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Master Level Thesis

European Solar Engineering School

No.169, August 2013

Techno-economic Appraisal of Concentrating Solar Power

Systems

Master thesis 18 hp, 2013 Solar Energy Engineering

Student: Maria Gasti Supervisor: Tara Chandra Kandpal

Dalarna University

Energy and Environmental

Technology

1

Contents

1 Introduction.....................................................................................................................................2 1.1 Aims...........................................................................................................................................2 1.2 Method......................................................................................................................................3 1.3 Related work.............................................................................................................................4 2 Simulations and Calculations.........................................................................................................5 2.1 Boundary Conditions and Assumptions.............................................................................6 2.2 Simulations..............................................................................................................................7 2.2.1 Parabolic Trough..............................................................................................................9 2.2.2 Linear Fresnel Mirrors...................................................................................................17 2.2.3 Molten Salt Power Tower.............................................................................................23 2.2.4 Dish Stirling....................................................................................................................27 2.2.5 Flat Plate PV...................................................................................................................31 2.2.6 High-X Concentrating PV............................................................................................35 3 Results............................................................................................................................................38 3.1 Parabolic Trough..........................................................................................................39 3.2 Linear Fresnel Mirrors...................................................................................................42 3.3 Molten Salt Power Tower.............................................................................................45 3.4 Dish Stirling.....................................................................................................................48 3.5 Base Cases Comparison.................................................................................................51 3.6 Comparisons....................................................................................................................54 4 Discussion and Conclusions.......................................................................................................57 5 References......................................................................................................................................60

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1 Introduction

The diffusion of Concentrating Solar Power (CSP) systems is currently taking place at a much slower pace than photovoltaic (PV) power systems. This is mainly because of the higher present cost of the solar thermal power plants, but also for the time that is needed in order to build them. Though economic attractiveness of different Concentrating technologies varies, still PV power dominates the market. The price of the CSP systems is expected to drop significantly in the near future and wide spread installation of them will follow. In this project it is proposed to examine the existing (CSP) systems in several different locations in Europe, including three different locations in Greece. An attempt to study the effect of various design, operation and economic parameters on the unit cost of electricity delivered by the CSP plants would be made in this project. Several feasible scenarios for operation of CSP systems shall be considered for analysis. Also a comparison of CSP systems with photovoltaic power plants shall be attempted. This will give an indication on how CSP can compete PV and maybe replace them in some cases. It has to do both with the technology and the financing of these systems. Last but not least, since CSP have the advantage to store power for many hours, the effect of design hours of storage on the unit cost of electricity would also be studied. So, an other question of this project is the definition of the more appropriate system for a particular geographical area. For the accomplishment of this work a literature study has been made. It includes the basic principles of the CSP systems and their possible future technological advancements (Lovegrove and Stein 2012).

1.1 Aims

The main aim of this project is the creation of different relevant case studies on solar thermal power generation and a comparison between them. The purpose of this detailed comparison is the techno-economic appraisal of a number of CSP systems and the understanding of their behaviour under various boundary conditions. The CSP Technologies which will be examined are the Parabolic Trough, the Molten Salt Power Tower, the Linear Fresnel Mirrors and the Dish Stirling. These systems will be appropriately sized and simulated. All of the simulations aim in the optimization of the particular system. This includes two main issues. The first is the achievement of the lowest possible levelized cost of electricity and the second is the maximization of the annual energy output (kWh). The input values that need to be changed and interpreted are many and a large number of combinations of them are possible, in case it gives better results. The project also aims in the specification of these factors which affect more the results and more specifically, in what they contribute to the cost reduction or the power generation. Also, a photovoltaic (PV) system will be simulated under the same boundary conditions to facilitate a comparison between the PV and the CSP systems. Depending on the boundary conditions, the selection of the system which performs better can be different.

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1.2 Method

The project is carried out with the use of the System Advisor Model (SAM) software of National Renewable Energy Laboratory (NREL). This tool considers for each type of technology various input parameters, which determine the system’s performance. A brief description of SAM and its input parameters is presented in the following paragraphs (SAM Help Contents).

To begin with, the most important parameter SAM takes into account is the climate. It refers to the location of the plant. Typical meteorological data for a certain area are taken from NREL weather database (Energy Plus weather data 2011). They include information about the latitude, longitude and the elevation of the particular location. Also, they include hourly data for direct normal and global horizontal radiation, dry bulb temperature and wind speed. It is worth noting that each file contains 8760 values for an entire year.

The next parameter is about the financing option of the examined CSP system. Utility Independent Power Producer is defined for the particular project, which refers to a seller who purchases electricity according to a power purchase agreement between two parties (seller-buyer). The financing option requires the following values to be determined from the SAM user. The installation cost and the operation-and-maintenance cost. More specifically, the total installed cost consists of the direct and the indirect capital cost. The direct one is related with the premeditated expenses for the completion of the project, like the solar field’s cost, the pumps and the piping and other various expenses which have already been defined. The Indirect one includes design and construction expenses, like permitting, engineering project and development activities and other issues which affect the project as an entity. It is also dependent on the land area and on the amount of the direct cost which has been subjected to sale taxes.

SAM considers financial and incentives assumptions too. The principal values for these assumptions are those of the inflation rate, the discount rate and the life time of the plant. Furthermore, the construction period and the loan parameters are taken into account in a long range planning. Here, the power purchase agreement price per kWh and its escalation rate is defined too. Regarding the type of investment, incentives can be considered if the project is based on them.

In addition, SAM assumes design parameters, such as the solar field, the power block, the solar collector assembly and the thermal storage. A short report for each of the aforementioned components of the plant is following. Important information regarding the solar field is the field’s layout, the solar multiple and the type and temperature of heat transfer fluid. The power block field includes values of the estimated net output after its reduction due to parasitic losses. The solar collector assembly refers to the physical properties of the CSP technology. This is about its dimensions, its optical characteristics and the relative heat loss parameters. The thermal energy storage indicates the thermal storage capacity that the power block delivers. This is described on number of hours. A more detailed explanation on the storage inputs concerns the type of the storage fluid and the limits of the amount of the storage energy.

As it was described in the previous lines, SAM software takes a great variety of input values. Due to this large amount of input data, some of them were not presented. It is worth noting to mention that SAM software contains defaults values. The project’s strategy is the alteration of one default input value in every simulation of CSP system and the detailed interpreting of its influence on the results. A combination of input values can be possible after that as well.

More specifically the procedure consists of the next steps. The first step is the variation of the climate parameter. The selected locations belong to Greece, Spain and Italy. The Greek locations are Athens, Thessaloniki and Andravida. Between them the climate is quite the same, as Greece is not a big country. The main characteristic of these regions is the intensity of direct radiation which is very high and the land availability, which makes them suitable for such installations. Weather data will be compared from different sources for these regions, but since the accuracy and the availability of

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them is insufficient, other locations will replace them. Also, a comparison in the results will be made between them and the Italian and Spanish location. The next step is the solar multiple and thermal storage energy variation. The last part of simulations includes potential improvements on the existing CSP systems and technological advancements. According to (Hank Price et al 2002), a higher reflectivity could be examined. But SAM already considers high reflectivity, which exceed the 0.9 and 0.93 in some cases. The CSP systems’ study will be continued with a detailed comparison between them. Then, a photovoltaic (PV) system will be simulated under the same boundary conditions and be compared with the relative CSP study cases.

The drawback of the SAM software is its limitation in modelling new CSP systems. Even small changes in the geometries and the systems component s combination is difficult and maybe impossible in some cases to be modelled. SAM uses only its existing models for the prediction of the results and rejects the option of simulating new CSP designs (Wagner and Zhu 2011).

1.3 Related work

In this paragraph a brief description of the literature study will be made. Except from the literature which concerns the System Advisor Model software a further study in the CSP systems was required. Principles and basic components of the CSP systems were provided from the (Lovegrove and Stein 2012) book. This book and (Hank Price et al 2002) includes future trends and technological advancements in the CSP systems. Finally, it provides information about various meteorological and climatic parameters which influence the CSP systems’ performance. More information about the Dish Stirling and its operation were taken from (Fraser 2008), where its components were studied in order to determine and to create a Dish Stirling prediction model. The report of (Yinghao Chu 2011) gives more information about the CSP and PV mechanisms. It also evaluates their performance and compares real CSP and PV systems according to their penetration on the market, their outputs and their costs. (Panos and Kretschmann 2010) is a relevant project where there are detailed comparisons of existing CSP systems and possible variations in their thermal storage capacity and their solar multiple. Last but not least, (NASA) website was used for the selection of further weather data.

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2. Simulations and Calculations

The simulation results which will be analysed are the levelized cost of electricity (LCOE) and annual electric output. The LCOE in other words is the unit cost of electricity which should be sold in order to cover the installed cost, the operation and maintenance costs and all the financing. SAM provides two kinds of LCOE, The real and the nominal one. Nominal LCOE refers to current value of money. Real LCOE considers inflation during the life time of the project in contrast with the nominal one which excludes it from its calculations. But the main difference among them relies on the analysis which will be made. The real LCOE is mainly used for the long term projects, rather than the nominal, which is suitable for short term schemes. In this project LCOE (real-w/o incentives) is considered for the comparison of the variants, in which tax credits and incentives payment is excluded. The annual electric output is the total annual output for the first year of the project’s cash flows. The thesis first presents the boundary conditions and the assumptions that have been considered in the study followed the simulations made in the study. Finally the results of the simulation studies are used to compare different scenarios of concentrator design and operational conditions.

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2.1 Boundary Conditions and Assumptions The boundary conditions refer to all the CSP system models which are going to be simulated. In this way the comparison between them is reasonable and feasible to be made. Some of them concern the financing and some others the technology. Here is a list of these assumptions.

A utility independent power producer is considered for the financing option. The CSP system is owned and operated by a producer who sells the produced energy at a certain price. This price is defined by the power purchase agreement (PPA). The project is performed from the owner’s and producer’s viewpoint.

Useful life of CSP systems is assumed to be 30 years

Nominal power of the system is 100MW. In SAM software it is mentioned as the estimated net output at design and describes the net output after the reduction due to the parasitic losses.

The financing mode of the system works according to a specified internal rate of return (IRR) target of 15%, which is the minimum required value. The IRR refers to the return on investment (the profits). In order to meet this target IRR value, SAM software calculates the PPA price, which is the defined amount of money the buyer pays for each kWh. It is worth noting that the PPA value will have an escalation rate of 1.2% per year.

A construction loan which covers the total installed cost of the CSP system. This applies to all the case studies. For example, when the solar multiple raises and the installed cost increases then, the loan also covers this capital cost.

Inflation rate of 2.5%

Solar multiple of 1 in the basic cases

Thermal storage capacity of 6 hours for the basic cases which have thermal storage

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2.2 Simulations Figure 1 illustrates the solar irradiation map of Europe (GeoModel Solar 2011). This project focuses on the Mediterranean countries and more specifically in the cities which are considered in the study. The selection of these countries is due to their location and their availability of direct normal irradiation. In the first place the project is supposed to focus on Greece, but there is the limitation of the availability of weather data. So for Greece, data for three cities are only available. Moreover, four Spanish and three Italian cities will be simulated.

Figure 1. Solar irradiation map of Europe

Solar recourses are enough in all of the considered cities, but each of them has different meteorological parameters which influence the power output. Here is a brief description of their importance and influence in the CSP system performance according to (Lovegrove and Stein 2012).

The primary parameter of a location with the greatest significance on a CSP plant is the direct normal irradiance (DNI). It determines the energy production capability (energy yield) and according to this value a location can be considered suitable or not for a CSP plant. The outputs are predicted according it and the rest of the parameters, which will be discussed later, can affect the result, but they are not as extremely significant as DNI.

The air temperature is described by the dry bulb temperature in Table 1. It can affect both the solar field’s and the power block’s efficiency. The effect of the ambient temperature is the convection losses of the heat transfer fluid and the collector’s area (reflectors). For the power block efficiency, humidity and ambient pressure have to be taken into account too. The elevation of each location

Thessaloniki

Athens Andravida Brindisi Palermo Malaga Cagliari Sevilla

Toledo

Alicante

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determines the ambient pressure. Also, relative humidity is considered in the weather data of each location, but there is not an annual mean value for it. Increased humidity and low air temperature reduces power block’s efficiency and therefore the overall energy output of the system.

High wind speeds have serious impact in the CSP system’s performance. This mainly happens for two reasons. The first is that they create vibrations and in some cases they may bend the mirrors. Then the focusing changes and power generation goes markedly down. So, construction is required for the resistance in the strong wind and the maintenance of the appropriate geometry. The second reason is that wind causes convective heat losses in the solar field.

Several characteristics of each location considered in the study are presented in Table 1. Every place combines different meteorological parameters, but in all of them DNI is plenty, which makes them suitable for studying. The simulation of them will determine which parameters have the greater effect on a CSP system and how they can be managed. The displayed numbers that refer to weather are the average annual values.

Table 1. Locations’ climate characteristics

Location DNI Wind Speed Dry Bulb Temperature Latitude Elevation

Units (kWh/m2) (m/s) (C°) (Degrees) (m)

Athens 1519.8 3.2 17.9 37.90 15

Thessaloniki 1372.7 3.1 15.4 40.52 4

Andravida 1151.7 2.8 16.7 37.92 12

Malaga 1988.1 6.7 18.0 36.67 7

Sevilla 1772.7 2.7 18.4 37.42 31

Toledo 1926.9 6.7 15.6 39.88 529

Alicante 2016.8 6.7 17.9 38.37 31 Cagliari 1013.1 4.0 16.4 39.25 18

Palermo 1516.9 3.8 18.8 38.18 34

Brindisi 1119.9 5.1 16.6 40.65 10

After the climate change, the solar multiple will be examined. A solar multiple of 1, expresses the required solar field aperture area to meet the power cycle demand. More specifically, it expresses the amount of thermal energy needed to drive the power cycle at design conditions. It is interesting to study the increase of this parameter and optimize the solar field size for a given power cycle capacity, location and thermal energy storage.

Thermal Storage Capacity is considered to be one of the advantages of the CSP systems. Its importance relies on the fact that it can shift the electricity use from high consumption periods to less expensive ones, by saving money and improving the overall system efficiency as well. The effect of this parameter is dependent from the power cycle thermal input. The simulations include a case without Thermal Storage and three others with gradual increase until 12 h of thermal storage.

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2.2.1 Parabolic Trough The simulations presented in this section will be used as the bases cases for each location for Parabolic Trough model. They will be used as references to be compared with different system variations. Afterwards, a comparison between the location and their possible variants will be done. The plant corresponds to a capacity of 100MW.

Figure 2 shows the estimated values of LCOE for three cities of Greece. Athens has the minimum LCOE while its value is highest for Andravida. The reason for the same may be noted from Figure 3 as the estimated annual electricity output is highest for Athens. Similar values of LCOE and annual electricity output for four cities of Spain are presented in Figures 4-5 and for three cities in Italy in Figures 6-7.

Figure 2. Base Case-LCOE for Greece Figure 3. Base Case-Annual Output for Greece

The base cases for the Spanish cities in Figure 4 bring much better results than in Greece. The LCOE for the four climates is low, with Malaga having the lowest value. Similar LCOE values have Toledo and Alicante and Sevilla has just a little bit higher price of electricity. The power outputs are higher for the locations with lower LCOE.

Figure 4. Base Case-LCOE for Spain Figure 5. Base Case-Annual Output for Spain

The Italian cities except from Palermo do not have such a good performance. Cagliari has the worse LCOE which reaches 122.95 Cents/kWh and Brindisi has very high LCOE as well. Palermo’s performance is better and its electric output is the highest of the Italian locations.

65.42

79.92 90.69

0

20

40

60

80

100

Athens Thessaloniki Andravida

LCOE(real without incentives)

92.39

75.56 66.56

0

20

40

60

80

100


Annual Output (GWh)

49.57

56.25

51.90

50.23

46

48

50

52

54

56

58

Malaga Sevilla Toledo Alicante


122.10

107.53

116.58

120.49

100

105

110

115

120

125


Annual Output (GWh)

Cents/kWh GWh

Cents/kWh GWh

10

Figure 6. Base Case-LCOE for Italy Figure 7. Base Case-Annual Output for Italy

The next set of simulations is about the variation of the solar multiple. Figures 8-13 show the variations in LCOE and annual output with changes in the values of the solar multiple.

To begin with, Athens has its minimum LCOE for 2.5 solar multiple. Here further increase or decrease does not produce the sufficient energy to minimize the LCOE and cover the initial cost. Thessaloniki has its minimum for solar multiple of 3 and Andravida for solar multiple of 2.5. In Figure 9, it is noticeable that the power output has a smaller increase between 2.5 and 3 solar multiple than between 2 and 2.5.

Figure 8. Solar Multiple-LCOE for Greece Figure 9. Solar Multiple-Annual Output for Greece

In Figure 10 all the Spanish climates have their power output for a 2.5 solar multiple. In Sevilla LCOE fluctuates in an interesting way, as solar multiple changes. For solar multiple of 2 Sevilla gets a high LCOE value equal to 35Cents/kWh. Then in solar multiple of 2.5, there is a sharp reduction in the price of electricity and it becomes equal to that of the Toledo. Finally, there is an increase again for a solar multiple of 3. The rest of the climates behave in the same way, but with smoother fluctuations.

122.95

66.12

105.34

0

20

40

60

80

100

120

140

Cagliari Palermo Brindisi


49.05

91.40

57.27

0

20

40

60

80

100


Annual Output (GWh)

40.4 38.0 38.7

48.3 44.0 43.8

55.1 51.8 52.7

0

10

20

30

40

50

60

2 2.5 3 3.5


205.4

248.2 272.7

171.6

213.9 240.7

150.4 181.7

199.9

0

50

100

150

200

250

300

2 2.5 3 3.5

Athens Thessaloniki Andravida Cents/kWh

Solar Multiple

GWh

Solar Multiple

GWh

Cents/kWh

11

Figure 10. Solar Multiple-LCOE for Spain Figure 11. Solar Multiple-Annual Output for Spain

Figure 12 presents the solar multiple variations for Italy. Cagliari and Brindisi reach their minimum LCOE for solar multiple of 3 and the related difference in the LCOE is big. On the contrary, Palermo has its minimum LCOE for solar multiple of 2.5. It is seen that the climates which do not have a high potential for a good CSP system performance reach their minimum LCOE for a solar multiple of 3 and not for 2.5 like the climates with the higher electric outputs. Moreover, the solar multiple changes cause them significant decrease in the LCOE, which does not happen in the other locations where the decrease is very small compare to them.

Figure 12. Solar Multiple-LCOE for Italy Figure 13. Solar Multiple -Annual Output for Italy

31.4 30.8

31.5

35.0

32.7

33.1 33.1 32.7

33.9

31.7

31.1

31.8

30.5 31

31.5 32

32.5 33

33.5 34

34.5 35

35.5

2 2.5 3 3.5

Malaga Sevilla

Toledo Alicante

237.8

288.4

318.8

251.0

288.6 311.7

262.1

304.2

332.6

265.4

307.0

335.2

225

245

265

285

305

325

345

2 2.5 3 3.5

Sevilla Toledo Alicante Malaga

71.7 63.7

59.9

40.9 38.6 39.1

62.9 56.4 55.0

0

10

20

30

40

50

60

70

80

2 2.5 3 3.5


115.4 147.7

175.6

202.9

244.5 270.2

131.7

166.7 191.4

0

50

100

150

200

250

300

2 2.5 3 3.5


Solar Multiple

Solar Multiple

Solar Multiple

Solar Multiple

Cents/kWh GWh

GWh Cents/kWh

12

Thermal Storage Capacity is the next variant. The examined capacity is between 0 and 12 hours for S.M of 1.

Athens appears to have almost the same electric output for 4h, 6h and 12h storage capacity. But a great difference in the LCOE is seen between them. So the 4h is the best thermal storage as it combines the same output in the lower price of electricity. The same thing happens with Thessaloniki and Andravida as well. And the best combination for them is the 4h storage.

Figure 14. Storage Capacity-LCOE and Annual Output for Greece-S.M:1

In Spain the electric outputs are very high and they exceed the 100GWh. Similar to the Greek climates, in Spanish cities the best case is the one of the 4h storage capacity, where the LCOE is very close to the case without storage and the power output is annual output similar to the one of the 12h thermal storage.

Figure 15. Storage Capacity-LCOE and Annual Output for Spain-S.M:1

In Cagliari and Brindisi the results are disappointing, as the LCOE are very high. Palermo case has much better results, close to those of Athens. Again the best combination for the three locations is the one for thermal storage of 4h.

0

20

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120

140 LCOE (real-w/o incentives)

Annual Output (GWh)

0

20

40

60

80

100

120


Annual Output (GWh)

13

Figure 16. Storage Capacity-LCOE and Annual Output for Italy-S.M:1

In the Solar Multiple variations, it is noticeable that in most of the cases the LCOE (real without incentives) takes its minimum value when S.M is 2.5. In some others the unit cost of electricity is the optimum for S.M of 3, but with very small difference than in the price that it gets for S.M of 2. So, for optimizing the size of the field it is interesting to simulate the Storage Capacity for a S.M of 2.5. In these graphs LCOE real is also presented, in which the unit cost of energy considers the incentives.

Figure 17 presents the thermal storage capacity variations for the parabolic trough model in Greece. For Athens and Andravida, solar multiple of 2.5 was the optimum and the results for the electric output become much higher with the storage variations. Athens combines the lower LCOE with and without incentives and the better annual output. The prices of electricity remains almost the same, but the output makes the difference. Thessaloniki and Andravida have similar behaviour. The optimum thermal storage capacity for Athens and Andravida is 12h and Thessaloniki is 6h, but the results with 12 h are very close.

Figure 17. Storage Capacity -LCOE and Annual Output for Greece-S.M:2.5

0 20 40 60 80

100 120 140 160 180 LCOE (real-w/o

incentives)

Annual Output (GWh)

0

50

100

150

200

250


Annual Output (GWh)

LCOE real

14

In Figure 18, there is the performance of the parabolic trough system in the four Spanish locations. The power outputs for 12h exceed the 300GWh and the LCOE remain in very low levels. All the cases have their best combination for this storage capacity.

Figure 18. Storage Capacity-LCOE and Annual Output for Spain-S.M:2.5

In Figure 19, the best combination of solar multiple and thermal storage for Palermo is in 12h. In Cagliari and Brindisi the optimum solar multiple was for 3 and not for 2.5. The expected results are not supposed to be good and the best case for them is this of the 6h storage.

Figure 19. Storage Capacity-LCOE and Annual Output for Italy-S.M:2.5

0

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350


Annual Output (GWh)

LCOE real

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Annual Output (GWh)

LCOE real

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The Installation cost for the previous cases is displayed in Figures 20-22. This cost differs from case to case, but remains the same between the countries (locations).

Base Case

The Installation cost is US $ 514.53 million for all the locations.

Solar Multiple Variations (Thermal Storage Capacity: 6h)

The solar multiple variations have great influence on the capital cost. From solar multiple of to 1 to 3, the installation cost is almost the double. The other solar multiple values correspond to high initial costs too.

Figure 20. Installation cost for different values of Solar Multiple (S.M)

Thermal Storage Capacity for S.M:1

In the thermal storage capacity variations, the initial cost does not increase as much as in solar multiple. For example in Figure 21 the increase between the storage of 6h and 12h is 173.72 million$ and the capital cost does not exceed the 700 million$.

Figure 21. Installation cost for Storage Capacity variation

514.53

726.96

833.17

939.38

0 200 400 600 800 1000

1

2

2.5

3

340.82

456.63

514.53

688.25

0 200 400 600 800

0h

4h

6h

12h

Solar multiple

Million $

Installation Cost

Installation Cost

Million $

16

Thermal Storage Capacity for S.M:2.5

Figure 22 refers to the intial cost of the combinations between the 2.5 solar multiple and the thermal storages. Here the capital costs are the higher as they correspond to better results.


659.45

775.27

833.17

1006.89

0 200 400 600 800 1000 1200

0h

4h

6h

12h

Million $

Installation Cost

17

2.2.2 Linear Fresnel Mirrors The following graphs are the base cases for the Linear Fresnel Mirrors model. The next simulations are for 100MW Nominal Power.

In Figure 23, the LCOE for the three Greek cities is presented and in Figure 24 are the related annual power outputs. The LCOE for the base cases are lower than in parabolic trough and the annual outputs as well. Athens has the best performance, the Thessaloniki and in the end Andravida. The power outputs are relatively low even for Athens which is the most suitable Greek location for CSP systems.

Figure 23. Base Case-LCOE for Greece Figure 24. Base Case-Annual Output for Greece

The LCOE in Figure 25 for the Spanish cities is lower than in the base cases for parabolic trough model. Again the annual output is not so good. Moreover in the previous simulations Malaga’s and Alicicante’s results were almost similar. But in Linear Fresnel Mirrors, Malaga performs much better. Also in Sevilla is the less efficient system, but the difference in the LCOE between the four locations is not as big as in parabolic trough’s simulations, due to the low power outputs and the less efficient system.

Figure 25. Base Case-LCOE for Spain Figure 26. Base Case-Annual Output for Spain

54.2

67.7 75.4

0

10

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40 50 60

70 80


LCOE (real-w/o incentives)

66.3

53.0 47.5

0

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70


Annual Output (GWh)

42.9

46.3

45.1 45.0

41

42

43

44

45

46

47



83.8

77.6

79.7 79.9

72

75

78

81

84

87


Annual Output (GWh)

Cents/kWh GWh

Cents/kWh GWh

18

In Figure 27, Palermo reaches a relatively low LCOE and the other two Italian cities have high unit costs of electricity. The annual outputs are low, like in the other countries. Palermo can be characterized a bit worse in its performance compared to Athens. Cagliari and Brindisi do not have any other limits for lower power outputs as their results are already bad. In this case Linear Fresnel Mirrors is not a good choice for these locations.

Figure 27. Base Case-LCOE for Italy Figure 28. Base Case-Annual Output for Italy

The presentation of results for Linear Fresnel Mirrors begins with the effect on LCOE of a variation in the Solar Multiple. The results for LCOE (real without incentives) and Annual Output are in the next graphs.

In Figure 28, the minimum LCOE is succeeded in the case with a solar multiple of 2 for all the Greek locations. But the difference with the electricity costs of the other solar multiples is not big. In Figure 29 is the annual output which increases sharply with the solar multiple changes, but still the results are not as good as in parabolic trough.

Figure 29. Solar Multiple-LCOE for Greece Figure 30. Solar Multiple-Annual Output for Greece

109.3

55.0

90.0

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120



32.7

65.2

39.8

0

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Annual Output (GWh)

35.9 37.1 39.3

41.4 41.7 43.5

48.6 49.7 51.8

30

35

40

45

50

55

1.5 2 2.5 3 3.5


149.9

168.9

181.6

130.0

150.1

163.8

110.7

125.9

137.5

100

110

120

130

140

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170

180

1.5 2 2.5 3 3.5


Cents/kWh

Solar Multiple

Cents/kWh GWh

Solar Multiple

GWh

19

In all the Spanish climates LCOE fluctuates in the same way as in Greece (Figure 31). The minimum value it gets is for solar multiple of 2 and between 2 and 3 the difference in price of electricity is almost 2 cents/kWh. In Figure 32, the electric output increase is great but no value exceeds the 230GWh. It is also significant to mention that Toledo has the worst performance and not Sevilla, as in the rest of the simulations. The best case will be determined from the lowest LCOE and this is for solar multiple of 2.

Figure 31. Solar Multiple-LCOE for Spain Figure 32. Solar Multiple-Annual Output for Spain

Here it is obvious that Palermo’s performance is outstanding compared to the other climates. Cagliari and Brindisi have their minimum LCOE in solar multiple of 2.5, while Palermo’s minimum is in 2. In Figure 34 the greater increase in the annual output is seen for Palermo, then in Brindisi and finally in Cagliari.

Figure 33. Solar Multiple-LCOE for Italy Figure 34. Solar Multiple- Annual Output for Italy

29.8

30.6

32.1

31.1

31.9

33.8

31.6

32.6

34.3

30.4 31.1

32.6

29

30

31

32

33

34

35

1.5 2 2.5 3 3.5


180.8

204.8

222.4

173.2

196.3

211.7

170.5

192.5

208.3

177.2

201.8

219.0

165

175

185

195

205

215

225

1.5 2 2.5 3 3.5


57.5 55.1 55.8

36.6 37.6 39.7

52.5 52.0 53.5

30

35

40

45

50

55

60

1.5 2 2.5 3 3.5


93.3

113.4

127.7

147.3

166.6

179.7

102.3

120.2

133.3

85

95

105

115

125

135

145

155

165

175

185

1.5 2 2.5 3 3.5


Solar Multiple

Cents/kWh

Solar Multiple

GWh

Solar Multiple

Cents/kWh

Solar Multiple

GWh

20

The power cycle always operates at its design point, but the produced energy from the solar field is not always enough for these standards. For this reason there is a backup system which replaces the amount of the missing energy. The fossil fill fraction refers to this amount of energy which fills the boiler.

For Solar multiple of 1, a percentage of auxiliary fractions from 0 to 75% will be tried in the system. In SAM it is called Fossil Fill Fraction and operates as a backup system, when the delivered solar energy is not enough to drive the power cycle at full load. This is the case of auxiliary fraction equal to zero. The auxiliary energy comes from a burner boiler and when the delivered solar energy drops below a certain fraction (which will be studied), the boiler supplies the rest of the required energy to reach the desirable.

The most interesting case is the one with the fossil fraction of 0.25, because fractions of 0.5 and 0.75 are very high to be used. The purpose of the project is to use the fossil fuel as a backup system and not as the main which will produce the most of the energy. So, the case of 0.25 fossil fuel fraction is the one which will be discussed. Between 0 and 0.25 fossil fraction, there is a great difference in the annual output. In all the Greek cities it exceeds the 150GWh. More specifically, in Athens it reaches 200GWh and in the other two climates it is very close to this value.

Figure 35. Fossil Fraction-LCOE and Annual Output for Greece In Figure 36 is the hybridization of the Linear Fresnel Mirrors system with fossil fuel fraction for Spain. All the Spanish locations exceed 200GWh power output in the case of 0.25 fossil fractions. For higher percentage of fossil fractions, the annual outputs become almost the same, as the generated energy from the fossil fuel sources are much more than the energy of the CSP system.

Figure 36. Fossil Fraction-LCOE and Annual Output for Spain

0 100 200 300 400 500 600 700


Annual Output (GWh)

LCOE real

0

100

200

300

400

500

600


Annual Output (GWh)

LCOE real

21

After the fossil fuel source integration in the hybrid system, the LCOE in Brindisi and Cagliari decrease significantly. It is noticeable that for 0.25 and for higher percentage of fossil fraction the annual outputs become the same for all the cases.

Figure 37. Fossil Fraction-LCOE and Annual Output for Italy

0

100

200

300

400

500

600


Annual Output (GWh)

LCOE real

22

The determination of the installed cost for the case studies follows:

Base Case

The Installation cost is US $ 281.30 million for the 100MW nominal capacity plants for all the locations. Compared to the parabolic trough installation cost for the base case, which is US $ 514.53 million, it is almost the half. But the results in the annual outputs are not as good as in the previous CSP system. Parabolic trough succeeded its maximum annual output for Malaga with 122.1 GWh and the maximum power output for Linear Fresnel Mirrors is for Malaga again and corresponds to 83.8 GWh.

Solar Multiple Variations

The solar multiple is the factor that causes the higher expenses in a plant. In the Linear Fresnel Mirrors case the cost is relatively low and even the case of solar multiple of 2.5 has similar cost to the parabolic trough base case.


Fossil Fill Fraction

The total capital cost is not affected from the variation of the fossil fill fraction and remains constant. It is the same as the base case (281.30 million$). This is because SAM does not consider the fossil backup system in its financing and gives 0$/kWe for it in the direct capital cost. But the fossil fuel cost will increase the operation cost during project’s lifetime.

450.27

533.32

616.37

0 200 400 600 800

2

2.5

3

Solar multiple Installation Cost

Million $

23

2.2.3 Molten Salt Power Tower Here are the Base Cases for the Molten Salt Power Tower Model. Like the previous CSP systems,

Molten Salt Power Tower plant has 100MW capacity. In the Molten Salt Power Tower, the Solar Multiple variations are not simulated. This is because it does not consider the heliostat field. SAM software provides a fixed Power Tower model, where the S.M is 1.9. For a possible change of that, the solar field layout (the heliostat scale) remains the same, as the software does not provide another layout. This is one of the SAM’s limitations as it was mentioned in the methods section. For this reason this CSP system will be simulated with S.M of 1.9.

Figure 39 shows the unit cost of electricity and Figure 40 presents the annual power outputs for the Power Tower system in Greece. Athens has the best performance, reaching 227.3GWh and the lowest electricity price. The second location is Thessaloniki, where the results are close to these of Athens and last is Andravida, which has the lower annual output. The power outputs are much higher than these of Parabolic Trough and Linear Fresnel Mirrors, because of the different solar multiple.

Figure 39. Base Case-LCOE for Greece Figure 40. Base Case- Annual Output for Greece

Molten Salt power Tower results are very good in Spanish cities. The minimum power output is the one of Sevilla and the maximum this of Alicante. The LCOE fluctuate from 24 to 25.9 cents/kWh which is the lowest price of electricity so far.

Figure 41. Base Case-LCOE for Spain Figure 42. Base Case- Annual Output for Spain

31.2 35.5

43.7

0

10

20

30

40

50



227.3 199.8

161.9

0

50

100

150

200

250


Annual Output (GWh)

24.1

25.9

24.9

24.0

23

23.5

24

24.5

25

25.5

26

26.5



295.1

274.0

285.3

296.1

260

270

280

290

300


Annual Output (GWh)

Cents/kWh

Cents/kWh

GWh

GWh

24

In Figure 43, Palermo has almost the same LCOE with Athens and its annual output is very close to the 227.3GWh of Athens. Cagliari and Brindisi take their lowest values of LCOE in this CSP system. Comparing them to the other base cases the LCOE are not so bad. But since power tower refers to a solar multiple of 1.9, these prices are high.

Figure 43. Base Case-LCOE for Italy Figure 44. Base Case- Annual Output for Italy

The results for the Thermal Storage Capacity variation are shown in the next charts.

In Figure 45 it is obvious that the cases without any storage have negative effects in the annual outputs. In the rest of the cases, the annual output is much higher but does not have great difference in GWh between 4h, 6h and 12h storage. In Athens, the LCOE remains the same between 4h and 6h. There is an increase in LCOE for the 12h storage, since this size is too big for the given size of the solar field. In Thessaloniki and Andravida, it happens the same for the 12h storage.

Figure 45. Storage Capacity-LCOE and Annual Output for Greece

60.4

31.7

51.7

0

10

20

30

40

50

60

70



117.0

223.7

136.7

0

50

100

150

200

250


Annual Output (GWh)

38 31 31 34 41 35 35 38 51 43 44 47

26 21 22 23 29 24 25 27 35 30 30 33

0

50

100

150

200


Annual Output (GWh)

LCOE real

Cents/kWh GWh

25

In Figure 46, the Spanish locations have more extreme increase in their power outputs as the storage capacity become larger. In Malaga, Toledo and Alicante the annual output increases rapidly from 4h to 6h storage. On the contrary, this does not happen to Sevilla and in Greece, which had their maximum output for 6h storage but with very small difference compare to 4h and 12h. Also, it is noticeable that in Toledo the LCOE reach its minimum in the case for 6h thermal storage.

Figure 46. Storage Capacity-LCOE and Annual Output for Spain

In Figure 47, the annual outputs for Palermo are far better than those of the other two Italian cities and they exceed 200GWh. Cagliari and Brindisi do not reach even 150GWh, which proves their bad performance. The LCOE of Palermo is equal to this of Athens for 4h. But this value does not correspond to the optimum case, which is the one of the 6h storage and it has the highest output.

Figure 47. Storage Capacity-LCOE and Annual Output for Italy

30 24 24 26 32 26 26 28 31 26 25 27 30 24 24 26 21 17 17 18 22 18 18 19 22 18 17 19 21 17 17 18

0

50

100

150

200

250

300


Annual Output (GWh)

LCOE real

62 59 60 65

38 31 32 34 57 50 52 56

43 41 42 45 27 22 22 24

40 35 36 39

0

50

100

150

200


Annual Output (GWh)

LCOE real

26

The determination of the installed cost for the case studies follows:

Base Case

The Installation cost is US $ 610.95 million for all the locations. As it was mentioned before, this price corresponds to a solar multiple of 1.9. The previous CSP systems of solar multiple of 2 had the following installation costs. Linear Fresnel Mirrors had US $ 450.27 million and Parabolic Trough had US $ 726.96. The Power Tower has lower installation cost and it provides better results than Parabolic Trough.

Thermal Storage Capacity Variation

The installation cost is not as high as it was expected when the thermal storage capacity becomes the double, from 6h to 12h. In the previous CSP systems, the increased amount of money was greater


557.01

592.97

610.95

664.89

500 550 600 650 700

0h

4h

6h

12h

Installation Cost

Million $

27

2.2.4 Dish Stirling In the Dish Stirling model, the variation of the Solar Field size and the layout of the network will be expressed in Solar Multiple which corresponds to different capacity. Dish Stirling consists of a Stirling Engine, which is a closed-cycle engine and operates individually. The unit nameplate capacity of each Dish is 25 kW. There will be a variation in the number of collectors and in the total solar field area. The primary number of collectors is 4000 and corresponds to a total capacity of 100MW. For these values the Base Cases are the followings.

The base cases for Greece are presented in Figure 49 for the LCOE and in Figure 50 for the annual output. The produced energy for Athens is 113.4GWh, which is the highest so far for solar multiple of 1. Also Thessaloniki and even Andravida have reached very high power outputs.

Figure 49. Base Case-LCOE for Greece Figure 50. Base Case- Annual Output for Greece

In Figure 51 the Spanish LCOE are just a little bit higher than these of Molten Salt Power Tower. Moreover, the power outputs in Figure 52 are very high, with Toledo having the lowest value this time instead of Sevilla. Also, Malaga is the most efficient location for the Dish Stirling system.

Figure 51. Base Case-LCOE for Spain Figure 52. Base Case- Annual Output for Spain

31.1 35.3

39.6

0

10

20

30

40

50



113.4 99.6

81.4

0

20

40

60

80

100

120


Annual Output (GWh)

25.2

25.7

26.1

24.9

24

24.5

25

25.5

26

26.5



140.1

137.4

135.3

141.9

132

134

136

138

140

142

144


Annual Output (GWh)

Cents/kWh GWh

Cents/kWh GWh

28

From the Italian cities only Palermo brings good results, with power output which exceeds the 100GWh per year in a low LCOE. In Cagliari and Brindisi, the main issue is the annual output which is very low. It is higher than Linear Fresnel mirrors, but still very low.

Figure 53. Base Case-LCOE for Italy Figure 54. Base Case- Annual Output for Italy

In this case the solar multiple does not refer to the power cycle, since it does not exist. The total number of the dish collectors will be changed. According to this number the solar field area and the electrical output of the solar field are changed automatically. Before the simulations it is important to present the solar multiple which is expressed in total number of collectors.

Table 2. Capacity and number of collectors expressed in Solar Multiple

Solar Multiple Total Number of Collectors Nominal Electric Output(MW)

1 4000 100

2 8000 200

2.5 10000 250

3 12000 300

So the annual output and the LCOE with and without the influence of the incentives are shown below. The increase of the produced energy is very high as well as the GWh that Dish Stirling succeeds. It is quite interesting that the LCOE remain the same while the annual output increases sharply and this is the factor that makes this CSP system so competitive.

Figure 55. Solar Multiple variation-LCOE and Annual Output for Greece

59.2

31.6

54.5

0

10

20

30

40

50

60

70



59.3

111.6

64.4

0

20

40

60

80

100

120


Annual Output (GWh)

31 31 31 31 35 35 35 35 40 43 43 43 23 23 23 23 26 26 26 26 31 31 31 31

0

50

100

150

200

250

300

350


Annual Output (GWh)

LCOE real

Cents/kWh GWh

Solar Multiple

29

The power output values exceed 400GWh and the LCOE are as low as in Power Tower. Especially in Spain, the climates have the potential to produce a great amount of electricity and these are the best results of all the CSP systems in this project. As it was mentioned before, the comparison is not valid since the solar multiples for 2, 2.5 and 3 do not refer in the same nominal power. But still the results for the base case are impressing.

Figure 56. Solar Multiple variation-LCOE and Annual Output for Spain

In Figure 57, the Italian simulations for different thermal storages are presented. Palermo has similar performance to Athens as it was noticed in the previous CSP systems. The power outputs and the LCOE without incentives are just a little bit higher in Athens. But the LCOE with incentive is quite the same. Cagliari has really low annual output in the case of 1 solar multiple. After the increase in the solar field, the output raises. The same happens to the Brindisi and both of them keep their LCOE stable like in the other locations.

Figure 57. Solar Multiple variation-LCOE and Annual Output for Italy

25 25 25 25 26 26 26 26 26 26 26 26 25 25 25 25 18 18 18 18 19 19 19 19 19 19 19 19 18 18 18 18

0

50

100

150

200

250

300

350

400


Annual Output (GWh)

LCOE real

59 59 59 59 32 32 32 32

54 54 54 54 43 43 43 43

23 23 23 23 40 40 40 40

0

50

100

150

200

250

300

350

400 LCOE (real-w/o incentives) Annual Output (GWh) LCOE real

Solar Multiple

Solar Multiple

30

Determination of the installed cost:

The installation cost in Dish Stirling for solar multiple of 1 is quite the same to this of the Linear Fresnel Mirrors in the base cases. But it is much lower from the one of the Parabolic Trough. This CSP technology brings the best power outputs in the lowest LCOE and with a relatively low capital cost.


289.32

578.65

723.31

867.97

0 200 400 600 800 1000

1

2

2.5

3

Solar Multiple Installation Cost

Million $

31

2.2.5 Planar PV Power Plants The Flat plate PV system and its components are based in Sandia models for PV modules and inverters (SAM Help Contents NREL: Performance Models). Some physical properties of the modules are presented in Table 3.

Table 3. Module’s property characteristics

Physical characteristics Material Mono-c-Si

Efficiency 18.71%

Max Power Voltage 54.7V

Max Power Current 5.58A

Max Power 305.226W

Module Area 1.631 m2

Number of Cells 96

It is also important to refer to the PV array sizing. Table 4 includes some interesting parameters about the desired layout.

Table 4. Parameters for the desired Layout

Array Sizing

Total Number of Modules 327624

Modules per String 8

Strings in Parallel 40953

Number of Inverters 301

Desired Array Size 100MWdc

The next simulations are provided in order to be compared with the previous CSP systems. In the case of the photovoltaic PV the Annual Output decreases gradually during the lifetime of the project. Since the useful life of the installation is 30 years, they will be presented three values of the Annual Output for each location. They will correspond to the 1st, the 15th and the 30th year. It is also significant to highlight that the Array Size is 100MW.

To begin with Greece, the LCOE are really low and the annual outputs relatively high, especially for Athens and Thessaloniki. Even after 30 years they exceed the 100GWh. In this system there are two points which should be noticed. Thessaloniki and Athens do not have a great difference in their annual outputs when in all the CSP systems the difference was significant and Athens’ results were far better than Thessaloniki’s. In Andravida the results are not as good as the other cities, but they are satisfactory and much better than in most of the CSP systems base cases.

Figure 59. LCOE with and without incentives for Greece

22.2 22.8

25.4

21.2 21.8

24.3

19

20

21

22

23

24

25

26



LCOE real

Cents/kWh

32

Figure 60. Annual Output for the 1st, 15th and 30th year for Greece

In Spain the most appropriate location for a PV plant is Alicante and then Malaga. The third in the queue is Sevilla and finally Toledo. But the LCOE and the prices show that the performance in the four climates in good and the results are quite similar. Toledo is the last in performance due to its high elevation, but more details about this will be discussed in the Conclusion and discussion section. During 30 years lifetime, the PV lose more than 20GWh of their power output.

Figure 61. LCOE with and without incentives for Spain

148 138

128 144

134 125 129 121 112

0 20 40 60 80

100 120 140 160

Annual Output (GWh)

19.9

20.3

20.7

19.6

19.0

19.4 19.8

18.7

17.5

18

18.5

19

19.5

20

20.5

21



LCOE real

Cents/kWh

Years

GWh

33

Figure 62. Annual Output for the 1st, 15th and 30th year for Spain

In Figure 63 and 64 are the results for the Italian cities. The PV system in the three Italian locations performs much better that it was expected. Palermo LCOE is better than these of the Greek cities and its power output is higher, even from Athens which always performed better in the CSP systems. Cagliari and Brindisi did not perform well in any of the simulated CSP systems, but here their results are not so bad.

Figure 63. LCOE with and without incentives for Italy

166 154

143 162

151 140

159 148

137

168 157

145

0

20

40

60

80

100

120

140

160

180

Annual Output (GWh)

27.7

21.8

27.5 26.5

20.8

26.3

0

5

10

15

20

25

30



LCOE real

Cents/kWh

Years

GWh

34

Figure 64. Annual Output for the 1st, 15th and 30th year for Italy

The Total Installed Cost for this plant is US $ 370.52 million.

119 111

103

151 151

130 120

112 104

0

20

40

60

80

100

120

140

160

Annual Output (GWh)

Years

GWh

35

2.2.6 High-X Concentrating PV An other photovoltaic PV system which will be examined is the High-X Concentrating PV. This type of system requires a higher capital cost due to the trackers and it also requires a cooling system in order to avoid losses. Despite of that, there are savings in land area and in PV modules. For the simulated installation the studied modules have the next characteristics which are presented in Table 5.

Table 5. High Concentration Photovoltaic Module’s property characteristics

Physical characteristics

Maximum Power 375.553kWdc

Module Efficiency 29.8058%

Number of Cells 20

Concentration Ratio (lens area/cell area) 700X

Module Area 1.4m2

The array configuration is described in Table 6. Again it is important to mention that the system nameplate capacity is 100MWdc.

Table 6. Array Configuration

Array Sizing

Number of Trackers 1780

Modules on each Tracker 150

Number of Inverters 293

System Nameplate Capacity 100MWdc

The LCOE and the annual output are presented in Figure 65 and 66. To begin with the power outputs are lower than these of the PV plant. Also the LCOE is high enough and very close to some of the base cases of CSP systems, like the parabolic trough. The results of the outputs for Athens and Thessaloniki are not bad, but the further decrease of the produced energy leads to lower outputs, which does not make them competitive in long run.

Figure 65. LCOE with and without incentives for Greece

61.9 70.2

86.2

41.2 46.8 57.4

0

20

40

60

80

100



LCOE real

Cents/kWh

36

Figure 66. Annual Output for the 1st, 15th and 30th year for Greece

In Spain the LCOE are not so high and this time Sevilla has the worst performance. Also, the annual outputs are satisfactory. The PV plant reaches much higher values. It is noticeable that Alicante produces energy of 154GWh the first year of its operation and Malaga 149GWh, which are closer to the 146GWh of Toledo. So Malaga does not perform so well in the Concentrating PV system.

Figure 67. LCOE with and without incentives for Spain

Figure 68. Annual Output for the 1st, 15th and 30th year for Spain

113 106 98 100 93 86 81 76 70

0 20 40 60 80

100 120

Annual Output (GWh)

47.1 52.0

47.9 45.6

31.4 34.7 31.9 30.4

0

10

20

30

40

50

60



LCOE real

149 139 129 135 126 116 146 136 127

154 143 133

0

50

100

150

200

Annual Output (GWh)

Cents/kWh

Years

Years

GWh

GWh

37

The last simulations are about Italy and they are presented in Figure 69 and 70. The results are disappointing for all of the locations. Even in Palermo which exceeded Greece outputs in the PV plants simulations. Actually, the power outputs are quite the same between Palermo and Athens. The total installed cost is US $ 170.58 million higher than the PV plant and the results are much worse for all the countries and all the climates.

Figure 69. LCOE with and without incentives for Italy

Figure 70. Annual Output for the 1st, 15th and 30th year for Italy

The Total Installed Cost for this plant is US $ 541.1 million.

102.6

62.3

95.7

68.4 41.5

63.7

0

50

100

150



LCOE real

68 64 59

113 105 97 73 68 63

0 20 40 60 80

100 120

Annual Output (GWh)

Cents/kWh

Years

GWh

38

3. Results In this section a detailed analysis of the simulations is presented. This includes matrices in the aid of which the results will be determined. The comparisons will be made according to the following sequence. The first step is to select the system with the best performance for every CSP technology. Then for each location a certain CSP system will be optimized. The best CSP system variation for every climate will be compared both with the base cases and the other locations. After that, the optimized CSP systems for each location will be compared to determine the most suitable and cost effective system.

The procedure of the comparison will be made according to the following criteria. In the next matrices the best results for each variation are presented. The determination of the optimum solar multiple will be made according to the power output and LCOE. The case which reaches the maximum annual output and the lower LCOE will be the best one. In many cases the results are clear since the LCOE reaches its minimum for a certain value of the solar multiple.

In the case of the selection of the thermal storage capacity things are quite different, as almost all the values for the power output are proportional to the storage hours increase. The decision will be made according to the maximum difference between the LCOE and the annual output.

Moreover, they will be presented some more possible cases. In the Linear Fresnel Mirrors, there is the fossil fraction combination. This case is not presented in order to be compared with the other technologies but in order to be discussed and to show the fluctuation of the LCOE and the annual output.

In the Dish Stirling case, the base case corresponds to a nominal electric output of 100MW and can be compared with the other CSP technologies. Also, in the related matrices the case of the optimum solar multiple is presented as well. But it is not comparable. This is because an increase in the Solar Multiple is accompanied with an increase in the nominal electric output. From the related simulations, the solar multiple increase is proportional to the nominal electric output. It is important to mention that the LCOE (with incentives and without) remains constant with the solar multiple variations. So the best results are those of the higher solar multiple, which is 3.

Tables 19 and 20 show the base cases comparison and Tables 21 and 22 present the optimum scenarios comparison. It is significant to mention that the value of the Annual Output for the Flat Plate PV is this of the 15 year, which is supposed to present an average value of the Annual Output during the 30 years life time of the plant.

39

3.1 Parabolic Trough

Table 7. Comparison of Parabolic Trough’s simulations for Greece

GREECE

Units Base Case Optimum S.M

Optimum T.S (S.M=1)

Optimum T.S (S.M=2.5)

Athens

LCOE without incentives

Cents/kWh 65.42

(2.5) 38

(4h) 59

(12h) 43

LCOE with incentives

Cents/kWh - - - 30

Annual Output

GWh 92.39 248 92 260

Thessaloniki


Cents/kWh 79.92 (2.5) 44

(4h) 72

(6h) 44


Cents/kWh - - - 30

Annual Output

GWh 75.56 214 76 214

Andravida


Cents/kWh 90.69 (2.5) 51.8

(4h) 82

(12h) 59


Cents/kWh - - - 40

Annual Output

GWh 66.56 182 67 191

Installation Cost

Million $ 514.53 833.17 (2.5)

456.63 (4h) 833.17 (6h) 1006.89(12h)

There are some points which do worth to be mentioned for the three Greek locations. The LCOE with incentives is the same for Athens and Thessaloniki in their optimum thermal storage hours for the same solar multiple (2.5).The LCOE without incentives is quite the same too. But the most important difference is in their annual output, which in Athens is 46 GWh more than Thessaloniki. Also, their optimum case corresponds in different storage hours. It is noticeable that the great decrease in the LCOE comes after the increase of the solar multiple.

On the contrary, Andravida’s results are quite bad. The unit cost of electricity is much higher than in the other climates. Despite the modifications in the solar field and the storage hours, the LCOE does not decrease enough. More specifically, it takes the same value as in Athens’ system for a solar multiple of 1. But parabolic trough in Athens receives further improvement, which Andravida’s does not.

To sum up, the capital cost for an installation with 12 hours storage capacity is US $ 173.72 higher than that of 6 hours storage. This expensive investment needs to receive high power outputs and climate is the key factor for it. Andravida is not definitely the location which can come up with these expectations. The other two cities have more efficient parabolic trough systems.

40

Table 8. Comparison of Parabolic Trough’s simulations for Spain

SPAIN


Optimum T.S (S.M=1)


Malaga


Cents/kWh 49.57

(2.5) 30.8

(4h) 47

(12h) 33


Cents/kWh - - - 23

Annual Output

GWh 122.1 307 122 338

Sevilla


Cents/kWh 56.25

(2.5) 32.7

(4h) 51

(12h) 37


Cents/kWh - - - 26

Annual Output

GWh 107.53 288.4 108 301

Toledo


Cents/kWh 51.9

(2.5) 32.7

(4h) 47

(12h) 35


Cents/kWh - - - 24

Annual Output

GWh 116.58 288.6 117 321

Alicante


Cents/kWh 50.23

(2.5) 31.1

(4h) 45

(12h) 34


Cents/kWh - - - 23

Annual Output

GWh 120.49 304.2 121 335

Installation Cost

Million $ 514.53 833.17 (2.5)

456.63 (4h) 1006.89 (12h)

Spanish climates have really high annual outputs, which makes all of them suitable locations for parabolic trough system from the beginning of their comparison. All of the above locations reach their optimum output in 12 hours thermal storage capacity. The LCOE fluctuates from 33 to 37 Cents/kWh with the lowest cost in Malaga and the higher in Sevilla. But after taking into consideration the incentives in the unit cost of electricity, the price becomes lower and same in the case of Malaga, Alicante and almost the same with them in the case of Toledo.

The annual power output exceeds the 300GWh, but the most interesting results are those of Malaga and Alicante. The changes in the solar field with a solar multiple of 2.5, produced much more energy than the double of outpout of the base cases. The unit cost of electricity was not as high as in the Greek cities and as a result, the decrease in the electricity price is lower.

It is worth noting that the in the case for the optimum thermal storage for solar multiple of 1, the results correspond to 4 hours. The base case is exactly the same but with storage of 6h. According to the simulations there are two significant points which need to be mentioned. The first is the slight increase in the annual output in most of the cases and the second is the decrease in the LCOE. Of course, there is a decrease in the Installation cost. Solar multiple and storage capacity are interdependent and their combination has been optimized in each case. Finally, the Installation cost is the similar to that of the 12 storage hours in Greece. The difference in the outputs in GWh is really big.

41

Table 9. Comparison of Parabolic Trough’s simulations for Italy

ITALY

Units Base Case Optimum S.M Optimum T.S (S.M=1)


Cagliari


Cents/kWh 122.95

(3) 59.9

(4h) 110

(6h) 64


Cents/kWh - - - 44

Annual Output

GWh 49.05 175.6 49 148

Palermo


Cents/kWh 66.12

(2.5) 38.6

(4h) 59

(12h) 44


Cents/kWh - - - 30

Annual Output

GWh 91.4 245 91 257

Brindisi


Cents/kWh 105.34

(3) 55

(4h) 95

(6h) 56


Cents/kWh - - - 39

Annual Output

GWh 57.27 191.4 57 167

Installation Cost

Million $ 514.53 833.17(2.5) 939.38 (3)

456.63 (4h)

833.17 (6h) 1006.89 (12h)

The unit cost of electricity in the base case for Cagliari and Brindisi exceeds 100Cents/kWh. Only in these two climates out of all, the price of electricity reaches such high values. In addition to this, the annual output is very low. It was mentioned above for the other two countries that the optimum solar multiple was 2.5 and higher values in the solar field area did not correspond to efficient systems. But in Cagliari and Brindisi the optimum solar multiple is 3, which means that there is an inverse relationship between the solar multiple and the LCOE. The best case appears to be the one with the maximum S.M (3) and storage capacity of 6 hours. The results are close to these of Brindisi, which had annual output 191GWh. But in that case the S.M was 2.5 and the storage was for 12 hours. Furthermore, these systems do not need 12 hours storage but their capital cost is very high. On the other hand Palermo’s results are much better. The annual output is close to that of Athens and the LCOE as well.

42

3.2 Linear Fresnel Mirrors

Table 10. Comparison of Linear Fresnel Mirror’s simulations for Greece

GREECE


Fossil Fraction

Athens


Cents/kWh 54.2

(2) 35.9

(0.75) 6


Cents/kWh - - 4

Annual Output

GWh 66.3 149.9 624

Thessaloniki


Cents/kWh 67.7

(2) 41.4

(0.75) 6


Cents/kWh - - 4

Annual Output

GWh 53 130 624

Andravida


Cents/kWh 75.4

(2) 48.6

(0.75) 6


Cents/kWh - - 4

Annual Output

GWh 47.5 110.7 623

Installation Cost

Million $ 281.30 450.27 281.30

Linear Fresnel Mirrors do not have any storage capacity in SAM and this is the system’s drawback. The annual outputs can only be optimized according to the solar field area. The results are much lower than those of parabolic trough. A possible increase in the solar multiple does not correspond to a high power output. Athens which has the potential of higher electricity production show very low values. The same happens with Thessaloniki. Andravida’s output is also very low. In parabolic trough simulations, the optimum output for Andravida exceeded the Athens case in this system, which proves the bad performance of L.F.M.

Also, Table 10 displays the hybridization of the system with fossil fuels. In the case of 0.75 fossil fraction all the cases become the same in power output and in cost. The annual output is extremely high and shadows the energy production from the examined CSP system. So, in this case only a small fraction of fossil fuel is acceptable for this combination. Comparing the annual output of the base cases with the fossil fraction combination, it is seen that the L.F.M systems contributes only in a very small part of the total generated energy. The initial cost may be the same, but the yearly expenses to fuel will be high. So definitely, this is not a positive alternative choice.

43

Table 11. Comparison of Linear Fresnel Mirror’s simulations for Spain

SPAIN


Fossil Fraction

Malaga


Cents/kWh 42.9

(2) 29.8

(0.75) 6


Cents/kWh - - 4

Annual Output

GWh 83.8 180.8 627

Sevilla


Cents/kWh 46.3

(2) 31.1

(0.75) 6


Cents/kWh - - 4

Annual Output

GWh 77.6 173.2 625

Toledo


Cents/kWh 45.1

(2) 31.6

(0.75) 6


Cents/kWh - - 4

Annual Output

GWh 79.7 170.5 629

Alicante


Cents/kWh 45

(2) 30.4

(0.75) 6


Cents/kWh - - 4

Annual Output

GWh 79.9 177.2 626

Installation Cost

Million $ 281.30 450.27 (2) 281.30

The performance of the Linear Fresnel Mirrors in Spanish cities is not so good similarly to the Greek locations. In Malaga is expected the higher electric output and it is only 222.4GWh for solar multiple of 3. In this case the values of the LCOE and the annual output are very close between the different climates.

The most important point of the L.F.M is that it does not have thermal storage and the installation cost is lower. But still, such an investment even in Spanish climates does not fulfil the expectations of an efficient and productive CSP system.

44

Table 12. Comparison of Linear Fresnel Mirror’s simulations for Italy

ITALY

Units Base Case Optimum S.M Fossil Fraction

Cagliari


Cents/kWh 109.3

(2.5) 55.1

(0.75) 6


Cents/kWh - - 4

Annual Output

GWh 32.7 113.4 622

Palermo


Cents/kWh 55

(2) 36.6

(0.75) 6


Cents/kWh - - 4

Annual Output

GWh 65.2 147.3 621

Brindisi


Cents/kWh 90

(2.5) 52

(0.75) 6


Cents/kWh - - 4

Annual Output

GWh 39.8 120.2 622

Installation Cost

Million $ 281.30 450.27 (2) 533.32 (2.5)

281.30

Extremely low outputs are presented in Table 12 for the performance of the L.F.M for the Italian climates for the given installation cost. Despite that, the LCOE in the base cases are lower than these of the parabolic trough. Cagliari and Brindisi perform better for a 2.5 solar multiple, as further increase in the solar field area would not produce sufficient extra energy to balance the electricity cost and more specifically to decrease it. Palermo has optimum solar multiple of three like the rest of the Mediterranean locations.

45

3.3 Molten Salt Power Tower

Table 13. Comparison of Molten Salt Power Tower’s simulations for Greece

GREECE

Units Base Case Optimum Thermal Storage S.M (1.9)

Athens


Cents/kWh 31.2

(6h) 31.2


Cents/kWh - 21

Annual Output

GWh 227.3 227.3

Thessaloniki


Cents/kWh 35.5

(4h) 35


Cents/kWh - 24

Annual Output

GWh 199.8 200

Andravida


Cents/kWh 43.7

(6h) 43.7


Cents/kWh - 30

Annual Output

GWh 161.9 161.9

Installation Cost

Million $ 610.95 592.97 (4h) 610.95 (6h)

To begin with, for the Power Tower there is a limitation of the design in the SAM software, as it was mentioned in the previous pages. For this reason any intervene on the solar field (structure or area) is not possible. For the given Power Tower system, only the thermal storage capacity was examined.

The simulation results for storage capacity of 6 hours are the optimum for the case of Athens and Andravida. Thessaloniki needs a smaller storage to reach its maximum. Athens and Andravida systems agree in their thermal storage, but always Andravida has the lowest annual outputs. In the case of Thessaloniki, the LCOE and the annual output remains the same. The only thing that changes is the capital cost, which has been decreased US $ 179.79. The results of Athens and Andravida appear to be good and they are close to those of parabolic trough.

The Installation cost is the one that makes this CSP system competitive compare to the other CSP technologies. The presented and highlighted capital cost is much lower than Linear Fresnel Mirrors and Parabolic Trough model. For the given installation costs the annual outputs are the highest so far. Parabolic trough model may succeed a more attractive output but the installation cost which corresponds to this electric output is much higher. In addition to the installation cost, the LCOE with incentives is very low, as well as the LCOE with incentives. Taking into account SAM’s limitations, low LCOE and relatively high outputs, Molten Salt Power Tower could be characterised as the most efficient system so far.

46

Table 14. Comparison of Molten Salt Power Tower’s simulations for Spain

SPAIN

Units Base Case Optimum Thermal Storage S.M (1.9)

Malaga


Cents/kWh 24.1

(6h) 24.1


Cents/kWh - 17

Annual Output

GWh 295.1 295

Sevilla


Cents/kWh 25.9

(6h) 25.9


Cents/kWh - 18

Annual Output

GWh 274 274

Toledo


Cents/kWh 24.9

(6h) 24.9


Cents/kWh - 17

Annual Output

GWh 285.3 285.3

Alicante


Cents/kWh 24

(6h) 24


Cents/kWh - 17

Annual Output

GWh 296.1 296.1

Installation Cost

Million $ 610.95 610.95 (6h)

The annual outputs for the Spanish cities approach the 300GWh. There are lower than the ones of parabolic trough, but the installation cost is again much lower. All the climates have their optimum storage for 6hours. The best location is Alicante this time, with 295.1 GWh. Malaga is the second climate with the greater performance and Toledo the third. The LCOE without incentives are very low taking the price of 17 or 18Cents/kWh.

47

Table 15. Comparison of Molten Salt Power Tower’s simulations for Italy

ITALY

Units Base Case Optimum Thermal Storage (S.M 1.9)

Cagliari


Cents/kWh 60.4

(4h) 59


Cents/kWh - 41

Annual Output

GWh 117 117

Palermo


Cents/kWh 31.7

(6h) 31.7


Cents/kWh - 22

Annual Output

GWh 223.7 223.7

Brindisi


Cents/kWh 51.7

(4h) 50


Cents/kWh - 35

Annual Output

GWh 136.7 137

Installation Cost

Million $ 610.95 592.97 (4h) 610.95 (6h)

Palermo’s performance is as good as Athens (227 GWh) and the price of LCOE with incentives reaches the US $ 0.22. Cagliari and Brindisi have again their optimum results for a smaller thermal storage capacity of 4 hours. The storage reduction does not change the electricity price and the electric output, but it causes a reduction in the installation cost, as it happens in Spanish climates.

48

3.4 Dish Stirling

Table 16. Comparison of Dish Stirling’s simulations for Greece

GREECE

Units Base Case Optimum Solar Multiple

Athens


Cents/kWh 31.1

(3) 31


Cents/kWh - 23

Annual Output

GWh 113.4 341

Thessaloniki


Cents/kWh 35.3

(3) 35


Cents/kWh - 26

Annual Output

GWh 99.6 299

Andravida


Cents/kWh 39.6

(3) 43


Cents/kWh - 31

Annual Output

GWh 81.4 245

Installation Cost

Million $ 289.32 867.97

The base cases of Dish Stirling are for nominal power output of 100MW, as it was described in the results introduction section. For this reason only the highlighted numbers concern the comparison. To begin with Athens, the annual output of this CSP system comparing with Parabolic Trough (92.39GWh) and Linear Fresnel Mirrors (66.3) is higher. Power Tower base case was simulated for a solar multiple of 1.9, so the comparison with this base case of solar multiple of 1 is not valid. The results of this technology are much better than the other CSP systems for the same installation cost.

It is very interesting to see the capabilities of a Dish Stirling installation. An increase in the solar multiple is accompanied with an increase in the nominal output, as the Dish Stirling is operating as an individual machine. For the maximum and optimum solar multiple of 3 that was simulated, the annual outputs are the highest of all the optimum cases of all the CSP technologies. The LCOE remains the same, but the power output exceeds even the optimum cases of parabolic trough. The highest output is seen in Athens with 341GWh. Comparing it with parabolic trough’s performance, the difference is 81GWh. Also the annual output for Athens is higher than the optimum output of Malaga in the parabolic trough system, which has higher levels of direct normal radiation and would be a more suitable climate for CSP systems. Even Andravida and Thessaloniki have very good performance with a low installation cost.

49

Table 17. Comparison of Dish Stirling’s simulations for Spain

SPAIN


Malaga


Cents/kWh 25.2

(3) 25


Cents/kWh - 18

Annual Output

GWh 140.1 421

Sevilla


Cents/kWh 25.7

(3) 26


Cents/kWh - 19

Annual Output

GWh 137.4 413

Toledo


Cents/kWh 26.1

(3) 26


Cents/kWh - 19

Annual Output

GWh 135.3 407

Alicante


Cents/kWh 24.9

(3) 25


Cents/kWh - 18

Annual Output

GWh 141.9 427

Installation Cost

Million $ 289.32 867.97

Similar to Greek results, the power outputs for the Spanish cities are impressing. Malaga and Alicante have the highest annual outputs of all the base case simulation. But the most impressing results are those for the solar multiple of 3. All the produced energy exceeds the 400GWh and the LCOE with and without incentives is really low. For the Power Tower model the optimum cases corresponded to a LCOE without incentives of 17 and 18. Here, the same fluctuates between 18 and 19. The installation cost is higher in Dish Stirling case, but the annual outputs are almost the half times larger. More specifically, for Alicante the produced energy is 427GWh and in 296GWh. But the base cases are the ones which determine the potential of this CSP system and the results are much better than in the previous technologies.

50

Table 18. Comparison of Dish Stirling’s simulations for Italy

ITALY


Cagliari


Cents/kWh 59.2

(3) 59


Cents/kWh - 43

Annual Output

GWh 59.3 178

Palermo


Cents/kWh 31.6

(3) 32


Cents/kWh - 23

Annual Output

GWh 111.6 335

Brindisi


Cents/kWh 54.5

(3) 54


Cents/kWh - 40

Annual Output

GWh 64.4 194

Installation Cost

Million $ 289.32 867.97

The results for Palermo are good for the base case. Despite of that, Cagliari and Brindisi do not perform so well. But the inappropriateness of these climates is strongly seen in the optimum case. The previous systems had annual outputs more than 244GWh and these climates cannot even reach 200GWh. Moreover, the LCOE are still very high.

51

3.5 Base Cases Comparison

Table 19. Comparison between the Base Cases in each climate for 100MW CSP systems

Units Parabolic Trough

Linear Fresnel Mirrors

Power Tower

Dish Stirling

PV High–X Concentra- ting PV

Athens


Cents/kWh

65.42

54.2

31.2

31.1

22.2

61.9


Cents/kWh

- - - - 21.1 41.2

Annual Output

GWh 92.39 66.3 227.3 113.4 138 106

Thessaloniki


Cents/kWh

79.92

67.7

35.5

35.3

22.8

70.2


Cents/kWh

- - - - 21.8 46.8

Annual Output

GWh 75.56 53 199.8 99.6 134 93

Andravida


Cents/kWh

90.69

75.4

43.7

39.6

25.4

86.2


Cents/kWh

- - - - 24.3 57.4

Annual Output

GWh 66.56 47.5 161.9 81.4 121 76

Cagliari


Cents/kWh

122.95

109.3

60.4

59.2

27.7

102.6


Cents/kWh

- - - - 26.5 68.4

Annual Output

GWh 49.05 32.7 117 59.3 111 64

Palermo


Cents/kWh

66.12

55

31.7

31.6

21.8

62.3


Cents/kWh

- - - - 20.8 41.5

Annual Output

GWh 91.40 65.2 223.7 111.6 151 105

Installation Cost

Million $ 514.53 281.30 610.95 289.32 370.5 541.1

52

Table 20. Comparison between the Base Cases in each climate for 100MW CSP systems



Power Tower

Dish Stirling


Brindisi


Cents/kWh

105.34

90

51.7

54.5

27.5

95.7


Cents/kWh

- - - - 26.3 63.7

Annual Output

GWh 57.27 39.8 136.7 64.4 112 68

Malaga


Cents/kWh

49.57

42.9

24.1

25.2

19.9

47.1


Cents/kWh

- - - - 19 31.4

Annual Output

GWh 122.1 83.8 295.1 140.1 154 139

Sevilla


Cents/kWh

56.25

46.3

25.9

25.7

20.3

52


Cents/kWh

- - - - 19.4 34.7

Annual Output

GWh 107.53 77.6 274 137.4 151 126

Toledo


Cents/kWh

51.9

45.1

24.9

26.1

20.7

47.9


Cents/kWh

- - - - 19.8 31.9

Annual Output

GWh 116.58 79.7 285.3 135.3 148 136

Alicante


Cents/kWh

50.23

45

24

24.9

19.6

45.6


Cents/kWh

- - - - 18.7 30.4

Annual Output

GWh 120.49 79.9 296.1 141.9 157 143

Installation Cost

Million $ 514.53 281.30 610.95 289.32 370.5 541.1

53

To begin with, the base cases comparison will exclude the Molten Salt Power Tower, as it does not refer to a solar multiple of 1 but of 1.9. In Table 19 and 20, the results of the three best technologies are highlighted. Power Tower is one of them and brings the highest annual outputs since it corresponds to a bigger size of solar field. Dish Stirling is the others CSP system with the higher outputs in low LCOE and here the comparison is valid since all the CSP systems have the same nominal power and the same size of solar field. In some locations like Cagliari, Brindisi, Andravida and Thessaloniki, Dish Stirling performs really badly. It is significant to mention that the results of the Dish Stirling are much better from the Parabolic Trough’s and Linear Fresnel Mirrors’ for all the climates. The climates which have the potential of a CSP system are the Spanish ones, Athens and in the end Palermo, where the power outputs exceed the 100GWh. Furthermore what makes Dish Stirling really competitive is its installation cost, which is far the lower one of the examined technologies. Parabolic Trough for example has nice performance and in many locations its outputs exceed the 100GWh, but its installation cost is the highest after Power Tower. But the cost of a Parabolic Trough system which refers to a solar multiple of 2 is 726.96 and it is a much more expensive system than Power Tower. So, Dish Stirling is the most efficient and the less expensive CSP technology.

Furthermore, as it was mentioned in the previous lines Parabolic Trough requires the highest capital cost. It is quite interesting to examine and to compare this system when its solar multiple is 2 with the Molten Salt Power Tower. The installation cost of the Parabolic Trough is 116.01 million$ higher and the produced energy is taken for three representative locations. Malaga and Alicante are always the climates with the higher energy output and for Parabolic Trough of solar multiple 2, they produce 307 and 304.2GWh. Cagliari is the less suitable location for a CSP system and it produces 115.4GWh. The Molten Salt Power Tower outputs are quite close to those numbers, but they are just a little bit lower. Taking into account the lower installation cost and the fact that Power Tower’s solar multiple is 1.9 and not 2, this CSP system is preferable than Parabolic Trough and can be characterized more efficient. In the end are the Linear Fresnel Mirrors which have low installation costs, low power outputs, but relatively high LCOE.

The last highlighted system is that of the PV plant which has better performance than Dish Stirling. Many locations which have been characterized inappropriate for a CSP plant perform really well in this system. This happens as the PV do not only consider the direct normal irradiation, but the diffuse as well. Finally, the High-X Concentrating PV system can be put into a middle order, as its outputs are between those of the efficient and the less efficient systems. But the fact that has so high capital cost does not make it competitive in front of the normal PV.

54

3.6 Comparison

The next step is the selection of the most suitable technology for each location and the discussion of the effects of its climate characteristics to its performance. The following matrices assemble the best cases in each CSP technology for every location.

Table 21. Determination of most suitable CSP system in each climate



Power Tower

Dish Stirling


Athens


Cents/kWh

43 (12h, S.M 2.5)

35.9 (S.M 2)

31.2(6h,S.M 1.9)

31 (S.M3)

27.5

95.7


Cents/kWh

30 - 21 23 26.3 63.7

Annual Output

GWh 260 149.9 227.3 341 112 68

Thessaloniki


Cents/kWh

44(6h, S.M 2.5)

41.4 (S.M 2)

35 (4h, S.M 1.9)

35 (S.M3)

19.9

47.1


Cents/kWh

30 - 24 26 19 31.4

Annual Output

GWh 214 130 200 299 154 139

Andravida


Cents/kWh

59 (12h, S.M 2.5)

48.6 (S.M 2)

43.7(6h, S.M 1.9)

43 (S.M3)

20.3

52


Cents/kWh

40 - 30 31 19.4 34.7

Annual Output

GWh 191 110.7 161.9 245 151 126

Cagliari


Cents/kWh

59.9 (6h, S.M 3)

55.1 (S.M2.5)

59 (4h, S.M 1.9)

59 (S.M3)

20.7

47.9


Cents/kWh

- - 41 43 19.8 31.9

Annual Output

GWh 175.6 113.4 117 178 148 136

Palermo


Cents/kWh

44 (12h, S.M 2.5)

36.6 (S.M 2)

31.7(6h, S.M 1.9)

32 (S.M3)

19.6

45.6


Cents/kWh

30 - 22 23 18.7 30.4

Annual Output

GWh 257 147.3 223.7 335 157 143

Installation Cost

Million $ 833.17(6h, S.M 2.5)

1006.8(12h,S.M2.5)

939.38(6h,

S.M 3)

450.27 (S.M 2)

616.37

(S.M 2.5)

592.97 (4h)

610.95

(6h)

867.97 370.52 541.1

55

Table 22. Determination of most suitable CSP system in each climate



Power Tower

Dish Stirling


Brindisi


Cents/kWh

55 (6h, S.M 3)

52 (S.M2.5)

50(4h, S.M1.9)

54 (S.M3)

27.5

95.7


Cents/kWh

- - 35 40 26.3 63.7

Annual Output

GWh 191.4 120.2 137 194 112 68

Malaga


Cents/kWh

33 (12h, S.M 2.5)

29.8 (S.M 2)

24.1(6h, S.M 1.9)

25 (S.M3)

19.9

47.1


Cents/kWh

23 - 17 18 19 31.4

Annual Output

GWh 338 180.8 295 421 154 139

Sevilla


Cents/kWh

37 (12h, S.M 2.5)

31.1 (S.M 2)

25.9(6h, S.M 1.9)

26 (S.M3)

20.3

52


Cents/kWh

26 - 18 19 19.4 34.7

Annual Output

GWh 301 177.2 274 413 151 126

Toledo


Cents/kWh

35 (12h, S.M 2.5)

31.6 (S.M 2)

24.9(6h, S.M 1.9)

26 (S.M3)

20.7

47.9


Cents/kWh

24 - 17 19 19.8 31.9

Annual Output

GWh 321 170.5 285.3 407 148 136

Alicante


Cents/kWh

34 (12h, S.M 2.5)

30.4 (S.M 2)

24 (6h, S.M 1.9)

25 (S.M3)

19.6

45.6


Cents/kWh

23 - 17 18 18.7 30.4

Annual Output

GWh 335 173.2 296.1 427 157 143

Installation Cost

Million $ 939.38(6h,S.M 3)

1006.8(12h,S.M2.5)

450.27 (S.M 2)

616.37

(S.M 2.5)

592.97 (4h)

610.95

(6h)

867.97 370.5 541.1

56

From Table 21 and 22 the three most efficient technologies for the optimum cases are highlighted. The first one is again Dish Stirling with power outputs which exceed the 400GWh in Spanish climates. Dish Stirling has the best performance in every location compared to the other systems, even the PV. But as it was mentioned before, the nominal power increases as the solar field becomes bigger. For the other CSP technologies the nominal power remains equal to 100MW. But the LCOE is low and even the installation cost for such high output is reasonable.

Parabolic trough is the second in the order with the higher power outputs. After the modifications in the solar field and the storage capacity, the outputs were promoted. Most of the climates correspond to a solar multiple of 2.5 or 3, which raises the initial cost a lot and the same time makes the comparison with the Power Tower invalid. Here it is significant to mention that climates with weak outputs always require higher solar multiples and lower thermal storage capacity.

To sum up, Linear Fresnel Mirrors are the last in the order and the PV and High-X Concentrating PV systems did not have any modification in their results and they remain the same. So, these three systems provide the lower amounts of energy and have relatively high LCOE.

57

4. Discussion and Conclusions The outstanding CSP technologies are the same, but the results vary significantly. In the next lines some climatic parameters will be discussed and they will be interpreted accordingly.

Toledo is the location with the highest elevation in 529m. This has an influence on the performance of the CSP systems. To begin with the climate characteristics, direct normal irradiation (DNI) in Toledo is 1926.9 kWh/m2. It is worth noting that DNI in Malaga is 1988.1 kWh/m2 and in Sevilla is 1772 kWh/m2. So, it is expected that the annual output will fluctuate more closely to the Malaga’s one rather than in Sevilla’s. But their main difference is their elevation and the fact that Malaga and Sevilla are situated a bit more southern than Toledo.

A more detailed investigation includes the average atmospheric pressures and the ambient air temperatures of these areas. According to (NASA), the annual average atmospheric pressure and the mean air temperature for the Spanish regions according to their latitude, longitude and elevation is in Table 23. SAM takes input hourly values of atmospheric pressure in mbar. The used weather files of energy plus include an ASHRAE Design Conditions Design Day Data file. NASA also provides weather files recommended from SAM in TMY3 format. They are same files with different formats from different sources, but the average values are expected to be similar and quite close.

Table 23. Ambient conditions for Spanish regions

Units Toledo Alicante Malaga Sevilla

Mean Air Ambient Temperature °C 15 18.5 19 19.2

Annual Average Atmospheric Pressure kPa 95.6 101 101 101

From the Table 23, it is clearly seen that in the 529m elevation the mean air ambient temperature and the annual average atmospheric pressure are relatively low. Even Sevilla where DNI levels are lower has better ambient conditions because of the elevation. But it is also interesting to compare Toledo with the rest of the climates, where the DNI is much lower than in Spanish cities.

Table 24. Ambient conditions for Greek and Italian cities

Units Athens Thessaloniki Andravida Cagliari Palermo Brindisi

Mean Air Ambient Temperature

°C

19

15.6

18.6

18.2

18.8

18.3

Annual Average Atmospheric Pressure

kPa

101

101

101

101

101

101

Table 24. Ambient conditions for Greek and Italian cities

Comparing Toledo with the rest of the climates in Table 24, it is seen that it has the lowest mean annual air temperature and atmospheric pressure. But this does not mean that this climate is inappropriate for CSP plants since it has sufficient DNI levels. But this is the reason why Toledo does not have the expected output, but a bit lower one.

Sevilla is the Spanish region with the lowest levels of DNI, which is the crucial factor for the CSP systems. The DNI resources are sufficient for a CSP plant, but not as high as the other three Spanish climates. The base case simulations show high values for the annual output. In some cases, like the Dish Stirling and the Planar PV plants, the power outputs are higher than in Toledo and in the other CSP technologies the results are close to Malaga’s and Alicante’s. Despite the fact that Sevilla has DNI of 1772.7 kWh/m2, it has another characteristic which promote and support the CSP performance. This is the low wind speeds. All the Spanish cities have mean wind speed of 6.7 m/s and Sevilla has only 2.7 m/s. Due to this fact, the efficiency of the solar fields is higher. Wind speed

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increases the convective losses of the heat transfer fluid and of the collectors (Lovegrove and Stein 2012).

Alicante is the location with the highest DNI, which reaches 2016.8 kWh/m2. So for this climate the expected results are supposed to be the best of all the cases. To begin with the base cases, Malaga’s annual output almost always exceeds that of Alicante’s. But this does not happen in the Planar PV plant and the High-X Concentrating PV cases, when the results are the expected ones. This means that other meteorological parameters affect the CSP performance there. Both locations are near the sea and they have the same ambient conditions. This includes similar wind speeds and relative humidity. From the Table 23, both regions have atmospheric pressure of 101kPa and almost the same mean air temperature. Their difference is on their elevation and the fact that Malaga is situated in a smaller latitude angle than Alicante.

From the Greek areas Athens is the most appropriate one for a CSP installation. Thessaloniki and Andravida shows quite low values of annual output and high unit costs of electricity. They can be described as inappropriate locations. Thessaloniki has low elevation, but the DNI is not as high as in the other locations and moreover it has the disadvantage of the low air temperatures.

Athens’ mean DNI is 1519.8 kWh/m2 and it is almost similar with Palermo’s, which is 1516.9 kWh/m2. The results are very close, with Athens slightly exceeding in CSP technologies. In Planar PV plant case, Palermo’s power output is higher. But this might happens because Palermo has higher latitude and the ambient air is colder than this of Athens and the particular conditions are better for a Photovoltaic plant, rather than a CSP system. In the end Cagliari and Brindisi can be described as non suitable locations for a CSP plant. The Power Outputs are the very low, even worse than Andravida’s.

After the comparison of the base cases, the outstanding technologies have proved to be the Molten Salt Power Tower and the Dish Stirling. They provide the highest annual outputs in the lowest LCOE. Actually, the annual output of the Power Tower is bigger than the one which corresponds in a solar multiple of 1 (this is S.M of 1.9). The third more efficient system is the Planar PV plant. But it is worth noting that the values of the Photovoltaic cases are those of the 15 year during the lifetime of the project and the annual output decreases in a long range.

But this classification mainly happens in the suitable climates for CSP systems. As it was mentioned before Andravida, Cagliari and Brindisi are not good places for building CSP plants. So, in these cases the sequence of the optimum results (power outputs) is different and the PV plant is in the first place. Parabolic Trough does not have such a good performance in the base cases, but after the optimization of the system it reaches really high values. One of its main advantages is the thermal storage capacity, which is directly connected with the solar multiple. In the last place is the High-X Concentrating PV system.

But there are some points that should be highlighted. In the system optimization simulations the LCOE in the Dish Stirling remains the same as in the base cases. This happens as the increase of the solar multiple is accompanied with the increase of the nominal power output. In this case the capital cost raises proportionally to the annual output and the derived LCOE is again the same.

The other point which has been noticed is that in the non suitable climates have a minimum optimum solar multiple than the rest of the locations. In these cases the increase of the annual output is not enough for further LCOE minimization. On the contrary, the increase is so small that causes a bigger value of LCOE.

Last but not least, this project has been done in order to evaluate the CSP technologies and to determine possible variations which can promote them more. During the simulation procedure and the study of the results, some more points emerge. One of these is the interdependence of certain variants, like the thermal storage and the solar multiple. A bad variation could cause high expenses and inefficient systems. Also, there is a possible improvement of the LCOE of a system with

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hybridization (fossil fraction). But the most important is the influence of the climatic factors in the CSP performance. SAM can only consider meteorological parameters which are appropriate for the design limits, like the wind speed or the humidity levels. But in reality, these parameters can be destructive if they exceed certain values. From the above simulations, the deduction is every CSP system has individual technological requirements in which it optimize its performance.

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References

ENERGY PLUS WEATHER DATA (2011) U.S Department of Energy Available from: http://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data2.cfm/region=6_europe_wmo_region_6 accessed April 2013

FRASER P. (2008) Stirling Dish System Performance Prediction Model, University of Wisconsin-

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GEOMODEL SOLAR S.R.O (2011) SolarGIS irradiation map Available from: http://www.google.se/imgres?imgurl=http://www.greenrhinoenergy.com/solar/radiation/images/SolarGIS-Solar-map-Europe-en.jpg&imgrefurl=http://www.greenrhinoenergy.com/solar/radiation/empiricalevidence.php&h=562&w=796&sz=103&tbnid=AQyroNXIAyF9RM:&tbnh=90&tbnw=127&prev=/search%3Fq%3Dsolar%2Birradiation%2Bmap%2Bfor%2Beurope%26tbm%3Disch%26tbo%3Du&zoom=1&q=solar+irradiation+map+for+europe&usg=__SxnWWj3lhGyahuH5mQK-f5QFXw8=&docid=4nY9Gu_xC5Y6CM&sa=X&ei=S8eoUc6FGbPE4gTRmoGgBg&ved=0CDYQ9QEwAQ&dur=503 accessed April 2013

HANK PRICE, ECKHARD LUPFERT, DAVID KEARNEY, EDUARDO ZARZA, GILBERT COHEN, GEE and ROD MAHONEY (2002) Advances in Parabolic Trough Solar Power Technology, Journal of Solar Energy Engineering

KEITH LOVEGROVE and WES STEIN (2012) Concentrating Solar Power Technology: Principles developments and applications. 1st ed. United Kingdom: Woodhead Publishing Series in Energy

NASA Atmospheric Science Data Centre Surface Meteorology and Solar Energy Available from: https://eosweb.larc.nasa.gov/cgi-bin/sse/sse.cgi?+s01#s01 accessed May 2013

PANOS KONSTANTIN and JOHANNES KRETSCHMANN (2010) Assessment of Technology Options for Development of Concentrating Solar Power in South Africa The World Bank, Fichtner GmbH & Co. KG

SYSTEM ADVISOR MODEL (SAM) HELP CONTENTS National Renewable Energy Laboratory (NREL): Performance Models Available from: https://sam.nrel.gov/reference, accessed March 2013

WARNER M.J and ZHU G. (2011) Generic CSP Performance Model for NREL's System Advisor Model: Preprint, Conference Paper NREL/CP-5500-52473

YINGHAO CHU (2011) Review and Comparison of Different Solar Energy Technologies Global Energy Network Institute (GENI)

http://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data2.cfm/region=6_europe_wmo_region_6

http://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data2.cfm/region=6_europe_wmo_region_6

http://www.google.se/imgres?imgurl=http://www.greenrhinoenergy.com/solar/radiation/images/SolarGIS-Solar-map-Europe-en.jpg&imgrefurl=http://www.greenrhinoenergy.com/solar/radiation/empiricalevidence.php&h=562&w=796&sz=103&tbnid=AQyroNXIAyF9RM:&tbnh=90&tbnw=127&prev=/search%3Fq%3Dsolar%2Birradiation%2Bmap%2Bfor%2Beurope%26tbm%3Disch%26tbo%3Du&zoom=1&q=solar+irradiation+map+for+europe&usg=__SxnWWj3lhGyahuH5mQK-f5QFXw8=&docid=4nY9Gu_xC5Y6CM&sa=X&ei=S8eoUc6FGbPE4gTRmoGgBg&ved=0CDYQ9QEwAQ&dur=503








https://eosweb.larc.nasa.gov/cgi-bin/sse/sse.cgi?+s01#s01

https://sam.nrel.gov/reference

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