the voltage ratio becomes less than 0.04 the troughs are in focus. Figure 3.14 shows the tracking sensors on the receiver tube. To prevent cycling of the collectors when the clouds pass, a time delay is incorporated into the controller logic.

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

Figure 3.14: Sun-tracking sensors on the receiver tube. The first pair is on the upper side and the second pair is on the lower side of the tube.

3.1.8 Controller hardware

The solar pilot plant safe operation is achievable through communication of two main controllers: Master Controller (MC) positioned in the control room and Local Controller

(LC) positioned between the two collector rows in a steel containment channel. Each of the controllers is equipped with a Campbell Scientific CR1000 datalogger which is part of the control system [76]. CR1000 can be used in a wide range of measurement and control functions, reading the sensor electrical signals and converts them to the required units. The specifications of each sensor determine the multipliers and offsets required for the proper unit conversion. Table 3.3 presents the CR1000 channels, the sensors connected to them, and the signal levels. The data can be stored in the datalogger memory and transferred to

PC via external storage or telecommunications. The experimental measurements of the

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

sensors listed in Table 3.3, are presented in Appendix A for two months of July and

February 2013. Figure 3.15 shows the location of the LC in the solar field.

Figure 3.15: The Local Controller located at the solar field that communicates with the Master Controller located inside the control room via CR1000 datalogger

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

Table 3.3: The CR1000 channels, the connected sensors, and the signal levels of each sensor [72]

CR1000 Sensor Sensor type Signal level channel

1H Level status Switch closed if not tripped 2.0 V

2L Start-up sensor Switch closed if not tripped 0-2.5 V

Switch closed if above 3H Low-pressure switch 2.5 V minimum

3L Demand switch Dry contact closure 2.5 V

5L Collector pressure sensor Linear DC voltage output 5.0 VDC

P1 Anemometer TTL Pulse 0-5 V

Dry contact closure: C4 AC power status 5.0 V Open=No AC power Dry contact closure: C5 Flow status 5.0 V Open=No flow Temperature: MUX 1 Thermocouple 0-25 mV Absorber, Field A #1 Temperature: MUX 2 Thermocouple 0-25 mV Absorber, Field A #2 Flow Meter: MUX 3 Voltage 400-2000 mV collector field Temperature: MUX 4 Thermocouple 0-25 mV center solar field Temperature: MUX 5 Thermocouple 0-25 mV to the entire field Temperature: MUX 6 Thermocouple 0-25 mV from the entire field

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

3.2 Solar trough pilot plant simulation model developed

The numerical simulation of the solar pilot plant performance is implemented by applying an energy balance to the collector and the receiver tube. This method is used by the National

Renewable Energy Laboratory (NREL) to analyze and model a parabolic trough solar receiver [77]. The simulation includes the incident normal solar irradiance on the collector surface, optical losses, heat losses from the receiver, and heat gain into the HTF. One- dimensional energy balance solution provides reasonable results for receiver lengths of less than 100 m [77]. Figure 3.16 shows the heat transfer mechanisms around the receiver tube and the glass envelope. Equations 3.1 to 3.3 present the energy balance implemented in the solar pilot plant.

Figure 3.16: The schematic of the one-dimensional energy balance on the receiver tube developed for the solar pilot plant transient simulation

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

∂Tg q′ = m c (3.1) net,g g pg ∂t

∂Tp q′ = m c (3.2) net,p p pp ∂t

dT mHTFcp ∂THTF q′ − ṁ c ( HTF) = HTF (3.3) 12,conv HTF pHTF dx ∂t

where

cpg: Glass envelope specific heat (kJ/kg K)

cpp: Receiver tube specific heat (kJ/kg K)

cpHTF: Heat transfer fluid specific heat (kJ/kg K)

mg: Mass of glass envelope (kg/m)

mp: Mass of receiver tube (kg/m)

mHTF: Mass of heat transfer fluid in unit length of pipe (kg/m)

ṁ HTF: Heat transfer fluid flow rate (kg/s)

Tg: Glass envelope temperature (°C)

Tp: Receiver tube temperature (°C)

THTF: Heat transfer fluid temperature (°C)

The terms q'net, g and q'net, p are net heat transfer rates on the glass envelope and the receiver tube, respectively and are determined using Equations 3.4 and 3.5.

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

′ ′ q′net,g = q′5,SolAbs + q′34,rad + q′34,conv − q 56,conv − q 57,rad (3.4)

′ ′ ′ q′net,p = q′3,SolAbs − q 34,conv − q 34,rad − q 12,conv (3.5)

where the heat flux terms are defined in Table 3.4.

Table 3.4: Definition of the heat flux terms used in Figure 3.16 and Equations 3.4 and 3.5

Heat flux Heat transfer path Heat transfer mode (kW/m) From To Inner receiver tube q' Convection Heat transfer fluid 12, conv surface Outer receiver tube Inner glass envelope q' Convection 34, conv surface surface Outer glass envelope q' Convection Ambient 56, conv surface Outer receiver tube Inner glass envelope q' Radiation 34, rad surface surface Outer glass envelope q' Radiation Sky 57, rad surface

q' Solar irradiation Incident solar Outer glass envelope 5, SolAbs absorption irradiation surface

q' Solar irradiation Incident solar Outer receiver tube 3, SolAbs absorption irradiation surface

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

3.2.1 Convection heat transfer between the HTF and the absorber

The convection heat transfer between the inside surface of the receiver tube to the HTF is calculated using Equations 3.6 and 3.7.

q′12,conv = h1D2π(T2 − T1) (3.6)

k1 h1 = NuD2 (3.7) D2

where

q′12,conv: Convection from inner receiver tube surface to HTF (W/m)

2 h1: HTF convection heat transfer coefficient at T1 (W/m K)

D2: Inside diameter of the receiver tube (m)

T1: Mean temperature of the HTF (°C)

T2: Inside surface temperature of receiver tube (°C)

2 NuD2: Nusselt number based on D

k1: Thermal conductance of the HTF at T1 (W/m K)

The NuD2 is calculated using Equation 3.8 for both laminar and turbulent flow. For laminar flow in a pipe, Re < 2,300, and uniform surface heat flux the Nusselt number is constant and equal to 4.36 [78]. For turbulent flow the Gnielinski correlation [79] is valid over a wide range of Reynolds numbers including the transition region, 2,300 < Re < 5×106, and

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

0.5 < Pr < 2,000. The correlation also accounts for fluid property variations between the receiver wall temperature and the bulk fluid temperature.

0.11 (f2/8)(ReD2 − 1000)Pr1 Pr1 NuD = ( ) (3.8) 2 1/2 2/3 Pr 1 + 12.7(f2/8) (Pr1 − 1) 2

where

f2: Friction factor for the inner surface of the receiver tube

Pr1: Prandtl number evaluated at the HTF temperature, T1

Pr2: Prandtl number evaluated at the receiver inner surface temperature, T2

2 ReD2: Reynold number based on the receiver inner diameter, D

The friction factor correlation for the smooth surface condition, given by Equation 3.9, has been developed by Petukhov [78] :

−2 f2 = [0.79 ln(ReD2) − 1.64] (3.9)

3.2.2 Convection heat transfer from the receiver to the glass envelope

To calculate the convective heat transfer between the receiver and the glass envelope, two mechanisms are evaluated: free-molecular and natural convection.

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

Vacuum in annulus

Under vacuum condition (pressure < ~133.32 Pa), free molecular convection heat transfer occurs between the receiver tube and the glass envelope. From Reference [81], the heat transfer coefficient in Equation 3.10 is calculated using Equations 3.11 to 3.13.

q′34,conv = h34D3π(T3 − T4) (3.10)

k h = std 34 D (3.11) ( 3 ln(D /D ) + bλ(D /D + 1)) 2 4 3 3 4

(2 − a)(9γ − 5) b = (3.12) 2a(γ + 1)

(2.331×10−20)(T + 273.15) 34 (3.13) λ = 2 Paδ

where

q′34,conv: Convection from outer receiver tube surface to inner glass envelope

surface (W/m)

D3: Outer diameter of receiver tube (m)

D4: Inner diameter of glass envelope (m)

T3: Outer absorber surface temperature (°C)

T4: Inner glass envelope surface temperature (°C)

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

T34: Average temperature (T3+T4)/2 (°C)

2 h34: Convection heat transfer coefficient for the annulus gas at T34 (W/m K)

kstd: Thermal conductance of the annulus gas at standard temperature and

pressure (W/m K)

b: Interaction coefficient

λ: Mean-free-path between collisions of a molecule (cm)

a: Accommodation coefficient

γ: Ratio of specific heats for the annulus gas

Pa: Annulus gas pressure (mmHg)

δ: Molecular diameter of annulus gas (cm)

The coefficients in Equations 3.11 to 3.13 for three different annulus gases are given in

Table 3.5.

Table 3.5: Coefficients in Equations 3.11 to 3.13 for three annulus gases [77]

kstd Annulus gas b λ (cm) γ δ (cm) (W/m K) Air 0.02551 1.571 88.67 1.39 3.53e-8 Hydrogen 0.1769 1.581 191.8 1.398 2.4e-8 Argon 0.01777 1.886 76.51 1.677 3.8e-8

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

Pressure in annulus

When the receiver is non-evacuated for pressures > ~133.32 Pa, natural convection heat transfer occurs between the absorber and the glass envelope which can be estimated from

Raithby and Holland’s correlation [77], presented by Equation 3.14. The correlation

4 assumes long, horizontal, concentric cylinders and is valid for RaD3 > (D4/(D4 − D3)) .

RaD3 1/4 2.425k34(T3 − T4)(Pr. ) 0.861 + Pr34 (3.14) q′34,conv = 3/5 5/4 (1 + (D3/D4) )

in which

gβ(T − T )(D )3 Ra = 3 4 3 (3.15) D3 αν

where

D3: Outer absorber surface diameter (m)

D4: Inner glass envelope surface diameter (m)

T3: Outer absorber surface temperature (°C)

T4: Inner glass envelope surface temperature (°C)

k34: Thermal conductance of annulus gas at T34 (W/m K)

Pr34: Prandtl number

3 RaD3: Rayleigh number evaluated for D

β: Volumetric thermal expansion coefficient (1/K)

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

T34: Average temperature (°C)

3.2.3 Convection heat transfer from the glass envelope to the atmosphere

The convection heat transfer from the glass envelope to the atmosphere, given by

Equation 3.16, is the largest source of heat loss and is either forced or natural depending on whether there is wind. The Nusselt number in Equation 3.17 is calculated for both natural and forced convection.

q′56,conv = h56D5π(T5 − T6) (3.16)

in which

k56 h56 = NuD5 (3.17) D5

where

q′56,conv: Convection from outer glass envelope surface to ambient (W/m)

T5: Glass envelope outer surface temperature (°C)

T6: Ambient temperature (°C)

2 h56: Convection heat transfer coefficient for air at T56 (W/m K)

k56: Thermal conductance of air at T56 (W/m K)

D5: Glass envelope outer diameter (m)

NuD5: Average Nusselt number based on the glass envelope outer diameter

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

Without wind

In this case, the natural convection occurs between the pipe and the environment. The

Nusselt number can be evaluated by Churchill and Chu correlation [78], presented by:

2 0.387Ra1/6 ̅̅̅̅ D5 (3.18) NuD5 = {0.60 + 9/16 8/27} [1 + (0.559/Pr56) ]

in which

gβ(T − T )D3 5 6 5 (3.19) RaD5 = α56ν56

1 β = (3.20) T56

ν56 Pr56 = (3.21) α56

where

5 RaD5: Rayleigh number for air based on the glass envelope outer diameter D

β: Thermal expansion coefficient (1/K)

g: Gravitational constant (9.81 m/s2)

T56: Average temperature (T5+T6)/2 (°C)

2 α56: Thermal diffusivity for air at T56 (m /s)

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

Pr56: Prandtl number for air at T56

2 ν56: Kinematic viscosity for air at T56 (m /s)

With wind

If the wind blows, the Zhukauskas correlation for cross flow over a circular cylinder, given by Equation 3.22, is used [78].

Pr 1/4 ̅̅̅̅ m n 6 (3.22) NuD5 = CReD5Pr6 ( ) Pr5

in which the C, m, and n coefficients are given in Table 3.6.

Table 3.6: Coefficients in Equation 3.22 with respect to Reynolds number

ReD C m n

1-40 0.75 0.4 For Pr ≤ 10: n= 0.37 40-1,000 0.51 0.5

1,000-200,000 0.26 0.6 For Pr > 10: n = 0.36 200,000-1,000,000 0.076 0.7

3.2.4 Radiation heat transfer between the receiver and glass envelope

From Reference [78], the radiation heat transfer between the receiver and the glass envelope is evaluated using:

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

σπD (T4 − T4) q′ = 3 3 4 34,rad 1 (1 − ε )D (3.23) ( + 4 3) ε3 ε4D4

where

′ q 34,rad: Radiation from outer receiver tube surface to inner glass envelope

surface (W/m)

σ: Stefan-Boltzmann constant (W/m2 K4)

D3: Outer receiver surface diameter (m)

D4: Inner glass envelope surface diameter (m)

T3: Outer receiver surface temperature (K)

T4: Inner glass envelope surface temperature (K)

ε3: Absorber selective coating emissivity

ε4: Glass envelope emissivity

3.2.5 Radiation heat transfer between the glass envelope and sky

The temperature difference between the glass envelope and the sky causes radiation heat transfer. The envelope is considered as a small convex gray body in a large black body cavity (sky). The radiation between the glass envelope and the sky is calculated using:

4 4 q′57,rad = σε5πD5(T5 − T7 ) (3.24)

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

where,

q′57,rad: Radiation from outer glass envelope surface to the sky (W/m)

D5: Outer glass envelope diameter (m)

ε5: Glass envelope outer surface emissivity

T5: Outer glass envelope surface temperature (K)

T7: Effective sky temperature (K)

3.2.6 Solar irradiation absorption in the glass envelope

To simplify the model, the solar absorption into the glass envelope is assumed as a heat flux and estimated using Equation 3.25. This is not physically accurate since the solar absorption is a heat generation phenomenon and is a function of the glass thickness.

However, as long as the glass envelope is thin with a small absorption coefficient, the heat flux assumption introduces minimal error [77]. The parameters which influence the optical efficiency of solar troughs are presented in Table 3.7.

q′5,SolAbs = q′siηenvαenv (3.25)

where

q′5,SolAbs: Solar irradiation absorption on outer glass envelope surface (W/m)

q′si: Incident solar irradiation per receiver length (W/m)

ηenv: Effective optical efficiency of the glass envelope

αenv: Absorptance of the glass envelope

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

The effective optical efficiency of the glass envelope is given by:

ηenv = a1a2a3a4a5a6ρclK (3.26)

where

K: Incident angle modifier

a1: Shadowing error (bellows, shielding, supports)

a2: Tracking error

a3: Geometry error (mirror alignment)

a4: Dirt on mirrors error

a5: Dirt on receiver tube error

a6: Unaccounted errors

ρcl: Clean mirror reflectance

Table 3.7: Estimates of the optical efficiency terms in Equation 3.25 [77]

Parameter Value

a1 0.974

a2 0.994

a3 0.98

a4 Reflectivity/ρcl

a5 (1+ Dirt on mirrors)/2

a6 0.96

ρcl 0.935 * Reflectivity is typically between 0.88 and 0.93

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

3.2.7 Solar irradiation absorption in the receiver

The solar absorption by the receiver tube is calculated using Equations 3.27 and 3.28. This term can also be assumed as heat flux since it occurs close to the surface [77].

q′3,SolAbs = q′siηabsαabs (3.27)

ηabs = ηenvτenv (3.28)

where

q′3,SolAbs: Solar irradiation absorption on outer receiver tube surface (W/m)

q′si: Solar irradiation per receiver length (W/m)

ηabs: Effective optical efficiency of the absorber tube

αabs: Absorptance of absorber tube

τenv: Transmittance of the glass envelope

3.3 Solar trough pilot plant transient numerical model implementation

As described in Section 1.7, the transient numerical model of the solar trough pilot plant was developed using MATLAB®/Simulink® and Thermolib. A simplified schematic of the solar system model is presented in Figure 3.17. The solar trough block contains several sub-blocks, described in Appendix B, that estimate parameters such as thermophysical properties of the HTF, the receiver tube and the glass envelope at different temperatures, solar zenith and azimuth angles, and IAM. The parameters are utilized by another series of

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

sub-blocks which calculate the convection and radiation heat transfer rates based on the equations described in Section 3.2. The energy balance block calculates the HTF, the receiver tube wall, and the glass envelope temperature along the receiver length at each time step of one second using the estimated heat transfer rates. The simplification assumptions made in the simulation are listed in Table 3.8.

A research objective is to integrate controls in the simulations (see Section 1.6). Through the control blocks supplied by Simulink®, it is possible to model the Proportional–Integral

(PI) controllers used for the simulations without having to program new control blocks. As a part of the model development, a solar system using flat panels and heat storage for a

First Nation Social Enterprise Building located in downtown Winnipeg was modelled to eliminate their natural gas consumption. This opportunity was a stepping stone to develop the models and controls required for the integrated solar trough pilot plant simulation.

Simulation results for this simpler building application are presented in Reference [80].

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

and and Thermolib. The solar

Simulink

MATLAB

blocks which are described in Appendix in are blocksA. described which

everal sub everal

: : The solar trough pilot plant transient model developed in

Figure s consists of troughblock

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

Table 3.8: Simplification assumptions made in the transient model for the receiver simulation

Model component Simplification assumptions

- Solar irradiation is uniform along the receiver length and q'si circumference. - The bracket and the receiver tube shadowing is neglected.

q'3, SolAbs and q'5, SolAbs - Solar irradiation absorption is assumed as heat flux. - Convection between the HTF and the receiver tube is q'12, conv estimated assuming uniform flow. - T1 is the bulk temperature. - Wind direction is assumed normal to the receiver axis to q'56, conv estimate the convection heat transfer between the glass envelope and the atmosphere. - Radiation between the receiver tube and the glass envelope is estimated assuming grey surfaces. q'34, rad - Surfaces are made from long concentric isothermal cylinders. - Effect of annulus gas is neglected. - Radiation between the glass envelope and the sky is q' estimated assuming a small convex grey object in a large 57, rad black body cavity. - Sky temperature is 8°C below the ambient temperature. - Optical properties are uniform. - Optical properties are invariable for all eight receivers. Optical properties - Optical properties are not functions of temperature, except for the receiver tube coating emissivity. - Degradation with time is neglected.

3.4 Solar trough pilot plant model validation

For the validation of the solar pilot plant model, detailed in Sections 3.2 and 3.3, the computed outlet temperature of the collectors was compared to the solar field measured temperature. To do so, the model was configured to apply the same control approach as the

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

solar pilot plant. The operating temperature of the collectors was set at 170°C; i.e. once the

HTF temperature reaches the set-point, the air flow oil cooler starts to cool down the HTF.

The flow rate is constant at 1.2 kg/s as like the pilot plant. Simulations were performed for a period of 365 days. The validation results presented in this section correspond to a random period of 5 days, selected as an example for demonstration. The measured local weather data for the selected five days from September 13th to 17th, 2013 is shown in Figure 3.18.

The solar normal irradiance, wind speed, and ambient temperature were recorded every minute, interpolated per second, and used as input to the solar trough pilot plant model.

Figure 3.18: Ambient temperature, wind velocity, and solar normal irradiance measured at the solar field from September 13th to17th, 2013, recorded every minute by the pilot plant weather station

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

Figure 3.19 presents the comparison of the modelled and measured outlet collector temperature for September 13th to 17th, 2013. To clearly indicate the results, a five-hour period from 11 am to 4 pm on a sunny day is also shown in the figure.

Figure 3.19: Comparison of the HTF outlet temperature obtained from solar trough pilot plant model and experimental measurements for September 13th to 17th, 2013 at a constant HTF mass flow rate of 1.2 kg/s. A five-hour period from 11 am to 4 pm on September 13th is also shown.

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

As observed in the figure, the model results are in excellent agreement with the experimental data, especially during the heat generation periods. Table 3.9 shows the average deviation between the modelled and measured temperature for sunny days when heat is being generated, and for low/no solar irradiance periods such as cloudy days and nights. No model coefficients were calibrated separately to achieve improved results.

Table 3.9: Mean deviation between the modelled and measured outlet temperature of the solar field for high and low/no solar irradiance periods in September 2013

Sunny days Low/no solar Total (heat generation periods) radiation periods Average deviation (°C) 1.37 8.03 6.24

Unlike many experimental facilities which are under complete controlled conditions in a laboratory setting, the solar pilot plant is exposed to the outdoor environment. Therefore, accurate prediction of the system performance is more challenging. For example, the free convection mode when the troughs are in stow position has a significantly higher relative error compared to when the system is operating. The main sources of error are listed below:

• Accurate simulation of the actual conditions for each trough is difficult and would

require a CFD model to predict the wind velocity and its direction as this impacts

Equations 3.16 and 3.17.

• Several connections and piping sections with and without insulation were not

included in the model. Moreover, obtaining accurate boundary conditions and

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

precise insulation values for the piping precludes obtaining high accuracy during

free convection mode, as exemplified in Reference [40].

• Measurement errors are associated with various sensors and devices.

• Simplification assumptions have been applied as explained in Table 3.8.

The model also predicts the glass envelope and the receiver tube wall temperatures.

Figure 3.20 shows the HTF, the tube wall and the glass envelope temperatures at the outlet of each collector, obtained from the solar pilot plant model. The results are presented for a randomly chosen period of one-hour from 12 pm to 1 pm on September 13th, 2013.

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

As observed in Figure 3.20, the glass envelope temperature changes more intermittently compared to those of the receiver tube wall and HTF. This is caused by the direct exposure

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

of the glass envelope to the ambient. The variations of ambient temperature and wind speed and direction have more significant influence on the glass envelope than the tube and the

HTF.

To solve the one-dimensional energy balance equation using finite difference method, the number of nodes along the 50-m receiver tube was set to 30 and 70. As can be seen in

Table 3.10, the model running time with 70 nodes for a five-hour period of solar radiation is almost eight times the running time with 30 nodes, while the results deviation is in the order of 10-5. Therefore, to reduce the running time and computational cost, the number of nodes is set to 30. This is more beneficial when simulating for a month or a year, and also when additional components are added to simulate heating, storage, cooling, and power generation.

Table 3.10: Comparison of model running time and results for 30 and 70 nodes along the 50-m receiver tube

Running time for a five hour Average deviation of Number of Case period of solar radiation results between 1 and 2 nodes (hh:mm:ss) (%) 1 30 00:01:21 3.3 × 10-5 2 70 00:10:55

The developed model is reconfigurable; it can be expanded to simulate storage, heating, cooling, and power generation. Other types of solar technologies such as flat plate collectors can also be modelled and added as a new block. The approach is to eventually

Chapter 3 – Parabolic Solar Trough Experimental Pilot Plant and Numerical Model

be able to use the pilot plant in a hardware-based simulation. As will be described in

Chapter 4, in such approach, not all the hardware components are physically installed when the simulation model is set up. Moreover, it is possible to link the model to the MATLAB®

Optimization Toolbox™. The preferred objective functions can be generated by the solar pilot plant model and transferred to the Optimization Toolbox at each time step to be minimized or maximized by changing the decision variables. The optimization approach is described in detail in Chapter 5.

Chapter 4

Thermal Storage, Heating, Cooling and Power

Generation Models Using Hardware-Based Simulation

The validated transient model of the parabolic solar trough pilot plant, presented in

Section 3.2, is expanded to simulate storage, heating, cooling, and power generation using a Hardware-Based-Simulation (HBS) approach (see Section 1.7). These four energy applications are simulated as add-on components to the solar pilot plant depicted in

Figure 3.2. The model is implemented to use the recorded meteorological data and can either use the pilot plant measurements or, as in this chapter, simulate the collector field temperatures. The meteorological data is recorded by the weather station installed at the solar field, explained in Sections 3.1.4 and 3.1.5. Energy applications are integrated and modelled to address heating, cooling, power, and transportation (electrified vehicles) demands in cold climates in real-time as an extension of this research.

4.1 Latent heat thermal storage using phase change material

Thermal storage, categorized as sensible and latent heat, is a key component that allows the solar trough system to address variable community loads. Latent heat thermal storage devices have large energy storage density with comparatively smaller temperature change [82]. Figure 4.1 compares the heat storage process using sensible and latent heat

Chapter 4 – Thermal Storage, Heating, Cooling and Power Generation Models Using HBS

approach [83]. As shown in Table 4.1, the sensible heat per volume stored in solids and liquids within a temperature interval of 20°C can be significantly less than the heat stored in a phase change material (PCM) [83], especially when compared to salts. However, latent heat storage has known disadvantages due to the properties of the PCM, such as low thermal conductivity, change in density, stability of thermal properties and subcooling [82]. For the pilot plant, the heat storage range varies between 20°C to over

170°C depending on season and design of the integrated system. Therefore, both sensible and latent heat storage can be considered.

Figure 4.1: Schematic of the thermal storage process as sensible and latent heat adapted from Reference [83]

Chapter 4 – Thermal Storage, Heating, Cooling and Power Generation Models Using HBS

Table 4.1: Typical storage densities of sensible and latent heat storage methods [83]

MJ/m3 kJ/kg Comment Sensible heat Granite 50 17 ∆T = 20°C Water 84 84 ∆T = 20°C Latent heat of melting Water 306 330 Melting temperature = 0°C Paraffins 180 200 Melting temperature = 5°C-130°C Salt hydrates 300 200 Melting temperature = 5°C-130°C Salts 600-1,500 300-700 Melting temperature = 300°C-800°C

Studies have shown that a significant reduction of the storage tank volume can be achieved by using PCMs which results in lower material and construction costs [84]. However, according to the levelized cost of electricity calculator on National Renewable Energy

Laboratory (NREL) website [84], the levelized cost of electricity for a 60-MWe CSP plant with latent heat thermal storage is only 0.2 ¢/kWh less than sensible heat thermal storage.

The storage model was developed to use PCM in support of the pilot plant design as space was a concern in the control room.

To model latent heat storage tank with PCM, a moving boundary must be modelled. A schematic of the thermal storage tank design, used in the simulations, is shown in

Figure 4.2. The storage tank contains a number of concentric tubes with the PCM in the annulus and the HTF in tubes. The HTF flows through inner tubes separating it from the

PCM in the annulus. The design shown in Figure 4.2 corresponds to the pilot plant storage system design.

Chapter 4 – Thermal Storage, Heating, Cooling and Power Generation Models Using HBS

Figure 4.2: Schematic of the modelled latent heat thermal storage tank design adopted for the solar trough pilot plant

Esen et al. [85] used a formulation based on the enthalpy method to solve the moving boundary problem. In the enthalpy method, the temperature of the PCM is the only unknown, with enthalpy being a temperature dependent variable. This method generates the latent heat flow through the volume integration using the system enthalpy. The energy equation for the PCM is given by:

∂T (ρc ) = ∇ ∙ (k ∇T) (4.1) p PCM ∂t PCM

where it is assumed that there is no convection and source term inside the control volume.

In the absence of external work and energy source inside an arbitrary volume, V, the increase of the energy content of the volume with time equals the net heat transfer into the volume through its surrounding surface area, A. Thus, the enthalpy equation becomes:

d ∫ ρ h dV = ∫ k ∇T ∙ n dA (4.2) dt PCM PCM

Chapter 4 – Thermal Storage, Heating, Cooling and Power Generation Models Using HBS

where,

3 ρPCM: Density of the PCM (kg/m )

kPCM: Thermal conductivity of PCM (W/m K)

h: Specific enthalpy (kJ/kg)

n: Outward normal to area, A

The PCM enthalpy change is modelled by [85]:

cp,sT, T < Tm1 ∆H(T − Tm1) h(T) = cp,lT + , Tm1 ≤ T ≤ Tm2 (4.3) ∆Tm

{cp,lT + ∆H, T > Tm2

where

cp,s: Specific heat of solid phase (kJ/kg K)

cp,l: Specific heat of liquid phase (kJ/kg K)

Tm1: Lower melting temperature of PCM (°C)

Tm2: Higher melting temperature of PCM (°C)

∆Tm: Tm2 - Tm1 (°C)

∆H: Latent heat of fusion (kJ/kg)

Chapter 4 – Thermal Storage, Heating, Cooling and Power Generation Models Using HBS

The initial and boundary conditions are represented by:

Tf = T = Tambient at t = 0 { (4.4) Tf(z = 0, r) = Tf,in at t > 0

∂T ∂T (z = 0, r = r0 to R) = 0, (z = L, r = r0 to R) = 0 ∂z ∂z

∂T k (z, r = r ) = h(T − T ) (4.5) ∂r 0 r=r0 f

∂T (z, r = R) = 0 {∂r

Equation 4.6 is the discretization of Equation 4.2; the control volume element is considered as a part of a cylinder, as shown in Figure 4.3.

Figure 4.3: The control volume element (j, k) used to discretize Equation 4.2

Chapter 4 – Thermal Storage, Heating, Cooling and Power Generation Models Using HBS

∂h ∂T ∂T ∂T ∂T ρVj,k | = − hAk | + hAk−1 | + hAj | − hAj−1 | (4.6) ∂t j,k ∂R k ∂R k−1 ∂z j ∂z j−1

The finite difference method is used to evaluate ∂T/∂R and ∂T/∂z in Equation 4.6 as:

n n ∂T Tj,k+1 − Tj,k | = (4.7) ∂R k ΔR

n n ∂T Tj,k − Tj,k−1 | = (4.8) ∂R k−1 ΔR

n n ∂T Tj,k − Tj+1,k | = (4.9) ∂z j l

The volume of the element is given by:

2 2 Vj,k = πl|Rk−1 − Rk| (4.10)

with surface element areas estimated as:

A = 2πR l k k

Ak−1 = 2πRk−1l (4.11)

2 2 {Aj = Aj−1 = π|Rk−1 − Rk|

Chapter 4 – Thermal Storage, Heating, Cooling and Power Generation Models Using HBS

The energy equation for the HTF and its discretized form are given by Equations 4.12 and

4.13, respectively.

∂T dT (ρc ) πr2 HTF = −(ṁ c ) HTF + 2πr h(T − T ) (4.12) p HTF o ∂t p HTF dz o r=ro HTF

b 1 n−1 aTr=r + THTF,j−1 + THTF,j Tn = o Δz Δt (4.13) HTF,j b 1 a + + Δz Δt

where

2h ṁ HTF a = and b = 2 (4.14) (ρcp)HTFro ρHTFπro

The Nusselt number for laminar flow in a tube with constant surface temperature is set to

3.66 [78]. For turbulent flow, the Gnielinski correlation, presented by Equation 3.8 in

Section 3.2.1, can be used. The assumptions made to model the storage tank design, shown in Figure 4.2, include:

• Homogeneous and isotropic PCM.

• Negligible thermal resistance for the inner tube.

• PCM thermophysical properties are independent of temperature; however, the PCM

has different thermal conductivity and specific heat in solid and liquid phase which

are included in the simulation.

Chapter 4 – Thermal Storage, Heating, Cooling and Power Generation Models Using HBS

• Natural convection effect during melting is neglected.

• The storage tank is insulated and the heat loss to the environment is neglected.

The selection of PCM is important in the latent heat thermal storage tank design. For remote communities, and for HTFs with similar properties to the solar pilot plant HTF

(Therminol 59), the PCM chosen is Erythritol having lower and higher melting points of

115.7°C and 119.7°C. As experimentally shown by Agyenim et al. [86], Erythritol has the highest energy density in the temperature range of 90°C to 120°C compared to the other

PCMs, such as Magnesium Chloride Hexahydrate (MCHH), RT100, and Acetanilide. The high energy density combined with the fact that Erythritol is commercially available in large quantities makes it suitable for energy storage in the temperature range suitable for the solar trough pilot plant. The properties of Erythritol are presented in Table 4.2.

The storage model solves Equation 4.6 coupled with Equation 4.13. This model is added to the solar trough pilot plant model as a new Simulink block. By appropriately sizing the storage tank for specific applications, it is possible to develop an integrated solar trough design with storage using a HBS approach.

Chapter 4 – Thermal Storage, Heating, Cooling and Power Generation Models Using HBS

Table 4.2: Thermophysical properties of Erythritol [87]

Property Value and unit

Melting point, Tm 117.7°C Heat of fusion 339.8 kJ/kg Specific heat, liquid 2.76 kJ/kg K Specific heat, solid 1.38 kJ/kg K Thermal conductivity, liquid (140°C) 0.326 W/m K Thermal conductivity, solid (20°C) 0.733 W/m K Density, liquid (140°C) 1300 kg/m3 Density, solid (20°C) 1480 kg/m3

4.2 Solar space heating model with thermal storage

The model developed for the greenhouse space heating consists of the solar pilot plant and the greenhouse models integrated with simulated add-on components, including the latent heat storage tank and an auxiliary water heater. Using the meteorological data, measured and recorded by the solar field weather station, the solar pilot plant model computes the

HTF temperature at the outlet of the collector field. The thermal energy provided by the solar plant to the greenhouse is obtained. Control strategies implemented in the model mimic how an actual system would be controlled to operate autonomously.

The greenhouse building, shown in Figure 4.4, is 4 m high, 7.5 m wide and 23 m long. As discussed in Section 3.1, additional trough collector rows are required to provide the space heating demand of the full-size greenhouse. Therefore, for the purpose of this simulation, the greenhouse is downsized to 1/8 of the actual building. Since the greenhouse is located in Winnipeg, where natural gas is readily available, the auxiliary backup system is assumed

Chapter 4 – Thermal Storage, Heating, Cooling and Power Generation Models Using HBS

to be a natural gas water heater. To model the solar heating in remote communities, where natural gas is not available, the model requires to be modified to include heating oil as the auxiliary fuel. A hydronic heating system which circulates a water/glycol solution of 50/50 through radiators is modelled for the greenhouse space heating simulation. The glycol solution properties are presented in Table 4.3.

Figure 4.4: The greenhouse building located next to the solar field: (a) east view and (b) south view

Table 4.3: Thermophysical properties of 50/50 glycol solution [88]

Property Value and unit Freezing point -36.8°C Boiling point 107.2°C Specific heat, (4.4°C – 93.3°C) 3.3 – 3.6 kJ/kg K Dynamic viscosity, (4.4°C – 93.3°C) 6.5 – 0.7 cP

The schematic of the proposed solar heating system model is shown in Figure 4.5. The greenhouse indoor temperature and the glycol solution temperature at the radiator inlet are set to 21°C and 60°C, respectively. Whenever the HTF temperature is in the range of 80°C

Chapter 4 – Thermal Storage, Heating, Cooling and Power Generation Models Using HBS

to 130°C, a controlled 3-way valve directs the HTF to a heat exchanger to exchange heat with the glycol. To simulate other heat users, a heat dump system is included that limits the HTF temperature to 130°C. The remaining required greenhouse heat is supplied by a natural gas water heater. A PI controller is embedded into each 3-way valve controller modules that regulates the flow rate of the glycol solution through the heat exchanger and the water heater such that a temperature of 60°C is obtained at the inlet of the greenhouse radiator. A simplified view of the solar heating system model is shown in Figure 4.6. The greenhouse heating block highlighted in Figure 4.6 is presented in detail in Figure 4.7.

The approach chosen to estimate the size of the storage tank was to run the heating model, shown in Figure 4.6 and Figure 4.7, using different storage sizes to optimize the charging and discharging time. The control strategy is based on discharging the storage tank whenever the HTF temperature is lower than the tank temperature and the solar radiation level is not high enough to increase the HTF temperature to the minimum value of 80°C.

Consequently, the storage tank size is estimated to be 94 liters. The maximum temperature of the tank is set to be at 120°C.

Chapter 4 – Thermal Storage, Heating, Cooling and Power Generation Models Using HBS

system as expected when fully built fully systeminstalled. and when expected as

: : Simplified view of the greenhouse space heating model. System controls are embedded into the module

components to operate the integrated HBS HBS integrated operate to the components Figure

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: The details of the greenhouse heating block which is highlighted in Figure in is 4.6 Figure highlighted block which greenhouse the heating details The : of

Figure

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4.2.1 Results of the solar space heating model

The solar space heating model is applied for two clear days in fall and winter 2013 and results are compared with and without storage. The days are chosen to be October 3rd and

March 16th with ambient temperature ranges of 5°C to 20°C and -25°C to -10°C, respectively. Figure 4.8 shows the HTF and the PCM average temperature for March 16th,

2013. As observed, the storage tank temperature reached 115.7°C at 3 pm when the phase change process initiated. During the phase change period, the tank temperature varies slowly until the PCM melts completely at 119.7°C. The stored heat is used near sunset to heat the HTF until approximately midnight. Simulation of an actual community system would require optimizing the size of the storage tank more appropriately.

Storage charging Storage discharging

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Contributions of the natural gas water heater and the solar pilot plant in the greenhouse space heating demand supply is calculated by the space heating model. The results with and without thermal storage are shown in Figure 4.9, Figure 4.10, and Table 4.4 for the two selected days, October 3rd and March 16th.

(a)

(b)

Figure 4.9: The contributions of the solar pilot plant and the natural gas water heater in the greenhouse space heating demand supply (a) without storage, (b) with storage on October 3rd 2013, obtained from the integrated solar space heating and latent heat storage model. The storage volume is 94 liters. The meteorological data measured and recorded by the solar pilot plant weather station is used.

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(a)

(b)

Figure 4.10: The contributions of the solar pilot plant and the natural gas water heater in the greenhouse space heating demand supply (a) without storage, (b) with storage on March 16th 2013, obtained from the integrated solar space heating and latent heat storage model. The storage volume is 94 liters. The meteorological data measured and recorded by the solar pilot plant weather station is used.

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Table 4.4: The greenhouse space heating demand provided by the solar pilot plant and the natural gas water heater on October 3rd and March 16th with and without thermal storage

October 3rd March 16th

Greenhouse space heating demand (kWh) 124.7 464.5 Without With Without With thermal thermal thermal thermal storage storage storage storage Demand supplied by the solar pilot plant 6.6 10.2 128.0 140.3 (kWh)

Demand supplied by the natural gas water 118.1 114.5 336.5 324.2 heater (kWh)

Solar pilot plant contribution (%) 5.3 8.2 27.5 30.2

By using the 94-l thermal storage tank, the solar pilot plant contribution to the greenhouse space heating demand increased by about 3%. This share can be improved by using a larger storage tank and changing the control strategy to start discharging after the tank is fully charged, which could take several days. Including such a large storage tank in the solar space heating model increases the running time and is left for later studies.

4.3 Solar absorption cooling/refrigeration with thermal storage

About 30% of the global power consumption is for refrigeration and air conditioning purposes [89]. As fossil fuels are the major source of the global electricity generation, the increasing use of electricity operated refrigeration and air conditioning systems has negative environmental impacts. There is an increased interest in efficient cooling technologies powered by renewable energy such as solar assisted absorption cooling.

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Unlike vapor compression refrigeration systems, absorption systems require significantly less electric power [90][91], as there is no need to compress a vapor. Moreover, the heat generated by the solar plant is directly converted to cooling effect by using the absorption refrigeration cycle; vapor compression refrigeration systems, in contrast, require low quality heat to be first converted to electricity and then to the cooling effect.

In Canadian remote communities, homes and small buildings are considered as heating dominant since their heating demands are significantly higher than their cooling demands.

However, buildings such as ice arenas and grocery stores in such communities require refrigeration and are cooling dominant [92]. The cooling process in these buildings results in significant amounts of waste heat rejection and relatively higher monthly electricity bills.

Solar absorption refrigeration systems that use LiBr-H2O as refrigerant, can deliver cooling with a higher coefficient of performance (COP) at lower heat source temperatures [93].

The heat generated by the solar pilot plant during summer is used to provide the thermal energy required to provide cooling effect using an absorption cooling unit. A typical absorption cooling cycle consists of five main components shown in Figure 4.11.

The LiBr-H2O rich solution absorbs heat, supplied by the solar plant in, in the generator, which results in the partial evaporation of the LiBr refrigerant, leaving a LiBr-H2O poor solution that goes to the solution heat exchanger and then the absorber. The LiBr vapor flows to the condenser where it releases heat and returns to the liquid phase, then passes through an expansion valve and enters the evaporator at low pressure. The refrigerant

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evaporates by removing heat from its surrounding where the cooling effect is required. The refrigerant vapor then enters the absorber where it is absorbed by the LiBr-H2O poor solution and pumped back to the generator. The solution heat exchanger improves the process efficiency by heating the refrigerant solution before entering the generator using the heated refrigerant solution which is separated from the LiBr in the generator during the evaporation.

To model the absorption cooling system using the HBS approach, the absorption chilling unit and the storage tank models are added to the system. The proposed layout of the system is shown in Figure 4.12.

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Figure 4.12: Proposed solar assisted absorption cooling system design for the solar pilot plant. The HBS approach is applied to combine the solar pilot plant with the added absorption cooling and latent heat storage models. A glycol solution of 50/50 is used to transfer heat from the HTF to the LiBr-H2O solution.

Each component of the cooling unit, shown in Figure 4.11, is assumed as a control volume and the mass and energy conservation and heat transfer equations are solved between streams of the inlet and outlet flows. For validation, a 209-kW absorption cooling system was modelled separately and results were compared to Hosseini’s [90] predictions using input parameters listed in Table 4.5. As can be observed in Table 4.6, there is a good agreement between the simulation results of this work and Hosseini’s model. The validated model of the absorption cooling system is then added to the solar pilot plant model as shown in Figure 4.13. The highlighted Simulink absorption cooling block is shown in

Figure 4.14; a guide chart is also added to the figure which identifies the inlet and outlet of each absorption cycle component. The thermal storage tank model described in Section 4.1 is also included. For the HBS model, the absorption cooling unit is sized to 40 kW for the

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8-trough pilot plant. A 3-way valve controlled by a PI controller directs the HTF to a heat exchanger to exchange heat with the cooling cycle whenever the HTF temperature is in the range of 90°C to 140°C. As for the heating model, a heat dump system is used to model other energy users to ensure the HTF temperature does not exceed 140°C. The absorption chiller operates once the thermal energy is supplied by the solar pilot plant and/or the thermal storage; no auxiliary backup is included.

The same approach described in Section 4.2 is applied to select the size of the thermal storage tank. As the cooling HBS is performed during summer, when the solar irradiance is relatively high, a 170-l storage tank is found to be appropriate for the selected conditions.

The maximum storage temperature is set to 120°C.

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Table 4.5: Input values for the simulated absorption cooling system chosen to validate the model with results of Reference [94]

Input Unit Value Hot water mass flow rate kg/s 14.1 Hot water input temperature °C 85 Cooling water mass flow rate kg/s 20.1 Cooling water input temperature °C 30 Chilled water mass flow rate kg/s 10.08 Chilled water input temperature °C 12 Solution mass flow rate kg/s 8.03 Conductance of generator coils kW/K 24.3 Conductance of absorber coils kW/K 98.28 Conductance of condenser coils kW/K 17 Conductance of evaporator coils kW/K 53.17

Table 4.6: Comparison of the absorption cooling simulation results of the present work with Hosseini’s work

Present Hosseini Parameter ∆ (%) work [90] Mass flow rate of the LiBr to the absorber (kg/s) 0.09 0.089 1.12 Output temperature of LiBr from solution pump 67.05 66.95 0.15 (°C) Output temperature of strong LiBr 41.85 40.18 4.16 solution at the solution heat exchanger (°C) Output temperature of hot water from generator 79.75 80.05 3.75 (°C) Output temperature of cooling water from 35.95 35.96 2.78×10-4 condenser (°C) Output temperature of chilled water from 7.05 7.25 2.75 evaporator (°C) Load of evaporator (kW) 209.60 209.70 4.77×10-4

COP 0.70 0.71 1.41

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: The solar absorption cooling system model along with thermal storage thermal with along system model cooling solar The : absorption

re Figu

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Figure 4.14: The details of the absorption cooling block highlighted in Figure 4.13. The guide chart on the top identifies the inlet and outlet of each absorption cycle component.

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4.3.1 Results of the absorption cooling model

The thermal storage tank temperature and the HTF temperature leaving the storage tank are shown in Figure 4.15 for a 24-hour period starting from 7 am on July 1st, 2013. As observed, the tank charging time started from approximately 7 am and stopped at 7 pm.

The discharging mode then started and continued until 7 am on the next day. The period when the tank temperature varies slightly indicates that the thermal energy is either being stored or used as latent heat. During the charging mode, the PCM starts melting at 115.7°C and the temperature varies marginally until the PCM melts completely at 119.7°C. In this simulation, the PCM temperature reached a maximum of 117.5°C. The reverse process occurs when discharging the tank.

Storage charging Storage discharging

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The cooling effect produced in the absorption cooler evaporator is calculated by the model with and without thermal storage. These results for the chosen 24-hour period are shown in Figure 4.16.

(a)

(b)

Figure 4.16: The cooling effect provided from thermal energy generated by the solar pilot plant (a) without storage, (b) with storage for the 40-kW absorption cooling on July 1st 2013, obtained from the integrated solar absorption cooling and latent heat storage model. The storage volume is 170 liters. The auxiliary backup is not included. The meteorological data measured and recorded by the solar pilot plant weather station is used.

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The cooling effect provided by the solar absorption cooling system with and without thermal storage for a five-day period from July 1st to July 5th is calculated by the model to be 2,340 kWh and 2,250 kWh, respectively. As discussed in Section 4.2.1, by using a larger thermal storage tank and discharging only after the tank is fully charged would improve the cooling cycle performance. As observed from the simulations, the more the PCM temperature approaches the higher melting point of 119.7°C, the more latent heat is available which extends the discharging period. The system COP is 0.57 as calculated using:

COP = Qe / Qg (4.16)

where

COP: Absorption chiller coefficient of performance

Qe: Cooling effect produced in the evaporator (kWh)

Qg: Input energy to the generator (kWh)

Solar assisted absorption cooling systems bypass power generation, as discussed in

Section 4.3. In contrast, for solar electric cooling systems, solar heat must be first converted to electricity at relatively low efficiency using, for example, an ORC system. Cooling is then generated using this power. These two cooling methods are compared in Figure 4.17.

The HBS absorption cooling COP is three times higher when compared to a compressor driven cooling system, demonstrating the advantage of this approach.

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Figure 4.17: Comparison of the cooling COP for solar absorption cooling and solar electrical cooling using ORC

4.4 Solar power generation

To address remote communities’ energy needs using concentrated solar trough technology, it is critical to displace diesel as the main source of electricity generation (see Section 1.4).

As discussed by Djebbar [32], the literature data on concentrated solar thermal power generation systems in cold climates and high latitudes is limited. In this section, the ORC and CPV power generation systems are modelled using HBS approach. The effect of the ambient and HTF temperatures is examined. The ORC is chosen as it is widely discussed as a method suitable for low temperature electricity generation [95]. In contrast, the CPV is postulated to offer attractive economics and lower qualified operators requirements in remote communities.

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4.4.1 ORC power generation model

In order to evaluate the performance, power quality, and system efficiency of a low temperature ORC system in cold climates, a waste heat demonstration project was developed by Manitoba Hydro in collaboration with Spruce Products Limited and Waste

Gas Power. For the demonstration, a low-temperature ORC system was installed at Spruce

Products' lumber sawmill in Swan River, Manitoba that uses the excess low pressure steam from the mill’s waste wood fired boilers. The working fluid is R245fa, a non-flammable liquid with boiling point of 14.09°C at 101 kPa [96]. The system is cooled using an air- cooled condenser.

To develop the ORC power generation model, the Swan River ORC system parameters are used as input and the add-on components, provided in the Thermolib library (see

Section 1.7) are utilized. The confidential parameters are obtained through private communications from Manitoba Hydro. As presented in Figure 4.18, the Thermolib block components such as turbine, compressor, condenser, etc. are used to simulate the ORC unit.

A heat exchanger is utilized to transfer the thermal energy at 150°C from the solar pilot plant HTF to R245fa. This methodology circumvents having to select ORC system parameters from the literature, as reviews in Chapter 2 exemplified significant variances.

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ORC model

4.4.2 CPV power generation model

The power output of the CPV system is a function of the concentration ratio of the solar collector and the cell operating temperature. The efficiency of CPV cells ranges between

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15% to 40% [97]. By mounting the CPV cells on the absorber tube of the solar trough collectors, the HTF now also acts as a coolant and enhances the CPV performance by reducing cell temperature while producing useful thermal energy. For the integrated power generation simulations, the glass envelope is removed to provide additional cooling and improve the optical efficiency. Lowering the HTF temperature is consistent with research objectives to improve system safety in remote communities.

From Reference [98], the DC power of a trough CPV system is given by:

PDC = I ∙ CC ∙ δT ∙ ηmod ∙ Amod (4.17)

where

PDC: DC power of a trough CPV (W)

I: Irradiance (W/m2)

CC: Concentration coefficient

ηmod: Module efficiency under standard test conditions

2 Amod: Module area (m )

δT: Temperature efficiency of the system

The temperature efficiency of the system is presented by Reference [99] as:

δT = 1 + TC (Tmod − TSTC) + γ log10 I(t) (4.18)

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where

TC: Temperature coefficient assumed as a constant value

Tmod: Module temperature (°C)

TSTC: Reference temperature (°C)

γ: Solar radiation coefficient

I(t): Hourly solar energy incident on array (kWh)

Equation 4.18 is often presented in a reduced form as:

δT = 1 + TC (Tmod − TSTC) (4.19)

where the solar radiation coefficient, γ, is neglected [100]. There are various correlations in the literature for temperature efficiency of the CPV cells in which the numerical parameters are material and system dependent, with each equation developed for a specific geometry and system design [99]. In this section, Equation 4.19 is used for calculating the

CPV cell temperature efficiency [99]. The constant parameter values in Equations 4.17 and

4.19 are presented in Table 4.7. The HBS model developed for CPV power generation is similar to the one shown in Figure 3.7; the main difference is in the heat transfer rate calculations, as exemplified in Figure 4.19. In this model, the energy equation is solved for the absorber tube with the glass envelope removed, as this reflects how the solar pilot plant would integrate CPV in the first implementation phase.

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Table 4.7: Constant parameter values in Equations 4.17 and 4.19 [98]

Constant Value CC 40

η mod 25%

2 Amod (per each trough) 0.3411 m TC -0.003

TSTC 25°C

Figure 4.19: The heat transfer rate calculation blocks of the CPV power generation model. Heat transfer rates are estimated for an absorber tube without a glass envelope. The purpose for the glass envelope removal is to provide CPV cooling and improve optical efficiency.

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The HBS is performed using CPV cells with 25% efficiency obtained under standard test conditions. This value represents the efficiency of cells, as given in Reference [97], for a

1-axis tracking parabolic trough. It is assumed that the CPV cells are positioned on the focus line of the parabolic collectors when operating.

4.4.3 Results of the ORC and CPV power generation models

Simulations are performed over one year using the 2013 meteorological data recorded by the solar pilot plant weather station. The HTF outlet temperatures of 50°C and 150°C are selected to show the effect of the operating temperature on the power generation performance of the ORC and CPV systems. They represent the selected temperature boundaries for operating such systems in remote communities.

Figure 4.20 compares the annual heat and power generation along with the thermal losses of the solar combined heat and power system using CPV versus ORC. Figure 4.21 also shows the monthly power generation efficiency of the ORC and CPV systems obtained from the simulations. The results at operating temperature of 50°C is only shown for CPV as the ORC does not operate at such low temperature. The parasitic loads such as pump, instrumentation, controls, cooling tower fans, etc. are neglected in these efficiency calculations. Note that the ORC efficiency decreases to less than 4% if the parasitic power is included.

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CPV CPV ORC Operation temperature = 50 C Operation temperature = 150 C Operation temperature = 150 C Loss 8% Loss Loss 29% CPV power 35% 22% Heat Heat Heat generation 54% generation generation CPV power 61% 70% 17% ORC power 4%

Figure 4.20: Comparison of the annual thermal loss and the electricity and heat generation of CHP system using CPV versus ORC. The results are obtained from the transient solar power generation model for two solar plant operating temperatures of 50°C and 150°C. The ORC simulation can only be performed at operating temperature of 150°C. The meteorological data measured and recorded by the solar pilot plant weather station is used.

5 Solar electrial efficiency (%) efficiency electrial Solar 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

CPV (Tcol = 50ºC) CPV (Tcol = 150ºC) ORC (Tcol = 150ºC)

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As observed from Figure 4.20 and Figure 4.21, the CPV power generation efficiency is

22% at 50°C and 17% at 150°C; in contrast, 5% is the maximum efficiency reached by the

ORC system in summer. There is a significant difference between the ORC and CPV efficiencies that is more pronounced in the winter months, when the HTF temperature does not reach the required ORC set point. The CPV approach shows better performance at lower temperatures with almost constant efficiencies throughout the year.

To investigate the ORC performance in cold climates, a case study was also performed by

Cordova Electric Cooperative in Alaska’s cold climate. An ORC system was coupled with a diesel generator low-grade waste heat. With expected efficiencies from 5% to 7%, a 280- kW ORC unit was designed to operate in ambient temperatures between -30°C to 50°C.

However, the maximum power generation achieved during spring tests was 160 kW with efficiencies below 5% [101] excluding the parasitic loads. The 280-kW rated power could not be achieved because the cooling tower temperature was much above the required 0°C in spring. Form these spring tests, the project payback period was estimated to be 45 years, more than the 20-year ORC life time [101]. Due to additional design flaws, the project was terminated after 46 days of operation in spring 2013.

As the Alaska ORC system did not operate during the winter season, the impact of cold weather on the performance was not investigated. However, as observed in the Swan River system, low winter temperatures decreased the ORC rated output by increasing the subcooling effect in the air-cooled condenser. As per discussions with Manitoba Hydro, this problem was caused by low-temperature air increasing the outside heat transfer rate of

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the condenser tubes resulting in low head pressures, and consequently, reducing refrigerant flow throughout the ORC cycle. The low refrigerant flow in the evaporator caused low suction pressure and resulted in liquid refrigerant to back up into the condenser, further increasing the subcooling effect. This subcooling effect was not tested in the Alaska ORC system.

The low-temperature ORC case studies and HBS model results emphasize that some design considerations remain unresolved to operate such systems in cold climates. In contrast, the

CPVT introduced is found to be potentially suitable for remote community applications in cold climates. However, the CHP system with an ORC has the advantage of generating heat and power during night time using the thermal storage tank; thermal storage and batteries, or alternatively other generation systems, are required for the CPVT plant to meet heating and electricity loads.

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Chapter 5

Reliability and Controls for Parabolic Solar Trough

System Applications in Cold Climates

As discussed in Chapter 3, parabolic troughs collect and circulate the solar energy using a

HTF, normally a synthetic oil. Controls and reliability of system components within the

HTF loop is required. A reliable system prevents unplanned outages, reduces maintenance and repair requirements, and avoids component replacement. Solar Energy Generating

Systems (SEGS) consist of nine solar trough power plants, SEGS I-IX, which are located in Mojave Desert, California. Operation of these plants, built from 1984 to 1990, contributed to current knowledge of control and reliability. SEGS plants control strategies were developed for utility scale power generation in a warm climate with relatively high solar insolation. As a result of the marginal reliability of hardware components, occasional severe failures occurred in SEGS plants leading to HTF spills and fires [102]. For example, a circulating pump seal failed 42 times in a single year during the operation of SEGS-V.

The maintenance cost of this failure, excluding the cost for the loss of electricity generation due to the plant outage, was $250,000 [102].

Technical issues that occurred when operating the Winnipeg solar trough pilot plant in winter are presented in this chapter to highlight how cold weather can unexpectedly cause

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system outages. In addition, this chapter investigates control strategies and reliability issues for solar troughs to be potentially installed in remote cold communities. In particular, controls are optimized to reduce the power consumed by auxiliary system components and maximize combined heat and power generation revenues.

5.1 Solar trough pilot plant technical issues

A major barrier for implementing solar trough technology in cold climates and remote communities is the lack of documented performance data of such systems (see Table 2.2) and limited investigation of add-on component integration. Moreover, none of the literature, presented in Chapter 2, have studied reliability issues resulting from extremely low ambient temperatures. Therefore, cold weather related technical issues that the solar trough pilot plant encountered during the winter season 2013 and 2014 are listed below.

These issues support the objective presented in Section 1.6 that control strategies developed in southern latitudes need to be modified for high northern latitudes.

5.1.1 Flow meter

An accurate knowledge of the HTF flow rate through the solar field is required to reliably operate the plant at maximum efficiency. The flow meter is of critical importance as it impacts the circulating-pump controls; its failure can result in plant outages, including loss of coolant accidents. As discussed in Section 3.1.6, the solar pilot plant flow measurement device is a vortex type flow meter. This flow meter is not suitable for fluids with relatively high viscosity [103]. As shown by Goujon-Durand [104], increasing the liquid viscosity has a significant influence on the vortex meters performance; for liquids with a viscosity

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exceeding 9.5 mPa·s, the relative error is more than the acceptable limit [104]. During morning start-up, the pump has to circulate relatively high viscosity oil that was not circulated for an extended period, with pipes exposed to low winter ambient temperatures.

However, if the flow meter does not reliably measure a minimum HTF flow rate after pump start-up, the system controller will shut the pump. The viscosity of Therminol 59 increases from 5.05 mPa·s at 30°C to 132.55 mPa·s at -30°C. This means that although the pump is capable of circulating the oil, it does not operate because of the flow meter inaccurate readings resulting from the high viscosity oil. Consequently, on cold winter mornings, the pump does not start the HTF circulation even though the level of solar irradiance is sufficient. For example, the measured solar irradiance and the outlet temperature of the collectors for February 1st to 11th, 2013 are presented in Figure 5.1. As can be seen from the figure, there were periods of relatively high solar irradiance, however, the outlet temperature of the solar field did not increase as the controller shut the pump and stowed the troughs.

5.1.2 Stow-switch

The solar collectors tracking system consists of a screw jack driver and a stow-switch which are located inside a metallic channel, as shown in Figure 5.2. The controller logic to prevent overheating of collectors is to stow them by activating the stow-switch whenever a pump stop command is issued. In the winter time, snow and ice accumulated in the solar field, including the space between the collector rows, as shown in Figure 5.3.

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(a)

(b)

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Figure 5.2: The metallic channel holding the screw jack driver and the stow switch, located between the collector rows

Figure 5.3: The snow accumulated at the solar field during winter 2014

As the screw jack channel is not fully sealed, snow entered the box from the two side openings, causing the stow switch to eventually freeze. Figure 5.4 shows the frozen switch.

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Figure 5.4: The frozen stow switch caused by the snow entering the channel due to an improper enclosure design

The combination of the stow-switch freezing, the pump being shut off due to inaccurate readings of the flowmeter at high HTF viscosity (see Section 5.1.1), and partial focusing of the troughs, resulted in heat generation without flow circulation. Consequently, the absorber tubes and the heat transfer oil in the tubes overheated up to 370°C as a result of which:

• the black Chrome coating of the absorber tubes was oxidized;

• the heat transfer oil was degraded; and

• some of the absorber tubes were bent due to thermal stresses.

Figure 5.5 shows the bent tube and the oxidized coating.

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Figure 5.5: Bent receiver tube and oxidized coating as a result of overheating up to 370°C

5.1.3 Pressure relief valve

The role of the pressure relief valve is to control the system pressure and prevent over pressurization. Over pressurization is reduced by driving the pressurized fluid through an auxiliary passage. As discussed in the previous section, the troughs being left in tracking mode without the oil flow caused an oil temperature of 370°C resulting in a system pressure of 763 kPa. The high-pressure oil was then released to the control room space through the breakage of the pressure relief valve burst disk and the pressure dropped to 277 kPa, as the solar troughs continued to accumulate heat. The hot oil vaporized because of rapid

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depressurization and then condensed and formed oil droplets in the control room as shown in Figure 5.6.

Figure 5.6: Condensed oil droplets formed in the control room due to the release of oil

5.1.4 Summary

These three technical issues were caused by winter conditions and are not expected to occur in warm climates. This emphasizes the necessity of adapting the controller and system components to operate in cold climates. Such adaptations are important as there may be less frequent surveillance by operators in remote communities with reduced technical knowledge. The solar trough pilot plant was inoperable after February 2014, waiting for

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receiver tubes replacement, developing control procedures to operate the system safely, and obtaining approval from RRC to restart. The pilot plant may be operational by 2018.

5.2 Control schemes for the solar field circulating pump

The dynamics of solar systems are influenced by the intermittency of the solar irradiance.

Solar fluctuations can be slow, such as the changes in solar irradiance on a sunny day, or fast, such as those related to passing clouds. The main control variable which changes the solar field outlet temperature is the HTF flow rate. Therefore, the use of advanced control approaches is necessary to manipulate the HTF flow rate and consequently the solar field outlet temperature [105]. The use of variable speed pumps is an attractive option for systems with varying loads such as solar power plants. With variations in the system demand, a variable speed drive can change the pump speed more optimally to meet the demand, reducing the parasitic power. In many cases, a control strategy is necessary to set the outlet temperature at a specific value. For example, when it is required to maintain the storage tank temperature at a value of 100°C, there is no benefit of extracting HTF at 80°C.

The preferred approach is to use a variable speed drive, or turn the pump on and off at a fix flow rate.

5.2.1 Parasitic power reduction

Internal parasitic loads contribute to the plant operation and reduce the gross power output [106]. In solar power plants, the parasitic loads include the HTF pumps, cooling water pumps, cooling tower fans, instrumentation, controls, valve actuators, lighting, etc.

Reducing the parasitic power can improve the net solar power generation. As electricity is

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more expensive in remote communities and heat losses increase in cold climates (see

Chapter 1), such applications require to address parasitic loads more effectively using detailed transient simulations which integrate hardware components and controls (see

Chapter 4).

In this section, two control strategies are applied to the solar pilot plant model to compare the pump parasitic load. In Method-1, which is used by the solar pilot plant controller during the system operation, the pump is running continuously at its set speed throughout the day unless the clouds cover the sun for more than 10 minutes. In that case, the pump stops circulating the HTF automatically until the solar irradiance level increases and reaches the operational limit again. Therefore, the field outlet temperature fluctuates due to the solar radiation variations. In Method-2, a PI controller is added to the solar pilot plant model which regulates the HTF outlet temperature at 170°C by applying an on/off pattern to operate the pump. This method can also control the temperature based on varying demands during the day. Simulation of the pilot plant using a variable speed pump requires future work; multiple rapid system disturbances require a robust PI controller to be implemented.

Simulations of Method-1 and Method-2 utilize the 2013 meteorological data recorded by the solar pilot plant weather station. Figure 5.7 presents the measured solar normal irradiance for a clear and a cloudy day randomly selected.

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(b) (a)

Figure 5.7: Solar direct normal irradiance (1 sample per minute) on a randomly selected (a) clear, and (b) cloudy day from 11 am to 5 pm, obtained from the meteorological data recorded by the solar pilot plant weather station and selected to compare Method-1 and Method-2 controls.

Figure 5.8 shows the outlet temperature of the solar field obtained from the model using the two control methods on a clear day; results for a cloudy day are shown in Figure 5.9.

(a) (b)

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(a) (b)

As observed in Figure 5.8 and Figure 5.9, by using the PI controller for the pump, the outlet temperature of the collector field remains relatively constant at 170°C. It is also shown in

Figure 5.9a that by using the constant mass flow rate in Method-1 on a cloudy day, the

HTF temperature does not reach the set point of 170°C during the day. In contrast, in

Method-2, the PI controller regulates the pump so that the field outlet temperature remains relatively fixed at its set point value.

Figure 5.10 and Figure 5.11 show the HTF flow rates obtained from the two control methods. The mass flow rate values in Figure 5.10a and Figure 5.11a are obtained from the pilot plant flow measurements. As shown in Figure 5.10b and Figure 5.11b, Method-2 requires the pump to operate for 10 s to 15 s every 1-2 minutes. In order to show the mass flow rate, and consequently the pump power reduction compared to Method-2, the moving

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average profiles over 15 minutes are added. The results show that the pump power decreases by about 80% using Method-2.

Method-2 is more suitable to integrate hardware components into the HTF loop, and to reduce system dynamics. This method can also be used to change the solar field outlet temperature based on the demand during the day. Figure 5.12 shows how the PI controller sets the field outlet temperature at 170°C from 11 am to 1 pm, and at 120°C from 1 pm to

5 pm during a day, by manipulating the pump operating time.

Using the PI controller, as described in Method-2, may not always result in a higher thermal energy generation compared to Method-1, specifically on a clear day. However, it is beneficial for variable demands and applications with specific inlet temperature requirements to achieve the optimal efficiency, such as ORC and absorption chilling.

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(a)

(b)

Figure 5.10: The HTF mass flow rate obtained using the two control methods (a) without PI controller, and (b) with PI controller on a randomly selected sunny day from 11 am to 5 pm. The meteorological data measured and recorded by the solar pilot plant weather station is used. The mass flow rate values in (a) are based on the pilot plant measurements while the results in (b) are obtained from the solar pilot plant model. A 15-minute period is also shown for the variable mass flow rate from 1 pm to 1:15 pm.

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(a)

(b)

Figure 5.11: The HTF mass flow rate obtained using the two control methods (a) without PI controller, and (b) with PI controller on a randomly selected cloudy day from 11 am to 5 pm. The meteorological data measured and recorded by the solar pilot plant weather station is used. The mass flow rate values in (a) are based on the pilot plant measurements while the results in (b) are obtained from the solar pilot plant model. A 15-minute period is also shown for the variable mass flow rate from 1 pm to 1:15 pm.

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(a)

(b)

5.2.2 Maximizing the energy revenue using an optimization algorithm

There has been a growth in recent years in applying heuristic optimization methods to improve wind and solar systems performance. Heuristic methods are relatively simple and rapid in execution, providing acceptable solutions for complex applications; however,

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solutions are not necessarily the most optimal. This approach is used to accelerate the process of finding a valid optimal alternative solution for a problem based on the expertise, intuition, and experience of the decision maker. In contrast, meta-heuristic methods assign a computational method to optimize a problem by improving a solution, or population of solutions, through iterations considering a given measure of quality [107]. This latter approach provides near-optimal solutions. Genetic algorithm, evolutionary algorithms and scatter search are in the category of population-based meta-heuristic methods which use a population of solutions, and return a population of solutions after evolving during a specified number of iterations [108]. Suh et al. [107] used a heuristic method and genetic algorithm for energy optimization of a post office building. They found that genetic algorithm results in 33% of heating and cooling demand reduction against the original design while a 24% reduction achieved using heuristics.

5.2.2.1 Genetic algorithm

The nature of renewable energy problems makes them a good candidate for applying the genetic algorithm optimization. It is a robust method to address solar and wind intermittency as it can handle unsmooth and noisy fitness functions [107]. Genetic algorithm is also a popular type of evolutionary algorithms, compatible with multi- objective optimization problems. It starts with a set of solutions called population which is used to create a new population that is better than the old one. The new solutions are selected based on their fitness which is the value of the objective function. The process continues until the termination criteria are reached. These criteria can be chosen as the

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maximum number of iterations, the maximum iteration time, or the improvement of the solutions. The genetic algorithm optimization approach is based on the following process:

1. Initial population: At this starting stage, a random initial population is created by

the algorithm.

2. Fitness evaluation: The fitness of each individual in the initial population is

evaluated using the fitness function.

3. Next generation creation: The algorithm chooses the individuals with better fitness

values as parents who contribute their genes to their children. Three types of

individuals, shown in Figure 5.13, are generated for the next population:

o Elite: These individuals have the best fitness values in the current generation

which automatically subsist to the next generation.

o Crossover: These individuals are created by combining the vectors of a pair of

parents.

o Mutation: These individuals are generated by making random mutations to a

single parent.

4. Stopping criteria: The algorithm stops when the stopping criteria such as maximum

number of iterations, maximum iteration time, etc. are reached.

5.2.2.2 Multi-objective optimization of remote community CPVT system

Most real-world problems require simultaneous optimization of multiple objectives which are often conflicting. In engineering problems, typical objectives are for example to maximize performance, minimize cost, and maximize reliability. Since the optimal solution of one objective is not necessarily optimal for the other objectives, a set of solutions is

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required to address the optimal solutions for all objectives. Multi-objective optimization problems may also be subjected to multiple constraints. The general formulation for a multi-objective optimization problem is defined by Equation 5.1.

Figure 5.13: Three types of individuals for the next generation creation in the genetic algorithm

Min / Max fk(x), k = 1, 2, … , K

Subject to Li ≤ xi ≤ Ui, i = 1, 2, … , n (5.1) gj(x) ≤ 0, j = 1, 2, … , J

hm(x) = 0, m = 1, 2, … , M

where

fk(x): Objective function(s)

xi : Decisions variable(s)

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Li: Lower limit

Ui: Upper limit

gj(x): Inequality constraint function

hm(x): Equality constraint function

The solar pilot plant model, detailed in Section 3.2, is linked to the multi-objective, genetic- algorithm, MATLAB® toolbox to optimize the CPVT plant presented in Section 4.4.2. The solar trough CPVT model maximizes heat and electricity revenues generated in remote communities. The CPVT transient model first estimates the heat and power generated from solar energy. Objective functions are then maximized to displace heat and power production costs using fuel oil and diesel fuel. Thus, for the CPVT optimization, the generated heat and power is credited as equivalent revenue to the displaced cost of fossil fuel generation.

Using the HBS approach, the solar trough design parameters are either obtained from the pilot plant configuration or from add-on components, like the CPV cells detailed in

Section 4.4.2. In the optimization problem, the HTF flow rate is selected as the decision variable—the PI controller no longer controls the flow rate during the optimization. As discussed in Section 5.2, the outlet collector temperature is controlled by changing the HTF flow rate. As CPVT power decreases with increasing HTF temperatures, solar power generation revenues strongly impact the optimal HTF flow rate. Figure 5.14 presents a schematic of the multi-objective optimization problem; the optimization problem formulation is depicted by Equation 5.2.

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Figure 5.14: Schematic of the multi-objective optimization problem to maximize heat and electricity revenues of the CPVT plant

Maximize Re(T), Rh(T) (5.2) Subject to 0 ≤ ṁ HTF ≤ ṁ max

where

Re(T): Electricity revenue ($)

Rh(T): Heat revenue ($)

T: Temperature of the collectors (ºC)

ṁ HTF: HTF mass flow rate (kg/s)

ṁ max: Maximum HTF mass flow rate (kg/s)

5.2.2.3 Simulation and optimization integration

One of the advantages of linking the solar trough pilot plant model to the MATLAB®

Optimization Toolbox™ is that there is no requirement to define a mathematical equation for each objective function. The optimization algorithm starts by calling the fitness

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functions using an interface code. This code calls the solar pilot plant model by which the fitness functions are generated. The fitness values continue to be exchanged between the model and the optimization tool until the termination criteria are satisfied and a set of

Pareto optimal solutions are achieved. When none of the objectives can be improved without degrading at least one other objective, the set of solutions obtained is called Pareto optimal. The procedure is schematically shown in Figure 5.15.

The optimization problem is solved for a 3-hour period from 11 am to 2 pm, January 1st,

2013 using the meteorological data collected by the solar pilot plant weather station.

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Longer simulation time periods would require code parallelization or simplification of models. To calculate the cumulative heat and electricity revenue for the selected period, a remote community is assumed that has a similar weather data to that of the solar pilot plant location. The solar field HTF outlet temperature is set to 100ºC. Fuel oil and diesel prices are set to $0.12/kWh [109] and $1.5/kWh [20], respectively. Table 5.1 summarizes the optimization problem assumptions together with the input parameters to the multi- objective genetic algorithm optimization tool. The lower and upper bounds for the HTF mass flow rate are selected based on the solar pilot plant pump characteristics.

Table 5.2 shows a single Pareto solution that is representative of all the solutions as these only varied by non-significant digits. For the three-hour period selected, the cumulative electricity revenue is found to be almost twice the heating revenue, partly due to the higher price of electricity. The optimum flow rate obtained is approximate 3 kg/s. At higher mass flow rates, the HTF temperature decreases, improving CPV cells efficiency, resulting in increased power generation. As electricity is often more expensive than heat in off-grid communities, increasing the power generation results in higher revenues.

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Table 5.1: Optimization assumptions and input parameters for the multi-objective genetic algorithm optimization

Optimization problem assumptions Value

Meteorological data Solar pilot plant weather station, 2013

Solar pilot plant operation temperature 100ºC Fuel oil heating cost 12 ¢/kWh [109] Diesel electricity cost 150 ¢/kWh [20] Simulation period 3 hours

Optimization tool inputs Value

Number of variables 1 Lower bound 0 kg/s Upper bound 3 kg/s Population size 15 Generations* 20 * Number of iteration before stopping the optimization.

Table 5.2: Optimal HTF mass flow rate and the associated heat and power revenues for January 1st, 2013 from 11 am to 2 pm. The meteorological data measured and recorded by the solar pilot plant weather station is used. The operation temperature of the solar plant is 100ºC. Thermal storage is disabled.

Mass flow rate (kg/s) Electricity revenue ($) Heat revenue ($)

2.9 26.2 11.9

A requirement to adapt solar trough technology to remote communities in cold climates is to reduce the system payback period by implementing control strategies that lead to

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revenue maximization. In this regard, the optimization algorithm presented can be utilized to improve the total energy generation revenue by estimating the optimum HTF mass flow rate based on the heat and electricity demand during different seasons in an off-grid community.

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Concentrated Solar Heat and Power Preliminary

Economic Assessment for Remote Communities

To better understand the potential to install an integrated low-temperature CPVT system

(Section 4.4.2) in remote communities, a simplified economic model is implemented in the transient simulation model. To preserve access to high resolution meteorological data, and maintain the advantages offered by HBS, the economic assessment simulations are performed using the solar trough pilot plant model—it is assumed that Winnipeg is representative of weather conditions in remote communities. However, community energy costs are required as inputs to the economic assessment model. Moreover, this approach highlights that the CPVT system configuration, proposed in Section 4.2.2, may not be economical when using low energy costs in grid connected areas like Winnipeg.

In the transient model, the annual displaced cost of heat and power generation and the resulted simple payback period of the solar plant are estimated per unit area of the collector aperture. It should be noted that the economic results presented in this chapter are not conclusive due to the following assumptions:

• The economic analysis is a simplified investigation of the solar technology in

Winnipeg and a typical off-grid community. It does not include the cost of balance

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of plant and the cost of intermittent generation for heat and power. The primary

objective of this research (Section 1.6) is to develop a validated simulation tool to

study solar trough technology integrated with storage, heating, cooling and power

generation in cold climates. A detailed economic assessment of this technology is

left for a later phase.

• To calculate the revenue that can be credited, it is assumed that the solar energy

generated displaces the existing fossil fuel generation using natural gas, propane,

fuel oil, and diesel.

• The heat and power storage system is not included.

• The design of how the CPV cells are attached to the receiver tubes is not completed,

nor validated experimentally.

In addition to the preliminary economic assessment, the solar pilot plant model, including controls, is utilized to evaluate the concentrated solar heat and power generation potential of six towns located across Manitoba. Each simulation is performed over an entire year using 20-year averaged hourly meteorological data available for each town.

6.1 Economic assessment

The economic analysis of the concentrated solar thermal systems helps to understand their long-term viability for remote communities. The feasibility of a solar thermal project in such communities is sensitive to not only economics, such as the payback period and the annual displaced cost, but also to social and environmental impacts. In this research, the annual displaced cost is the actual savings in dollar that can be achieved by providing

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energy using solar system instead of fossil fuels. The value of savings is related to the price of natural gas, diesel, and heating oil, and for that reason, more savings can occur in off- grid communities. The following two cases are considered for displacing fossil fuels:

Case 1) Winnipeg – heat only, displacing natural gas and propane

In this case, the pilot plant model, explained in Section 3.2, is used to calculate the annual heat generation of the solar pilot plant for the year 2013. The generated solar heat obtained from transient simulations is used to displace heating cost in Winnipeg using natural gas and propane, allowing calculating the solar pilot plant payback period. As mentioned, the

2013 meteorological high-resolution data measured by the solar pilot plant weather station is used.

Case 2) Remote community – heat and power, displacing fuel oil and diesel

The cost of heat and power displaced are calculated based on the price of fuel oil and diesel in a typical remote community with meteorological data similar to Winnipeg. The CPVT model explained in Section 4.4.2 is used to evaluate the combined heat and CPV electricity production of the solar pilot plant. Similar to Case 1, the annual displaced cost and the

CPVT plant payback period are estimated using the 2013 meteorological data measured by the solar pilot plant weather station.

6.1.1 Annual displaced cost – Case 1

The annual displaced cost of fossil fuel is estimated using Equation 6.1 with assumptions presented in Table 6.1. As presented in Figure 6.1, the displaced cost of propane is

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distinctly higher due to the fact that it is a more expensive fuel used in non-integrated rural areas.

ADC = Qann,sol,th ∙ FFP (6.1)

where

ADC: Annual displaced cost ($/m2/y)

2 Qann,sol,th: Annual solar thermal energy generation (kWh/m /y)

FFP: Fossil fuel price ($/kWh)

Table 6.1: Assumptions for the annual displaced cost calculation in Case 1

Assumption Value Meteorological data Solar pilot plant weather station, 2013 HTF outlet temperature 100°C and 150°C Thermal storage Not included Natural gas fuel cost in Winnipeg1 4.0 ¢/kWh Propane fuel cost in Winnipeg2 11.3 ¢/kWh 1 Manitoba Hydro website, 2017. 2 Natural Resources Canada, 2017.

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$60 /y) 2 $50

$40 Natural gas $30 Propane $20

$10

Annual displaced cost ($/m cost displaced Annual $0 100 150

Operating temperature (ºC)

Figure 6.1: The annual displaced cost of natural gas and propane for heating in Winnipeg obtained from the solar pilot plant model. The HTF outlet temperatures are set to be 100°C and 150°C. The 2013 meteorological data measured and recorded by the solar pilot plant weather station is used.

6.1.2 Simple payback period – Case 1

The simple payback period of the solar trough pilot plant is calculated for heating based on the 2017 price of natural gas and propane in Winnipeg. Equation 6.2 is used to estimate the simple payback period with assumptions presented in Table 6.2.

SPBP = Pcoll/ADC (6.2)

where

SPBP: Simple payback period (y)

2 Pcoll: Price of solar collector field ($/m )

ADC: Annual displaced cost ($/m2/y)

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Figure 6.2 shows that by displacing natural gas, the simple payback period of the solar heating system is almost 20 years. The required price of natural gas to reduce the payback period to less than 10 years is estimated by the model to be 8 ¢/kWh.

Table 6.2: Assumptions for the simple payback period calculation in Case 1

Assumption Value Meteorological data Solar pilot plant weather station, 2013 HTF outlet temperature 100°C and 150°C Thermal storage Not included Cost of collector field per aperture area $340/m2 [110] Natural gas fuel cost in Winnipeg1 4.0 ¢/kWh Propane fuel cost in Winnipeg 2 11.3 ¢/kWh 1 Manitoba Hydro website, 2017. 2 Natural Resources Canada, 2017.

15 Natural gas Propane 10

Simple payback period (years)paybackperiod Simple 0 100 150

Operating temperature (ºC)

Figure 6.2: Simple payback period resulted from displacing natural gas and propane in Winnipeg obtained from the solar pilot plant model. The HTF outlet temperatures are set to be 100°C and 150°C. The 2013 meteorological data measured and recorded by the solar pilot plant weather station is used.

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The Alberta Market Price (AMP) 2026 projection for the primary cost of natural gas

(excluding the supplemental gas, transportation, and distribution costs) based on a reference case scenario is $0.016/kWh, which is an approximately 45% increase compared to 2017 [111]. This price is $0.024/kWh based on a high price scenario. Table 6.3 shows residential natural gas price rates for Manitoba in 2017 and projections to 2026. The 2026 rates for primary and supplemental gas are calculated considering a 45% increase; transportation and distribution prices are assumed to remain constant and fix monthly charges are not included in the calculations.

The projected residential natural gas rate, presented in Table 6.3, beside the declining cost of solar trough technology show that by 2026, the payback period of the solar pilot plant for heating would decrease to less than 10 years. According to Reference [110], the cost of solar field and the HTF system will reduce from $340/m2 in 2015 to $240/m2 (aperture area) in 2020. By using the latter price as input to the pilot plant model, the payback period is reduced to 14 years with the current price of natural gas. Figure 6.3 also shows the LCOE for the solar trough and the solar tower technologies in 2010 and 2015 [108].

By considering higher carbon taxes for the fossil fuels, the payback period of renewables decreases to a more acceptable value. The required price of carbon for natural gas to reduce the payback period of the solar pilot plant to less than 10 years is calculated by the model to be approximately $190/tonne CO2, which is significantly higher than the 2016 prices in

Canada; carbon prices in Alberta, British Columbia, and Quebec are $15/tonne CO2,

$30/tonne CO2 and $12.08/tonne CO2, respectively [113]. This is an indication that the

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carbon prices are much lower than required for encouraging low carbon technologies. Such high values may not yet be realistic to expect until such times when atmospheric CO2 levels exceed 500 to 600 ppm.

2026 rate2 2026 rate2 2017 rate1 ($/kWh) ($/kWh) ($/kWh) (reference case) (high price) Primary gas 0.0109 0.0161 0.0244 Supplemental gas 0.0152 0.0222 0.0339 Transportation to centra 0.0049 0.0049 0.0049 Distribution to customer 0.0090 0.0090 0.0090 Total 0.040 0.052 0.072 1 Manitoba Hydro website, 2017. 2 The 2026 prices are calculated based on a 45% increase due to Alberta Market Price projection.

25 21 ¢ 20 19 ¢ 8 6 Solar field 15 14 ¢ 12 ¢ Thermal storage 5 Power plant 10 5 5 4.2 Receiver/HTF LCOE LCOE (¢/kWh) 2 4 2 5 5 4 3.3 4 3 3 2.5 0 Trough Trough Tower Tower 2010 2015 2010 2015

Figure 6.3: Cost reductions for parabolic trough and solar tower technologies from 2010 to 2015, adapted from Reference [112]

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Table 6.4 shows the natural gas carbon price based on the current carbon tax of Alberta,

$15/tonne CO2, and the calculated carbon tax to reduce the solar pilot plant payback period,

$190/tonne CO2. This carbon price is obtained assuming natural gas produces

0.056 tonne CO2/GJ.

Table 6.4: Natural gas carbon price based on Alberta’s current carbon tax and the carbon tax calculated using the solar pilot plant model to reduce the payback period to 10 years. Natural gas produces 0.056 tonne CO2/GJ.

Carbon tax ($/tonne) Natural gas carbon price ($/GJ)

151 0.84 1902 10.64 1 Carbon tax in Alberta 2 Carbon tax calculated using the solar pilot plant model.

Results show that for Case 1 the solar system payback period is also influenced by the price of natural gas. The 2026 projected price of natural gas is $4.38/GJ [111]. Considering this price as an element in the payback period reduction, the required carbon tax to lower the solar system payback period to less than 10 years, decreases from $190/tonne CO2 to

$165/tonne CO2. The declining cost of solar trough technology also affects the payback period and results in less required carbon tax than estimated.

6.1.3 Annual displaced cost and simple payback period – Case 2

The annual displaced cost of fuel oil and diesel and the simple payback period of the solar

CPVT plant are estimated for a typical remote community in Manitoba. The heat and

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Chapter 6 – Concentrated Solar Heat and Power Preliminary Economic Assessment for Remote Communities

electricity generated by the solar system are calculated using the solar CPVT model, described in Section 4.4.2. The weather data is assumed to be similar to that measured and recorded by the solar pilot plant weather station in 2013. The fuel oil and diesel prices are presented in Table 6.5. As shown in Figure 6.4, the simple payback period of the solar

CPVT is calculated to be approximately three years, providing a much better return compared to heat only in Winnipeg.

Including the cost of intermittency and balance of plant will result in a higher investment cost, and consequently, longer payback period in off-grid communities. However, due to the higher power generation efficiency of the CPVT technology when using relatively low

HTF temperatures, results show this technology will be competitive with alternatives for remote communities. Moreover, as discussed in Section 2.3.2, although the CPV cells are more expensive than regular PV cells, there is a potential to reduce the system cost due to smaller area required to generate the equivalent power using regular PV cells while producing useful thermal energy. For the pilot plant, 2.8 m2 CPV cells are required. To address intermittency, thermal storage, batteries, or integration with other base load generation systems would be required.

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Chapter 6 – Concentrated Solar Heat and Power Preliminary Economic Assessment for Remote Communities

Table 6.5: Assumptions for the annual displaced cost and the simple payback period calculations in Case 2

Assumption Value Similar to the solar pilot plant weather data, Meteorological data 2013 HTF outlet temperature 50°C and 150°C Thermal storage Not included Concentration ratio 40 Cost of collector field per aperture area1 $600/m2 Fuel oil heating cost 12 ¢/kWh [109] Diesel electricity cost 150 ¢/kWh [20] 1 Does not include the extra costs for off-grid applications. An additional cost of $255 per m2 of the collectors aperture area is considered for concentrated PV cells [97].

$300 3.0

/y) 2 $250 2.5

$200 2.0

$150 1.5

$100 1.0

$50 0.5

$0 (year)paybackperiod Simple 0.0 Annual displaced cost ($/m cost displaced Annual 50 150 50 150 Operating temperature (ºC) Operating temperature (ºC) (a) (b)

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Chapter 6 – Concentrated Solar Heat and Power Preliminary Economic Assessment for Remote Communities

6.2 Concentrated solar thermal energy generation potential of Manitoba

In this section, the annual thermal energy generation of the solar pilot plant is calculated using a twenty-year average hourly weather data as input to the pilot plant model. The meteorological data is provided by Environment Canada and Natural Research Council of

Canada and includes data from 1981 to 2001. To evaluate the concentrated solar thermal potential of Manitoba, the weather data for the six cities of Brandon, Winnipeg, Dauphin,

The Pas, Thompson, and Churchill are included in the simulations. As presented in

Figure 6.5, which shows the Manitoba DNI map created based on a seventeen-year annual average direct normal solar resource, the cities are selected from regions with different DNI resources. The map is provided by Natural Resources Canada and represents data from

1998 to 2014. The assumptions made for the simulations are as the following:

• The maximum HTF outlet temperature is 100°C.

• The heat load is simulated as a heat exchanger which removes heat whenever the

HTF temperature exceeds the set point of 100°C.

• Thermal storage is not included.

• The pump speed and consequently the HTF flow rate are constant.

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Chapter 6 – Concentrated Solar Heat and Power Preliminary Economic Assessment for Remote Communities

Figure 6.5: Manitoba DNI map based on annual average data from 1998 to 2014, provided by Natural Resources Canada

The monthly concentrated solar thermal potential and the solar thermal efficiency for the six cities selected are presented in Figure 6.6 and Figure 6.7. Each data point is achieved by running the pilot plant transient model over a full year. Solar thermal efficiency is calculated using:

163

Chapter 6 – Concentrated Solar Heat and Power Preliminary Economic Assessment for Remote Communities

ηsol,th = Qgen/Qsol (6.3)

where

ηsol,th: Solar thermal efficiency

Qgen: Generated thermal energy by solar system at 100ºC (kWh)

Qsol: Total incident solar energy on the aperture area of collectors (kWh)

) 2

40 /month/m

th 30

(kWh 20

0 Concentrated solar thermal energy generation Concentrated thermal solar energy Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Churchill (58.77° N) Thompson (55.74° N) The Pas (53.83° N)

Dauphin (51.15° N) Winnipeg (49.90° N) Brandon (49.83° N)

Figure 6.6: Monthly concentrated solar thermal energy generation for six cities across Manitoba obtained from the solar trough pilot plant model. The HTF outlet temperature is 100°C. A 20-year averaged hourly meteorological data of the six cities is used.

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Chapter 6 – Concentrated Solar Heat and Power Preliminary Economic Assessment for Remote Communities

30 efficiency (%) efficiency 20

0 Concentrated solar thermal energy generation generation thermal energy Concentrated solar Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Churchill (58.77° N) Thompson (55.74° N) The Pas (53.83° N) Dauphin (51.15° N) Winnipeg (49.90° N) Brandon (49.83° N)

6.3 Concentrated PV power generation potential in Manitoba

The CSP potential using a steam-base system at relatively high temperature was analyzed by Djebbar et al. [32] for the Canadian provinces. They mapped fourteen-year-averaged yearly sums of DNI in Canada. The regions with the highest DNI are located in the south of Alberta, Saskatchewan, and Manitoba. Their analysis shows the Canadian CSP potential, without productive use of the residual heat, is 26,190 TWh/year when including land areas with less than 4% slope [32]. Table 6.6 summarizes their results and compares them to the total 2014 electricity generation in each province [110]. It can be seen that Saskatchewan has the greatest CSP potential, followed by Alberta and then Manitoba. From these results,

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Chapter 6 – Concentrated Solar Heat and Power Preliminary Economic Assessment for Remote Communities

the CSP potential compared to power generation is 524 times in Saskatchewan, 113 times in Alberta, and 85 times in Manitoba. As shown by Djebbar et al., there is a significant potential in Canadian provinces to generate electricity using CSP.

CSP potential (GWh/yr) DNI range BC AB SK MB ON Total (kWh/m2/yr) 1,500-1,600 31,742 3,093,048 4,735,610 2,866,668 48,295 10,775,363 1,600-1,700 13,350 3,031,725 6,064,488 479,724 - 9,589,287 1,700-1,800 - 2,571,706 2,707,494 - - 5,279,198 1,800-1,900 - 480,739 68,401 - - 549,140 Total (GWh/yr) 45,093 9,177,215 13,575,993 3,346,392 48,295 26,192,988

Total electricity generated in 67,863 81,342 25,880 39,479 167,171 381,735 2014 (GWh/yr)

In contrast, to investigate the CPV power generation potential at relatively low temperatures in a cold climate, the CPVT model, presented in Section 4.4.2, is applied to the same six Manitoba cities selected in Section 6.2. The assumptions made for these simulations are:

• The 20-year averaged meteorological data for Brandon, Winnipeg, Dauphin,

The Pas, Thompson, and Churchill is used.

• The HTF outlet temperature is limited to 100°C.

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Chapter 6 – Concentrated Solar Heat and Power Preliminary Economic Assessment for Remote Communities

• Storage is not included.

• The glass envelope is removed from the receiver tube to attach the CPV cells.

The monthly power generation for the six cities are presented in Figure 6.8. As can be seen, the power generation in Churchill is at a similar level compared to the other five cities in the spring and summer months. The long daylight hours during those months along with lower ambient temperatures improve the CPV cell performance, and consequently, result in higher power generation.

) 2

15 /month/m

e 10

(kWh CPV powerCPV generation 5

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Churchill (58.75° N) Thompson (55.74° N) The Pas (53.83° N) Dauphin (51.15° N) Winnipeg (49.9° N) Brandon (49.83° N)

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Chapter 6 – Concentrated Solar Heat and Power Preliminary Economic Assessment for Remote Communities

6.3.1 Simple payback period of the CPVT system in remote communities

Table 6.7 shows six remote regions that experience similar weather conditions as each of the cities selected in Sections 6.3. The annual combined heat and power generation values obtained from the CPVT model are used to calculate the simple payback period for each remote region. Assumptions for the payback simulations include:

• The HTF outlet temperature is 100°C.

• Storage is not included.

• The glass envelope is removed from the receiver tube to attach the CPV cells.

• The price of fuel oil and diesel are chosen to be 0.12 $/kWh [109] and

1.5 $/kWh [20], respectively.

The approach described in Section 6.1 is used to estimate the simple payback period of the system for the six remote regions and the results are shown in Figure 6.9. As observed, the maximum simple payback period is associated with the Remote Region 6 representing

Churchill.

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Chapter 6 – Concentrated Solar Heat and Power Preliminary Economic Assessment for Remote Communities

Table 6.7: Remote Regions assumed to calculate the simple payback period of the concentrated photovoltaic/thermal system in remote communities

Remote region with similar City in Manitoba weather data Brandon Remote Region 1 Dauphin Remote Region 2 Winnipeg Remote Region 3 The Pas Remote Region 4 Thompson Remote Region 5 Churchill Remote Region 6

3.5

3.0 2.99 2.66 2.71 2.5 2.25 2.60 2.0 2.01 1.5

1.0

0.5 Simple Simple payback period(year) 0.0 Remote Remote Remote Remote Remote Remote Region 1 Region 2 Region 3 Region 4 Region 5 Region 6

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Chapter 6 – Concentrated Solar Heat and Power Preliminary Economic Assessment for Remote Communities

Although the cost of installing a solar CPVT system in an off-grid community is significantly higher compared to a grid connected area, the simple payback period is much more attractive as a result of higher price of diesel. Furthermore, as the cost of concentrated solar collectors continue to decline, as shown in Figure 6.3, the payback period will improve. Moreover, as discussed in Section 1.4, the Canadian government spends millions of dollars every year to operate diesel plants in remote communities which include fuel costs, operation, maintenance, and remediating of contaminated soil. Finally, as part of applying RED policy in remote communities, the results obtained from the integrated

CPVT system simulations introduced for cold climate applications—having low temperature HTF, reduced operator qualifications requirements, and improved safety— demonstrate that this technology must be included in the renewable energy planning process of such communities.

170

Chapter 7

Summary, Conclusions, and Recommendations for

Future Work

7.1 Summary of research work performed

The objective of the present work was to develop a detailed transient numerical model to simulate and integrate solar trough systems to provide heat and power as a viable alternative to diesel generation and heating oil in cold climates. Of importance was to validate such model and document system performance in cold climates by collecting data from a 52-kW parabolic solar trough pilot plant installed in Winnipeg, experiencing winter conditions that are representative of those occurring in remote communities. The studied solar pilot plant was installed and commissioned in July 2012. A major cold-weather- related issue resulted in the pilot plant outage in winter 2014; the plant is expected to restart by 2018.

None of the literature studies till now have focused on the performance of solar trough collectors in extremely low winter temperatures and tailored a design to displace fossil fuels without relying on high qualified operators. The key points that have not been addressed in the previous works are:

171

Chapter 7 – Summary, Conclusions and Recommendations for Future Work

1. Performance documentation and analysis of parabolic trough collector under cold

weather conditions with temperatures as low as -40°C.

2. Transient simulation of a solar trough plant with integrated controls using non-

averaged meteorological data.

3. The influence of control strategies on the system parasitic power and increasing

heat and electricity revenues.

4. Investigation of integrated solar trough plants technical issues resulting from cold

climate operations expected in remote communities.

5. Transient numerical simulation of a solar CPVT system in cold climates as a

potential approach that improves safety and reduces operator requirements for a

solar-base CHP system, a proposed method to eliminate diesel and oil use in

Canadian First Nation remote communities.

6. Assess the economics of using concentrated solar heat and power generation in cold

climates and remote communities.

The transient numerical model was developed using MATLAB®, Simulink®, and

Thermolib. The one-dimensional energy balance equation was solved around the receiver tube and the temperature of the glass envelope, the tube wall, and the HTF was calculated along the receiver length. The model also includes a passive mode which simulates the system during night time and no solar radiation periods. This is specifically important to study the system start-up in cold winter mornings. The weather data used in the simulations, including solar normal irradiance, ambient temperature, and wind velocity, was measured and recorded by the solar pilot plant weather station every minute to capture rapid

172

Chapter 7 – Summary, Conclusions and Recommendations for Future Work variations that controls require to address. The solar field outlet temperature was calculated by the model and compared with the measured temperature for validation.

By implementing a HBS approach, the validated model was used to simulate integrated thermal storage, heating, absorption cooling, and power generation using ORC and CPV methods. A combined heat and power generation model was developed using CPVT technology. To study the impact of control strategies on the parasitic loads of the solar pilot plant, two control strategies were applied and the resulted pump power was estimated. An optimization algorithm was also applied to maximize the heat and electricity revenue of the CPVT system by applying an optimal HTF flow rate.

The solar pilot plant and CPVT models were utilized to estimate the concentrated solar heating and electricity generation potential in six cities across Manitoba. Finally, a preliminary economic analysis was presented to calculate the revenue and simple payback period of the CPVT system per unit aperture area of the collectors.

7.2 Conclusions

The developed solar pilot plant model was successfully validated using the measured data collected from the solar field. The outlet temperature of the collector field, calculated by the model, was compared with the measured temperatures. During the thermal energy generation periods, the numerical results showed an excellent agreement with the experimental measurements, having an average deviation of approximately 1°C. The average difference between the model results and experimental measurements was found

173

Chapter 7 – Summary, Conclusions and Recommendations for Future Work to be approximately 6°C which was dominated by the periods when troughs were stowed and heat losses were relatively high compared to the heat capacity of the stagnant HTF in the pipes. This deviation is mainly caused by the exposure of the solar pilot plant to the outdoor environment which is subjected to unpredictable boundary conditions.

The greenhouse space heating model was developed using HBS approach and simulations were performed for October 3rd and March 16th, 2013, with ambient temperature ranges of

5°C to 20°C and -25°C to -10°C, respectively. The solar pilot plant contribution in the greenhouse space heating demand on October 3rd was 8.2% with thermal storage and 5.3% without thermal storage. For March 16th, the contribution with and without thermal storage was 30.2% and 27.5%, respectively. The cooling effect obtained from the solar assisted absorption cooling HBS model for the first five days of July 2013 was 2,340 kWh and

2,250 kWh with and without thermal storage, respectively. The performance of ORC and

CPV power generation was compared at the HTF outlet temperatures of 50°C and 150°C.

At 50°C, the solar power generation efficiency obtained to be 22% using CPV; such low temperature was not suitable for ORC operation. At 150°C, the ORC efficiency, being at

4%, was significantly lower than CPV efficiency of 17%. Lowering the solar field operating temperature is of interest in cold climates and remote communities due to its twofold advantage: reducing the heat loss and increasing the plant safety with lower operator qualification requirements. Environmental issues resulting from a spill or pipe rupture is also considerably lessened when using more environmental friendly HTFs, such as glycol. Moreover, the CPVT technology was applied as a part of RED policy to increase

174

Chapter 7 – Summary, Conclusions and Recommendations for Future Work renewable energy generation and increase the energy conversion efficiency in remote communities.

To reduce the parasitic power of the solar plant and to manipulate the field outlet temperature concurrently, a control method was applied that used a PI controller to control the pump on/off pattern and regulate the outlet temperature at a specific set point. By using the PI controller, the pump parasitic power reduced by almost 80% compared to the control method which was based on continuous operation of the pump.

A preliminary economic analysis for a grid connected application resulted in a simple payback period exceeding 20 years by displacing natural gas heating with solar heating in

Winnipeg. To reduce the payback period to less than 10 years, it was estimated by the model that either the natural gas price is required to increase to 8 ¢/kWh or the carbon tax is needed to rise to $190/tonne CO2.

In contrast, the simple payback period of the solar CPVT system installed in remote communities, based on the local cost of heating oil and diesel, was estimated as less than three years. It is necessary to include the additional costs of installing such systems in off- grid communities as they have significant impact on the payback period calculations.

However, due to the high cost of operating diesel generators in off-grid communities, the

CPVT technology can become a viable approach to eliminate fossil fuels in remote communities and contribute to a renewable energy future.

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Chapter 7 – Summary, Conclusions and Recommendations for Future Work

7.3 Recommendations

The following aspects were set for future work:

1. Installation of a solar trough test facility in an off-grid remote community to

investigate the actual barriers, difficulties, and economics of such technology.

2. Investigation of the technology simplification methods to lower the shipment,

installation, and maintenance costs in remote areas.

3. Integration of a prediction element into the existing numerical model which can

read the weather forecast online and develop a control strategy for the solar system

based on the upcoming weather conditions to better address community loads.

4. Development of an advanced controller for the integrated solar pilot plant which

can be connected to the solar pilot plant model and apply the selected control

strategy based on the weather forecast.

5. Analysis of the data resulted from the simulation to achieve a single equation for

calculating the solar system output energy and avoid the computational cost of the

detailed transient simulation.

6. A critical component left out of the return on investment analysis is the cost of

balance of plant and the cost of intermittency. These aspects need to be considered.

7. An optimization algorithm is required to evaluate the storage design parameters

based on the size and capacity of the solar plant and the energy demand.

176

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190

Appendix A

Sample experimental results for two selected months of June and February are shown in this section. The voltage readings of the thermocouples connected to channels MUX1,

MUX2, MUX4, MUX5, and MUX6 are converted to degrees Celsius using the T-type thermocouple reference tables shown in Figure A.1. Figure A.2 to Figure A.9 show the experimental results for July 2013. The same results are also presented for February 2013 in Figure A.10 to Figure A.17.

191

Appendix A

Figure A.1: Thermoelectric voltage as a function of temperature for T-type thermocouple

192

Appendix A

180

160

140

120

C) ° 100

60 Temperature ( Temperature

961

5761 1921 2881 3841 4801 6721 7681 8641 9601

18241 30721 10561 11521 12481 13441 14401 15361 16321 17281 19201 20161 21121 22081 23041 24001 24961 25921 26881 27841 28801 29761 31681 32641 33601 34561 35521 36481 37441

Minute

Figure A.2: Temperature of the receiver tube at the center of the first row, July 2013. The thermocouple is connected to channel MUX1 of CR1000.

180

160

140

120

100

60 Temperature ( Temperature

961

6721 1921 2881 3841 4801 5761 7681 8641 9601

13441 20161 10561 11521 12481 14401 15361 16321 17281 18241 19201 21121 22081 23041 24001 24961 25921 26881 27841 28801 29761 30721 31681 32641 33601 34561 35521 36481 37441

Minute

Figure A.3: Temperature of the receiver tube at the center of the second row, July 2013. The thermocouple is connected to channel MUX2 of CR1000.

193

Appendix A

180

160

140

120

100

Temperature ( Temperature 60

961

1921 2881 3841 4801 5761 6721 7681 8641 9601

36481 10561 11521 12481 13441 14401 15361 16321 17281 18241 19201 20161 21121 22081 23041 24001 24961 25921 26881 27841 28801 29761 30721 31681 32641 33601 34561 35521 37441

Minute

Figure A.4: Temperature of the receiver tube at the intersection of the two rows, July 2013. The thermocouple is connected to channel MUX4 of CR1000.

180

160

140

120 C)

100

60 Temperature ( Temperature 40

1098 2195 3292 4389 5486 6583 7680 8777 9874

14262 18650 23038 10971 12068 13165 15359 16456 17553 19747 20844 21941 24135 25232 26329 27426 28523 29620 30717 31814 32911 34008 35105 36202 37299

Minute

Figure A.5: Temperature of the receiver tube at the supply pipe, July 2013. The thermocouple is connected to channel MUX5 of CR1000.

194

Appendix A

180

160

140

120

100

60 Temperature ( Temperature

7680 8777 9874 1098 2195 3292 4389 5486 6583

10971 12068 13165 14262 15359 16456 17553 18650 19747 20844 21941 23038 24135 25232 26329 27426 28523 29620 30717 31814 32911 34008 35105 36202 37299 Minute

Figure A.6: Temperature of the receiver tube at the return pipe, July 2013. The thermocouple is connected to channel MUX6 of CR1000.

2000

1800

1600

1400

1200

1000

800

V_FlowMeter (mV) V_FlowMeter 600

400

200

4389 1098 2195 3292 5486 6583 7680 8777 9874

25232 10971 12068 13165 14262 15359 16456 17553 18650 19747 20844 21941 23038 24135 26329 27426 28523 29620 30717 31814 32911 34008 35105 36202 37299 Minute

Figure A.7: The flow meter voltage measured by the MUX3 sensor, July 2013

195

Appendix A

2.5

1.5

1 V_LevelSwitch (V) V_LevelSwitch 0.5

1098 2195 3292 4389 5486 6583 7680 8777 9874

36202 37299 10971 12068 13165 14262 15359 16456 17553 18650 19747 20844 21941 23038 24135 25232 26329 27426 28523 29620 30717 31814 32911 34008 35105

Minute

Figure A.8: The level switch voltage measured by the 1H sensor, July 2013. The voltage indicates that the level of oil in the expansion tank is within the operational limit.

2.5

1.5

V_StartUp (V) V_StartUp 1

0.5

1098 2195 3292 4389 5486 6583 7680 8777 9874

10971 20844 30717 12068 13165 14262 15359 16456 17553 18650 19747 21941 23038 24135 25232 26329 27426 28523 29620 31814 32911 34008 35105 36202 37299

Minute

Figure A.9: The start-up voltage measured by the 2L sensor, July 2013. This voltage indicates that the solar irradiance and wind speed are within the operational limits to start tracking.

196

Appendix A

170 150 130 110

C) 90 ° 70 50

30 Temperature ( Temperature 10 -10 -30

-50

961

5761 1921 2881 3841 4801 6721 7681 8641 9601

18241 30721 10561 11521 12481 13441 14401 15361 16321 17281 19201 20161 21121 22081 23041 24001 24961 25921 26881 27841 28801 29761 31681 32641 33601 34561 35521 36481 37441

Minute

Figure A.10: Temperature of the receiver tube at the center of the first row, February 2013. The thermocouple is connected to channel MUX1 of CR1000.

170 150 130 110

C) 90

70 50

30 Temperature ( Temperature 10 -10 -30

-50

961

6721 1921 2881 3841 4801 5761 7681 8641 9601

13441 20161 10561 11521 12481 14401 15361 16321 17281 18241 19201 21121 22081 23041 24001 24961 25921 26881 27841 28801 29761 30721 31681 32641 33601 34561 35521 36481 37441

Minute

Figure A.11: Temperature of the receiver tube at the center of the second row, February 2013. The thermocouple is connected to channel MUX2 of CR1000.

197

Appendix A

170 150 130

110 C)

90 70 50

30 Temperature ( Temperature 10 -10 -30

-50

961

1921 2881 3841 4801 5761 6721 7681 8641 9601

36481 10561 11521 12481 13441 14401 15361 16321 17281 18241 19201 20161 21121 22081 23041 24001 24961 25921 26881 27841 28801 29761 30721 31681 32641 33601 34561 35521 37441

Minute

Figure A.12: Temperature of the receiver tube at the intersection of the two rows, February 2013. The thermocouple is connected to channel MUX4 of CR1000.

170 150 130 110

70 50 30

Temperature ( Temperature 10 -10 -30

-50

1098 2195 3292 4389 5486 6583 7680 8777 9874

14262 18650 23038 10971 12068 13165 15359 16456 17553 19747 20844 21941 24135 25232 26329 27426 28523 29620 30717 31814 32911 34008 35105 36202 37299

Minute

Figure A.13: Temperature of the receiver tube at the supply pipe, February 2013. The thermocouple is connected to channel MUX5 of CR1000.

198

Appendix A

170 150 130 110

C) 90

70 50

30 Temperature ( Temperature 10 -10 -30

-50

7680 8777 9874 1098 2195 3292 4389 5486 6583

10971 12068 13165 14262 15359 16456 17553 18650 19747 20844 21941 23038 24135 25232 26329 27426 28523 29620 30717 31814 32911 34008 35105 36202 37299 Minute

Figure A.14: Temperature of the receiver tube at the return pipe, February 2013. The thermocouple is connected to channel MUX6 of CR1000.

2000

1800

1600

1400

1200

1000

800

V_FlowMeter (mV) V_FlowMeter 600

400

200

4389 1098 2195 3292 5486 6583 7680 8777 9874

25232 10971 12068 13165 14262 15359 16456 17553 18650 19747 20844 21941 23038 24135 26329 27426 28523 29620 30717 31814 32911 34008 35105 36202 37299 Minute

Figure A.15: The flow meter voltage measured by the MUX3 sensor, February 2013

199

Appendix A

2.5

1.5

1 V_LevelSwitch (V) V_LevelSwitch 0.5

3292 4389 5486 6583 7680 1098 2195 8777 9874

10971 12068 13165 14262 15359 16456 17553 18650 19747 20844 21941 23038 24135 25232 26329 27426 28523 29620 30717 31814 32911 34008 35105 36202 37299

Minute

Figure A.16: The level switch voltage measured by the 1H sensor, February 2013. The voltage indicates that the level of oil in the expansion tank is within the operational limit.

2.5

1.5

V_StartUp (V) V_StartUp 1

0.5

1098 2195 3292 4389 5486 6583 7680 8777 9874

10971 20844 30717 12068 13165 14262 15359 16456 17553 18650 19747 21941 23038 24135 25232 26329 27426 28523 29620 31814 32911 34008 35105 36202 37299

Minute

Figure A.17: The start-up voltage measured by the 2L sensor, February 2013. This voltage indicates that the solar irradiance and wind speed are within the operational limits to start tracking.

200

Appendix B

Incident angle modifier calculator block

Figure B.1: The IAM calculation block developed in Simulink

The block shown in Figure B.1 calculates the IAM term for the PT-1 solar trough module using an equation provided by Abengoa. To estimate the IAM, the incident angle, θ, is input to the block from the local measurements. The solar zenith, θz, and azimuth, γs, angles are also used from the data collected at the solar plant location. In case the measurement data is lacking, the block is capable of calculating all the required solar angles through the sub-blocks shown in Figure B.2. From Reference [26], the incident angle for tracking aperture about the horizontal east-west axis is calculated using:

cos θ = √1 − (cos δ)2×(sin ω)2 (A.1)

201

Appendix B where

δ: Declination angle (deg)

ω: Hour angle (deg)

Figure B.2: The sub-blocks used to calculate the IAM using the related equation

Properties calculator block

The block shown in Figure B.3 calculates the thermophysical properties of the HTF, receiver tube, annulus gas, and glass envelope as a function of temperature. The optical properties estimation of the receiver coating and glass envelope is also included in this block.

202

Appendix B

Sub-blocks

Figure B.3: The property calculator block along with the sub-blocks used to estimate the thermophysical and optical properties of the solar system components

203

Appendix B

Heat transfer rates calculator blocks

The heat transfer rates described in Section 3.2 are calculated in the blocks shown in

Figure B.4. The equations presented in Section 3.2 are used by the blocks to estimate the heat transfer rates at each time step of one second.

Figure B.4: Heat transfer rates calculator blocks which use the equations presented in Section 3.2

204

Appendix B

Energy balance block

The heat transfer rates calculated by the blocks shown in Figure B.4 are used by the energy balance block to solve the energy equation around the receiver tube using finite difference method. The outputs of this block are the HTF, receiver tube, and glass envelope temperatures along the receiver length. The block is shown in Figure B.5.

Figure B.5: The energy balance block estimates the receiver tube, HTF, and glass envelope temperature using the data provided by the blocks shown in Figure B.4

205