Evaluation of dry cooling option for parabolic trough (CSP ... · (CSP) plants including related...

Assessment of Dry Cooled Parabolic Trough (CSP) plants

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„Evaluation of dry cooling option for parabolic trough (CSP) plants including related technical and economic

assessment”

“Case study CSP Plant in Ma’an/Jordan”

By

Ahmad Abdel-Latif Mohammad Liqreina

A Thesis Submitted in partial fulfillment of the requirements for the degree

Master of Science in “Renewable Energy and Energy Efficiency”

College of Engineering Kassel University Cairo University

Supervisors

Prof. Dr.Ing Adel khalil Prof. Dr. sc. techn. Dirk Dahlhaus Cairo University Kassel University

Dr. Louy Qoaider German Aerospace Center – DLR

Date of approval

March 15, 2012


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Acknowledgements

I would like to express my most sincere gratitude to my advisors, Dr. Adel khalil, Dr. Louy

Qoaider and Dr. Dirk Dahlhaus for their guidance and encouragement throughout this work. I

also like to thank Dr. Klaus Pottler, Dr. Christoph Richter, Dr. Marc Roeger, Mr.

FabianWolfertstetter, Mr. Stefan Wilbert and Mr. Christoph Prahl who with their advice and

kindness were great help during my stay in the German Aerospace Center – (DLR) in Almeria in

Spain. I would like to express my thanks to Dr. Ahmad al-Salaymeh from Jordan University and

Mr. Firas Alrimawi the manager of Ma’an Development Area (MDA) for their support. I would

like also to thank my professors and collogues from Birzeit University Dr.Allan Tubaileh, prof.

Hasan Shibleh, prof. Afif Hasan, Dr. Ahmad abu Haneia, Dr. Mohamad Karaeen and Mr.Sameh

abu Awwad who inspire me.

My special thanks go to my friends Alaa’, Anan, Ahmad, Mohammad, Ali, Hani, Rabee’, Tariq,

Hana, Nour, wala,Suad, Najah, Shadi, Osama, Rmai Ayman, Abdallah, Ashraf, Moath, Momen,

Zaher, Ibrahim, Karim, Mutaz Martin, Younis, Laith, Fadi, Noha, Rana for their support

throughout my study. Sincere thanks to my mother, Fatheia, my father, Abdelatif who brought

me up and always supported me, great thanks to my brothers Mohammad, Mahmoud, Montaser

and my sisters Abeer, Iman and Haneen, to my uncles Abdallah, Mustafa, Ibrahim, to my aunts

Fatima, Rasmia, Nihaya, and to my brother in law Anwar.


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Table of Contents Acknowledgements 2

Table of Contents 3

List of Tables 5

List of Figures 7

List of Symbols 9

Abstract 11

1. Introduction 13

1.1 Background ....................................................................................................................................... 13

1.2Research need .................................................................................................................................... 13

1.3 Objectives .......................................................................................................................................... 14

1.4 Methodology ..................................................................................................................................... 14

2. Parabolic trough power plants 15

2.1 Overview of CSP technologies ........................................................................................................... 15

2.1.1 Fresnel ........................................................................................................................................ 15

2.1.2Central receiver systems ............................................................................................................. 17

2.1.3 Dish‐Stirling ................................................................................................................................ 19

2.1.4 Parabolic Trough ......................................................................................................................... 21

2.2 Parabolic trough power plant ........................................................................................................... 23

2.2.1 Introduction ............................................................................................................................... 23

2.2.2 Solar field ................................................................................................................................... 24

2.2.3 Storage system ........................................................................................................................... 25

2.2.4 Power block ................................................................................................................................ 27

3. Site Assessment of the case study area 33

3.1 Site Location ...................................................................................................................................... 33

3.1.1 Best locations for CSP ................................................................................................................. 33

3.1.2 Ma’an Plant location .................................................................................................................. 35

3.2 Solar resources assessment .............................................................................................................. 38

3.2.1 Source and quality o f metrological data ................................................................................... 38

3.2.2 Solar recourses ........................................................................................................................... 40

3.3 Study of dry cooling as an option ...................................................................................................... 45


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4. Planning of the Ma’an power plant 48

4.1 Pre‐Design of Ma’an power plant ..................................................................................................... 48

4.2 Power cycle and cooling system ........................................................................................................ 50

4.3 Simulation Inputs .............................................................................................................................. 51

5. Simulation, optimization and comparison 57

5. 1 Simulation of base design (Andasol) ................................................................................................ 57

5.1.1 Andasol in Spain ......................................................................................................................... 57

5.1.2Andasol in Ma’an ......................................................................................................................... 60

5.2 Optimization of Ma’an plant ............................................................................................................. 63

5.2.1 Wet Optimization ....................................................................................................................... 64

5.2.2 Dry Optimization ........................................................................................................................ 69

5.3 Technical Comparison between wet and dry cooling in Ma’an ......................................................... 74

5.3.1 Same design ............................................................................................................................... 75

5.3.2 Optimized design ........................................................................................................................ 77

5.4 Economic Comparison between wet and dry cooling in Ma’an ........................................................ 84

5.5 Suggestions to make the project economically feasible ................................................................... 87

5.5.1 Minimum required tariff ............................................................................................................ 88

5.5.2 Minimum required grant ............................................................................................................ 90

5.5.3 Tariff and grant ........................................................................................................................... 92

6. Economic Analysis 97

6.1 Introduction: ..................................................................................................................................... 97

6.2 Electricity Prices ................................................................................................................................ 97

6.3 Environmental impacts ..................................................................................................................... 97

6.3.1 Plant construction: ..................................................................................................................... 98

6.3.2 Plant operation: ......................................................................................................................... 98

6.3.3 CO2 emission reduction ........................................................................................................... 100

6.4 SWOT Analysis ................................................................................................................................. 101

6.5 sensitivity analysis ........................................................................................................................... 102

7. Conclusions 106

8. Recommendations 108


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List of Tables

Table2.1: Selected properties of SKAL‐ET150 collector. ................................................................ 25

Table4.1:Ma'an pre‐design similar to Andasol.............................................................................. 48

Table4.2: Power block design conditions for wet and dry ............................................................ 51

Table4.3: Simulation inputs for project site .................................................................................. 52

Table4.4: Simulation inputs for solar field .................................................................................... 54

Table4.5: Simulation inputs for Storage and Power Block ............................................................ 55

Table5.1 Simulation results of Andasol design in Spain ................................................................ 58

Table5.2 Monthly Power production and overall efficiency for Andasol design in Spain ............ 57

Table5.3 Simulation results of Andasol destine in Ma’an with Spain economics ......................... 61

Table5.4 Monthly Power production and overall efficiency for Andasol design in Ma’an ...... Error!

Bookmark not defined.

Table5.5: optimization steps of wet cooled case .......................................................................... 64

Table5.6: optimization steps of dry cooled case ........................................................................... 69

Table5.7: LCOE at different TES hours and solar multiple, for dry cooled plant in Ma’an ............ 72

Table5.8: Power output and overall plant efficiency for same design case. ................................. 75

Table5.9: Power output and overall plant efficiency for Optimized design case .......................... 78

Table5.10: Power block parasitics for Optimized design case ....................................................... 80

Table5.11: Summary of all technical simulation results ............................................................... 82

Table5.12: Technical comparison between expected plants in Ma’an.......................................... 83

Table5.13: Economic Simulation inputs for Jordan (Costs, Financing, and Timing) ................... 85

Table 5.15: Economic comparison between expected plants in Ma’an (sample one) .................. 86

Table5.16: Economic comparison between expected plants in Ma’an (sample two)................... 87

Table5.17: Economic comparison between expected plants in Ma’an with minimum required

tariff (sample one) ................................................................................................................ 88


tariff (sample two) ................................................................................................................ 89

Table5.19: Economic comparison between expected plants in Ma’an with grant (sample one) . 90


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Table5.20: Economic comparison between expected plants in Ma’an with grant (sample two) . 91

Table5.21: Economic comparison between expected plants in Ma’an with tariff and grant

(sample one) ......................................................................................................................... 92


(sample two) ......................................................................................................................... 93


(sample three) ....................................................................................................................... 94


(sample four) ......................................................................................................................... 95


(sample five) .......................................................................................................................... 96

Table6.1: Expected water consumption for Dry/Wet 50 MW with 7.5 TES in Ma’an‐Jordan ....... 99

Table6.4: Cost assumptions recommended by SAM software, adjusted to Greenius inputs ..... 103

Table6.5: Different simulation results for different specific solar filed cost, including the

recommended costs ............................................................................................................ 103

�


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List of Figures

Figure1.1: Fresnel lens based on the conventional lens ................................................................ 16

Figure1.2: Fresnel mirror based on the Fresnel lens ..................................................................... 16

Figure1.3: Fresnel collectors ......................................................................................................... 17

Figure1.4: Sketch of Tower Systems ............................................................................................ 18

Figure1.5: Central receiver filed and its receiver. Source DLR .................................................... 18

Figure1.6: Cavity receiver Pant and schematic of cavity receiver. (PS10, 20 Spain) ................... 19

Figure1.7: Sterling Generator Package. ........................................................................................ 20

Figure1.8: Sterling Dishes at PSA. ............................................................................................... 21

Figure1.9: Rays collecting mechanism ......................................................................................... 22

Figure1.10: Parabolic Trough collector, at PSA. .......................................................................... 23

Figure2.1: Schematic diagram of parabolic trough CSP plant with indirect two-tank storage. ... 24

Figure2.2: Two tanks molten salts storage of Andasol. ................................................................ 26

Figure2.3: Simple representation of a steam Rankine thermal power cycle. ................................ 28

Figure2.4: Schematic diagram of a cooling water system ........................................................... 29

Figure2.5: Cross flow natural draft cooling tower ........................................................................ 29

Figure2.6: Induced draft, double-flow crossflow tower. ............................................................... 30

Figure2.7: Direct dry cooled condenser ........................................................................................ 31

Figure2.8: Hybrid cooling systems use an air-cooled condenser and a wet-cooled condenser in

parallel. ................................................................................................................................. 32

Figure3.1: Best locations for CSP. ............................................................................................... 33

Figure3.2: Exclusion map for MENA region. ............................................................................... 34

Figure3.3: Plant location. .............................................................................................................. 35

Figure3.4: Picture from MDA site. ............................................................................................... 36

Figure3.5: Meteonorm main user window. ................................................................................... 40

Figure3.6: One day clear sky irradiance in Ma’an. ....................................................................... 41

Figure3.7: Mean Monthly Diurnal of DNI, based on enerMENA station ................................... 44

Figure3.8: Mean Monthly Diurnal of DNI, based on Meteonorm data ........................................ 44

Figure3.9: Monthly plot of hourly averaged dry bulb temperature in Ma’an. .............................. 46


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Figure3.10: Duration curve of dry bulb temperature. ................................................................... 47

Figure4.1: layout of Andasol plant ............................................................................................... 49

Figure4.2: Process Matrix for 50 MW Dry Cooled Power Block ................................................ 50

Figure4.3: Process for Greenius simulation .................................................................................. 51

Figure5.1: Monthly Net Power for Andasol design in Spain ........................................................ 59

Figure5.2: Plant Overall Efficiency for Andasol design in Spain ................................................. 60

Figure5.3:Monthly Net Power for Andasol design in Ma’an ....................................................... 62

Figure5.4:Plant overall Efficiency for Andasol design in Ma’an ................................................. 62

Figure5.5:Gross electrical power of wet case,before optimization ............................................... 65

Figure5.6:Gross electrical power of wet case,after optimization ................................................. 65

Figure5.7:Dumped solar energy of wet case,before optimization ............................................... 66

Figure5.8:Dumped solar energy of wet case,after optimization .................................................. 67

Figure5.9:Charging and discharging of TES of wet case,before optimization ............................. 68

Figure5.10:Charging and discharging of TES of wet case,after optimization .............................. 68

Figure5.11:Gross electrical power of dry case,after optimization ................................................ 70

Figure5.12:Dumped solar energy of dry case,after optimization................................................. 70

Figure5.13:Charging and discharging of TES of dry case,after optimization .............................. 71

Figure5.14: Contour representation of LCOE as function of solar multiple and storage hours ... 73

Figure5.15: plot of LCOE as function of solar multiple for each TES capacity .......................... 74

Figure5.16: Monthly Net power for same design ......................................................................... 76

Figure5.17: Monthly parasitics loads for same design ................................................................. 76

Figure5.18: Monthly overall efficiency for same design .............................................................. 77

Figure5.19: Monthly Net power for Optimized design case ......................................................... 79

Figure5.20: Monthly overall efficiency for Optimized design case ............................................. 79

Figure5.21: Dry cooled power block parasitics for Optimized design case ................................. 81

Figure5.22: main operational charctersitics of the expexted dry plant in ma’an,(23-Jun) ........... 84

Figure6.1: LCOE senstivity analysis .......................................................................................... 105


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List of Symbols

CSP Solar Thermal Power

CO2 Carbon dioxide

DAR Direct Absorption Receiver

DSCR Debt Service Coverage Ratio

Cp Specific Energy

GDP Gross Domestic Product

GHG Greenhouse Gas

Gw Gigawatt

Gwh Gigawatt hour

HTF Heat Transfer Fluid

HEX Heat exchanger

IPP Independent Power Producer

IRR Internal Rate of Return

K Kelvin

J Joule

KfW Kreditanstalt für Wiederaufbau

kg Kilogram

kv Kilovolt

Kilowatt hour (thermal / electrical) kWh ther / el

LCOE Levelized Cost of Electricity

LS Type of parabolic collector Luz system

MENA Middle East and North Africa

Mtoe Million ton oil equivalent

MW Megawatt

NERL National Renewable Energy Laboratory

NPV Net Present Value

O&M Operation & Maintenance


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PPA Power Purchasing Agreement

R&D Research and Development

RE Renewable Energy

SAM System Advisor Model

SST Siemens Steam Turbine

SWOT Strength, Weakness, Opportunity, Thread

T Temperature in Kelvin

TSO Transmission System Operator

TWh Tera watt hour

UNEP United Nations Environment Programme

W Watt


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Abstract

In south Jordan water is expensive, not enough and restricted thus dry cooling for power

production is the only option. This work is a comprehensive study of a 50 MW parabolic trough

solar thermal power plant with dry cooled system, and 7.5 full load storage hours in Ma’an,

Jordan, comparison between Dry and Wet cooled plants from technical and economic point of

view is done, also an assessment of dry cooled plant in this site.

Simulation tool (Greenius) which is developed by DLR is used to simulate parabolic trough plant

and also used for optimization, based on Site measured data from enerMENA station in this site,

design parameters that are similar to Andasol are used in the comparison between Ma’an site and

Andasol for both cooling options, but after the first comparison was finished, an optimization of

design is done for wet base plant and the dry cooled plant for better assessment in Ma’an.

Constant-capacity design was assumed, thus the dry plant has a larger turbine and solar field to

accommodate the lower cycle efficiency, The expected wet cooled plant in Ma’an has 444720

m2 effective solar field area, with 183879.4MWhe annual energy yield, 4162 operating hours,

14.9% annual mean overall efficiency, a capacity factor of 41.98 and water consumption of

717981 m3/a.

While the dry plant has 523200 m2 effective solar field area, with 182173.5MWhe energy

yield, 4190 operating hours, 12.9% annual mean overall efficiency, a capacity factor of 41.59%,

and water consumption of 41820 m3/a,

The solar field area increased by 17.64%, the efficiency reduced by 2%, the water consumption

reduced by 91.3%, the energy yield reduced by0.93%, the investment cost increased by 16.42%,

the LCOE increased by 16.12%.

A dry cooled plant in Ma’an will have the same solar field size as the Andasol wet cooled plant,

but with a larger turbine; both have the same TES full load hours (7.5 hours), but instead of a

970MWht thermal capacity in Andasol a 1100MWht in Ma’an, because of higher thermal input

of dry cooled turbine at same capacity. And the expected Energy yield is 35.23% higher than

Andasol.

The technical simulation showed good results, because Ma’an has high DNI and Normal ambient

temperatures, from technical point of view the dry cooling option in Ma’an still very good, but

CSP technologies are expensive. The economic simulation showed that the project is unfeasible


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if the energy to the grid is sold at the same price of Jordanian electricity 0.084€/kWhe, without

feed in tariff. Different suggested financial scenarios have been simulated to make the project

feasible. The minimum required tariff 0.17€/kWhe, or a grant of 163.3 million €, or

(0.146€/kWhe with 50 million €), or (0.13€/kWhe with 100 million €)


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Chapter 1

Introduction 1.1 Background The world population is increasing and the thus their needs, mainly power, food and water.

Electrical power has been generated for the last two hundred years depending on conventional

resources in relatively efficient ways. However, these resources are depleting and therefore

limited so that their costs increase steadily. Moreover the available conversion techniques are

associated with the emission of greenhouse gases. These restrictions and negative effects lead the

world to search for alternative resources. In this regard, the utilization of renewable energy

resources should be a good solution, in which some technologies are well developed and others

still under development. Solar energy that is represented by solar radiation is one of the most

promising renewable sources. Photovoltaic, which is direct conversion from light energy into

electricity, and concentrated solar thermal power called also concentrated solar power (CSP) are

the two known technology types to utilize the solar energy. While PV is suitable for small or off-

grid solutions, CSP showed attractive features to be installed in large scale. On-grid CSP power

plants with a thermal storage should stabilize the grid secure the dispatchability of power.

1.2Research need CSP direct normal irradiation which is very high in deserts rather than the cloudy and humid

coastal areas, but such as conventional plants, require water for cleaning. Normally conventional

plants are sited near good water sources coastal areas. If water is expensive not enough or

restricted, dry cooling an option, it is clear that the use of such a system is not to compete with

wet cooling but it be used in many attractive locations for CSP.

Previous studies done by NREL and DLR showed that dry cooling could save more than 90% of

water consumption1, on the other hand, the overall performance of such a power plant is reduced

under higher ambient temperatures. Such losses would be compensated inter alia by increasing

the thermal energy input, from the solar, field in CSP case by increasing its size, and through the

1 Water Use in Parabolic Trough Power Plants: Summary Results from WorleyParsons' Analyses.P(25)


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selection of turbines. Hence the design of a CSP plant, especially its solar field, depends also on

which cooling system is used; a bigger Solar field for dry cooling than of that for wet cooling for

the same power output. This results in higher investment costs for dry cooled power plants than

those for wet cooled plant with the same capacity.

CSP power plants require huge initial investments and any additional costs, e.g. the cooling

tower and a bigger solar field, are undesired and would threaten securing project finance. In this

context a trade-off between all options should be made for each specific site to know whether to

use dry cooling or not. For many locations, dry cooling is the only affordable option and

therefore must be considered.

1.3 Objectives The main objective of this master thesis is to evaluate the use of dry and wet cooled CSP

parabolic plants in Ma’an site in Jordan. This will be done by establishing a comparison between

both options. The comparison is held also for the technical performance and the economics of the

plant options, to show that high DNI could compensate for the defects of a dry plant. This is to

prove that a larger solar field produces more electricity in days of low ambient temperature,

finally an assessment for a dry cooled plant in Ma’an site will be resulted.

1.4 Methodology First of all a literature review is done, which are about comparison of cooling options for

parabolic trough CSP plants, assessment of power plants, site selection and simulation tools. Site

measured data from enerMENA station in Ma’an is used.

Simulation tool (Greenius) which is developed by DLR is used to simulate parabolic trough plant

and also used for optimization,

The pre-design parameters are decided to be similar to Andasol, that gave a good comparison

reference, but after the first comparison is finished, an optimization of design is done for better

assessment in Ma’an.

The Comparison between dry and wet is done based on identical input parameters, the weather

input data is only changed when comparing the other site which is Andasol in Spain.

Three steps are done, simulation of Andasol design in Spain and in Jordan as a reference,

optimization of wet/dry that made the design suitable to Ma’an site, and finally


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Chapter2

Parabolic trough power plants 2.1 Overview of CSP technologies Concentrated solar power technologies are categorized to point focus and line focus technologies

according to their focal point geometry. In the point focus the sun rays concentrated on one point

by three dimensional collectors, result in high rays density that lead to high temperature, two of

its main technologies are the tower and dish. The line focusing collectors concentrate incident

rays on a line resulting moderate temperatures, also two of its main types are the parabolic

trough and Fresnel.

The solar energy is then evacuated from the focus into the power cycle by heat transfer mediums

such as water, oil, air, gas, or sometimes liquid salts. The collected heat can be stored in an

isolated storage or it can be directly used. Herein a description of elements used in the previous

technologies will be discussed, but first it is important to define the concentration ratio” the

ability of a collector to concentrate or elevate the intensity of solar radiation”, the theoretical

concentration ratio which is the ratio of the aperture area to the absorber area, and the actual

concentration ratio which is the ratio of the solar flux absorbed by the absorber to the solar flux

received at the aperture.

2.1.1 Fresnel

This technology is based on Fresnel mirrors to concentrate the sun rays on a line, it is also based

on the Principle of Fresnel lens; named after the French inventor Augustin Fresnel in1819; which

is described schematically in figure1.1, he aimed to use this technology for house lighting by

reducing the material and the price of the conventional lens, now the same principle is used in

these types of concentrators.


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Figure1.1: Fresnel lens based on the conventional lens2

The following figure shows the principle of the Fresnel concentrator, which is simply the

opposite of a lens, by replacing the segments of Fresnel lens by mirrors.

Figure1.2: Fresnel mirror based on the Fresnel lens3

2 http://en.wikipedia.org/wiki/File:Fresnel_lens.svg

3 DLR enerMENA capacity building course eM-CB01:U4


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The new Fresnel collectors are developed in Germany and investigated in Spain, their

disadvantage is their low optical efficiency but still this technology has a huge potential since it

has lower construction costs, easy cleaning processes, simple structure due to lower wind stress,

they need less area because of the smaller distance between mirrors, also it is a direct steam

generation (DSG) which means another reduction in price due to the elimination of the HTF

system, but don’t have a storage option, this technology is suitable for thermal process systems

and also electrical generation, figure1.3 shows a real Fresnel power plant.

Figure1.3: Fresnel collectors4

2.1.2Central receiver systems

One receiver is used or in other words one central receiver for the same solar field, this receiver

is supported and elevated by a tower, simple nearly flat mirrors (Heliostats) are controlled

individually to concentrate the sun rays at the receiver. Obviously the concentration ratio here is

relatively high, something between 200-1000. To reduce the distances between heliostat and to

increase capacity a high tower is necessary, figure1.4 shows a sketch of a central receiver system.

This technology is categorized according to the type of receiver, which also changes the solar



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field design.

Figure1.4: Sketch of Tower Systems

External receiver:

This type of receiver has high an acceptance angle thus it can receive solar rays from all

directions, also it is simply constructed but it’s subjected to high thermal losses. Figure1.5 shows

the receiver and its solar field. The development of this type is in the coating of the receiver

which increases absorptivity and reduces emissivity.

Figure1.5: Central receiver filed and its receiver.5

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Mainly this receiver is tubular which means a set of tubes are a direct heat exchanger from the

solar heat into a fluid, the fluid circulates inside these tubes, it should withstand high

temperatures, for that reason molten salts are used here which are also stored well.

Cavity receiver:

In the cavity receiver, the concentrated radiation will be admitted through an aperture to be

absorbed by the internal cavity walls, it is subjected to lower losses but has a smaller acceptance

angle. Here the absorption techniques are deferent and still under development, types of such

receivers are Tubular Receiver with pressurized air, Volumetric Receiver which is similar to the

previous one but without tubes, and Direct Absorption Receiver (DAR) where a moving working

fluid passes through a radiation flux and absorbs radiation directly. Figure1.6 shows a cavity

receiver schematic and a real power plant.

Figure1.6: Cavity receiver Pant and schematic of cavity receiver. (PS10, 20 Spain)6

2.1.3 Dish‐Stirling

The technology here uses two dimensional mirrors to concentrate normal irradiance on one point,

the surface mirror has a parabolic shaped mirror able to rotate along two axis; to make its

manufacturing easy they divided the surface into several segments. At the focal point a high

density of solar flux exists due to high concentration ratio (over that 1000), and thus high

temperatures and high performance. What’s deferent here is that a Stirling generator located in

6 http://en.wikipedia.org/wiki/File:PS20andPS10.jpg


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the focal point is used to convert heat into electricity directly. Figure1.7 shows a Stirling

generator’s components for a system at PSA, used by DLR in the dish research and development.

Figure1.7: Sterling Generator Package.7

Each dish system is a complete power production device due to the high temperatures and the

help of Stirling, this leads to a distributed generation, that means to generate electricity

separately and then collecting it, this is not found in Tower and parabolic trough which are

centralized power systems.

The main design problem is related to the structure which has to withstand high loading stress of

gravity, bending moments, tensional loads and structural forces due to thermal expansion, and

also high wind forces due to large aperture area. Figure1.8 shows a group of dishes with different

designs at PSA.

7 DLR


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Figure1.8: Sterling Dishes at PSA.8

2.1.4 Parabolic Trough

A Parabolic Trough Collector is simply a parabolic shaped mirror that reflects the direct normal

irradiation from its normal axis to its axis, as shown in figure1.9, the cross section of the mirror

is a parabola curve (Trough) for that reason it is named, the typical curvature radius is between 1

to 4 and the focal length is half of the curvature radius, and the typical concentration ratio is

around 80 but they could reach higher values by lager accurate troughs. This shape is extended

along an axis that passes through the focus which results in a focal line, where a heat collecting

element; also called absorber tube; is placed. Mirrors and tubes are mounted on a steel structure

to fix and support the assembly, see figure1.10, this assembly tracks the sun as it moves across

the sky around the trough axis, and the other axis is fixed normally aligned North –South.



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Figure1.9: Rays collecting mechanism9

The fluid inside the Absorber tube –usually synthetic Oil - heats up, thus the collected energy is

evacuated by circulating this fluid to a heat exchanger, that transmits heat to water cycle inside a

conventional steam cycle, this process is called indirect steam generation or two cycles, the other

option is to be used directly if the HTF is water this technology is called direct steam generation

DSG or one cycle. the attractiveness of this technology are clear, the potential of storage in the

indirect steam generation where the oil or molten salts are used to store huge amounts of thermal

energy, and it can be used on demand or whenever the sun is not shining.

This technology is justified and used in USA since 1981, recently new power plants are installed

-hybrid systems or only solar-, in Spain, Egypt, Morocco, Algeria and UAE. The solar field cost

is still relatively expensive.



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Figure1.10: Parabolic Trough collector, at PSA.10

2.2 Parabolic trough power plant

2.2.1 Introduction

The power plant under study has a capacity of 50MW and 7.5 hours energy storage. The capacity

of the plant and storage system are similar to those of Andasol-1 plant in Spain which is the first

commercial parabolic trough plant in Europe and the first plant with storage system in the world.

Since the design of parabolic trough plants require vast experience, and it’s good to have a real

reference plant, the configuration of Andasol-1 plant will be adopted for this study.

A schematic description of the CSP plant under study is illustrated in the figure2.1. In this stand-

alone configuration, the plant consists of three main components: the solar field, the storage

system and the power block. The three components are coupled through two heat exchangers.

A heat transfer fluid (HTF) is heated as it circulates through the receivers in the solar field. It

runs through a multiple heat exchangers to generate high-pressure steam. The steam is then fed

into a separate cycle (Rankine cycle) to drive a conventional steam turbine. The discharged

steam from the turbine is condensed into liquid ready to be re-heated in the steam generator to

10 DLR


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complete the cycle.

The thermal energy storage (indirect two-tank system) is charged when the output thermal power

of the solar field exceeds the power block requirements. Where the surplus heat is transferred to

the molten salt through a heat exchanger, the heated molten salt is stored in the hot salt tank.

Discharging salt from the hot tank to reheat the HTF occurs in the same heat exchanger except

the flow is reversed when the solar field does not provide the sufficient power for steam

generation.

Figure2.1: Schematic diagram of parabolic trough CSP plant with indirect two-tank storage.11

2.2.2 Solar field

The major element in the solar field is the collector; the field consists of parabolic trough

collectors which are currently the most proven solar thermal electric technology. This is

primarily due to nine large commercial-scale solar power plants since 1984 with a total capacity

of 354MW.

SKAL-ET150 collector with a continuous tracking system will be used for solar radiation

collection. Some selective properties of the collector are presented in table2.1 below. The size of 11 Solar Millennium (2008, p.13)


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the field aperture area will be similar to that in Andasol-1 which is 510,120m² with a solar

multiplier of 1.712. Given the fact that the DNI in Ma’an is greater than in Andasol-1, the annual

energy output is expected to be higher as will be demonstrated in chapter5, and more solar

energy will be dumped, thus the design should be optimized to have a better base when

compared with dry cooling.

The most widely used HTF is hydrocarbon oil, which has a wider liquid temperature range than

water, but a lower thermal capacity and higher viscosity. It is a eutectic mixture of two very

stable compounds, biphenyl (C12H10) and diphenyloxide (C12H10O).

Table2.1: Selected properties of SKAL-ET150 collector.13

Parameter Value Unite

Focal Length 1.71 m m

Average distance to focus 2.11 m m

HCE Absorber Radius 3.5 cm cm

HCE Length 4 m m

Aperture width 5.75 m m

Aperture area 817.5 m2

Length 150 m m

Number of modules 12 ---

Mirror reflectivity 93.5 %

Absorber absorptivity 96 %

Envelop transmissivity 96.3 %

Overall optical efficiency 78 %

2.2.3 Storage system

As mentioned herein before, the storage system consists of hot and cold water tanks. The storage

media is Nitrate molten salt (60% NaNO3 + 40% KNO3). The heat capacity, thermal conductivity 12 Solar Millennium (2008, p.8)

13 Herrmann, Nava (p.3)


26

and viscosity of the salt are given as functions of temperature shown in the equations below14. In

addition, the salt has a high melting point (239 oC).

cp(T)=1443 +0.172 T [J/kg/K]

k(T) =0.443 +0.00019 +T [W/m/K]

μ(T) =0.001+22.714 -0.12 T +0.0002281T 2-0.0000001474 T 3 ) [Pa s]

The storage designed for Andasol provides the rated power output of the plant for 7.5 hours, the

main technical data of this storage is written down; a picture of this storage is shown in figure2.2

• Cold tank temperature: 292 oC

• Hot tank temperature: 386 oC

• Flow rate: 948 kg/s.

• 14 m height, 38 m diameter.

• 1085 MWh capacity = 7.5 equivalent hours = 28.500 tonnes.

Figure2.2: Two tanks molten salts storage of Andasol.15

14 Kopp (2004, p.16)

15 Solar Millennium (2008, p.18)


27

2.2.4 Power block

The thermal energy will be transferred by the HTF to the power generation side through the heat

exchanger. The generation side consists of conventional Rankine steam cycle. Because of

thermal stability of HTF it is only kept up to working temperatures of 400 oC, the maximum

steam temperature in the power cycle will be nearly 370 oC16.

The steam turbine type is condensing turbine single reheat and six steam extractions. Siemens

turbine SST-700 is selected for this study, capacity and operation parameters of the turbine are as

follows:

‐ Nominal Capacity 50.0 MW.

‐ Conversion efficiency 38%.

‐ Turbine Inlet Conditions 100 bar 370°C, reheat 16.5 bar 370°C.

‐ Nominal Steam Flow 59 kg/s.

‐ Design Back Pressure 0.08 bar.

Cooling tower will cool the water that is used to condense the steam flowing out of the turbine

using water from the municipality despite the proximity of the plant to the sea, or using dry

cooling system. Because this part of the plant is very important to our study, the theory of

Rankine Cycle and the cooling options are discussed next.

Steam Rankine Power Cycle

Conventional coal plants and nuclear plants are Steam power plants; also here CSP parabolic

trough and sometimes tower technologies are based on such types of steam power plants. These

power cycles simply converts heat into work (figure2.3), it inputs high-quality thermal energy

from boiler or here by solar field, produce electric power, and reject low-quality heat at the

condenser and cooling system, The cooling phase is using heat sink which is the ambient .the

maximum conversion efficiency defined by the ideal thermal cycle efficiency (the Carnot

efficiency) is proportional to 1 minus the ratio of heat sink absolute temperature to the heat

source absolute temperature

16 Kopp (2004, p.16)


28

Source

k

TT

EfficiencyCyc sin1le −=

The previous equation shows that the efficiency increases as the difference between the two

temperatures increases, also for the same source temperature but lower sink temperature we get

high efficiency or sometimes called better performance of the plant. Around this equation a lot

of research and development of CSP technologies is proceeding, for example changing the HTF

to reach high temperatures.

Figure2.3: Simple representation of a steam Rankine thermal power cycle.17

Cooling systems

Conventional Steam power plants, those operating on Rankin cycle requires a medium to reject

heat out of the condenser, the two obvious mediums are water and air, another option is a hybrid

system which benefits from wet cooling in hours of high ambient temperatures.

• Wet cooling

This type of cooling is connected with the name of cooling tower, which is a device used to

reduce the temperature of a water stream by extracting heat from water and rejecting it to the

ambient. Figure2.4 shows that a cooling tower based on the evaporation of water whereby some

of the water is evaporated and carried by a moving air stream and discharged to the atmosphere. 17 Water Use in Parabolic Trough Power Plants: Summary Results from WorleyParsons' Analyses.P(3)


29

Figure2.4: Schematic diagram of a cooling water system 18

There are two main types of cooling towers natural draft and mechanical draft, natural draft

make use of the difference of air temperatures between inside the tower and the ambient

temperature, the hotter with lower density goes up and the fresh colder air intakes from down the

tower, the tower is concrete its height reaches 200 m, figure2.5 shows a picture of this type.

Figure2.5: Cross flow natural draft cooling tower19

18 Pacific Northwest National laboratory, 2001


30

The other type is mechanical draft which uses large fans to circulate air, mostly they are site

erected except the concrete towers, the water is sprayed or dropped on the air to increase the

cooling performance, This types of towers is mostly used in large heat duties to be cost effective

due to high cost of construction, it should be indicated that many technologies and different

designs are used and here we only mention the preliminary concept. Figure2.6 shows a schematic

for one type.

Figure2.6: Induced draft, double-flow crossflow tower.20

• Dry cooling

In this type of cooling no water is required, thus it is used whenever water is scarce, it’s also

economic feasible in cold areas, the steam to be condensed and cooled passes through air cooled

finned tubes without contact between condensate and air, The heat transfer rate is a function of

the surface area of the fins and the velocity of the air flow, the next figure2.7 show a schematic

of a dry cooled condenser.

19 Gulf Coast Chemical Commercial Inc, 1995 20 Cooling tower fundementals,2nd edition


31

Figure2.7: Direct dry cooled condenser21

• Hybrid cooling

Since the air cooled condenser plants suffer from reduction of performance during hot days of

summer, plants operators started looking for other alternatives.

This cooling option benefits from both cooling systems dry and wet, the synchronization

between both is function of ambient temperature, in medium and low air dry bulb temperature

the air cooled condenser is activated, in summer when the air temperature gets high and the 21 Design and Specification of Air-Cooled Steam Condensers M.W. Larinoff, W.E. Moles and R.

Reichhelm, Hudson Products Corporation,Houston, Texas


32

performance of the plant reduces, the wet cooling tower carries part of the load. It is clear that

this system has the cost of ACC and WCC, so it’s not economically feasible for most sites,

figure2.8 shows one type of hybrid cooling systems.

Figure2.8: Hybrid cooling systems use an air-cooled condenser and a wet-cooled condenser in

parallel. 22



33

Chapter 3

Site Assessment of the case study area 3.1 Site Location

3.1.1 Best locations for CSP

Obviously the best locations for CSP plants are those with high solar insulation, thus locations

lying in the solar belt are the most attractive, figure3.1 shows the world map with sites ranked

according to their suitability to such type of plants.

Figure3.1: Best locations for CSP. 23

Sites with good solar recourses are not enough to be chosen as plant locations, since parabolic

trough plants requires large areas and special foundations, also they are like steam power plants

that require special needs, in the next paragraph most of the important site characteristics are

clarified. 23 Solar Millennium


34

Direct normal irradiation more than 1800kwh/m²a -with 3000 shining hours or more- are the

economical solar requirements. The landscape of the required area of plant or mainly the solar

field should be nearly flat, a small slope of 1-2% is considered excellent; protected areas for

animals or plants are excluded, also the type of soil should be stable, the plant should not be in a

valley that has floods risks, the owner of land should be known where public land is cheaper

and has simple property issues, last and not least the availability of infrastructures mostly roads,

telecommunications-GSM and GPRS, water source-<30km- grid and substation -<10km-, near

airports or ports, proximity to gas/fossil fuel pipe line is important during construction phase and

for energy makeup, city services which are important for the plant operators and workers.

Figure3.2 shows an exclusion map.

Figure3.2: Exclusion map for MENA region.24

These site requirements are studied by experts to select the plant site, they fill them in what is

called site identification matrix and then they rank the locations, finally the highest site record is

considered the best, this decision became a preliminary base to plan a CSP plant for the most

suitable sites.



35

3.1.2 Ma’an Plant location

The site chosen for this study is a Ma’an development area near Ma’an city, which is 200 km

south of Amman the capital of Jordan, and only 9 km from the city of Ma’an, also it is 100 km

from Aqaba port. The coordinates of the site are (N 30.17° E 35.78°) see figure3.3

&figure3.4.It’s not the best location for CSP in MENA region, but a location where such kind of

a project is attractive, due to the impacts on the development of this area the Jordanian

government is willing to invest in such projects, also the solar radiation is higher than the solar

economic potential making it an attractive location.

Figure3.3: Plant location.25

25 Source: Google earth


36

Figure3.4: Picture from MDA site.26

The main prerequisite of power plant are also available , such as the area big enough with

public property ,flat terrain, good infrastructure, but the water availability in all south Jordan is

scarce, water in small quantities is provided by MDA, table3.1 shows some site characteristics.

26 Mr. Firas Rimawi, Director, Business Development for MDA 2011


37

Table3.1: Ma’an site characteristics

Criteria Unit Site name MDA

Region/Municipality Jordan/Ma'an

Latitude N 30.17°

Longitude E 35.78°

Elevation/altitude 1015 m

Time zone Hours GMT +3

Annual sum DNI 2772kWh/m²a

Topography Flat

Terrain Slope and Direction Deg -

Approximate Land Size 7.5 km²

Soil compacted sand and gravel

Land protection No

Land Ownership MDA

Flooding risk No

Fire risk No

Armed/Social conflicts No

HV substation 33 kV

Availability of Water yes-few

Source of Water MDA-underground

Distance to source less than 1 km

Road/railway yes (highway)

Aprox. Distance to Road/Railway 0.2km

Gas / Fossil Fuel Pipeline NO

Telecom Yes


38

3.2 Solar resources assessment

3.2.1 Source and quality o f metrological data

Planning for this type of project requires high quality solar resources, based on long term data –

more than 10 years- and resolution less than 15 minutes which is unavailable in many promising

locations specially in MENA region, for that reason satellite data is used in prefeasibility studies

to identify the locations potential, a ground data station installed in the promising site to be used

as a base of final design, also its important to mention that depending only on one year is not

enough while the long term satellite data give an indication if the solar irradiation is good

always.

Parabolic Trough CSP plant is the first of all steam power plants that depends on other weather

parameters such as, dry bulb temperature, air pressure, and relative humidity. These important

yearly values plus the DNI and time series should be measured and purified for a complete year,

typical meteorological years (TMY) provide reasonable sized annual data sets, hourly values and

for more than 20 years, there are Different methods to create these file types, metrological data

sources provide different format, table3.1 below shows sources of metrological data with their

period and precision.


39

Table3.1: Metrological data sources and characteristics27

Product Input Area Temp Resolution Period Provider Spatial

Resolution

NASA SSE

World averag. daily

profile

1983-2005 NASA 100 km

Meteonorm

World synthetic hourly/min

1981-2000 Meteotest 1 km

(+SRTM)

Solemi

1h 1991> DLR 1 km

Helioclim

15min/30min 1985> Ecoe de

Mines 30 km // 3-

7 km

EnMetSol

15min/1h 1995> Univ. of Oldenburg

3-7 km // 1-3 km

Satel-light

Europe 30min 1996-2001 ENTPE 5-7 km

PVGIS Europe

Europe averag. daily

profile

1981-1990 JRC 1 km

(+ SRTM)

ESRA

Europe averag. daily

profile

1981-1990

Ecole de Mines 10km

In this paragraph Meteonorm is described, this Software is well known because it covers the

entire world, since it uses climatic data algorithms to generate weather data. Based on ground

data, Satellite assisted interpolation between stations, Stochastic models to derive higher



40

resolution data and Global to tilted models. In this study the data taken by the software is used to

generate TMY2 format weather data for Ma’an site this data as mentioned before is not accurate

enough to be used as an only source for large projects, but it is enough for prefeasibility studies,

“The radiation data was subjected to extensive tests. The error in interpolating the monthly

radiation values was 9% and for the temperature 1.5°C”28. Figure3.5 shows the main window of

the software, it requires four steps to generate a weather file, a site, Data, formatting, and finally

the results. Here the site is defined using the map choice with a station option; the nearest station

was Ma’an airport, while the error in interpolation increases as the distance from the station

increases.

Figure3.5: Meteonorm main user window.

3.2.2 Solar recourses

The energy flux from the sun outside the atmosphere is known as solar constant, due to the solar

geometry which is the spherical trigonometry and position of the sun with respect to the

receiving surface, the received flux only after the geometry is not constant but still known and

calculated with high accuracy. The problem is the atmosphere state specially clouds and aerosols

28 Meteonorm 6.1 user guide


41

that dominate the changes of irradiance reaching a target on earth. Figure3.6 shows the measured

irradiation at Ma’an during a cleared sky day, from the enerMENA ground station, the data is

measured and purified by DLR and CSP services. This source of data is used in this study, but

the station is still new(less than one year), Meteonorm Software was necessary to generate a

Meteodata file which is a Typical Meteorological Year version 2 (TMY2), that was only used

during optimization phase, but this data is not accurate enough thus it was not used in the final

assessment while the ground data completed one year in februray2012.

Figure3.6: One day clear sky irradiance in Ma’an.29

The red line -what is important for CSP- is the direct normal irradiation (DNI) which is the

irradiance received by a plane normal to the solar incident rays, it’s measured by a device called

Pyrheliometer mounted on an accurate double axis tracker. The value of DNI is changed severely

by the presence of clouds that is inaccurate to be calculated based on satellite data only.

tables3.2 show the average monthly data from enerMENA station, and tables3.3 show the

29 enerMENA High Precision Meteo Station in Ma’an, Jordan . DLR /CSP Services Company


42

average monthly data from Meteonorm, the monthly data are taken from Greenius, and tables

was created by separate excel sheet.

Table3.2: Monthly Average Ground data30

Month Daylight(1) DNI (2) Temperature (3) Wind speed (4)

[hours] [kWh/m²] [deg C] [m/s]

January 12 5.211937 8.532256 3.465053

February 13 5.69143 10.80223 4.984672

March 13 7.85187 12.62003 4.11156

April 14 7.149566 16.62529 4.507222

May 15 7.836742 21.29423 4.311559

June 15 10.40243 24.55055 4.033055

July 15 9.249483 28.34423 3.69113

August 14 9.139579 26.76895 3.512634

September 13 8.130232 24.26709 3.497223

October 13 7.083063 19.40955 3.065458

November 12 7.027534 10.57999 3.366806

December 12 5.211937 8.490456 3.479301

annual avg - 7.498817 17.6904 3.835473

30enerMENA High Precision Meteo Station in Ma’an, Jordan . DLR /CSP Services Company


43

Table3.3: Monthly Average Meteonorm data 31

Month Daylight(1) DNI (2) Temperature (3) Wind speed (4)

[hours] [kWh/m²] [deg C] [m/s]

January 10 4.946903 7.565322 3.101479

February 10 6.786321 9.092115 1.300001

March 11 7.006 12.31303 1.493145

April 13 8.220466 16.77194 2.002917

May 13 9.912291 20.95444 1.992338

June 13 10.9112 23.74431 2.299583

July 13 10.67306 25.57783 2.199865

August 13 9.238678 25.67097 2.399463

September 11 7.9347 23.49126 1.895139

October 11 6.280031 19.54032 1.700806

November 10 5.433467 13.32569 1.502083

December 9 4.731904 9.019756 0.991398

annual avg - 7.672919 17.25558 1.906518

(1) Monthly averaged daylight hours.

(2) Monthly averaged direct normal radiation.

(3) Monthly averaged air temperature at 10m.

(4) Monthly averaged wind speed at 10m.

Ground data showed that the DNI is lower than expected; the dry bulb temperature is higher,

wind speed is higher, and the shining hours are lower, also from the durational figures it was

clear that the distribution of yearly DNI is different, for all of these reasons the previous studies

done based on Meteonorm is not accurate enough, Ground data is adopted in this study.

Figure3.7 and figure3.8 show the seasonal monthly average or mean monthly diurnal, the

monthly data is from Greenius software, while the figure is done on separate Excel.

31 Meteonorm


44

Figure3.7: Mean Monthly Diurnal of DNI, based on enerMENA station

Figure3.8: Mean Monthly Diurnal of DNI, based on Meteonorm data


45

These types of figures are widely used because they give a clear representation about yearly

DNI, the distribution of irradiance during one day from sun rise to sunset. It is obvious that the

highest DNI is always around noon reaching its extreme in June. the ground data showed that the

two months April and may have low DNI as expected, from the diffused irradiation data it

showed that, the reason is the existence of clouds and not measurement error, for that reason

ground data for more than one year gives a more accurate view about clouds, but still this data is

the best for this location and later the output of simulation is near to realty.

3.3 Study of dry cooling as an option

What is more important to this study is the need for a Dry cooling CSP parabolic trough plant,

because water recourses are scarce in Jordan especially the south part which is rich of solar

irradiation, it should be indicated that a previous study was already done for this location under a

project named EMPOWER, but their metrological data source was Meteonorm, since it is not

accurate enough the output of CSP power plant was not realistic and thus the economic results.

Herein a high accurate metrological data is supported by DLR. specially DNI, dry-bulb

temperature and relative humidity, this will change the simulation results and the design

optimization also the plants economic results. On the other hand it will give us the ability to

compare between the dry and wet cooling option for our site.

Dry Cooling for CSP means a larger plant where the solar field has the largest effect on the

economics of the plant, using the storage enables the plant to work at night where the ambient

temperature is low; more over this location has high altitude and desert weather properties in

cold nights. Later the energy yield will be simulated, for both a dry and wet plant with the same

conceptual design as Andasol-I, after that modification and optimization is done to have a new

base design for wet plant and compare it with a dry cooling option. What’s important to show is

how the higher energy yield; resulted from larger solar field and low ambient temperature

periods; will reduce the LCOE.

Dry bulb temperature is an important parameter in dry cooling plants designs, that affects the

performance of the plant, for CSP plant it will reduce the annual energy yield and the design of

solar field beside the power block, where high temperature means low performance, freezing

hours are distinguished by the plot of dry bulb temperature, figure3.9 shows the hourly average

of dry bulb temperature in Ma’an for all months.


46

Figure3.9: Monthly plot of hourly averaged dry bulb temperature in Ma’an.

First of all the overlook on the previous figure, showed that Ma’an is not an extremely hot area,

and it has very low probability of freezing hours thus the operation of plant is accepted, also the

higher air temperature occurred at noon, which is not always true in other locations that are

shifted from noon, this property is good because the reduction in performance is compensated by

higher thermal input instead of dumping it.

The number of hours of temperature occurrence is noticed by another figure representation, that

is the duration curve, see figure 3.10, it is clear that around 600 hours with temperature over 30

degree Celsius, and around 7000 hours with temperature less than 25 C, this situation is nearly

excellent for the dry cooling option, this can be justified by the high altitude of Ma’an location.


47

Figure3.10: Duration curve of dry bulb temperature.

As a result of this section, dry cooling option in Ma’an is accepted, and the performance of plant

will not be affected harmfully but further investigation is done in next chapters.


48

Chapter 4

Planning of the Ma’an power plant

4.1 Pre‐Design of Ma’an power plant

In this section the Andasol design characteristics are specified, to be used later as a base

reference for our plant. As mentioned earlier the first comparison between wet and dry cooling

will be done for the reference designs, then some optimization is going to be necessary to fit the

local conditions, Table4.1 shows the main specifications of Andasol design where the underlined

items are the ones that will be changed.

Table4.1:Ma'an pre-design parameters similar to Andasol

Location Project name Ma'an-MDA

Location 200 km south Amman

Terrain approx.195 hectar (1300m x1500m)

High voltage line 33kV

Solar field Aperture area 510,120m²

Solar multiplier 1.7

Collector Assembly (SKAL-ET 150)

Storage Cold tank temperature 292 C

Hot tank temperature: 386 C

Flow rate 948 kg/s.

Hours 7.5

Size 14 m height, 38 m diameter.

Capacity 1085 MWh =28.500 tonnes

Power Block Turbine SST-700

Nominal Capacity 50.0 MW


49

Conversion efficiency 38%

Turbine Inlet Conditions 100 bar 370°C , reheat 16.5 bar

370°C

Nominal Steam Flow 59 kg/s

Cooling system wet

Design Back Pressure 0.08 bar

Figure4.1 depicts the general layout of the CSP plant where the collectors are N-S. Collector

loop configuration has been set according to the current engineering layout for oil-cooled

parabolic trough solar fields with each loop consisting of four collectors.

Figure4.1: layout of Andasol plant 32

32 Herrmann and Nava, 2008; Prieto et al., 2008; Herna´ndez et al., 2008


50

4.2 Power cycle and cooling system

Changing the cooling system will change the operation of the whole power cycle, Greenius

software defines the power cycle as a look-up table, which is generated by external power station

simulation softwares, such as Ebsilon Professional, IPSEpro, or GateCycle. For this study it’s

very good to have access to one of these softwares but that is not possible, so the capacity of

plant is 50MW similar to Andasol solves the problem, because these look-up tables are available

in Greenius expert version, not only for the wet cooling but also for the dry cooling. the process

matrix for a 50 MW dry cooled power block is shown below, it is taken from the Greenius library

which is the same used for the simulation in this study, another matrix for the wet cooled case is

used.

Figure4.2: Process Matrix for 50 MW Dry Cooled Power Block33

33 Greenius library


51

The rows marked in yellow are the range of ambient temperature and design conditions; the

ambient temperature range covers all the ambient temperature of our location which is very

good. The design conditions are tabulated for both wet and dry, see table4.2.

Table4.2: Power block design conditions for wet and dry

Design item

Design value

Wet Dry

Solar thermal heat 129222.5 147440

Inlet temperature 391 393

Ambient temperature 30 25

Ambient pressure 0.9 0.99

Relative air humidity 20 60

Condenser pressure 0.08 0.144

Load type freeload freeload

4.3 Simulation Inputs

Greenius software is one of the leading tools used for simulating renewable energy systems

specially concentrated solar power systems; it is developed by DLR and still under development.

In this study a parabolic trough with storage will be simulated optimized and adapted, for both

dry and wet cooling. One should be able to deal with these large input variables and different

outputs, herein this section the procedure is described based on its manual and self learning

through program interface, see the figure4.3.

Figure4.3: Process for Greenius simulation


52

The next tables clarify the inputs of simulation, in table4.3 project site inputs, where the

underlined items mean they were changed through different simulation cases, most of them

describing the technology used and the local conditions, items without reference are

recommended by software. The financial simulation is very simple without any incentives, that

were identical for both cases, and further financial study could be done separately after the

economical results of this study. The load curve is undefined because it’s a free load design,

where all the produced electrical energy is fed to grid; also the Boiler is not included.

Table4.3: Simulation inputs for project site

Project Site Nation: Jordan location

Rem

uneration T

ariffs

type flat Geographical location

Name Ma'an- MDA year 2011 latitude 30.17 N

Electricity Jordan 0.084 €/kwh Spain 0.27 €/kwh longitude 35.78 E

Heat/cooling 0 Altitude 1069 fuel usage 0 Time zone +3

Taxes

Income tax rate 0 Properties of Ground

Ground structure Clay

Property tax rate 0 Roughness length 0.03

Tax holidays 0 Albedo factor 0.2 loss forwarded 0 Average slope 0

Discount R

ate

investment cost 6% specific land cost Jordan0.5€/m²Spain 2€/m²

running costs 6% load curve

Prices of D

elivery

Fuel price 0.05€/kwhth Water price 0.5€/m3

undefined-free load purchased from the grid

Jordan 0.084€/ kwh34 Spain 0.15 €/kwh

year 2011 Metrological input

Escalati

on Rates

Electricity 0% Typical Metrological year ( Meteonorm ) Ma’an Airport

O&M 0% Replacement 0%

34 NEPCO, annual report 2005.(P 40)


53

Fuel Jordan 0% Spain 12%

Specific Reference

Values

levelized electricity costs 0.050€/kwh

One year ground data

DLR

CO2 emissions -electricity 0.63235

levelized Heat costs 0

CO2 emissions -Heat 0.3

The next table shows the solar field design specification, the values are based on Andasol design

supported by Greenius team; also the underlined items are the changeable inputs for different

simulation cases.

35 UNEP, 2000 - "The GHG Indicator"


54

Table4.4: Simulation inputs for solar field

Technology Part 1

collector Assembly collector Field

General inform

ation and dimensions

Length 148.5 m General and

dimensions

Name Andasol

Aperture width 5.75 m land use 1900000m²

Aperture area 817.5 m² Reference

irradiation 800w/m²

Focal Length 1.71 m

Orientation

Distance between

rows 17.3m

HCE Diameter 0.0655m Distance between

collectors 1m

Nominal optical

efficiency 77.00%

Tracking axis tilt

angle 0

Therm

al Parameters

Tracking axis

Azimuth 0

Field parameters

Number of rows 156

No. of

collectors/loop 4

Field size 510120

Total header length 6823m

mean header

diameter 0.381

Header specific

mass 60.29kg/m

length fraction cold

header 0.5

pipe length in loops 6807m

Incidence A

ngle M

odifier

Coefficient a1 0.000525

Coefficient a2 2.86E-05 pipe diameter in

loops 0.0525m

Coefficient a3 0


55

Table4.5 shows the technical data for storage and power block, which is loaded from Greenius

library, these are valid only for the wet cooled case in Andasol and Ma’an case before

optimization, where the dry cooling case requires different power block as mentioned before, the

results of the final optimization of dry cooling is discussed briefly in chapter5

Table4.5: Simulation inputs for Storage and Power Block

Technology Part2

Thermal storage Power Block

Name Andasol 50 MW

Type Two Tank Molten Salts

Technical D

ata


56

Table4.6: Economic Simulation inputs for Spain (Costs, Financing, Timing)

Economics

Costs Financing major equipment costs minimum internal rate of return 12%

Non-C

onventional Costs

Reference year 2011 Financing sources

specific costs 320 €/m² Grant

Funding

none conventional parts 0%

specific O&M costs 4 €/m² conventional parts 0%

specific replacement costs 0.2%/a Dept

funding 70%

Guarantee period 0 Equity Funding 30%

specific insurance cost 0%/a Dept financing

Conventional costs-

Power B

lock

Reference year 2009 A loan w

ith portfolio Share 60%

land use 10000m² Interest rate 5.40% specific costs 950 €/kw Dept term 10 years

specific O&M costs 3 €/m² Upfront fee 0% specific replacement

costs 0.2%/a Commitment fee 0.4% of the amount drown

Guarantee period 0 grace period 0 specific insurance cost 0%/a bridge loan No

Storage

Reference year 2009 B loan w

ith portfolio

Share 40% land use 7500 Interest rate 6.00%

specific costs 35 €/kwth Dept term 12 years specific O&M costs 1 €/m² Upfront fee 0% specific replacement

costs 0.2%/a Commitment fee 0.5% of the amount drown

Guarantee period 0 grace period 0 specific insurance cost 0%/a bridge loan No

Other C

osts infrastructure costs 0 Timing-(Project Schedule)

Project development 5% Reference year of discounting 2012 insurance during

construction 1% Construction period 2

supervision and Startup 3% First year of operation 2014 contingencies 5% Operation period 25

Depreciation type linear Depreciation period 15

Cost distribution during

construction 25% per half year


57

Chapter 5

Simulation, optimization and comparison

5. 1 Simulation of base design (Andasol) First of all the Andasol input in Spain is simulated with dry and wet cooling, then a simulation

with the same design and economic inputs in Ma’an is done, this would be a good base for

comparison. During steps Energy yield, parasitic loads, Gross power, Net power, investment

cost, water consumption and LCOE, are the main outputs that are analyzed

5.1.1 Andasol in Spain

Table5.1 shows monthly power production and overall efficiency, table5.2 shows the simulation

results of the Andasol design in Spain based on the same economic inputs of Spain, thus an

economic reference for the next steps was created.

Table5.1 Monthly Power production and overall efficiency for Andasol design in Spain

Wet Dry

Month Net

power [MWh]

Gross power [MWh]

overall efficiency

[ %]

Net power [MWh]

Gross power [MWh]

overall efficiency

[%] Jan 4335.76 4928.26 6.71422 3965.92 4487.75 6.1392

Feb 6280.6 7075.74 10.1821 5685.71 6419.61 9.2165

Mar 11778.1 13374.3 13.6355 10749.7 12291.2 12.443

Apr 13411.2 15193.1 15.6361 12245.3 13997.8 14.265

May 16222.3 18337 15.7938 14978.4 17091.5 14.581

Jun 18263.3 20655 15.6429 16601.9 18981.6 14.218

Jul 19746.4 22297.5 15.4337 18057 20670.1 14.108

Aug 17403 19661.2 16.1173 15730.5 17978.9 14.568

Sep 13105 14826.8 14.7125 11749.1 13424.9 13.189

Oct 8624.36 9756.06 12.0788 7662.73 8710.31 10.73

Nov 5453.07 6127.06 8.09658 4908.25 5514.55 7.2863

Dec 3347.23 3787.97 5.86989 3050.49 3432.41 5.3413


58

Table5.2 Simulation results of Andasol design in Spain

General information

Number of loops 156

Effective Collector Area 510120m²

Direct normal irradiance (DNI) 2052kWh/(m²·a)

Cooling type Comparison element Wet Dry Unite

Energy yield 134715.8 126184.27 MWh/a

Capacity factor 31.709968 28.8091941 %

Thermal output of solar field 442908.3 458833.01 MWh/a

Economic results Internal Rate of Return (IRR) on Equity 9.69 7.28 %

Net Present Value 109.11 59.32 €

Payback Period 12.35 13.96 yrs.

Discounted Payback Period 15.88 20.77 yrs.

Total Incremental Costs 262 474 023 280 190 787 €

Minimum ADSCR 1.01 0.91

Required Tariff (LCOE) 0.301 0.341 €/kWh

Incremental LEC 0.152 0.179 €/kWhe

Calculation of LEC Levelized Electricity Costs (LEC) 0.2024 0.2293 €/kWhe

Total Investment Costs (IC) 274 259 498 282 859 352 €

Annuity of IC 0.0782 0.0782

NPV of Running Costs (OC) 74 320 528 75 473 190 €

Annuity of OC 0.0782 0.0782


59

The net power which is the final electrical power that is ready to be fed to the grid, is shown in

figuer5.1, where the wet case has high power production due to the higher efficiency of the

turbine, the dry cooling and wet cooling have nearly the same output during the period between

October and march, due to the lower ambient temperature that improves the turbine performance.

Figure5.1: Monthly Net Power for Andasol design in Spain

A graph of the overall efficiency is shown in figure5.2, which indicates the conversion factor

from solar energy to final electrical energy, the wet cooled case always has higher efficiency than

the dry case; this difference reduces at a lower ambient temperature. In summer the overall

efficiency for wet and dry is reduced because of the dumped solar energy, it is expected that the

dry cooling will have more reduction because of its lower performance but this effect is reduced

by using mores solar energy instead of dumping it.


60

Figure5.2: Plant Overall Efficiency for Andasol design in Spain

5.1.2Andasol in Ma’an

Herein four simulation steps are done. dry and wet cooling systems are attached to Andasol

design and simulated in Jordan, once with Spain economic inputs from Greenius and the other

with Jordan Economic, this section is important to compare between the Dry and wet cooling in

those two countries. The same design for both plants means that only cooling system is changed.

Table5.3 Monthly Power production and overall efficiency for Andasol design in Ma’an

Wet Dry

Month Net

power [MWh]

Gross power [MWh]

Overall efficiency

[%]

Net power [MWh]

Gross power [MWh]

Overall efficiency

[%] Jan 8160.2 9090.2 8.47 7318.9 8181.0 7.59 Feb 8491.5 9600.8 10.70 7622.8 8677.5 9.60 Mar 17694.6 19995.3 14.34 16280.9 18626.4 13.20 Apr 16207.3 18358.8 15.04 14989.0 17208.9 13.91 May 19102.7 21653.5 15.46 18069.2 20830.2 14.62 Jun 23188.1 26334.6 14.62 22779.4 26412.2 14.37 Jul 23440.2 26638.2 16.05 22226.6 25772.0 15.22 Aug 22866.8 25999.3 16.12 21674.0 25105.1 15.28 Sep 18882.3 21515.3 15.39 17138.4 19830.1 13.96 Oct 14895.3 16753.4 13.36 13027.8 14803.9 11.68 Nov 10985.5 12288.3 10.44 9826.3 11056.5 9.33 Dec 8117.6 9046.1 8.40 7278.8 8156.67 7.54


61

Table5.4 Simulation results of Andasol destine in Ma’an with Spain economics

General information Number of loops 156

Effective Collector Area 510120m² Direct normal irradiance (DNI) 2802.2kWh/(m²·a)

Cooling type Comparison element Wet Dry Unite

Energy yield 191999.2 178232.3 MWhe/a

Capacity factor 43.84 40.69 %

Thermal output of solar field 625580.2 626682.2 MWh/a

Economic and financial results Internal Rate of Return (IRR) on Equity 18.38 15.27 %

Net Present Value 295.79 238.68 €

Payback Period 6.31 8.33 yrs.

Discounted Payback Period 8.15 10.75 yrs.

Total Incremental Costs 225 860 347 247 656 288 €

Minimum ADSCR 1.39 1.25

Required Tariff (LCOE) 0.211 0.236 €/kWh

Incremental LEC 0.092 0.109 €/kWhe

Calculation of LEC Levelized Electricity Costs (LEC) 0.142 0.1587 €/kWhe

Total Investment Costs (IC) 274 259 498 285 719 994 €

Annuity of IC 0.0782 0.0782

NPV of Running Costs (OC) 74 320 528 75 856 614 €

Annuity of OC 0.0782 0.0782

Figuer5.3 shows the net Power, where the wet case has high power production due to the higher

efficiency of the turbine, in the period between December and February, the dry cooling and wet

cooling have nearly the same output that is due to the lower ambient temperature that improves

the turbine performance, and mainly due to the usage of dumped energy in dry cooling instead of

being lost in wet cooling. In Ma’an the DNI is higher than Andasol thus the net power of both

dry and wet cooling is higher too, the summer ambient temperatures are higher in Ma’an, that

increases the difference of power production between wet and dry cooling,


62

Figure5.3:Monthly Net Power for Andasol design in Ma’an

The overall efficiency is shown in figure5.4, the wet cooled case always has higher efficiency

than the dry case, this difference reduces at a lower ambient temperature. In summer (Jun) the

overall efficiency for wet and dry is reduced because of the dumped solar energy, it is expected

that the dry cooling will have more reduction because of the lower performance but the effect is

reduced by using more solar energy instead of dumping it. This figure shows that the difference

in the overall efficiency between wet and dry cooling in Ma’an is more than the difference in

Andasol, Mainly due to higher ambient temperature.

Figure5.4:Plant overall Efficiency for Andasol design in Ma’an


63

Because Ma’an has higher DNI the Andasol design is not suitable, in other words it is not the

optimized, where the solar field is oversized and storage full load hours for dry cooling should

be increased as the turbine requires higher thermal input, for these reasons the comparison can’t

be generalized before an optimization of the plant in Ma’an, that is done in the next section.

5.2 Optimization of Ma’an plant

During last sections simulation the design was fixed as Andasol, constant thermal input

comparison criteria, in this section another comparison criteria is constant capacity, thus

optimization is required to compensate the lower efficiency of dry cooling by higher thermal

input keeping the capacity at design conditions fixed, for both dry and wet cooled plants .

The design of a plant is site specific due to the dependency on local metrological characteristics,

specially the DNI, ambient temperature, atmospheric pressure and relative humidity that change

the operation and performance of power plants.

Using another plant design (Andasol) for our location is not accurate, the miss-optimized design

results in differences that can be characterized as, over sized solar field and lower sized, but in

our case it is over sized due to the higher DNI and relatively similar other destine requirements.

During periods of high DNI the solar field produces higher amount of needed thermal energy

while no space in the storage system is available, thus more wasted energy-Dumped energy-,

from an economical point of view larger a solar field adds investment and operating costs

without effecting the reduction of LCOE, on the other hand the dry cooled plant will benefit

from that extra solar energy, because the turbine requires more thermal input, as a result the

comparison would senseless, for those reasons a new design optimization is required with a new

base for a wet plant, then to be used for a dry cooled plant with the same design , finally a last

optimization for the dry cooled plant is done for good economical results.

The procedure for optimization is discussed in this paragraph, the first step started with Andasol

design in Ma’an that was used in the previous simulations, and then several reduction steps of

the solar field area are made, followed by simulation each time while keeping the same storage

system size. The Gross output power, the Energy yield, thermal energy into system, the storage

level, the Storage input/output and the Dumped solar Heat, are analyzed, all are typical

operations yearly with hourly data, besides the LCOE and the investment cost, daily figures are

also noticed consequently and some of them are included in the next figures.


64

5.2.1 Wet Optimization

As we discussed before an optimized wet cooled plant is important to be a reference valid for the

local conditions, table5.5, shows the several simulation trials to reach the best LCOE, where the

storage still has the same design characteristics as Andasol, also the power block, but the size of

solar field is changed, it should be indicated that these economic values are depending on the

cost inputs, but the sequence and thus the consequence are the same

Table5.5: optimization steps of wet cooled case

Wet optimization

# run #

loops

Effective mirror area

[m²]

Thermal output of solar filed

[MWhth]

Energy yield [MWhel]

Investment cost [€]

LCOE [€/kWhe]

1 156 510120 644378.5 198497.9 272 336 160 0.1305 2 152 497040 627918.6 196043.3 269 940 951 0.1298 3 148 483960 611435.2 193448.8 262 755 322 0.1292 4 144 470880 594963.5 190553 257 964 903 0.1287 5 140 457800 578498.8 187345.5 253 174 483 0.1285 6 138 451260 570268.8 185598.4 250 779 274 0.1285 7 136 444720 562028.1 183879.4 248 384 064 0.1284 8 134 438180 553796.4 182043.9 245 988 855 0.1285 9 132 431640 545583.8 180200.7 243 593 645 0.1285 10 130 425100 537345.0 178186.3 241 198 435 0.1287

The main optimization resulted in that 444720m² mirror areas is the optimum size for the solar

field, for 50Mw capacity,7.5 TES hours, a wet cooled condenser, a capacity factor of 41.98%,

plant in Ma’an Jordan. The next two figures (5.5&5.6) show the net power before and after

optimization, it’s clear that little changed on the annular figure, represented by the white area

meaning that the operating hours reduced little, but still economically better, because larger solar

field cover those days and dump the solar energy in other periods, which increases the

investment cost without a big share in the reduction of LCOE.


65

Figure5.5:Gross electrical power of wet case,before optimization

Figure5.6:Gross electrical power of wet case,after optimization


66

The dumped energy is the energy that is thrown away because it is larger than the allowable

thermal input for the steam turbine, and can’t be stored because the storage is full, it can’t be

zero if the solar field is optimized, because the design reference of DNI is 800w/m² that covers

the majority of hours and not only the peak summer hours, where in many hours it is more than

this value, part of the extra energy is stored and part is thrown by shutting some rows. The next

two figures (5.7&5.8) show that the dumped energy is reduced, especially for the summer

months.

Figure5.7:Dumped solar energy of wet case,before optimization


67

Figure5.8:Dumped solar energy of wet case,after optimization

The next two figures (5.9&5.10) show the thermal energy entering the storage and going out, the

charging and discharging rates, they give an indication wither the storage is fit with the solar

field or not, relatively the two figures before and after optimization are not changed, that means

the extra energy was mostly dumped energy, in periods where the storage is not full higher

energy yield was before optimization, but again this energy was insignificant compared with the

cost of extra rows. generally from march to April the storage capacity was used completely

except for a few hours, which is a good selection of storage capacity and the charging

/discharging rates are suitable for the solar field and turbine design points, the fact that the

storage for Andasol is perfectly optimized, thus the reduction in the solar field effective area is

due to higher DNI in Ma’an than Andasol.


68

Figure5.9:Charging and discharging of TES of wet case,before optimization

Figure5.10:Charging and discharging of TES of wet case,after optimization


69

5.2.2 Dry Optimization

Herein the power block is changed to be dry cooled, since the thermal input for the turbine

147440Mwth is higher than the wet case which is 129233Mwth, the TES full load hours are

lower than 6.3 hours, so the storage capacity and the charging/discharging rates are changed to

meet the design point, table5.6 shows these simulation steps. The same procedure as wet

optimization is done; the higher investment costs and LCOE are due to the higher costs of the

cooling system, a larger turbine, a larger storage and lower efficiency of the power cycle.

The main optimization resulted in 510120m² mirror area as an optimum size for the solar field,

for 50Mw capacity, 7.5 TES hours, a dry cooled condenser, a capacity factor of 41.59%, and a

plant in Ma’an Jordan.

Table5.6: Optimization steps of dry cooled case

Dry optimization

# run

# loops

Effective mirror

area [m²]


[MWhth]

Energy yield

[MWhel]

Investment cost [€]

LCOE [€/kWhe]

1 136 444720 562803.6 163431.0 265 209 798 0.1525 2 140 457800 579262.3 167801.6 270 000 217 0.1512 3 144 470880 595709.3 171846.3 274 790 637 0.1502 4 148 483960 612171.0 175639.8 279 581 056 0.1496 5 152 497040 628603.9 179071.8 284 371 475 0.1492 6 154 503580 636834.5 180652 286 766 685 0.1491 7 156 510120 645061.7 182173.5 289 161 894 0.1491 8 158 516660 653296.5 183657.2 291 557 104 0.1492 9 160 523200 661515.3 185109.8 293 952 313 0.1492 10 164 536280 677988.6 187874.3 298 742 733 0.1494

Since the same procedure as wet case optimization, then only figures after optimization are

included, the next figure5.11 shows the gross power after optimization. What is different here is

that the gross power is reduced in hot summer days, while only a small increase in cold days,

which is clear from the figures output curve, more than 50MWe in cold days and lowers than

50MWe in hot ones.


70

Figure5.11:Gross electrical power of dry case,after optimization

Figure5.12 shows the dumped solar energy which is lower than the wet case, because the plant

has larger storage and higher thermal input to turbine.

Figure5.12:Dumped solar energy of dry case,after optimization


71

Figure5.13shows the charging/discharging rates, the difference here is larger rates is needed to

meet the turbine to its design point, the optimization here is better than for wet because more

effective thermal energy is provided by larger storage and turbine.

Figure5.13:Charging and discharging of TES of dry case,after optimization

The Simulation software called SAM has a powerful parameterization and optimization tool.

Nearly similar inputs of this Greenius model are entered there, the values are not exactly as

Greenius but this step is essential to be sure that the optimization of the dry cooled plant is

correct, table5.7 shows how LCOE changes by the full load hours of TES and by the solar

multiple.


72

Table5.7: LCOE at different TES hours and solar multiple, for dry cooled plant in Ma’an LCOE

Nominal Solar Multiple

[c€/kWhe] 1 1.25 1.5 1.75 2 2.25 2.5 2.75 3 3.25

full load hours of TE

S

0 19.71 19.3 20.1 21.3 22.54 23.99 25.42 26.89 28.41 30.11.5 20.91 19.2 19.5 20.2 21.19 22.32 23.53 24.81 26.08 27.63 22.58 19.9 19.4 19.8 20.57 21.42 22.39 23.45 24.49 25.7

4.5 24.24 21.2 19.6 19.6 20.1 20.78 21.55 22.4 23.29 24.36 25.91 22.6 20.3 19.7 19.89 20.38 21.01 21.73 22.46 23.3

7.5 27.56 23.9 21.3 20 19.91 20.16 20.62 21.2 21.81 22.59 29.22 25.2 22.4 20.6 20.05 20.04 20.36 20.83 21.36 22

10.5 30.86 26.5 23.5 21.5 20.47 20.17 20.25 20.59 21.03 21.612 32.52 27.9 24.6 22.5 21.27 20.44 20.24 20.41 20.73 21.2

Another representation of the last table is simply plotted in figure 5.14, the upper left and lower

right corners of the figure, are colored by red closely spaced lines which means that the values

are very high and the increase in storage hours or solar multiple consequently, have low effect on

energy yield because the solar field is not suitably chosen, thus more costs without effective

energy yield. In the middle of the figure wide spaced blue lines are changing around both axes,

the blue color means low values of LCOE, and the distance means small change in solar multiple

or storage hours have strong effect on energy yield.


73

Figure5.14: Contour representation of LCOE as function of solar multiple and storage hours

Figure5.15 shows the relation between LCOE and the solar multiple for each storage capacity, it

is clear that the best LCOE of energy for 7.5 storage hours is at a solar multiple of two, lower

values can be achieved without storage and smaller solar multiples, but that is not our case

because the plant works only on solar energy and we need a higher capacity factor, the 9 hours

are also suitable for solar multiple and with similar LCOE, this matter of the capacity factor

needed. As a conclusion, the solar multiple two with storage of 7.5 hours will be adopted also for

a dry plant and it is technically and economically an excellent choice.


74

Figure5.15: plot of LCOE as function of solar multiple for each TES capacity

5.3 Technical Comparison between wet and dry cooling in Ma’an

Comparison between a wet cooling and dry cooling plant is based on two criterias, constant

thermal input and constant capacity. In the constant thermal input the turbine input thermal

energy is fixed, thus the dry cooled will have lower capacity, in the second criteria (used in this

study) the capacity is fixed by adjusting the turbines design point conditions by feeding them

more thermal energy, this adjustment is supported by Greenius and included in the two power

blocks models.

Here two situations are used, the first is using similar design specifications to compare the output

and performance of both plants and the other is by using the optimized plants.

As mentioned before the base wet cooled plant is the optimized plant for Ma’an site conditions,

in the dry similar to the wet only the cooling system is changed keeping the solar field and

storage fixed, while in the optimized design the dry cooled plant is optimized to meet the

economic condition of lowest LCOE. It should be indicated that economic results also somehow

represent the best technical performance.


75

5.3.1 Same design

Table5.8: Power output and overall plant efficiency for same design case.

Optimized Wet Plant Dry same design as wet

Month T amb

[°C]

Net power

Gross power

Overall efficiency

Net power

Gross power

Overall efficiency

[MWhe] [MWhe] [%] [MWhe] [MWhe] [%] Jan 6.28 7308.4 8131.1 8.70 6585.8 7334.7 7.84 Feb 10.64 7705.8 8689.9 11.14 6867.8 7789.0 9.92 Mar 12.60 16622.4 18768.7 15.46 14738.3 16825.7 13.70 Apr 16.63 15392.9 17435.7 16.39 13640.9 15636.5 14.52 May 21.28 18664.4 21182.5 17.32 16730.2 19287.2 15.53 Jun 24.55 23551.8 26829.2 17.03 21331.2 24775.9 15.43 Jul 28.33 23325.6 26552.6 18.31 20489.4 23766.7 16.09 Aug 26.78 22596.9 25735.9 18.27 19824.7 22946.7 16.03 Sep 24.27 17910.7 20374.6 16.74 15403.6 17783.0 14.40 Oct 19.59 13501.7 15139.1 13.88 11704.8 13247.0 12.04 Nov 10.59 9944.5 11086.3 10.84 8867.6 9934.8 9.66 Dec 8.35 7354.3 8179.0 8.73 6575.8 7339.4 7.81

Table5.8 shows the simulation results of the power output and the overall efficiency for the

whole plant. The net power for the wet case and thus for the efficiency was always higher than

for the dry case, due to the lower conversion efficiency the of dry cooled plant; the efficiency

was reduced about 1% for dry cooled, except in June and July about 2%, because the ambient

temperature is higher than design point condition, in cold months January and December the

reduction in efficiency was less than 1%. It is also clear that the difference between the net

power and the gross power increases in summer, because of the high power block parasitics

loads.

in figure5.16 the net power was plotted, it represents the previous table, the net power for the wet

case is always higher than for the dry, but the two plots come closer from [October to march],

where the ambient temperature is low and the dry cooled plant can generate electricity with

efficiency near or higher than the wet cooled, thus the monthly sum of energy becomes similar.


76

Figure5.16: Monthly Net power for same design

Figure5.17 show the parasitics loads for both the dry and wet cooling, where for the wet cooling

case the parasitics is higher, that not means the dry cooling has lower hourly parasitics, because

the monthly value is reduced due to the lower operating hours because it requires higher thermal

input, thus the full load hours are lower than for the wet cooling, this is the main reason

justifying the figure.

Figure5.17: Monthly parasitics loads for same design

In the next figure5.18, the overall wet cooled plant efficiency is higher than the dry cooling

efficiency because It operates more hours and the dumped solar energy is reduced by larger

storage. But the difference in both efficiencies is reduced in colder months due to higher


77

conversion efficiency of dry cooled plants, but still it is lower than the wet because the

irradiation is not enough to operate the dry cooled plant in many hours.

Figure5.18: Monthly overall efficiency for same design

5.3.2 Optimized design

Here in this case the turbines design point is different than the wet case, where more thermal

input is fed and the conversion efficiency is lower than for the wet cooled condenser, this was

discussed in section 3.3. The solar field area and the storage capacity are adjusted and optimized

in section5.2; table 5.9 shows the power output and the overall efficiency for both the optimized

dry and wet cooling cases.


78

Table5.9: Power output and overall plant efficiency for Optimized design case

Optimized Wet Plant Optimized Dry Plant

Month T amb

Net power

Gross power

Overall efficiency

Net power

Gross power

Overall efficiency

[°C] [MWh] [MWh] [%] [MWh] [MWh] [%] Jan 6.28 7308.4 8131.1 8.70 7580.9 8474.34 7.86 Feb 10.64 7705.8 8689.9 11.14 7862.29 8958.74 9.90 Mar 12.60 16622.4 18768.7 15.46 16696.7 19133.3 13.53 Apr 16.63 15392.9 17435.7 16.39 15365.7 17667.9 14.26 May 21.28 18664.4 21182.5 17.32 18423 21277.7 14.90 Jun 24.55 23551.8 26829.2 17.03 22951.5 26658.8 14.48 Jul 28.33 23325.6 26552.6 18.31 22546 26192 15.44

Aug 26.78 22596.9 25735.9 18.27 22065 25610.1 15.56 Sep 24.27 17910.7 20374.6 16.74 17607.9 20415 14.34 Oct 19.59 13501.7 15139.1 13.88 13414.3 15261.1 12.03 Nov 10.59 9944.5 11086.3 10.84 10133.1 11406.2 9.62 Dec 8.35 7354.3 8179.0 8.73 7527.08 8434.73 7.79

Figure5.19 shows the monthly net power, it is clear that both outputs are identical except for the

small extra power in the dry case at low ambient temperature, but the dry plant has 20 loops

more than the wet plant, also from this figure we can prove that the optimization led us to see

that the required outputs are identical, which is good for the comparison. If the plant is in a

location hotter than Ma’an, the solar field would then be larger to reach for results similar to this.


79

Figure5.19: Monthly Net power for Optimized design case

Figure5.20 shows the overall efficiency where the dry cooled plant is subjected to [2-2.88]%

efficiency reduction in summer, but only [0.85-1.2]% reduction in winter. Compared to the

previous case the difference between the wet and dry condition is increased because of higher

dumped energy in summer resulted from larger solar field.

Figure5.20: Monthly overall efficiency for Optimized design case


80

The next table includes the monthly power block parasitics loads that are also plotted in

figure5.21, the reason for including this table is to show why the overall efficiency reduced and

where the extra thermal input goes, another factor is the lower conversion efficiency due to

higher turbine’s back pressure.

Table5.10: Power block parasitics for Optimized design case

The previous table is plotted in figure5.21, it is clear that the parasitics load is higher for dry

cooling, where in summer the difference increased due to the high ambient temperature,

assuming that both cases operate for the same number of hours because of enough solar energy

in summer that also able them to operate 7.5 hours at night, on the contrary for the period

between October to march where there is nearly no deference between the two figures due to

two reasons, the first is the low ambient temperature where the fans run at lower speed, the

second reason is the lower number of operation hours of the dry cooled plant because it requires

high thermal input.

Wet Dry

Month [MWhe] [MWhe] Jan 408.412 446.995 Feb 499.151 564.466 Mar 1055.32 1227.22 Apr 993.996 1136 May 1205.58 1372.41 Jun 1536.83 1735.88 Jul 1526.7 1728.72

Aug 1484.67 1685.57 Sep 1162.46 1352.44 Oct 862.376 996.269 Nov 640.943 726.179 Dec 474.243 528.993


81

Figure5.21: Dry cooled power block parasitics for Optimized design case

In table5.11 the summary of the previous cases are tabulated, where the dry cooling case similar

to the wet design is just used for comparison, because it is not optimized it does not give the

maximum possible energy yield. Both the optimized wet and dry cases for Ma’an are the logical

technical and economical cases for comparison, because they have nearly similar output

requirements, the Dry cooling has 16.121% (2.07c€/kWhe) higher in LCOE and 16.417% in the

investment cost. The optimization of the wet plant in Ma’an was essential because the higher

DNI in Ma’an made the Andasol wet design operate at lower performance due to the high

dumped solar energy; this was justified by the optimized dry case in Ma’an nearly similar to the

wet case in Spain but with higher energy yield and thus lower LCOE.


82

Table5.11: Summary of all technical simulation results

Number of loops

Effective mirror

area


Energy yield

Full load hours

Capacity factor

Investment cost

LCOE

------ [m²] [Mwht] [MWhe] [Hours] [%] [€] [€/KWhe] Optimized Wet/Ma’an

136 444720 562028.1 183879.4 4162 41.98 248 384 064 0.1284 Dry similar to Wet design/Ma’an

136 444720 563128.2 162310.3 3719 37.06 259 844 561 0.1514 Optimized Dry/Ma’an

156 510120 645061.7 182173.5 4190 41.59 289161894 0.1491 Andasol Wet/Spain

156 510120 442908.3 134715.8 3468 31.71 274 259 498 0.1967 Andasol Dry/Spain

156 510120 458833.01 126184.27 3175 28.81 282 859 352 0.2183 Andasol Wet/Ma’an

156 510120 625580.2 191999.2 4345 43.84 274 259 498 0.142 Andasol Dry/Ma’an

156 510120 626682.2 178232.3 4093 40.69 285 719 994 0.1587

After the optimization of the plant in Ma’an the technical specifications of both options are

tabulated in table 5.12, where larger size of the dry plant is adopted to give the same capacity at

the design point conditions.


83

Table5.12: Technical comparison between expected plants in Ma’an

Comparison between two plants in Ma’an Item Unit Wet Dry

General characteristics Direct normal irradiation [kWh/(m²·a)] 2802.2 2802.2

Annual Thermal output of solar filed [MWhth] 562028.1 645061.7 Annual Energy yield [MWhel] 183879.4 182173.5

Full load hours [h/a] 4162 4190 Capacity factor [%] 41.98 41.59

Plant area [m²] 1710000 2010000 Water consumption [m3/a] 717981 41820

Solar field Aperture area [m²] 444720 510120

Solar multiplier --- 1.74 2 Number of loops --- 136 156

Storage Cold tank temperature [C] 292 C 292 C Hot tank temperature [C] 386 C 386 C

Full load hours hours 7.5 7.5 Capacity [MWht] 970 1100

Power Block Turbine --- SST-700 SST-700

Nominal Capacity [MW] 50.0 50.0 Conversion efficiency [%] 38 34 Design Back Pressure [bar] 0.08 0.144

Thermal Input [MWt] 129.2225 147.440


84

Figure5.22: main operational charctersitics of the expexted dry plant in ma’an,(23-Jun)

The red line is the thermal power of the solar field, its coincident with the DNI, the green line is

the charging and discharging of TES during the day, while the blue line is the net electrical

output power, it increases during night due to lower ambient temperature and the very low solar

field parasitics mainly the pumping parasitics.

5.4 Economic Comparison between wet and dry cooling in Ma’an

In table5.13 all the costs and financial inputs are repeated but with small adjustment, two project

periods are simulated with two minimum required internal rate of returns, also one soft loan is

used.


85

Table5.13: Economic Simulation inputs for Jordan (Costs, Financing, and Timing)

Economic inputs Costs Financing

Non-C

onventional Costs

Reference year 2011 minimum required internal rate of return6% 9%

specific costs 320 €/m² Financing sources

specific O&M costs 4 €/m² Grant Funding

None conventional 0% 30% 60%

specific replacement costs 0.2%/a Conventional 0%

Guarantee period 0 Dept funding 70% specific insurance cost 0%/a Equity Funding 30%

Conventional costs-Pow

er B

lock

Reference year 2011 Dept financing

land use Wet 10000m² Dry 30000m² loan w

ith portfolio

Share 100%

specific costs Wet 800 €/kw Dry 1000€/kw

Interest rate 5.3%,5.4%,5.5%,6%

specific O&M costs 3 €/m² Dept term 10,15,17,18,20 years specific replacement costs 0.2%/a Upfront fee 0%

Guarantee period 0 Commitment fee 0.4% of amount drownspecific insurance cost 0%/a grace period 0

Storage

Reference year 2010 bridge loan No land use 7500 Timing-(Project Schedule)

specific costs 35 €/kwth Reference year of discounting 2012 specific O&M costs 1 €/m² Construction period 2 years

specific replacement costs 0.2%/a Upfront fee 0% Guarantee period 0 First year of operation 2014

specific insurance cost 0%/a Operation period 30 years 40 years

Other C

osts

infrastructure costs 0 bridge loan No Project development 5% Depreciation type linear

insurance during construction

1% Depreciation period 15 years

supervision and Startup 3% Cost distribution during construction

25% per half year contingencies 5%

The simulation results show that the project is not economically feasible according to Jordan

electricity prices without feed in tariff, even without taxes on renewables. Table5.14 shows the


86

financial inputs that are simulated, two cases are tabulated in table 5.15 and table 5.16

consequently.

Table5.14: Simulated financial inputs

project period

Electricity Tariff

min required

IRR 30 0.084 6% 30 0.084 9% 40 0.084 6% 40 0.084 9%

Table 5.15: Economic comparison between expected plants in Ma’an (sample one)

Cooling type Comparison element

Wet Dry Unite

Project period 30 30 years Electricity tariff 0.084 0.084 €/kWhe

Minimum required IRR 6 6 % Simulation results

Internal Rate of Return (IRR) on Equity -0.31 -2.12 % Net Present Value -109.41 -157.94 million € Payback Period 0 0 yrs.

Discounted Payback Period 0 0 yrs. Total Incremental Costs 198 520 169 248 590 623 €

Minimum ADSCR 0.35 0.28 Required Tariff (LCOE) 0.13 0.151 €/kWh

Incremental LEC 0.078 0.099 €/kWhe Calculation of LEC

Levelized Electricity Costs (LEC) 0.1284 0.1491 €/kWhe Total Investment Costs (IC) 248 384 064 289 161 894 €

Annuity of IC 0.0726 0.0726 NPV of Running Costs (OC) 76 689 532 84 808 070 €

Annuity of OC 0.0726 0.0726 Environmental Aspects:

Annual CO2 Reduction 116211.8 115133.6 tCO2 CO2 Avoidance Costs 124.1 156.86 €/tCO2


87

Table5.16: Economic comparison between expected plants in Ma’an (sample two)

5.5 Suggestions to make the project economically feasible

The pervious simulations showed that the project was not feasible under the economic conditions

in Jordan, thus some essential suggestions are simulated that will make the project feasible.

Those are the minimum required tariff, the minimum required grant, and the tariff with grant.

During simulation of the three mentioned cases, the loan portfolio changed to increase the

ADSCR, the factor should be more than one, unless the project has a shortage of liquid assets;

also the loan portfolio was changed in each case according to the required minimum ADSCR.


Wet Dry Unite


Minimum required IRR 9 9 % Simulation results

Internal Rate of Return (IRR) on Equity 1.6 0.13 % Net Present Value -97.47 -146.89 million € Payback Period 31.14 39.16 yrs.

Discounted Payback Period 0 0 yrs. Total Incremental Costs 193 877 985 244 813 556 €








88

5.5.1 Minimum required tariff

Table (17&18) show the minimum required tariff, that the project need to be feasible, the wet

plant require 0.152€/kWhe while the dry plant require 0.17€/kWhe. The loan is soft loan with

term 15 years, and interest rate 5.5%, the tariff is more than the required tariff based on required

IRR because of the ratio ADSCR that should be more than one, as mentioned before.


tariff (sample one)


Wet Dry Unite


Minimum required IRR 6 6 % Dept term 15 15 years

interest rate 5.5 5.5 % Simulation results

Internal Rate of Return (IRR) on Equity 9.69 8.8 % Net Present Value 57.91 51.26 million € Payback Period 14.1 15.33 yrs.

Discounted Payback Period 18.59 20.36 yrs. Total Incremental Costs 198 520 169 248 590 623 €

Minimum ADSCR 1.05 1 Required Tariff (LCOE) 0.128 0.148 €/kWh







89


tariff (sample two)


Wet Dry Unite


Minimum required IRR 9 9 % Dept term 15 15 years

interest rate 5.5 5.5 % Simulation results

Internal Rate of Return (IRR) on Equity 10.34 9.54 % Net Present Value 84.96 81.25 million € Payback Period 14.1 15.33 yrs.









90

5.5.2 Minimum required grant

Table (19&20) show the minimum required grant, that the project need to be feasible, the wet

plant require 123810048€ The loan is soft loan with term 18 years, and interest rate 5.5%, while

the dry plant require 163238400€ with loan term 20 years, and interest rate 5.3%,. The tariff in

both cases is 0.084€/kWhe which is the electricity price in Jordan.

Table5.19: Economic comparison between expected plants in Ma’an with grant (sample one)


Wet Dry Unite


Minimum required IRR 6 6 % Grant 123810048 163238400 million €

Dept term 18 20 years interest rate 5.5 5.3 %

Simulation results Internal Rate of Return (IRR) on Equity 7.63 6.36 %

Net Present Value 11.84 2.46 million € Payback Period 16.56 18.41 yrs.









91

Table5.20: Economic comparison between expected plants in Ma’an with grant (sample two)


Wet Dry Unite














92

5.5.3 Tariff and grant

Table (21&22) show the minimum set of tariff and grant that are required to make the project

feasible, the wet plant require 0.13€/kWhe, while the dry plant require 0.146€/kWhe, both are

with grant of 50 million€ and loan is soft loan with term 15 years, and interest rate 5.5%.


(sample one)


Wet Dry Unite














93


(sample two)


Wet Dry Unite














94

Table (23&24) show the minimum set of tariff and grant that are required to make the project


with grant of 100 million€ and loan is soft loan with term 15 years, and interest rate 5.5%.


(sample three)


Wet Dry Unite














95


(sample four)


Wet Dry Unite














96

Table (25) shows the minimum set of tariff and grant that are required to make the project


with grant of 100 million€ and loan is soft loan with term 10 years, and interest rate 6%, this

table representing the normal soft loan that is already given to renewable projects, the net present

values for both cases are high because the high tariff compared to the minimum required tariff,

while the ratio ADSCR is equal to one because the loan term is only ten years that lead into high

loan payments the first ten years, by other financial solutions the tariff could be reduced such as

two loans, or another budget to cover the deficit in the first ten years.


(sample five)


Wet Dry Unite



Dept term 10 10 years interest rate 6 6 %


Net Present Value 77 122.04 million € Payback Period 10.89 10.97 yrs.









97

Chapter 6

Economic Analysis 6.1 Introduction:

The economic feasibility analysis examines the economic effects of the CSP power plant

in Ma’an MDA. Generally the objective of an economic feasibility is to give an assessment from

a macro perspective, whereas the financial feasibility analysis assesses the operators’ point of

view. Thus, the financing costs are not considered within the economic but only in the financial

analysis. The analysis also considers externalities which can be defined as positive or negative

effects that describe the uncovered costs (e.g. pollution in conventional power plants).

Furthermore, since the economic analysis considers the macro-level it does not take into account

cash transfers within the economy. Within an economic feasibility analyses real prices are used

so changes over time aren’t taken into account. The economic feasibility analysis can be further

subdivided into two major parts, the analysis of the direct and the indirect effects respectively.

Direct economic impacts relate only to the construction of new power plants, whereas indirect

effects are economic impacts by demand in the supply value chain. However, most factors have

direct and indirect effects, thus they won’t be separated in this analysis.

6.2 Electricity Prices

Under current estimations the plant will produce electricity at a price of 12.74–15.94 €c/kWh

(without transmission and distribution) which is higher than the end-user price. Thus, in the short

term electricity prices will rise for industry and consumers and thereby negatively affect

competitiveness and reduce the purchasing power. According to different recommended specific

solar filed costs, the most convenient values are 14.19–16.0€c/kWh

6.3 Environmental impacts

The construction and operation of a CSP project leads to several environmental and social

impacts that have to be identified, assessed, monitored and mitigated. Therefore, this project

follows environmental guidelines of the respective national institutions. Within this section the


98

major aspects as well as their mitigation are listed; these are site-specific rather than regional.

6.3.1 Plant construction:

• Land use: In the previous chapter, it was shown that the pant required less than 497

acres; this is quite huge for a 50MW conventional plant due to the low energy density of

solar thermal power. Yet, when comparing the required land with other alternatives like

conventional power plants, the land demand for excavation, transport and processing has

to be considered. This plant justifies the need of this land, by other environmental

attractiveness such as the free fuel that doesn’t need excavation and transportation and

zero emission sources. Another important point for site selection, the land used for the

plant itself, and the precluding other use of the adjacent lands are very critical.

Alternative locations were compared previously under the project of EMPOWER and the

site was carefully selected to avoid impact with recreational areas. Furthermore, it is

recommended from this study that the local government makes consultation with nearby

communities, in the other hand this plant is required for this site due to its positive impact

on the development of the area, for that reason the government and MDA interested in

such project.

• Construction impacts: some harmful impacts during construction might occur, these are

evaluated and the plant must put regulations on the consultant to be sure that a safe

construction process is followed that’s also parallel with safe waste disposal. Some

relevant effects from associated infrastructure are also evaluated early in the process for

example opening temporary roads; parking land preparation equipments, labors housings,

here the MDA is prepared well and can supply the required services.

• Fire risks: due to high temperatures at some sections of plant parts including risk of out-

gassing from panel components, mitigation and safety measures against fire are needed,

such as overheating (coolants) and relevant warning / monitoring systems.

• Flora and Fauna: the land is desert that does not mean neglecting those impacts, impacts

of these components on environment are critical; a Re-establishment of local flora and

fauna plan is included to the construction phase if it is urgent and possible.

6.3.2 Plant operation:

• Chemical discharge: There is a risk of ordinary or accidental release of chemicals, e.g.


99

anti-freeze or rust inhibitors in coolant liquids. Also heat transfer fluids are likely to

contain harmful chemicals. Therefore it is essential to include in the plant some safety

measures against such possible releases through leak-proof, regularly maintenance and

cleaning as well as periodical replacements of components.

• Safety issues for workers: due to high levels of radiation workers should use special

sunglasses, hats, skin protection and other protective devices if required.

• Water requirements: this element is the most important and attractive, since this plant is

dry cooled it requires fewer amount of water where the annual consumption is only

41820m3 , while the wet cooled plant requires 717981m3 , that means 94.18% of water

saving, other studies showed that around 93% of water consumption is reduced by dry

cooling. The water consumption is not calculated by Greenius because it is not possible,

thus it is calculated by SAM software under the assumptions of 0.6 l/m² aperture and 63

annual washes, which are recommended by SAM software, table6.1 shows the expected

water consumption monthly.

Table6.1: Expected water consumption for Dry/Wet 50 MW with 7.5 TES in Ma’an-Jordan

Water consumption (m3) 50 MW 7.5 TES


100

Month Wet cooled

[m3] Dry cooled

[m3] Saving

[%] Jan 21870.5 774.8 96.46 Feb 31664.3 1128 96.44 Mar 50395.4 1744 96.54 Apr 68028.8 2240 96.71 May 82907.3 2509 96.97 Jun 92620.0 2668 97.12 Jul 90028.1 2631 97.08 Aug 89484.1 2611 97.08 Sep 74386.7 2298 96.91 Aug 49207.1 1661 96.62 Nov 29990.2 1055 96.48 Dec 20588.6 721.3 96.46

Sum+Other water usage

717981 41820 94.18

NREL study36

3.5m3/Mwh 54900

0.3m3/Mwh633500

91.33

Reflections from the solar field: visual impacts are important when using tower technology, but

since our plant is a parabolic trough, the points of focus of the concentrators will be relatively

close to the reflector itself. However, further consideration should be given to the impacts on any

residences, facilities and transport within line of sight of the reflector field.

Regarding this plant the water and land are not critical, land is available and dry cooling doesn’t

require much water, so due to water scarce in this region such plant is needed. Since it is clean

with no greenhouse gas emissions, and provides a fixed cost of energy produced, this plant is an

attractive feature for Jordan a country that imports 95% of its energy. Also this plant is required

because it’s a good tool for the government to meet the national renewable energy plan, the

major thing that should be taken into consideration in this environmental study is to secure

public acceptance of the plant. Therefore we need careful management processes and some

media advertisement to highlight the environmental features, and other social attractiveness such

as job creation, development of local technologies research and educational benefits.

6.3.3 CO2 emission reduction



101

Since this plant will feed the grid with electricity, the amount of CO2 reduction is calculated

based on electricity to carbon conversion factor. Each kWhe of grid electricity participates in

0.632kgCO237 emissions. So the project avoids 115995.69 tonCO2, which is equal to

115.8€/tCO2 based on the country’s electricity prices. Regarding the carbon trading for CSP the

cost is 30$/tCO238 .thus the avoided emission can be sold for 2.4359095million €, as an

additional profit for the plant.

6.4 SWOT Analysis

This section summarizes the effects of the power plant construction in a SWOT analysis.

Strengths: Governmental support, production of clean and sustainable energy at relatively fixed

costs, job creation, reduction of CO2 emissions and CDM potential, provides excellent

conditions for research as well as for technology transfer; the energy storage system supports

the grid, low water consumption and the plant participate in the development of Ma’an.

Table6.2: SWOT analysis table

Strengths: Clean energy at fixed costs,

Weaknesses: low local know-how and operating experience

37 UNEP, 2000 - "The GHG Indicator”

38 Desert Power: The Economics of Solar Thermal Electricity for Europe, North Africa, and the Middle

East(p25)


102

Independent of fossil fuels, Job creation, Research and technology development of Ma’an low water consumption

High electricity prices today Most products have to be imported 2% lower efficiency than wet cooled plant

Opportunities: Development of new industry Reduction of energy costs in the future

Threats: Opposition by local population can’t attract investors without feed in tariff law

Weakness: there is no local know-how and operating experience, all the equipments for

the operation are imported, very high land and water consumption, high LCOE today.

Opportunity: fixed energy price even with rising fossil fuel costs, creation of local

experts and manufactures that will reduce the levelized cost of this technology in the long

run.

Threats: some environmental risks, such as a sand storm which will affect the operation and

cost, a threat that the project may not attract workers due to high solar radiations and difficult

working conditions, social rejection and demonstrations against the power plant.

Comparison between opportunity and threats: It can be seen that the risks can be controlled

which means overcoming the major weakness. Thus, their strengths and opportunities provide a

great chance to contribute to a long term stable and clean energy supply and to foster sustainable

development in Jordan. Furthermore, there is great potential to develop new industries and foster

R&D which both support long term job creation, and technology transfer.

6.5 sensitivity analysis

Since the input costs used in this study affect the Investment cost of the LCOE, and these costs

should be recommended by experts, a search is done on them, table 6.4 shows the cost

assumptions recommended by SAM software the other important simulation software. Also in

table6.5different costs are simulated by Greenius for the optimized wet and dry design in Ma’an.


103

Table6.4: Cost assumptions recommended by SAM software, adjusted to Greenius inputs39 Component Value Unite Value Unite Solar Field 295 $/m²

270 €/m² HTF System 90 $/m²

Storage 80 $/kwht 56 €/kwht Power Block (Wet-Cooled) 940 $/kW 658 €/kw Power Block (Dry-Cooled) 1160 $/kW 812 €/kw

Table6.5: Different simulation results for different specific solar filed cost, including the recommended costs

Investment cost and LCOE for different specific solar field cost Specific solar

filed cost Project Period

Wet Dry LCOE Investment cost LCOE Investment cost

[€/m²] years [€/kWhe] [€] [€/kWhe] [€] Greenius320 25 0.136 248 384 064 0.1580 289 161 894

320 this study 30 0.1284 248 384 064 0.1491 289 161 894 315 30 0.1273 245 839 154 0.1478 286 242 733 310 30 0.1261 243 294 244 0.1465 283 323 571 305 30 0.1249 240 749 334 0.1451 280 404 409 300 30 0.1238 238 204 423 0.1438 277 485 247 295 30 0.1226 235 659 513 0.1425 274 566 086 290 30 0.1214 233 114 603 0.1441 271 646 924 285 30 0.1202 230 569 693 0.1398 268 727 762 280 30 0.1191 228 024 783 0.1415 265 808 601 275 30 0.1179 225 479 872 0.1402 262 889 439

270 SAM 30 0.1167 222 934 962 0.1388 259 970 277 265 30 0.1156 220 390 052 0.1375 257 051 116 260 30 0.1144 217 845 142 0.1362 254 131 954 255 30 0.11.32 215 300 232 0.1348 251 212 792 250 30 0.1121 212 755 321 0.1335 248 293 630 245 30 0.1109 210 210 411 0.1322 245 374 469 240 30 0.1097 207 665 501 0.1308 242 455 307

237 DLR study40 30 0.1090 206 138 555 0.127 240 703 810 235 30 0.1085 205 120 591 0.1295 239 536 145

39 Parabolic Trough Reference Plant for Cost Modeling with the Solar Advisor Model (SAM) (p3) 40 EFCOOL- Wassereffiziente Kühlung solarthermischer Kraftwerke (p33)


104

The simple sensitivity analysis is made by a separate excel sheet based on several simulation

results, because Greenius has no option for sensitivity, starting with 100% of base value which is

the original result of this study, then the investment cost is increased and reduced keeping all

other inputs fixed, the same method done for O&M costs of solar field. The energy yield was

difficult to change, so the characteristics of storage and solar field were changed, keeping the

investment cost and the variable costs at the same values of the original case, the result was

changing in energy yield as required without any other changes, separate excel tools can be used

rather than this, see table6.6.

Table6.6: Sensitivity analysis

Sensitivity

Investment cost Annual solar field

O&M costs Annual Energy Yield Percent

of base IC LCOE O&M LCOE EY LCOE [€] [€/kWhe] [€] [€/kWhe] [MWhe ] [€/kWhe] [%]

346994272.8 0.1768 2448576 0.1514 218608.200 0.1290 120 332536178.1 0.1719 2346552 0.1508 209499.525 0.1361 115 318078083.4 0.1653 2244528 0.1503 200390.850 0.1411 110 303619988.7 0.1583 2142504 0.1497 191282.175 0.1433 105 289161894.0 0.1491 2040480 0.1491 182173.500 0.1491 100 274703799.3 0.1455 1938456 0.1486 173064.825 0.1528 95 260245704.6 0.1389 1836432 0.148 163956.150 0.1565 90 245787609.9 0.1323 1734408 0.1475 154847.475 0.1735 85 231329515.2 0.1257 1632384 0.1469 145738.800 0.1867 80

The sensitivity results shown in the previous table are plotted in figure6, the blue line describes

the investment cost, it is obvious that LCOE reduced as the investment cost decreased.10% and

20% increase in IC resulted in 1.62c€ and2.77c€ increase in LCOE consequently, 10% and 20%

decrease in IC resulted in 1.02c€ and2.34c€ decrease in LCOE consequently, the reduction of IC

cost is the more convenient. the red line describes the solar field operating and maintenance

costs, also the LCOE reduced as the O&M costs decreased.10% and 20% increase in O&M costs

resulted in 0.12c€ and 0.23c€ increase in LCOE consequently, 10% and 20% decrease in IC

resulted in 0.11c€ and0.22c€ decrease in LCOE consequently. The Energy yield is inversely

proportional to the LCOE, 10% and 20% increase in EY resulted in 0.8€ and2.01c€ decrease in

LCOE consequently, 10% and 20% decrease in EY resulted in 0.74c€ and 3.76c€ increase in


105

LCOE consequently

Figure6.1: LCOE senstivity analysis

Jordan today is highly dependent on imported fossil fuels, (for example the frequent cuts of the

imported Egyptian gas, costs the government 3 million Euro daily). This situation is expected to

worsen due to economic growth and rising prices for fossil fuels on international markets. Thus,

renewable energy provides a good solution despite higher prices today. Solar energy plays a

strong role in the ambitious plans of the Jordanian government which is to reach a 10%

renewables share by 2020, so CSP is one of the promising renewable technologies for Jordan.

The economic analysis identified large positive effects which can be achieved by the application

of solar thermal power in Jordan. Yet, most of them (development of new industry, reduction of

fossil fuel imports, job creation, development in south of Jordan etc.) only come into effect when

CSP technology is applied on a wider basis. Thus, the results of a single plant with 50MW are

rather low – yet, large potential when a shift to more CSP is undertaken as expected.

The SWOT analysis summarized the results of all previous chapters. It was concluded, that

despite higher electricity prices today the strengths and opportunities outweigh the threads and

weaknesses.


106

Conclusions

It will be a 50MW solar thermal power plant operating on a dry cooled (air cooled) Rankine

cycle with thermal storage for 7.5 hours. The proposed turbine is the SST-700 from Siemens, for

the solar field the SKAL-ET150 from Solar Millenium is selected. Based on the ground data

from enerMENA station in the site and a North-South installation, the expected yield reached

around 182173.5MWhe. This yearly amount is greater than the yearly output of Andasol-1 (a wt

cooled plant in Spain) which generates around 134715.8 MWhe and has the same power and

storage capacity as proposed plant; this can be attributed to the higher direct normal radiation in

Ma’an.

Constant-capacity design was assumed, thus the dry plant has a larger turbine and solar field to

accommodate the lower cycle efficiency, The expected wet cooled plant in Ma’an has 444720

m2 effective solar field area, with 183879.4MWhe annual energy yield, 4162 operating hours,

14.9% annual mean overall efficiency, a capacity factor of 41.98 and water consumption of

717981 m3/a.

While the dry plant has 523200 m2 effective solar field area, with 182173.5MWhe energy

yield, 4190 operating hours, 12.9% annual mean overall efficiency, a capacity factor of 41.59%,

and water consumption of 41820 m3/a,

In addition, the annual mean overall efficiency is 12.9% which is low compared to the current

fossil fuel technology like the combined cycle that can have an efficiency of 60%. It should be

pointed out that the solar irradiation that falls in the areas between the collectors is accounted for

in this efficiency.

The solar field area increased by 17.64%, the efficiency reduced by 2%, the water consumption

reduced by 91.3%, the energy yield reduced by0.93%, the investment cost increased by 16.42%,

the LCOE increased by 16.12%.

A dry cooled plant in Ma’an will have the same solar field size as the Andasol wet cooled plant,

but with a larger turbine; both have the same TES full load hours (7.5 hours), but instead of a

970MWht thermal capacity in Andasol a 1100MWht in Ma’an, because of higher thermal input

of dry cooled turbine at same capacity. And the expected Energy yield is 35.23% higher than

Andasol.

The technical simulation showed good results, because Ma’an has high DNI and Normal ambient


107

temperatures, from technical point of view the dry cooling option in Ma’an still very good, but

CSP technologies are expensive. The economic simulation showed that the project is unfeasible

if the energy to the grid is sold at the same price of Jordanian electricity 0.084€/kWhe, without

feed in tariff. Different suggested financial scenarios have been simulated to make the project

feasible. The minimum required tariff 0.17€/kWhe, or a grant of 163.3 million €, or

(0.146€/kWhe with 50 million €), or (0.13€/kWhe with 100 million €)


108

Recommendations

• Water recourses are not restriction factors against CSP in Ma’an, dry cooling requires less

amounts, and the performance is not affected too much, because of high attitude and very

low hours with extreme ambient temperatures

• This result is site specific, for other locations with high ambient temperatures,

improvements in the cooling system and re-optimization are essential.

• Active approach to support the build-up of local industry and local added value (inclusion

in tenders).

• Active approach to get support from local communities.

• Usage of soft loans and grants from international and regional donors,

• Thinking of selling electricity to neighbor countries that have high electricity prices,

Ma’an wins from the development of its area, and experts are prepared for further

Jordanian projects

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