FEASIBILITY ANALYSIS OF SOLAR AND WIND ENERGY PROJECTS ...€¦ · approaches. To do this, Chile has signed different laws in favor of the development of renewable energy technologies

FEASIBILITY ANALYSIS OF

SOLAR AND WIND ENERGY

PROJECTS ALONG THE

CENTRAL INTERCONNECTED

SYSTEM (CIS), CHILE Thesis submitted in fulfilment of the requirement for the

degree of Master of Renewable and Sustainable Energy

Alexeis P. Huaiquin Rosas – Chemical Engineer [email protected]

Murdoch University – Western Australia School of Engineering and Information Technology

Supervisor: Dr. Almantas Pivrikas

Coordinators: Dr. Jonathan Whale

Dr. Xiangpeng Gao

November 2017

1

Declaration

I declare this thesis is my own account of my research and contains as its main content work

which has not been previously submitted for a degree at any tertiary education institution.

Alexeis P. Huaiquin Rosas, December 2017.

2

“To see a World in a Grain of Sand

And a Heaven in a Wild Flower

Hold Infinity in the Palm of your Hand

And Eternity in an Hour”

William Blake

3

Contents

Abbreviations ....................................................................................................................................... 5

1. Abstract ........................................................................................................................................... 7

2. Background and Literature Review .................................................................................................. 8

3. Objectives of the Research ............................................................................................................ 14

4. Methodology ................................................................................................................................. 15

5. Resource Assessment .................................................................................................................... 18

5.1 Resource Assessment Tools ..................................................................................................... 18

5.1.1 Solar Explorer Model Explanation ..................................................................................... 18

5.1.2 Solar Explorer Model Validation ....................................................................................... 20

5.1.3 Wind Explorer Model Explanation .................................................................................... 21

5.1.4 Wind Explorer Model Validation ....................................................................................... 23

5.2 Data Generation ...................................................................................................................... 25

5.2.1 Solar Explorer Data Generation ........................................................................................ 25

5.2.2 Wind Explorer Data Generation ........................................................................................ 29

5.2.3 Transmission Infrastructure Considerations ..................................................................... 32

5.2.4 Site Selection .................................................................................................................... 34

5.3 Sites Selected ........................................................................................................................... 36

5.3.1 Sites Location .................................................................................................................... 36

5.3.2 Resource Availability Results ............................................................................................ 37

6. Project Details ................................................................................................................................ 39

6.1 Defining the Scale of the Project ............................................................................................. 39

6.1.1 Room Left for Utility-Scale Energy Projects ...................................................................... 39

6.2.2 Why PV Power Plant and Economical Scale Range ........................................................... 42

6.2.3 Why Onshore Wind Farm and Economical Scale Range ................................................... 45

6.2 Initial Costs .............................................................................................................................. 47

6.2.1 Initial Cost of the Solar Energy Project.............................................................................. 47

6.2.2 Initial Cost for the Wind Energy Project ............................................................................ 50

6.3 Operation and Maintenance Costs .......................................................................................... 53

6.4 Transmission Costs .................................................................................................................. 54

6.5 Cost Summary .......................................................................................................................... 55

7. Software Feasibility Analysis .......................................................................................................... 56

7.1 Project Set Up .......................................................................................................................... 56

4

7.2 Solar Power Plant Technical Data ............................................................................................ 58

7.2.1 Orientation and Tracking System ...................................................................................... 59

7.2.2 Module Model and Quantity ............................................................................................ 60

7.2.3 Efficiency and losses ......................................................................................................... 61

7.2.4 Solar Energy Tab Summary ............................................................................................... 62

7.3 Wind Farm Technical Data ....................................................................................................... 63

7.3.1 Wind Shear ....................................................................................................................... 65

7.3.2 Wind turbine Model and Quantity .................................................................................... 66

7.3.3 Losses and Availability ...................................................................................................... 68

7.3.4 Wind Energy Tab Summary............................................................................................... 68

7.4 Costs Details Data Input ........................................................................................................... 69

7.5 Financial Analysis Tab and Electricity Price .............................................................................. 70

7.5.1 Financial Data ................................................................................................................... 70

7.5.2 Energy Market and Electricity Price .................................................................................. 72

7.5.3 Power Capacity Income .................................................................................................... 74

7.5.4 Subsidies, Incentives ......................................................................................................... 75

7.5.5 Financial Analysis Tab Summary ....................................................................................... 77

7.6 Sensitivity and Risk Analysis Tab .............................................................................................. 78

8. Economic Performance Indicators and Sensitivity Analysis ........................................................... 79

8.1 Economic Indicators to be Used .............................................................................................. 79

8.2 Levelized Cost of Energy Results .............................................................................................. 83

8.3 Internal Rate of Return ............................................................................................................ 86

8.4 Projects Qualification ............................................................................................................... 89

9. Discussion and Conclusions ........................................................................................................... 91

9.1 Discussion of the Results and Limitations ................................................................................ 91

9.2 Conclusions and Further Works ............................................................................................... 97

10. Annex ......................................................................................................................................... 101

10.1 Solar and Wind Explorer Software Project Sites .................................................................. 101

10.2 Costs Section Data ............................................................................................................... 110

10.3 Solar and Wind Resource Data ............................................................................................ 112

10.4 Levelized Cost of Energy and Internal Rate of Return Calculus Sheets ................................ 113

References ....................................................................................................................................... 118

5

Abbreviations

CLP: Chilean Peso (currency).

CIS: Central Interconnected System.

CER: Renewable Energy Certificate.

GHG: Greenhouse Gas.

GIZ: German Federal Enterprise for International Cooperation.

HPV: Holding Period Return.

HVT: High Voltage Transmission Line.

IRR: Internal Rate of Return.

LCOE: Levelized Cost of Energy.

kWh/m2day: Daily solar radiation (kiloWatt-hour) in horizontal surface m2.

m/s: Wind speed in meters per second.

MW: Power units in Mega-Watt.

MWh: Energy units in Mega-Watt-hour.

NCRE: Non-Conventional Renewable Energy.

NPV: Net Present Value.

OECD: Organization for Economic Cooperation and Development.

O&M: Operation and Maintenance.

6

PPA: Power Purchase Agreement.

PV: Photovoltaic Solar.

ROI: Return Over Investment.

USD: American Dollar (currency).

WT: Wind Turbine.

7

1. Abstract

This assessment aims to estimate the levelized cost of energy (LCOE) and the internal rate of

return (IRR) of utility-scale solar and wind energy projects to identify which are the most

profitable regions for each type of project in the Central Interconnected System (CIS) of Chile.

For this, the Solar and Eolic (Wind) Explorer online tools developed by the University of Chile

were used for selecting the sites with best resource availability, while the feasibility analysis

was done with RETScreen, software specialized for renewable energy projects. Two solar

and two wind plants with 40 MW of capacity were evaluated for every region in the CIS, the

LCOE and IRR profiles of all these projects were obtained and tested in a sensitivity analysis,

comparing these results to the current average energy prices observed in the CIS market (78

USD/MWh), and a discount rate commonly used for evaluating these projects (10%) in Chile.

For the 18 solar projects evaluated, it was found that the regions with the best profitability

were concentrated in the north of Chile (III and IV regions), with LCOE values that ranged

from 54.0 to 59.1 USD/MWh, and between 67.1 to 73.6 USD/MWh when increasing the

installed cost in 30%. Also, the IRR resulted to range between 25.4 and 30.9% in the base

scenario, and from 10.3 to 13.3% with a decrease of 30% in the electricity rates in the long

term.

In the case of the 18 wind projects evaluated, it was found that the far north and south

(regions IV, VIII and X) had the best results, with LCOE values from 56.5 to 61.0 USD/MWh in

the base scenario and between 64.7 and 67.7 USD/MWh in the risk scenario, and IRR values

of 32.6 – 36.3% and 10.2 – 12.1% in their respective scenarios.

8

2. Background and Literature Review

The last decade, Chile has experienced some difficulties in its energy supply, being some of

the most relevant extensive droughts hitting hydropower input, a source that accounts for

nearly 40% of the energy supply. Also, because of energy issues of his former main exporter

of natural gas, Argentina, Chile has been and is still relying on the importation of fossil fuels

for running around 50% electricity load required in the country, being the electricity price

highly influenced by the highs and lows of the global economy.

Both problematics had such influence on the electricity that the spot price of the electricity

surpassed the record of 300 USD/MWh the year 2008 (National Energy Commission, 2012),

which has a direct influence on the growth perspective of the country and the quality of life

of their citizens.

The country has been very aware of the world discussion of the global warming issue and the

past years has begun to address the energy problematic with sustainable development

approaches. To do this, Chile has signed different laws in favor of the development of

renewable energy technologies since 2010, highlighting the Law 20.698 (National Library of

Chile, 2013) of non-conventional Renewable Energies, which sets the goal of reaching a 20%

of renewable energy supply by the year 2025. The rapid increase in the renewable matrix

was recognized the past year 2016 by the New Energy Finance Climascope (2016) ranking,

elaborated by Bloomberg New Energy Finance and the Interamerican Bank of Development,

where Chile reached the first place in the continent and second place in the world ranking of

clean energy investments, being placed behind China.

9

The two biggest electric systems are the Central Interconnected System (from now on

referred CIS) and the Interconnected System of the North. The electric market power

capacity composition of generators market (Generators Chile, 2017) is shown in Table 2.1

and Figure 2.1.

The country accounts with high renewable resource availability, especially hydro resources

which were very important for the development of the energy sector in the past. Even

considering that in the CIS system more than half of the energy is already renewable, taking

into account that under the current legislation of Renewable Energy Modified Law 20.257

(National Library of Chile, 2013), for the hydro energy only run-of-river hydro with a capacity

below 20 MW are considered non-conventional renewable energy (NCRE), which means that

for Chilean standards the current power capacity of NCRE in the country is about 19%. Today

it is highly expected to reach the goal before 2025, thus a new law decree was published the

year 2016, the Decree 148, setting a new goal of 70% renewable energy supply by the year

2050 (National Library of Chile, 2016).

Considering the current circumstances in which renewable energy technologies are rapidly

gaining space in the electric market, this evaluation will attempt to study how profitable

these types of projects are in Chile in the present, focusing on the Central Interconnected

System (CIS) which concentrate most of the population (over 91%), making it the biggest

most important electric system of the country.

10

Table 2.1: Electric market power capacity composition in Chile by August 2017.

Generator Capacity

CIS + ISN CIS

MW % MW %

Hidro (Dam) 3,402 14.9 3,402 19.4

Hidro (RoR) 3,300 14.4 3,289 18.7

Wind Power 1,444 6.3 1,354 7.7

Solar Power 1,537 6.7 1,218 6.9

Biomass 503 2.2 503 2.9

Natural Gas 4,450 19.4 2,514 14.3

Oil Derivatives 3,100 13.5 2,773 15.8

Coal 5,162 22.5 2,494 14.2

Renewable 10,186 44.5 9,766 55.7

NCRE 6,784 29.6 6,364 36.3

Fossil 12,712 55.5 7,780 44.3

Total 22,898 100 17,547 100

Reference: Generators Chile (2017).

Figure 2.1: Electric market power capacity composition in Chile by August 2017. Reference: Generators Chile (2017).

About the renewable resource in the country, in the case of solar and wind power energy

Chile has a high availability, factor which has attracted many investors (thus the rapid

increase in NCRE projects in the past years), and having the country around 6,000 km of

coastline, 120 active volcanoes and one of the places with the most radiation of the world in

11

the north of the country, according to Renewable Energy World (2016) the country’s

renewable energy market’s potential is very high.

Because of the importance of NCRE projects and all that was mentioned, is that the aim of

this work is to generate actualized economic performance indicators of solar and wind power

projects in the most important electric market of the country to identify where are the best

places to install said projects. The two main indicators that were used for measuring the

profitability of these projects were the Levelized Cost of Energy (LCOE), widely used for

energy projects, and the Internal Rate of Return (IRR), used for any type of project. Not much

information about the IRR is available for the case of Chile, but plenty of studies for

estimating the LCOE were made in the past, where some of them generated results for the

country as a whole, while others focused on specific locations but with more detail.

Some of these studies which will be referenced later this work are acknowledged below:

1) Chile’s Clean Energy Future (Bloomberg New Energy Finance, 2011): A wide variety of

utility-scale power projects are evaluated in this work, where the output of this study is the

LCOE in the country by 2011 and the projections of it for the years 2020 and 2030. The results

were intervals of LCOE for each technology for the entire country, with no deeper detail on

the differences between regions or any other economic indicators.

In this study, it was found that the LCOE for cSi-PV utility-scale solar projects ranged from 92

– 143 to 74 – 115 USD/MWh from 2020 to 2030 respectively, and from 60 – 121 to 52 – 105

USD/MWh in the case of wind farms.

12

2) Photovoltaic performance and LCOE comparison at the coastal zone of the Atacama Desert

(Araya et al. 2015): Study focused on comparing the energy yield and costs of two different

PV cell technologies, thin film and mc-Si on the north of Chile, obtaining LCOE values of 156.6

and 177.1 EUR/MWh.

3) Sensitivity analysis of a photovoltaic solar plant in Chile (Bustos et al. 2015): By using the

resource data available in RETScreen database, this study simulated 20 PV solar plants of a

size of 30 MW, placed in different cities from the north to the south of the country (mostly

regional capitals). The objective was to evaluate solar power plants located in advantageous

places inside cities, not considering better places with higher resource quality which could

be found outside of cities. It was found that for all the projects evaluated, the IRR values

obtained were below the discount rate, obtaining negative net present values in all the cases

that were evaluated without incentives. It must be noted that for this study, the total initial

cost was approximately 6.7 MUSD/MWh.

4) Assessment of wind energy potential in Chile (Oses et al. 2016): A more recent study

projects the LCOE for 70 existing projects under environmental evaluation in locations from

the I to the X region, by doing a resource assessment utilizing the Wind Explorer, the same

online tool which was also used in this work. The results obtained were LCOE values from 50

to 120 USD/MWh for different scenarios of investment cost.

From the mentioned studies, Bloomberg New Energy Finance (2011) is highlighted because

it aimed to estimate the LCOE of NCRE projects in the entire country, but as will be seen in

the discussion section, these estimations resulted to have important deviations compared to

13

the values that can be observed today, and the same could be said for the other studies

mentioned. This opens a research opportunity, motivating the aim towards generating

actualized economic performance indicators for identifying the best regions for placing solar

and wind plants, and not only from the life cycle cost perspective by estimating the LCOE,

but also, from the utility perspective by calculating the IRR.

Thus, some of the questions that motivated the interest of this work, and that will be

addressed in detail in the last section, are:

- How the solar and wind resources are distributed in Chile and where the resource

availability is higher?

- Why investing in utility-scale solar and wind power projects and with which

characteristics?

- How economically feasible would these projects be along the country?

- Which are the best regions for investing in utility-scale solar and wind power projects?

14

3. Objectives of the Research

Aim:

Determine the economic feasibility of solar and wind power utility-scale projects in the

Central Interconnected System (CIS-Chile) to identify the most profitable regions and

projects.

Objectives:

1) Identify promising sites in all the regions in the CIS and get the average daily solar

radiation on a horizontal surface and wind speeds for later feasibility project analysis.

2) Determine the basic technical characteristics of the solar and wind power plants to

be evaluated, along with the cost and financial variables needed to make a feasibility

analysis for every site selected in objective 1.

3) Using all the information gathered from objectives 1 and 2, estimate the levelized

cost of energy and the internal rate of return of each project.

4) Determine the sites and regions with the best economic performance and sensitivity

analysis resistance.

15

4. Methodology

The present work approaches its aim and objectives by following the 5 steps presented in Figure 4.1:

Figure 4.1: Methodology scheme.

Section 5: Resource Assessment.

Availability of resource data for project evaluations is important to make them possible in

first place. Unfortunately, substantial amounts of money are needed to assess the resource

availability for a project, and more if what a country wants are assessments of the renewable

resources on a big region or the whole national territory.

The University of Chile, with the cooperation of the Energy Ministry and the German Federal

Enterprise for International Cooperation (GIZ), worked in diverse ways with the objective to

facilitate the accessibility to resource information. This way, the University of Chile has been

developing since 2010 public online tools for renewable resource data assessment, including

solar, wind, hydro and other resources. This work will mainly focus on the assessment of

solar and wind resource so the tools that will be used to gather all the data needed for the

Section 5:

Resource

Assessment

Section 6:

Project

Details

Section 7:

Software

Analysis

Section 8:

Economic

Performance

Section 9:

Feasibility

Results

• Solar and wind sites selection.

• Solar and wind sites data generation.

• Technology and scale. • Costs information.

• RETScreen data input. • Set financial and other

miscellaneous inputs.

• Estimation of the LCOE. • Estimation of the IRR. • Sensitivity Analysis.

• Discussion. • Conclusions of the

feasibility analysis.

16

evaluations will be the Solar Explorer 3 and Wind Explorer 2, which corresponds to the last

version of each software.

Several project’s sites will be selected for each region, and the monthly average daily solar

radiation on horizontal surface and monthly average wind speed data will be retrieved from

it along with distance to the grid. All this information will be saved for being used later as

inputs in the feasibility analysis software.

Section 6: Project Details.

A bibliographical review from journals, websites and articles of current costs, technical data

and other necessary information for doing the feasibility analysis will be retrieved in this part

of the work to get results that would reflect the economic performance of solar and wind

power projects using up to date information.

Section 7: Software Feasibility Analysis.

For each site, the resource, costs and technical data retrieved in the previous sections will

be loaded to RETScreen Excel-based software to run a feasibility analysis of each project on

each of the sites selected in section 5. RETScreen is a clean energy project pre-feasibility

assessment tool, which can be downloaded for free in the Canada’s governmental website:

Natural Resources Canada (2017).

Although RETScreen is a free software, the enhanced version RETScreen Expert will be used

in this study, which can be purchased on the same website. The annual subscription fee for

the access of several devices has an annual cost of $869, according to the software’s license.

17

The end results of this section will be used to get the LCOE and the IRR, which will be tested

in a sensitivity analysis in section 8.

Section 8: Economic Performance Indicators and Sensitivity Analysis.

The LCOE and the IRR will be obtained in this section. These economic performance

indicators will later be tested in a sensitivity analysis which will test how these indicators vary

in different scenarios of risk, like a rise in the total initial cost, a decrease of long-term

electricity rates, lack of currently existing incentives, among others.

The performance results of every scenario considered in the sensitivity analysis will be

classified in two categories, feasible and non-feasible, a method which will help to identify

which are the projects that fail to withstand the risk scenarios and which are the most likely

to be feasible in every case.

Section 9: Discussion and Conclusions.

The end results, which are the economic performance indicators for different projects the

CIS, and the sensitivity analysis which identify the best and most robust projects, will be

discussed in this last section, along with the final conclusions of this research.

18

5. Resource Assessment

The first step consists in gathering all the resource data needed for being utilized in the

RETScreen software, which will be done with the help of the Solar Explorer and Wind

Explorer, which basically are maps of Chile containing a database of solar and wind,

highlighting the places with high concentration.

5.1 Resource Assessment Tools

5.1.1 Solar Explorer Model Explanation

The software uses satellite data and mathematical models to estimate the total solar

radiation on a horizontal surface. Figure 5.1 shows a flowchart of the steps that the Solar

Explorer takes to employ all the data from a location to get the total solar radiation on a

horizontal surface.

Figure 5.1: Solar Explorer Model. Reference: Modified and translated from University of Chile (2012).

This tool considers the effects of the different components of the atmosphere on the total

amount of solar radiation reaching the surface of the planet. Molecules such as ozone, water,

Solar Radiation

Model

•SORCE Satellite Data

•Metheorology Models

•Topoplgy Model

Empiric Model

•GOES EAST Satellite Data

•Sky Clearness Model

Solar Radiation on Horizontal Surface

19

carbon dioxide and aerosols absorb photons depending on their wavelength while causing

Rayleigh dispersion. According to the Solar Explorer user manual (University of Chile, 2012),

the model works doing two main steps, which are explained below:

1) CLIRAD-SW Radiation Model of Chou and Suarez (1999): This separates the radiation

into 11 spectrum bands to consider the interactions of the radiation of those bands

with the different components atmosphere independently, while also considering

conditions such as temperature and humidity. It also takes into account 100

atmospheric vertical layers (which varies with the surface’s topography), having the

superior level the total solar radiation measured by the Total Irradiance Monitor

(TIM) on board of SORCE satellite (Solar Energy Explorer, 2016), which considers the

different cycles of sun distance, corrected by latitude.

- The temperature and humidity data of the atmosphere are taken from NCEP/NCAR

of NOAA/ESRL Physical Sciences Division (Kalnay et al. 1996).

- Aerosols data is retrieved from the Monitoring Atmospheric Composition and Climate

(MACC) in combination with the data monitoring ECMWF model (Stein et al. 2011).

- The thickness of each of the 100 layers of the atmospheric column that are taken into

consideration for the model is determined for each point on the Energy Explorer

software, using the topography data from the Shuttle Radar Topography Mission

(SRTM), which has a resolution of 500 meters (Kopp and Lawrence, 2005).

2) The radiation of “clear” sky obtained in the first step is contrasted and re-estimated

with the cloud cover data from the Geostationary Satellite Service (GEO-NOAA) GOES

20

EAST 12 satellite from 2001 until 2012 and GOES EAST 13 from 2013 until now (Hillger

and Schmit, 2007). The time resolution of this satellite is 30 minutes during the entire

year and gives images on 5 different spectral channels (the visible and four infrared),

with a resolution near one kilometer.

5.1.2 Solar Explorer Model Validation

The latest version of the Solar Explorer Manual (University of Chile, 2016) explains that for

validating the model predictions and accuracy of the radiation maps, the data were evaluated

according to the readings of 78 monitoring stations in the country, being 10 of those stations

installed by the GIZ with the Ministry of Energy of Chile and the remaining stations belonging

to the private and public sector. The results are shown in Figure 5.2, obtaining that the

average annual values of the daily global irradiance predicted by the model, the data

deviations were inferior to 10% (and a root mean squared error r2 of 10.6%) when comparing

to the real data measured by the stations, presenting more error the stations in the far south

because of the prevalence of cloudy weather, distorting the result of the model.

21

Figure 5.2: Model validation (left) and position of the 78 monitoring stations (right). Reference: Translated from University of Chile (2016).

5.1.3 Wind Explorer Model Explanation

Just like the Solar Explorer, the Wind Explorer was created by the University of Chile with the

cooperation of the Energy Ministry of Chile and GIZ. As it is explained in the Wind Explorer

User Manual (University of Chile, 2012), this online tool shows a wind map of the country

created with resource data generated by a software that runs a numeric simulation of the

atmosphere according to the WRF (Weather Research and Forecasting) mesoscale model.

This model was created by national agencies and universities of the United States of America

and is one of the most well-known and developed models for weather forecasting. WRF has

been widely used for wind resource assessment according to Basu et al. (2009) and for

evaluation of wind power generation (Dvoraka et al. 2010).

22

The model aims to represent the properties of the atmosphere in a non-hydrostatic state for

three-dimensional volumes, considering multiple parameters such as the three vectors of

the wind speed, atmospheric pressure, temperature, humidity and various microphysical

variables which represent distinct phases of water. The equations of the model are run for a

given resolution (size of the atmospheric volume), simulating for amounts of times that

depend on the resolution that was chosen, being necessary more time for computing in

smaller and more detailed resolutions. The results are generated for intervals of one hour

for the entire year (a total of 8760 intervals), obtaining the hourly mean wind speed at

different altitudes.

The current version of the Wind Explorer simulated the entire year 2010, from January to

December to consider all seasonal effects, saving the state of the model every 1 hour. This,

for a horizontal resolution of 1 km2 and 42 atmospheric layers in the vertical axis of 5 to 10

meters for the lower levels. The model also considers the topography of the terrains along

all the surface, using the data from the global satellite Shuttle Radar Topography Mission

(SRTM), which has a resolution of 90 meters (University of Chile, 2012). The model also

considers other properties of the surface, such as the roughness, vegetation, type of soil and

others. This data was retrieved from the Moderate Resolution Imaging Spectrometer

(MODIS) from NASA, this along with the complementation of surface information from the

data banks of the National Forest Corporation (CONAF), the National Commission of the

Environment (CONAMA), and other related works developed by: Gajardo (1994), Austral

University of Chile et al. (1999), Luebert and Pliscoff (2006).

23

5.1.4 Wind Explorer Model Validation

According to the Wind Explorer User Manual (University of Chile, 2012), the wind speed

predictions from the model were compared to real data from 420 anemometers installed

along the country (from the 1st to the 10th region), information that was retrieved from the

Meteorological Observations Data Base of the Geophysics Department (DGF) of the

University of Chile and from the Energy Ministry, using data from 2008. Most of the

anemometers recorded data at an altitude between 5 and 10 meters above the surface, so

the model data used belonged to predictions at the altitude of the corresponding instrument.

The results of the comparison between the model predictions of the wind speeds and the

data measured from anemometers at different altitudes and various locations around the

country (shown in the Figure 5.3), indicates that the standard deviation between 0.5 and 1.1

m/s (a root mean squared r2 error of 74 and 86%) for the DGF and the Ministry stations

comparisons respectively.

24

Figure 5.3: Model validation (left) and the 380 DGF’s (blue) and 40 Energy Ministry’s (red and

green) anemometers (right). Reference: Translated from University of Chile (2012).

25

5.2 Data Generation

All the resource data for the economic evaluations on RETScreen was extracted from the

mentioned Solar and Wind Explorers, which basically shows colored maps of the country

where their intensity of said colors are related to the availability of resources on the site.

The whole explanation of how the resource data was retrieved from each software will be

explained in this section.

5.2.1 Solar Explorer Data Generation

The Solar Explorer1 consists of an interface map that contains all the solar resource data

generated by the model previously mentioned. Figure 5.4 shows a complete screenshot of

the first look after selecting any site with the cursor. Also, the coordinates (longitude and

latitude) can be written in the tab at the right part, as shown in the same figure.

After selecting the site, the Summary tab opens showing general annual information of the

global radiation on a horizontal and tilted surface, along with other related meteorological

information. By selecting the second tab labeled as Graphs, and then the Select Graph tab as

shown in Figure 5.5, a lengthy list of different information is generated, which is listed in

Table 5.1.

Table 5.1: List of Information generated for the selected site.

Tab Units Information

Radiation

Annual Cycle kWh/m2day Direct horizontal – tilted. Plot. Diffuse horizontal – tilted. Plot. Daily Cycle W/m2

Year to Year kWh/m2day

1 Minenergia.cl/exploradorsolar

26

Surface reflected (on tilted surface). Plot.

Daily Cycle – Global Horizontal W/m2

Global. Grid. Daily Cycle – Global Tilted

Daily Cycle – Global Direct Normal

Topography and Shadows

Daily Cycle %

Shadow Frequency Plot.

Annual Daily Cycle Shadow Frequency. Grid.

Cloud Cover

Annual Cycle %

Cloud Frequency. Plot. Daily Cycle

Year to Year

Annual Daily Cycle Cloud Frequency. Grid.

Temperature

Annual Cycle °C

Ambient Temperature. Plot.

Daily Cycle

Monthly Daily Cycle Ambient Temperature. Grid.

Wind

Annual Cycle m/s

Wind Speed. Plot.

Daily Cycle

Monthly Daily Cycle Wind Speed. Grid.

Figure 5.4: Solar Explorer website interface screenshot.

Reference: Modified and translated from University of Chile (2016).

27

Figure 5.5: Select Graph tab (left) and annual cycle graph example (right).

As can be seen in Table 5.1, plenty of data can be generated, but for the evaluation in

RETScreen what is required is the mean daily global radiation on a horizontal surface, listed

as Annual Cycle in the Solar Explorer tab. Figure 5.5 (right) shows an example of the useful

information which is already plotted for comparing different sites while searching in the

explorer’s map. The numerical data that was used for plotting the results were estimations

made by the radiation model explained before, and these results can be downloaded from

the Downloads tab shown in Figure 5.6. In this section, csv (comma-separated values) format

files that contains all the raw data can be downloaded, but also, the Summary Table which

contains all the organized data (in xlsx format) listed in Table 5.1 can be downloaded along

with a report in pdf format, contains commented tables and graphs of the table’s content.

28

Figure 5.6: Downloads tab in the Solar Explorer.

By downloading the Summary Table excel file, from the ghi tab the monthly values of the

monthly average daily radiation on a horizontal surface can be obtained as shown in Figure

5.7. The results are in W/m2, so all the hourly radiation must be summed and multiplied by

10-3 to get kWh/m2 units required for the RETScreen solar resource input fields.

Figure 5.7: Solar Explorer xlsx format data generated.

29

All the resource data gathered was saved and classified according to their geographic

coordinates along with the wind resource data for further usage.

5.2.2 Wind Explorer Data Generation

The interface of the Wind Explorer2 is very similar to Solar Explorer’s, website once opened

it will show a map of the country which highlights with yellow and red colors the sites with

high wind speed and blue colors for the sites with less wind resource availability. Figure 5.8

shows how the website looks like, and in this case, the map generated is showing annual

averages of wind speed at 78 meters, for ranges between 0 and 10 meters, all this

information can be modified to get the information needed. Which is important are the View

and Report buttons on the left, these tabs will generate all the information needed for the

wind power project’s site.

The View tab will show the information listed in Table 5.2, which look like Figure 5.9, where

a monthly average wind speed bar graph is immediately generated, showing also the annual

average wind speed for the selected site at the specified height, which will be 78 meters,

almost the same height of the wind turbines model selected according to the considerations

of the next section.

After selecting a site and seeing the site’s information in the View tab, by selecting the Report

buttons (see Figure 5.10) it is possible to generate a document in pdf format which basically

shows all the information listed in Table 5.2. These documents contain the average daily wind

2 walker.dgf.uchile.cl/Explorador/Eolico2/

30

speed for each month, which is the data required for the RETScreen input fields in the

resource section.

Figure 5.8: Wind (Eolic) Explorer website interface screenshot.

Reference: University of Chile (2012).

Table 5.2: List of information generated for the selected site in the View tab.

Tab Information

Summary

- Coordinates of the site. - Elevation. - Height. - Monthly average wind speeds bar graph.

Daily Cycle Graph of the daily (24h) wind speeds for each month.

Daily-Monthly Cycle Colour scale graph of the daily (24h) wind speeds for each month.

Simple Profile Wind shear graph, for monthly averages (wind speed versus height).

Daily-Cycle Profile Wind shear graph, for daily profile.

31

Figure 5.9: View tab summary section. Example of wind speed data at 45 meters.

Figure 5.10: Report tab in the Wind Explorer (left) and format selection (right).

32

5.2.3 Transmission Infrastructure Considerations

The electricity transmission infrastructure available is another important feature of the

projects. The losses during electricity transportation are related to the amount of current.

For this reason, high voltage lines (HVT) operates at ranges between 23 and 500 kV,

maximizing the power transported while decreasing the current and the losses. This also

lowers the diameter of the cables, thus lowering the quantity of material and the total cost

of the transmission system.

The total cost that the HVT lines in an energy project depend on the next 2 key factors:

- Amount of energy transmitted and voltage specifications.

- The distance between the project’s site and the nearest SIC line or substation.

- Cost per kilometer of HVT line.

The first factor suggests that the more power and voltage will make the HVT line more

expensive. The second point is related to the length of the transmission lines that must be

installed for the project to be connected to the grid. The third factor is the unitary cost per

installed kilometer of HVT line, which will be determined in the Costs section.

Regarding the distance data, it will be retrieved from the Governmental online tool Energia

Maps, from the National Commission of Energy. This map is similar to the Solar and Wind

maps, with the difference that this one can display all the energy infrastructure in the

country, which can be visible on the map and the information of this infrastructure can be

retrieved from it also.

33

Figure 5.11 shows how the online software looks like, the Menu tab displays a catalog which

gives the possibility to generate the electric infrastructure in the map. In the mentioned

figure, in the Transmission tab, the CIS HVT lines were generated, shown as orange and green

lines and gray squares respectively. In this case, the color of the line represents the operating

voltage of the HVT lines.

The map has a tool to retrieve the distance between selected points on the surface (as shown

in Figure 5.12), which will be used for estimating the length of the HVT lines required for each

project’s site. The total additional cost in HVT lines for each project will be determined in the

next section.

Figure 5.11: Energia Maps, transmission infrastructure.

Reference: National Commission of Energy (2017).

34

Figure 5.12: Distance measuring tool.


5.2.4 Site Selection

The CIS is the biggest electric grid of Chile, containing most of the regions of the country, and

integrating the 91.58% of the total population, making this grid the widest and most

important of Chile. The regions (represented by their respective code numbers) in the CIS

and the population of each one, are shown in Table 5.3 from north to south. Note that the

XIV region is geographically placed between IX and X regions.

Table 5.3: Regions and population for 2017.

Code Region Population

III Atacama 346.692

IV Coquimbo 817.801

V Valparaiso 1.974.880

M Metropolitana de Santiago 7.419.042

VI O'Higgins 966.828

VII Maule 1.083.322

VIII Bíobío 2.134.902

IX La Araucanía 1.046.322

XIV Los Ríos 407.300

X Los Lagos 937.495

III - X Regions 17.134.584

Nation 18.710.232

Fraction of population in CIS 91.58%

National Institute of Statistics (2014).

35

Two different sites will be selected for each case (solar and wind resource), according to the

resource availability and closeness to the CIS. The coordinates and monthly resource

information needed for the resource input in RETScreen were saved, along with the distance

between the site and the grid for the cost section.

It must be noted that due its small size, the region of Santiago, the capital of Chile, will not

be covered in this study.

36

5.3 Sites Selected

As mentioned before, most of the population in Chile is distributed between the 9 regions

connected in the CIS, from the 3rd to the 10th region. In this assessment, two different sites

were selected per region, for each case of solar and wind power projects, giving a total of 36

projects to be evaluated (18 for each technology).

5.3.1 Sites Location

All project’s sites, coordinates and distances from the grid are listed in Table 5.4, by region

and from north to south. Note that the negative values of latitude and longitude are related

to south and west frame of reference.

Table 5.4: Distance from each project’s site and the CIS grid.

Region/Site Coordinates Project Distance (km) Annex

Solar Wind Solar Wind Figures

III-1 29.14 S – 70.92 W 28.85 S – 71.44 W 2.00 38.47 Fig A.1

III-2 26.31 S – 69.92 W 26.92 S – 69.57 W 2.61 3.74

IV-1 31.19 S – 71.05 W 32.14 S – 71.48 W 0.87 2.29 Fig A.2

IV-2 30.05 S – 70.72 W 30.79 S – 71.67 W 0.23 10.62

V-1 32.87 S – 70.58 W 32.71 S – 71.03 W 1.78 5.95 Fig A.3

V-2 32.86 S – 70.97 W 32.19 S – 71.51 W 1.54 0.41

VI-1 34.23 S – 71.62 W 34.54 S – 71.30 W 1.90 8.12 Fig A.4

VI-2 34.24 S – 71.42 W 34.07 S – 71.66 W 2.57 4.36

VII-1 36.20 S – 71.85 W 35.88 S – 72.53 W 1.86 8.62 Fig A.5

VII-2 35.20 S – 71.38 W 35.28 S – 71.59 W 1.41 5.10

VIII-1 37.45 S – 72.25 W 37.27 S – 73.54 W 1.76 26.47 Fig A.6

VIII-2 36-39 S – 71.90 W 36.89 S – 72.65 W 1.16 10.70

IX-1 38.93 S – 72.60 W 38.39 S – 72.02 W 3.20 61.11 Fig A.7

IX-2 38.43 S – 72.44 W 37.70 S – 72.67 W 2.72 8.19

XIV-1 40.14 S – 72.85 W 40.14 S – 73.01 W 2.15 8.60 Fig A.8

XIV-2 39.59 S – 72.25 W 39.97 S – 72.92 W 0.69 6.15

X-1 42.24 S – 73.70 W 42.49 S – 73.86 W 0.84 4.68 Fig A.9

X-2 40.77 S – 73.17 W 40.87 S – 73.61 W 1.88 41.99

37

5.3.2 Resource Availability Results

In the case of the monthly average solar radiation on a horizontal surface for the 18 different

sites, and as can be seen in Figure 5.13, there are two important characteristics that define

the solar resource in the country. One is that during the entire year, there will be more solar

radiation available in the northern regions, variable that will decrease when going to the

south, which is expected because there will be less radiation available the further away site

is from the equator, as the solar altitude angle will be lower. The second thing to consider is

the notorious seasonality between summer and winter, also normal for places far from the

equator, having in this case reductions of around half of daily solar radiation availability

comparing summer and winter.

For the wind resource, there are no clear differences between regions when studying how

wind speed changes between seasons, as for some of them there is more wind speed during

summer and there is more during winter for others, whereas for other regions, it is stable

during the entire year. For this reason, the yearly average wind speed was plotted and is

shown in Figure 5.14, where it can be concluded that there is more wind speed in the

extreme regions of the country with around 7 to 8 m/s (annual average at 78 meters), while

for the rest of the regions located at the centre of the country there is less wind resource.

All the monthly average daily solar radiation on a horizontal surface and monthly average

wind speed data (plotted above) are available in Tables A.1 and A.2, which corresponds to

the input data that will be set in the RETScreen resource tab input fields for the feasibility

evaluation of each site.

38

Figure 5.13: Monthly average daily solar radiation on a horizontal surface during the year.

Figure 5.14: Yearly average wind speed per region.

0

1

2

3

4

5

6

7

8

9

10

Dai

ly S

ola

r R

adia

tio

n o

n H

ori

zon

tal S

urf

ace

kW

h/m

2-d

ayAverage Daily Solar Radiation per Region/Site III-1

III-2

IV-1

IV-2

V-1

V-2

VI-1

VI-2

VII-1

VII-2

VIII-1

VIII-2

IX-1

IX-2

XIV-1

XIV-2

X-1

X-2

2

3

4

5

6

7

8

9

10

Year

ly A

vera

ge W

ind

Sp

eed

m/s

Site

Wind Speed per Region/Site

39

6. Project Details

In this section, the feasibility design details of a photovoltaic and wind turbine farms will be

determined with the purpose of setting all the costs and technical details associated with the

various parts of the project.

6.1 Defining the Scale of the Project

The first thing to be defined is the scale of the project. It is expected that for low power

output power plants, for instance lower than 5 MW, the power of negotiation of the client

company interested in investing is reduced, while other fixed costs will be the same regarding

of the size of the project, making small-scale plants have a high initial capital cost per each

megawatt installed. Although, there is a point at which the unitary cost reduction is minimal

when increasing the size of the project.

Firstly, the energy market in Chile will be briefly studied to conclude if there is any room for

utility-scale energy projects in the CIS in first place.

6.1.1 Room Left for Utility-Scale Energy Projects

The first things to be defined are some aspects of the electricity market in the CIS grid, being

some of them a number of renewable energy projects being studied for future

implementation and the real growth of installed capacity growth of it in relation to the entire

market.

40

In relation to the future projects, there is a huge gap between the growth of conventional

and nonconventional energy technologies in the last decade, which can be seen in Figure

6.1. In the year 2016 more than half of the projects were NCRE, expected to be installed by

the year 2020. From this, it can be concluded that the renewable energy market is reaching

its maturity, as investors no longer doubt about preferring these technologies.

But the mentioned projects are not totally related to the actual capacity growth, even the

approved ones could be left on paper or totally changed. Hence, the real growth of the

generation capacity in the last years is studied and exposed in Figure 6.2, where it can be

seen that the capacity increased from 16,087 MW the year 2015 to 16,842 MW on 2016 in

the CIS, around 4.5% per year, and an overall increase of 92% in ten years (from 2006).

A linear projection of the energy supply increase was made from the total capacity on 2016

and its annual growth to determine the amount of net capacity growth. This is shown in Table

6.1, where it can be concluded that the installed capacity of the CIS is growing around 800

MW per year, and therefore, medium size utility projects below the 10% of the total supply

increase should be considered.

41

Figure 6.1: Expected Total Installed Capacity (MW) Projects in Chile. Reference: Translated from National Commission of Energy (2016).

Figure 6.2: Growth in the Installed Capacity (MW) Projects in Chile (SIC: CIS).

Reference: Translated from National Commission of Energy (2016).

42

Table 6.1: Installed capacity growth in the Central Interconnected System (CIS)

Year Total Capacity MW Increased Capacity

2016 16,842 -

2017 17,597 755

2018 18,385 788

2019 19,209 824

2020 20,069 861

6.2.2 Why PV Power Plant and Economical Scale Range

As it was mentioned, from the distinct types of utility-scale solar power plants, the three

dominant technologies in the market in the last decade were photovoltaic (PV), concentrated

PV, concentrated solar power (CSP) through and CSP power plants. According to Bolinger

and Seel (2016), in the United States of America the installed price of utility-scale PV power

projects has been almost always lower than the initial cost of CSP through and tower

technologies, where the prices for these type of PV projects have been dropping constantly

to prices below 3 USD/W the year 2015, as can be seen in Figure 6.3. For this reason, a PV

power plant project will be evaluated.

The scale of the project is as important as the technology selected, as the economies of scale

play a key factor when deciding which size of power plant should be considered. Two

variables were studied in this matter, one is the installed cost per megawatt and the LCOE.

Farrell (2016) indicates for the year 2015 in the United States, rooftop commercial scale PV

projects had an initial cost of almost as twice the cost observed in the utility-scale solar PV

projects. As it is shown in Figure 6.4, it can also be noted that for projects of a size over 5

MW and up to 100 MW the costs stabilizes, around 2.20 USD/W. The exact scenario can be

43

observed in Figure 6.5 for the levelized cost of energy in different project sizes, being the

most cost efficient the range above 5 MW utility projects, with values a bit over 4.0 ¢/kWh.

Therefore, a 40 MW utility-scale PV power plant will be evaluated, taking into consideration

that this covers only around 5% of the annual growth in the energy capacity of the CIS, while

fitting into the lowest installed cost and levelized cost of energy for the size ranges.

It must be mentioned that a fixed capacity is required for running feasibility analysis on

RETScreen, hence the mentioned capacity of 40 MW was set Cost section input field. It was

tested that even if changing the plant size, for example between 40 to 100 MW, the internal

rate of return would vary from 28.3 to 28.9%, a difference below 1%. This is expected

considering that the initial cost, and its influence because of economy scales, was already

set. Therefore, the results of this arbitrary capacity of 40 MW will represent the results that

utility-scale projects around that range (up to 100 MW) would have.

Figure 6.3: Installed price for solar power projects by technology.

Reference: Bolinger and Seel (2016).

44

Figure 6.4: Installed cost in USD/W of solar power projects by size in the USA, 2015.

Reference: Farrell (2016).

Figure 6.5: Levelized cost electricity in ¢/kWh of solar power projects by size in the USA, 2015.


45

6.2.3 Why Onshore Wind Farm and Economical Scale Range

In the case of wind power, two major technologies to be considered are onshore and

offshore wind farms. Most of the bibliography and the current cost data that was studied

from different websites related to energy indicates the same conclusion, which is that

offshore technologies are still considerably more expensive than onshore wind farms. As can

be seen in Table 6.2, according to the International Renewable Energy Agency (2012) the

initial cost required in offshore wind farms of doubles the cost of the onshore counterpart,

being one of the main reasons the increase in the grid connection and construction costs.

The LCOE follows the same trend, doubling the onshore costs. Therefore, an onshore wind

farm power plant project will be evaluated.

For the scale of the project to be considered, according to Bolinger and Wiser (2016, August)

and as can be seen in Figure 6.6, the installed cost of onshore wind farms of sizes below 5

MW are not as competitive when comparing to the ranges between 5 and 200 MW, where

steady values between 1700 and 1900 USD/kW can be observed. Something similar happens

with the LCOE for different project scales as shown in Figure 6.7 (Farrell, 2016), where only

marginal benefits of economies of scale can be observed beyond 20 MW.

Hence, a 40 MW utility-scale wind farm will be evaluated, following the same energy supply

market and cost efficiency criteria. Also, the scales of both PV and wind farm projects were

set as equal for later comparison.

46

Table 6.2: Installed cost for solar power projects by technology in developed countries, 2011.

Reference: International Renewable Energy Agency (2012).

Figure 6.6: Installed cost of wind farm projects by size in the USA, 2015.

Reference: Bolinger and Wiser (2016).

Figure 6.7: Levelized cost electricity of wind farm projects by size in USA, 2011-2015.


47

6.2 Initial Costs

In the previous section, the best range of plant size was defined from a cost perspective. In

this section, the real costs of the projects will be studied in this section, information needed

for the RETScreen feasibility analysis section.

6.2.1 Initial Cost of the Solar Energy Project

When it comes to PV technologies, the next three technologies are studied because of their

relevance in the market:

- Crystalline Silicon (c-Si): Monocrystalline or polycrystalline. First generation PV

systems and most matured technology.

- Thin Film: Amorphous a-Si, Cadmium-Telluride (CdTe) and Copper-Indium-Selenide

(CIS). Named as “thin film” due to the low thickness (in the range of micrometres) of

the cells, made by semiconducting materials. Second generation PV systems which

are starting to reach some maturity in the market.

- Third generation PV technologies which are still on an experimental scale, still far

from a large commercializing phase.

According to the International Renewable Energy Agency (2016), crystalline silicon PV

modules currently account for more than 90% of new installations due to their relatively high

efficiency and low costs. Meanwhile, thin film PV systems are starting to consolidate

especially in the utility-scale market, accounting for almost all the market remaining, around

10%. As shown in Figure 6.8, the trends imply no major differences in the price per watt of

48

capacity for the mentioned technologies in the last 4 years, but due to the high consolidation,

reliability and market size of crystalline silicon PV, this will be the technology to be evaluated

in this assessment.

About the installed cost of utility-scale projects such as 40 MW solar power plant, these could

be divided into three main categories (International Renewable Energy Agency, 2016). :

- Soft costs: System design studies, permitting and financing.

- Installation: Main equipment, electrical-mechanical installation, inspection and

supervision.

- Hardware: Safety, mounting, monitoring, cabling and grid connection.

In Figure 6.9 it can be seen that for the year 2015, in most of the world, engineering and

financial studies accounts for less than 40% of the total cost, while equipment and

installation account nearly 60%. These costs could be as low as 500 USD/kW for countries

like China and Germany which produces and compete in PV module manufacturing industry,

while in Chile the total cost is around 1250 USD/MW, a medium cost when comparing to the

prices in other parts in the world. The influence of the total initial cost on the performance

indicators for each site will be addressed in the sensitivity analysis.

In the case of the tracking system, various articles such as Green Tech Media (2012) and Solar

Power World (2016) mention that by adding one-axis tracking system, a marginal increase of

0.08 to 0.1 USD/W in the initial cost is expected, but it would increase the amount of energy

generated from 10 to 40% depending on the latitude and the geographic location. About

the operation and maintenance (O&M) costs compared to fixed systems, an increase of only

49

3% one-tracking is expected according to NextTracker (2017). This increase in the capital cost

would mean a final initial cost of 1350 USD/kW

One-axis option seems to be the preferred tracking system by the market due to its price and

performance increase. On the other side, it is mentioned in the same articles that by adding

two-axis the increase of area would double. Therefore, a one-axis tracking system was

considered for this evaluation.

Figure 6.8: Global PV module price trends between 2010 and 2016.


50

Figure 6.9: Total installed cost of utility-scale solar energy projects in 2015.


6.2.2 Initial Cost for the Wind Energy Project

Onshore wind turbine technology was selected over offshore mainly because the total

installation doubles its counterpart. For onshore utility-scale projects, it is common for

individual wind turbines to have capacities around 1 MW. According to Bolinger & Wiser

(2016), in the range between 1 and 3 MW the cheapest total installed costs can be obtained,

around 1700 USD/kW the year 2015 in the USA (see Figure 6.10). On the other side, the same

authors explain that for hub heights above 80 meters, the generation capacity of individual

towers has been increasing up to 2 MW by the year 2015 (see Figure 6.11). Taking this into

consideration, All the wind resource data was generated at 78 meters, which is the closest

default height available to be selected in the Wind Explorer software.

51

According to an article of Electricidad (2016) magazine, the cost structure of utility-scale wind

energy projects is constituted by 75% the wind turbines equipment, 15% construction,

terrain works and electricity installations, and 10% of services and studies. The same article

shows that currently, the installation costs have dropped up to 1500 USD/kW in Chile.

Another article of Dinero (2015) states that in the same country, the installed cost of wind

farm projects ranged between 1200 and 1700 USD/MW in 2015.

Finally, the International Renewable Energy Agency (2015) also studied the initial cost of

utility-scale wind energy projects in different regions, unveiling a weighted average of 2200

USD/kW in South America the year 2014 (see Figure 6.12).

Using all the information retrieved, the wind energy projects will be evaluated as the

maximum wind power project initial cost observed in Chile, of 1700 USD/MW. The variations

of initial and how it hits the performance indicators will be evaluated in the risk analysis

section.

Figure 6.10: Installed cost per individual turbine capacity in the USA.


52

Figure 6.11: Evolution of tower capacity and height.


Figure 6.12: Wind energy projects installed costs 2014.


53

6.3 Operation and Maintenance Costs

The O&M costs will be defined as a function of the amount of energy produced, this is

USD/MWh. In the case of utility-scale PV plants, Bolinger and Seel (2016) state that in the

USA, for 30 different projects (a total of 546 MW) the average O&M costs of fluctuated

between 6.3 and 15.6 USD/MWh, an average of 10.1 USD/MWh in 2015.

For wind farms, Bolinger and Wiser (2016) mention that the O&M for projects installed after

2010 would be 9 USD/MWh in average in the USA. Another report made by the International

Renewable Energy Agency (2015) found that the 2014 average for O&M costs in the

Organization for Economic Cooperation and Development (OECD) countries, in which Chile

participates, were between 20 and 30 USD/MWh. An average between the lower OECD

range and USA prices will be considered.

Summarizing, the average between the minimum and maximum O&M costs found, equal to

10.1 and 19,7 USD/MWh will be used for solar and wind power plants respectively.

54

6.4 Transmission Costs

Even if it is assumed that the total installed costs of the projects of both technologies includes

electrical installation, voltage transformation and grid connection, the transmission cost is

not considered and must be addressed separately, as each project is situated at different

distances (see Table A.11) and the total amount of infrastructure (and investment) would be

different.

Yli-Hannuksela (2011) studied the total costs (in EU currency) of HVT lines, considering the

costs of materials, installation, civil, engineering and commissioning cost, also estimating the

influence of the amount of power input (in MVA) and the voltage characteristics. Using the

conclusive results of Figure A.10, the total investment cost (with a rate of 1.18 USD/EU, by

August 2017) of a HVT line for a 40 MW plant would be 224,000 USD/km, considering the

cost of the 72.5 kV HVT, the closest to the 66 kV HVT lines existing in the Central

Interconnected System. Table 6.3 shows the final HVT cost for each of the 38 projects.

Finally, the cost of the HVT transformer for a power input of 40 MVA will be included in the

total HVT item cost, which will be equal to 750,000 USD, a rough average of the observed

current prices for this equipment (Alibaba, 2017).

55

6.5 Cost Summary

As it mentioned before, by adding a one-axis tracking system to the PV modules in the solar

project case, the initial cost would increase in 0.1 USD/W, and their O&M in 3%. Table 6.3

summarizes the unitary cost (in USD/MW) and total cost of the power plants for each

technology and their O&M costs. Lastly, the total sum of the power plant and their HVT can

be seen in Table 6.4, presented in USD/kW as those are the units required in the input fields

on RETScreen.

Table 6.3: Initial cost of the power plants (without transmission costs).

Project Initial Capital Cost O&M

USD/kW MM USD USD/MWh

Solar Power Plant 1,350 54 10.4

Wind Power Plant 1,700 68 19.7

Table 6.4: Final installed cost of each project (transmission costs included).

Region/Site Initial Capital Cost (USD/kW)

Solar Projects Wind Projects

III-1 1,380 1,934

III-2 1,383 1,740

IV-1 1,374 1,732

IV-2 1,370 1,778

V-1 1,379 1,752

V-2 1,377 1,721

VI-1 1,379 1,764

VI-2 1,383 1,743

VII-1 1,379 1,767

VII-2 1,377 1,747

VIII-1 1,379 1,867

VIII-2 1,375 1,779

IX-1 1,387 2,061

IX-2 1,384 1,765

XIV-1 1,381 1,767

XIV-2 1,373 1,753

X-1 1,373 1,745

X-2 1,379 1,954

56

7. Software Feasibility Analysis

The resource availability data, along with the technical characteristics and cost data for each

type of project retrieved in section 5 and 6 will be used in this section to obtain the economic

results for each site.

7.1 Project Set Up

Figure 7.1 shows the first look upon opening RETScreen. Many types of projects can be

selected, from industrial energy efficiency to power projects, being this last mentioned the

one that will be used, which can be selected in Facility Types – Power Plants option as

indicated in the figure.

The Location tab should be open for the next stem (Figure 7.2). In this section, weather data

can be obtained by selecting the Climate Data Location button, which contains data from

various ground monitoring stations and NASA’s satellite data. Some of the data that is

automatically filled after selecting the location will be needed later for the estimation of the

energy produced, like the ground temperature which affects the solar power production. But

other fields like the solar radiation and wind speed columns must be erased and replaced by

the solar or wind resource data obtained previously, as shown in Figure 7.3.

57

Figure 7.1: RETScreen home window.

Figure 7.2: Location window.

Figure 7.3: Location tab.

58

7.2 Solar Power Plant Technical Data

After setting the climate location as the nearest to the project's site, the type of technology

must be set, which can be done by selecting project Type (photovoltaic or wind turbine) in

the Facility tab. After this, in the Energy tab, the type of project must be set again as it is

shown and highlighted in Figure 7.5. It is important to note that after adding the power plant

type in this tab (photovoltaic in this example), the Level 2 analysis method must be selected,

this will allow being more specific with the inputs that can be specified in this section.

This scheme for utility solar power plants contains equipment that is necessary to be met the

power supply quality required before the connection to the grid. The implementation of an

inverter transforming the DC current of the PV array into AC before the connection to the

grid. This device also performs the function of maximum power point tracker (MPPT). No

battery or diesel/gas generator will be considered in this application.

Taking this into consideration the simplified scheme of the power plant would go as shown

in Figure 7.4, which is the scheme that is related to the input fields in the Energy tab of

RETScreen.

The inputs in the Energy tab corresponds to:

- Resource assessment: Type of solar tracking mode, the slope of the modules and

their orientation.

- Photovoltaic: Model of the PV modules (which can be selected from the RETScreen

database), total capacity, efficiency and miscellaneous losses.

- Inverter: Miscellaneous losses and other inverter characteristics.

59

- Summary: Initial costs per kW and O&M annual costs can be set in this section, which

can be specified with more detail eventually in the Cost tab.

Figure 7.4: Simplified solar power plant scheme.

7.2.1 Orientation and Tracking System

A slope equal to the latitude angle will be set, which is a common criterion when installing

solar power plants because it maximizes the total yearly solar exposure. This means that for

each project from north to south, the slope field in RETScreen will be different. Also, the

azimuth will be set at 180°, which means that the modules will point to the equator, following

the same goal of maximizing the solar exposure on their surfaces. All the mentioned

considerations are to be set in the Energy tab as shown in Figure 7.5. Finally, as it was

mentioned, a 1-axis tracker will be added.

Figure 7.5: Photovoltaic project’s Facility and Energy tabs.

60

7.2.2 Module Model and Quantity

As stated in the previous section, crystalline silicon technology was selected over thin film.

Also, and as stated by Energy Informative (2017), due the higher silicon purity,

monocrystalline are more efficient and widely used in utility-scale projects, compared with

the lower silicon purity of polycrystalline, more frequently seen used in the residential

market.

After this, the manufacturer must be chosen before setting the module’s model, as shown

in the Product Database tab in Figure 7.6. In this matter, Trina Solar is chosen due to its

leadership in the utility market according to the Think Solar (2016) and Natural Energy Hub

(2017) magazine’s most recent rankings.

As can be seen in the mentioned figure, the TSM-DC05/275W module was selected, which

was the one with the highest efficiency in the database. An amount of 145,455 modules of

275 Watt each must be installed to reach the 40 MW capacity, and with a need of land of

around 95 hectares (four times the total modules frame area), this taking into consideration

the area required by a similar solar power plant in Antofagasta (north of Chile), which uses

217 hectares of the generation of 50 MW (Ministry of National Properties, 2017). This is not

a huge amount considering other solar PV projects like the 600 MW plant in Tarapacá in the

north of Chile, having a similar usage of land in relation to the size of the plant (University of

Chile, 2016).

Other efficiency factorss like the temperature coefficient are automatically set my RETScreen

according to the module model selected.

61

Figure 7.6: Product database.

7.2.3 Efficiency and losses

The efficiency of the modules was set upon the selection of the model, only the

miscellaneous losses of the PV array must be set separately, which corresponds mainly the

losses due to the dirt factor according to RETScreen manual (soiling losses).

The soiling losses depend on many factors, such as the weather (amount of dust or rain on

the site), human activity nearby the plant, frequency of cleaning, tilt angle and others. An

article of Solar Professional (2013) mention that the soiling losses increases during the year

when there is no cleaning, accumulating values up to 15% of losses during dry periods, which

62

could be fixed by cleaning procedures, which could recover the losses to 0%. In this matter,

for utility-scale PV power plants, Bosch et al. (2014) found an average of cumulative losses

0.21% per day, which could be rewritten as a total average of 3.15% considering a monthly

cleaning of the PV array.

In the case of the efficiency of the inverter, a typical efficiency of 96.4% for utility-scale

models will be chosen (Solar Facts, 2011), while the miscellaneous losses such as those

related to the DC/AC conversion will be set to 0.25% (Sola Power World, 2016). Finally, the

capacity must be set at an amount equal to the capacity of the electricity output of the PV

array, equal to 40,000.125 kW.

7.2.4 Solar Energy Tab Summary

All the input information of the solar power plant project (Level 2 analysis) is summarized in

Table 7.1, in same the order that the input fields appear in RETScreen. Auto-filled data like

the efficiency of the modules are not considered in this table.

Table 7.1: Energy tab - Solar project input summary.

Item Units Input

Solar tracking mode - One-axis

Slope Degrees Depends on location

Azimuth Degrees 180

Type - Mono-Si

Manufacturer - Trina Solar

Model - Mono-Si-TSM-DC05A/275W

Number of Units Modules 145.455

Miscellaneous losses (soiling) % 3.15

Efficiency (inverter) % 96.4

Capacity (inverter) kW 40,000.125

Miscellaneous losses (inverter) % 0.25

63

7.3 Wind Farm Technical Data

Wind turbines also require additional equipment to be connected to the grid. For this plant

design which is directly connected to the grid, an induction generator type of wind turbine

will be considered as it is the default setting available in RETScreen Energy tab. Induction

machines require reactive power to operate which can be obtained from the grid or from a

reactive compensator (Chakraborty et al. 2008) as shown in Figure 7.7. This figure also shows

a simplified scheme wind power plant to be evaluated in RETScreen. Once again, no battery

or diesel/gas generator will be considered.

The same procedure is made for setting a wind power project, by selecting wind turbine type

in the Facility tab and by adding a wind tower power plant in the Energy tab, as shown in

Figure 7.8. In this case, a Level 3 of project detail must be set, this will allow specific settings

like the wind shear, which is not available in the lower levels.

The inputs in the Energy tab are classified into these four sections:

- Resource assessment: Energy resource availability per month (which should be the

same as the input data set in the Location tab), height of the measurement and wind

shear (which can be estimated from the site satellite images).

- Wind Turbine: Model of the wind turbines (available in the RETScreen database), total

capacity (number of wind turbines) and efficiency. Also, the power curve is shown in

this section, which corresponds to the power output depending on the current wind

speed of the site, which is automatically set after selecting a wind tower model.

64

- Losses: Derating factors or losses and availability, which corresponds to the

downtime for maintenance or failures.

- Costs: Initial cost per kW and annual O&M cost can be set in this section.

Figure 7.7: Wind generator equipment and simplified wind power plant scheme.

Reference: Chakraborty et al. (2008).

Figure 7.8: Wind power project Facility and Energy tabs.

65

7.3.1 Wind Shear

The wind shear exponent α will define how the terrain influence the wind speed vh at

different altitudes h, at a given reference altitude h0 and wind speed v0, following the

Equation 7.1.

𝑣ℎ = 𝑣0 (ℎ

ℎℎ)

𝛼

𝐸𝑞. 7.1

This variable is needed by RETScreen for estimating the total power generation of the wind

turbine that depends of the wind speed at different heights. Gipe (2009) describes the

different wind shear exponent values that fits the characteristics and roughness of different

surfaces in Table 7.2, values that are tabulated for each wind power project site in Table 7.3

by using the satellite photos. Also, the roughness value of 0.25 for hilly and mountainous

terrain is added to expand the range of possible terrains (The Engineering Toolbox, 2017).

Table 7.2: Surface roughness and wind shear exponent.

Reference: Gipe (2009).

66

Table 7.3: Surface wind shear exponent.

Site Wind Shear Figure

III-1 0.25 A.1

III-2 0.25

IV-1 0.29 A.2

IV-2 0.25

V-1 0.25 A.3

V-2 0.25

VI-1 0.29 A.4

VI-2 0.29

VII-1 0.43 A.5

VII-2 0.43

VIII-1 0.43 A.6

VIII-2 0.43

IX-1 0.43 A.7

IX-2 0.43

XIV-1 0.43 A.8

XIV-2 0.43

X-1 0.43 A.9

X-2 0.43

7.3.2 Wind turbine Model and Quantity

Just like in the case of the solar power plant, a wind turbine manufacturer and model must

be selected. Vestas company has been in the business for almost 5 decades with high success

the last years because of the huge increase of wind power in the electricity market

worldwide, has the most relevant market share in the renewable energy market in Chile

according to an article of The Latin American Energy Review (2017), holding 68% of the

market and 886 MW operating in 2017. Vestas has proved his reliability since 2011 when the

first wind farm was installed, for these reasons Vestas wind turbine models will be considered

in the assessment.

After choosing the manufacturer, the product database must be opened for the selection of

the wind turbine model, which as mentioned in Section 7, should be a wind tower with a hub

67

height of at least 80 meters for this project size. Many models with these characteristics can

be found, but just a few show the power curve data, which includes the amount of power

that can be harness at different wind speeds, as can be seen in Figure 7.9. Hence, the curve

that reaches values near its maximum at the minimum wind speeds possible will be selected,

which would be the V82-0.9/1.65 MW-78m model (0.9 MW of capacity for each wind

turbine).

With 44 wind turbines and a total of 39.6 MW, this project would need around 27 hectares,

which is acceptable considering other wind farms in Chile of similar characteristics like El

Arrayan, with 50 turbines, 115 MW and the usage of 62 hectares (Mineria Chilena, 2014).

Figure 7.9: Wind turbine product database tab.

68

7.3.3 Losses and Availability

Array losses: Caused by the interaction of multiple turbines, where some cast shadow effect

upon others, being this the reason of why the positioning of the towers and their orientation

is important. Compton et al. suggest an average of approximately 5.7% for 7 wind farms in

different states in the USA, losses that consider all array, and other factors such as turbulence

and control losses.

Airfoil losses: Losses due to the accumulation of dirt, dust or snow in the wind blades that

distort their aerodynamic characteristics. Will be considered as 1% as (Compton et al. 2000)

Miscellaneous Losses: Electrical losses, between 2 and 3% according to Campiñez et al. Will

be considered as 2.5%.

Availability: The downtime percentage considered for maintenance and failures will be set at

96%, typical amount considered by many operators and investors for modeling the

performance of this type of projects (Williams, 2014).

7.3.4 Wind Energy Tab Summary

The input information of the wind power project (Level 3) is summarized in Table 7.4.

Table 7.4: Energy tab - Wind project input summary.

Item Units Input

Resource data m/s Values of Table A.2

Measured at m Values of Table A.2

Wind shear exponent - Values of Table 7.3

Manufacturer - Vestas

Model - VESTAS V82-0.9/1.65 MW-78m

Number of Units Wind Turbines 44

Array losses % 5.7

Airfoil losses % 1

Miscellaneous losses % 2.5

Availability % 96

69

7.4 Costs Details Data Input

After setting the technical specifications of each project, the cost detail can be set in the Cost

tab as shown in Figure 7.10, where only a Level 1 cost detail will be enough for setting user-

defined initial cost, as higher levels contain Green House Gases (GHG) studies and other

specific details that will not be covered in this assessment.

Finally, the annual O&M cost must be set in the same Energy tab (Summary section). This

must be done in terms of USD/kW and USD/MWh for the initial cost and O&M respectively

as suggested in Figure 7.10. By doing this, RETScreen automatically estimates the total annual

costs according to the size of the power plant, filling the input fields of the Cost Analysis tab

as shown in the previously mentioned figure. The initial cost and O&M input data were

estimated for each project in Table 6.4 in the previous section.

Figure 7.10: Energy tab cost summary section and Cost Analysis tab.

70

7.5 Financial Analysis Tab and Electricity Price

The last tab in which inputs are needed to get the economic performance indicators is the

Financial tan, which is shown in Figure 7.11. These inputs are divided into three sets:

- General: Inflation rate, discount rate and project life period.

- Finance: Information related to bank loan financing the project.

- Income Tax Analysis: Tax rates and shields information.

It must be noted that the Level 2 analysis button must be selected to be able to set most of

the previous input fields mentioned above.

7.5.1 Financial Data

According to the Central Bank of Chile (2017), the expected long-term inflation rate should

be around 3%, as it has been controlled between the range of 0 and 5% for almost a decade

(see Figure A.11).

The discount rate that best fit for renewable energy projects in Chile is 10% according to the

Chilean Renewable Energy Association and Natural Resources Defense Council (2013). As for

the project life, an amount of 25 years will be considered, just like many other wind farms

like El Arrayan in Pelambres which has a similar scale compared to the projects to be

evaluated in this study (Mineria Chilena, 2014).

For the finance inputs, no incentives will be considered. As for the loan, it is commonly

expected in projects of this scale because it lowers the risk for the investors and increases

the economic performance indicators. An article of Sustainable Earth (2017) highlight that

71

for small renewable energy projects like wind farms in Chile, the annual interest is around

6%, with a 75% of leverage and debt terms of over 10 years, while other sources like the

Financial Journal (2016) suggest interest rates around 5.1% for large companies and projects

like this one. Therefore, a leverage of 75% with 5.1% annual interest rate for 15 years will be

used as inputs in this section.

Figure 7.11: Financial Analysis tab.

The income tax which is paid once a year in Chile varies between 26 and 27% depending on

the regime selected by the company, so 26% tax rate will be used. Finally, electric projects

have many different assets that can be depreciated at different regimes, and to simplify this,

a linear depreciation rate of 6 years will be considered, as it is allowed for electrical

installations and buildings according to the table of working life of goods and immobilized

assets accelerated depreciation in the current tax regulation (Intern Tax Service, 2017).

72

7.5.2 Energy Market and Electricity Price

An additional input is required here for the software to generate the results, which is the

price of the energy. This input can be found back in the Energy tab as shown in Figure 7.12,

along with the type of revenue which in this case corresponds to electricity export rate since

the focus of the projects that are being evaluated is to export to the grid (CIS).

Being the CIS the main vehicle for commercialization of electric energy, there are three main

formats for selling electricity: Via spot price market at the marginal cost of the electricity

upon injection, via direct contracts with a consumer at negotiated prices (PPA) and via

stabilized price for small non-conventional renewable energy generators with a capacity

below 9 MW, which is not this case.

The spot price has been decreasing gradually the last decade because of the introduction of

renewable energy projects in the CIS the last decade, as shown in Figure A.12 (National

Commission of Energy, 2017), stabilizing in the past years with an average of 62.3 USD/MWh

(National Commission of Energy, 2016). On the other side, the Medium Market Price, which

is basically the average price at which the supplied electricity is sold through private contracts

(PPA), was 93.6 USD/MWh in 2016 (Generators Chile, 2017). Therefore, this evaluation will

consider the average of 78.0 USD/MWh as the base scenario, which will be tested later with

the RETScreen Risk Analysis tool, to see how the economic performance is influenced by

when decreasing the price.

As a mean of comparison, as it was mentioned there is another mechanism of revenue

allowed only for non-conventional renewable energy generators below the 9 MW of capacity

73

(small distributed generation), which leads access to a stabilized market price that is

approximately equal to the average of the contract prices (PPA) observed in the past 4 years.

As can be seen in Figure A.14 (Systep, 2016), the price has been following a stabilized regime,

hitting a minimum of 75 USD/MWh during 2016. Also, according to Mozó (2016), this price

should experience an increase up to 100 - 110 USD/MWh in the next 15 years. Hence, it is

safe to consider the base scenario at a conservative 78 USD/MWh, as it is near the minimum

observed in the PPA market and far from positivistic price perspectives.

Finally, a conservative 0% of electricity export escalation rate will be set. The relationship

between the variation of the price of electricity and the economic performance indicators

will be observed nonetheless in the sensitivity analysis, being this the reason for keeping this

input field in at 0%. This setting can be modified in the Financial Analysis tab (Figure 7.14).

Figure 7.12: Electricity rate settings.

74

7.5.3 Power Capacity Income

The power capacity available also perceives incomes, and according to the National

Commission of Energy (2016) and from what can be seen in Figure 7.13, the prices have been

experiencing some variability, where the average price in the CIS is equal to 10,628 USD/MW-

month, or 127.536 USD/kW-year in 2016. The aim of this mechanism is to maintain the

reliability of the electric system by giving revenues to generators like thermoelectric plants

who sells electricity mainly during peak load periods at high rates, which only pays their costs

of electricity but not the investment costs of the project. Since these plants have 100%

availability they are paid for the whole capacity they have, and in the case of renewable

energy, they would only receive revenues for the fractional power capacity they really have,

considering their low capacity factors.

The capacity factor of solar and wind power plants will vary depending on the resource

availability, and during the evaluation of the resources in RETScreen, it was found that for

solar power it ranged between 10 and 30%, while for wind power it ranged from 20 to 40%,

considering all the sites evaluated. Therefore, a capacity factor of 20% and 30% for solar and

wind power plants respectively will be considered for estimating the annual revenue for

available power capacity.

This revenue can be added by marking the checkbox in Other Revenue section in the Financial

Analysis tab as shown in Figure 7.14.

75

Figure 7.13: Power Capacity Price between 2006 – 2016 (CIS in Blue).


7.5.4 Subsidies, Incentives

No direct subsidies will be considered for this evaluation. However, regulatory incentives are

granted to renewable energy in order to accomplish the NCRE goals for 2024 established in

the Law 20.257 (National Library of Chile, 2013). These incentives come in the form of

Renewable Energy Certificates (CER), where companies who retrieve energy from the system

must demonstrate the usage of at least 10% to 18% by 2024 of NCRE, paying penalty fees

from 0.4 to 0.6 UTM for every MWh of non-renewable energy over the limit, around 37

USD/MWh (Internal Tax Service, 2017).

This way, a market for CER certificates was created and will continue existing while the

energy goals for the increase of renewable energy supply are maintained considering the

new goal of 70% NCRE supply by the year 2050 according to the Decree 148 (National Library

of Chile, 2016). The price of the CERs in the market were just a bit below the penalty fee of

76

37 USD/MWh the first years, but according to an article of Alberto Hurtado University (2015),

these prices had decreased and stabilized around 7.1 to 10.2 USD/MWh (4,500 – 6,500

CLP/MWh as shown in Figure A.13) between the 2010 and 2014 period, and the because of

the continuous entrance of renewable energy generators, it is expected for this price to

continue dropping. Therefore, a conservative and constant value of 4 USD/MWh for the CERs

will be set with no future increase in the price and for a duration of only 6 years assuming a

future scenario in which there will be no incentives of any kind for the renewable energy

industry after 2024. These inputs are shown in Figure 7.14. Additional incomes like those

related to the reduction of GHG (USD/ton CO2) will not be considered in this assessment.

Figure 7.14: Annual revenues in Financial Analysis tab.

77

7.5.5 Financial Analysis Tab Summary

All the financial input data (for the Level 2 analysis option), which will be used for both solar

and wind power projects, is summarized in Table 7.5.

Table 7.5: Financial Analysis tab – Input summary.

Item Units Input

Inflation rate % 3

Discount rate % 10

Project life Years 25

Debt ratio % 75

Debt interest rate % 5.1

Debt term Years 15

Effective income tax rate % 26

Loss carryforward - Yes

Depreciation method - Straight-line

Depreciation period Years 6

Electricity export escalation % 0

Solar project capacity (available)

kW 8,000

Wind project capacity (available)

kW 12,000

Rate (power capacity) USD/kW-year 127.5

CE production credit rate (price)

$/MWh 4

CE production credit duration Years 6

78

7.6 Sensitivity and Risk Analysis Tab

Once all the resource, technical, cost and financial information is set, the end results can be

generated in the Sensitivity Analysis tab. There, the internal rate of return can be tested by

selecting the Perform Analysis field, where a 30% of sensitivity range will be set along with a

threshold of 10%, which will highlight IRR results below that value that is equal to the

discount rate, limit at which the project is feasible. Figure 7.15 shows the IRRsensitivity upon

the initial cost and the electricity export rate. The LCOE and IRR results of each project will

be studied in the next section.

Figure 7.15: Sensitivity and risk analysis tab.

79

8. Economic Performance Indicators and Sensitivity Analysis

The two main economic performance indicators that will be studied are the levelized cost of

energy and the internal rate of return. The IRR is calculated by RETScreen automatically after

all the necessary input fields a filled. On the other side, the life cycle cost analysis variables

needed for the estimation of the LCOE are not determined by the software, so they will be

calculated separately.

8.1 Economic Indicators to be Used

Firstly, the LCOE (USD/MWh) according to the Darling (2011) is defined by Equation 8.1,

basically representing the cost of the energy necessary to pay the life-cycle cost of the

project. For this, and taking into consideration the effects of the discount rate and inflation,

these variables must be actualized by following the steps of Equations 8.2, 8.3 and 8.4

(Murdoch University, 2017).

𝐿𝐶𝑂𝐸 =𝐿𝑖𝑓𝑒 𝐶𝑦𝑐𝑙𝑒 𝐶𝑜𝑠𝑡

𝐿𝑖𝑓𝑒𝑡𝑖𝑚𝑒 𝐸𝑛𝑒𝑟𝑔𝑦 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝐸𝑞. 8.1

𝑁𝑃𝐶 = 𝐶𝑖 + 𝑃𝑊𝐹(𝑡, 𝑖, 𝑛) · 𝐴𝐶 𝐸𝑞. 8.2

𝑃𝑊𝐹 =1 − (

1 + 𝑖1 + 𝑡)

𝑛

(1 + 𝑡1 + 𝑖) − 1

𝐸𝑞. 8.3

𝐿𝐶𝑂𝐸 =𝑁𝑃𝐶

𝐿𝑖𝑓𝑒𝑡𝑖𝑚𝑒 𝐸𝑛𝑒𝑟𝑔𝑦 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 · 𝑃𝑊𝐹 𝐸𝑞. 8.4

Where:

NPC (USD): Net present value, equal to the actualized life-cycle cost of the project.

80

Ci (USD): Initial cost of the project.

AC (USD/year): Annual operating and maintenance costs.

PWF (-): Present worth factor for series of equal payments, which actualizes all the annual

costs and energy production, that at the same time depends on the value of the discount

rate t, inflation rate i and the evaluation period n.

NPV (USD): Defined as the measure of how much value would be created or added (or saved)

in the present by doing a certain investment. It is equal to the sum of the actualized value of

all the cash flows Fn at a defined discount rate t of the project evaluation period n.The NPV

is one of the most used performance indicators for evaluating projects, but it should not be

used for comparing projects with different initial capital cost, like in this case.

Payback Period (Years): Amount of time needed for an investment to create the cash flows

sufficient to get back the initial capital cost Ci. For an even annual cash flow Fi, the Equation

8.7 represents how to calculate the payback, but since the cash flows will not be even

because of several factors such as inflation and other factors, the software estimates the

payback considering the cumulative cash flow until it reaches the initial capital cost. The

disadvantage as an economic indicator is that it speaks little about how profitable a project

is, allowing to take conclusions only in terms of years needed to recover the investment in

comparison to the life of the project.

IRR (%): Internal rate of return, which is basically the value of the discount rate at which the

NPV of a project is exactly zero. A good measure for easily identifying which project is

profitable or not when comparing similar projects, as it is needed in this assessment. On the

81

other side, RETScreen uses the estimated value of the net present value for calculating the

IRR, by using Equations 8.5 and 8.6 (Jordan et al. 2003):

𝑁𝑃𝑉 = ∑𝐹𝑛

(1 + 𝑡)𝑛

𝑛

𝑖=0

𝐸𝑞. 8.5

∑𝐹𝑛

(1 + 𝐼𝑅𝑅)𝑛

𝑛

𝑖=0

= 0 𝐸𝑞. 8.6

𝑃𝑎𝑦𝑏𝑎𝑐𝑘 =𝐶𝑖

𝐹𝑛 𝐸𝑞. 8.7

As can be noted, the LCOE is a specific performance indicator for energy projects, whereas

the IRR is an indicator who speaks about the profitability of projects in general. But should

the IRR be used as the main feasibility indicator? To explain this, other relevant economic

performance indicators relevant for evaluating economic performance will be highlighted.

ROI (%): The return on investment, also known as rate of return (ROR), shows information of

how profitable a project is in a way easy to understand and dimension. It can be calculated

with Equation 8.8, just taking into consideration the relation between the initial and end

value of a project, being the end value equal to the sum of all the cash flows (after tax benefit)

in the project life of n years (Phillips, 2002 and Planprojections 2017).

𝑅𝑂𝐼 =𝐸𝑛𝑑 𝑉𝑎𝑙𝑢𝑒

𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝑉𝑎𝑙𝑢𝑒=

∑ 𝐶𝑎𝑠ℎ 𝐹𝑙𝑜𝑤𝑖𝑛𝑖=0

𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝑉𝑎𝑙𝑢𝑒 𝐸𝑞. 8.8

Should the ROI be used for comparing projects in the pre-feasibility analysis? Aho and

Virtanen (1982) made a study of ROI and IRR, and some of their conclusions are listed below:

82

- IRR considers the value of money in time as it accounts all cash flows. ROI does not.

- IRR reflects the profitability of the project on an annual basis. ROI accounts for the

whole period.

- Inflation increases the final net profitability for ROI. IRR also considers these increases

but end up showing the profitability on a present value basis.

Therefore, ROI does not estimate properly the profitability of a project as it does not consider

the effect of the inflation and the debt ratio or bank leverage, among other factors when

evaluating different projects through cash flow, and it is more widely used to study the

performance of an ongoing business.

HPR (%): Holding Period Return, one of the most basic profitability indicators, and a variation

of the ROI which is calculated through Equation 8.9, mostly used for analyzing the

profitability of an asset or portfolio, and not necessarily projects on a cash flow basis

(Financetrain, 2017). This indicator (as can be observed in Equation 8.9), does not consider

the effects of the discount rate, inflation or debt ratio, so it cannot be used for the purpose

of deciding which projects are feasible in the same way it was concluded for ROI.

Nevertheless, this parameter will be used to compare the profitability of the projects to the

alternative of mutual funds and long-term deposits, at the rates observed in Chile.

𝐻𝑃𝑅 (𝑃𝑒𝑟𝑖𝑜𝑑) = (𝐸𝑛𝑑 𝑉𝑎𝑙𝑢𝑒 − 𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝑉𝑎𝑙𝑢𝑒

𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝑉𝑎𝑙𝑢𝑒+ 1)

1𝑛

− 1 𝐸𝑞. 8.9

Nevertheless, ROI and HPR will be estimated in the last section for the best projects identified

in the sensitivity analysis to complement the main performance indicators.

83

8.2 Levelized Cost of Energy Results

The LCOE results along with the sensitivity analysis and average resource availability are

shown in Figure 8.1 and 8.2 for all the 38 solar and wind power projects respectively. The

LCOE was also evaluated against the variations up to 30% of the initial cost to be compared

to the base scenario.

Considerations for the LCOE evaluation:

- The LCOE values estimated are not dependent on the incomes of electricity sales or

the annual negative flows of the debt payment.

- The LCOE is only dependant to the initial cost and regular annual costs (O&M) at the

selected discount and inflation rates over 25 years.

- Hence, when comparing the LCOE only against the projected electricity price, the

number of projects that could result feasible under these criteria will be different

when comparing the number of projects feasible in the IRR analysis, this is because

the power capacity and CER certificate sales are not reflected in the mentioned

electricity price.

- Results of LCOE below the average market price of electricity of 78 USD/MWh

indicates that the costs of the projects are below the electricity rates, and will be

considered as feasible projects.

Analysing the solar projects in Figure 8.1:

- Base LCOE Scenario: In the central scenario with no variations in the initial cost, the

12 of the projects located in the north and the central regions resulted to be feasible

84

- (+)30% Initial Cost: Only 4 projects located in the north of the country resulted

feasible in this scenario.

- It can be observed that it is needed an average daily solar radiation on horizontal

surface of at least 5 kWh/m2day to achieve values of LCOE below the electricity

market price, and over 6 kWh/m2-day in the case of an increase of 30% in the initial

costs.

Analysing the wind projects in Figure 8.2:

- Base LCOE Scenario: In the central scenario with no variations in the initial cost, 15 of

the projects resulted feasible. Most of the non-profitable projects were located in the

central regions.

- (+)30% Initial Cost: 10 of the projects resulted to be feasible in this scenario.

- It can be observed that it is needed an average wind speed of at least 6 m/s to achieve

values of LCOE below the electricity market price in the base scenario, and over 7 m/s

in the case of a 30% higher initial cost.

85

Figure 8.1: LCOE sensitivity analysis of the results for the solar power projects.

Figure 8.2: LCOE sensitivity analysis of the results for the wind power projects.

2

3

4

5

6

7

8

50

60

70

80

90

100

110

120

130

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Solar Radiation (kWh/m2-d)

LCO

E (U

SD/M

Wh

)

Solar Projects - Levelized Cost of Energy Sensitivity Analysis

Solar Radiation

Base Energy Price

Base ScenarioLCOE

+30% Initial CostUSD/MW

2

3

4

5

6

7

8

9

10

50

70

90

110

130

150

170

190

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18Wind Speed (m/s)

LCO

E (U

SD/M

Wh

)

Wind Projects - Levelized Cost of Energy Sensitivity Analysis

Wind Speed

Base Energy Price

Base ScenarioLCOE


86

8.3 Internal Rate of Return

The IRR results, along with the sensitivity analysis and average resource availability are shown

in Figures 8.3 and 8.4 for all the solar and wind power projects respectively, where this value

was also evaluated against an increase 30% on the initial cost and a drop 30% of the

electricity rate in the long term.

Considerations:

- The scenario in which the incomes related to energy like the electricity rate, the

power capacity annual revenue and CER certificates sales, is considered to be the

base scenario from which the sensitivity analysis is done considering 4 additional

scenarios: Only energy and capacity sales, only energy sales, +30% initial cost in the

base scenario and -30% electricity rate in the base scenario.

- The IRR evaluation takes into consideration the incomes related electricity, capacity

power and CER certificates sales, debt terms and financial rates, while LCOE only

considers annual costs and initial cost, which will lead to differences in the results.

- Results of IRR over the discount rate of 10% indicate a positive value of the NPV and

will be considered as feasible projects.

Analysing the solar projects in Figure 8.3:

- Energy, Capacity and CER (Base Scenario): 14 of the projects in the northern and

central regions are feasible.

- Energy and Capacity: 13 of the projects in the northern and central regions are

feasible.

87

- Only Energy Sales: 6 of the projects located in the north are feasible.

- +30% Initial Cost: 6 of the projects located in the north are feasible.

- -30% Energy Price: Only 3 of the projects located in the north are feasible.

- An average daily solar radiation on horizontal surface of at least 4 kWh/m2day is

needed to achieve values of IRR over 10%, and at least 6 kWh/m2day in the worst-

case scenario, a decrease of 30% in electricity rates.

Analysing the wind projects in Figure 8.4:

- Energy, Capacity and CER (Base Scenario): 17 of the projects placed in the regions of

the country are feasible.

- Energy and Capacity: 17 of the projects placed in all the country are feasible.

- Only Energy Sales: 12 of the projects located in the north and south are feasible.

Most of the non-profitable projects were located in the central regions.

- +30% Initial Cost: 11 of the projects located in the north and south are feasible.

- -30% Energy Price: Only 3 of the projects located in the north and the south are

feasible.

- An average wind speed of around 5.5 m/s is needed to achieve values of IRR over

10%, and at least 8 m/s in the worst-case scenario with a drop of 30% in electricity

rates.

88

Figure 8.3: Sensitivity analysis of the internal rate of return results for the solar power projects.

Figure 8.4: Sensitivity analysis of the internal rate of return results for the wind power projects.

2

3

4

5

6

7

8

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18Solar Radiation (kWh/m2-d)

IRR

(%

)

Solar Projects - Internal Rate of Return Sensitivity Analysis

Solar Radiation

Discount Rate %

Energy-Power-CERSales (Base Scenario)


-30% Energy PriceUSD/MWh

Energy-Power Sales

Only Energy Sales

2

3

4

5

6

7

8

9

10

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Windspeed (m/s)

IRR

(%

)

Wind Projects - Internal Rate of Return Sensitivity Analysis

Windspeed

Discount Rate %

Energy-Power-CERSales (Base Scenario)


-30% Energy PriceUSD/MWh

Energy-Power Sales

Only Energy Sales

89

8.4 Projects Qualification

By using the final sensitivity analysis results shown in Tables A.9 and A.10 (Annex section), all

the solar and wind projects were qualified in Tables 8.1 and 8.2 respectively, with a score

scale from 0 to 7, where the projects were given 1 point if they showed profitable results in

each sensitivity analysis scenario. From this analysis, it was found that only 3 solar and 3 wind

projects withstood all scenarios, including the energy price drop of 30% which resulted to be

the most difficult scenario to overcome.

These end results are summarized and studied in the last section of discussion and

conclusions.

Table 8.1: Solar projects feasibility analysis summary.

Site

Energy, Power &

CER (Base Scenario)

Energy &

Power

Only Energy

+30% Initial

Capital Cost

-30% Energy Price

Variation

Base Scenario

LCOE

(+)30% Initial Cost

LCOE

Score (0 – 7)

III-1 ✓ ✓ ✓ ✓ ✓ ✓ ✓ 7

III-2 ✓ ✓ ✓ ✓ ✓ ✓ ✓ 7

IV-1 ✓ ✓ ✓ ✓ ✖ ✓ ✓ 6

IV-2 ✓ ✓ ✓ ✓ ✓ ✓ ✓ 7

V-1 ✓ ✓ ✓ ✓ ✖ ✓ ✖ 5

V-2 ✓ ✓ ✓ ✓ ✖ ✓ ✖ 5

VI-1 ✓ ✓ ✖ ✖ ✖ ✓ ✖ 3

VI-2 ✓ ✓ ✖ ✖ ✖ ✓ ✖ 3

VII-1 ✓ ✓ ✖ ✖ ✖ ✓ ✖ 3

VII-2 ✓ ✓ ✖ ✖ ✖ ✓ ✖ 3

VIII-1 ✓ ✓ ✖ ✖ ✖ ✓ ✖ 3

VIII-2 ✓ ✓ ✖ ✖ ✖ ✓ ✖ 3

IX-1 ✓ ✖ ✖ ✖ ✖ ✖ ✖ 1

IX-2 ✓ ✓ ✖ ✖ ✖ ✖ ✖ 2

XIV-1 ✖ ✖ ✖ ✖ ✖ ✖ ✖ 0

XIV-2 ✖ ✖ ✖ ✖ ✖ ✖ ✖ 0

X-1 ✖ ✖ ✖ ✖ ✖ ✖ ✖ 0

X-2 ✖ ✖ ✖ ✖ ✖ ✖ ✖ 0

90

Table 8.2: Wind projects feasibility analysis summary.

Site

Energy, Power &

CER (Base Scenario)

Energy &

Power

Only Energy

+30% Initial

Capital Cost

-30% Energy Price

Variation

Base Scenario

LCOE

(+)30% Initial Cost

LCOE

Score (0 – 7)

III-1 ✓ ✓ ✓ ✓ ✖ ✓ ✓ 6

III-2 ✓ ✓ ✓ ✓ ✖ ✓ ✓ 6

IV-1 ✓ ✓ ✖ ✖ ✖ ✓ ✖ 3

IV-2 ✓ ✓ ✓ ✓ ✓ ✓ ✓ 7

V-1 ✖ ✖ ✖ ✖ ✖ ✖ ✖ 0

V-2 ✓ ✓ ✖ ✖ ✖ ✖ ✖ 2

VI-1 ✓ ✓ ✖ ✖ ✖ ✖ ✖ 2

VI-2 ✓ ✓ ✖ ✖ ✖ ✓ ✖ 3

VII-1 ✓ ✓ ✓ ✖ ✖ ✓ ✖ 4

VII-2 ✓ ✓ ✓ ✓ ✖ ✓ ✓ 6

VIII-1 ✓ ✓ ✓ ✓ ✖ ✓ ✓ 6

VIII-2 ✓ ✓ ✓ ✓ ✓ ✓ ✓ 7

IX-1 ✓ ✓ ✖ ✖ ✖ ✓ ✖ 3

IX-2 ✓ ✓ ✓ ✓ ✖ ✓ ✓ 6

XIV-1 ✓ ✓ ✓ ✓ ✖ ✓ ✓ 6

XIV-2 ✓ ✓ ✓ ✓ ✖ ✓ ✓ 6

X-1 ✓ ✓ ✓ ✓ ✓ ✓ ✓ 7

X-2 ✓ ✓ ✓ ✓ ✖ ✓ ✓ 6

91

9. Discussion and Conclusions

9.1 Discussion of the Results and Limitations

The end results, which correspond to the LCOE and IRR of all the 38 solar and wind projects

are shown in Tables A.7, A.8, A.9, A.10. The mentioned tables show a lengthy list of indicators

and their sensitivity analysis results, from which the best projects are identified through the

qualification method done in the last section, which details are summarized in Table 9.1:

Coordinates of the site, resource availability, installed cost, ROI, HPR, Payback, LCOE and IRR

in the base scenario and the -30% energy rate scenario, which resulted to be the one that

influenced the economic performance the most. All this under the considerations given in

Tables 9.2 and 9.3.

These projects obtained perfect qualification as they withstood all the sensitivity analysis

tests, this is, having an internal rate of return higher than the discount rate of 10% and at the

same time a levelized cost of energy lower than the market’s average electricity price in all

scenarios.

About the solar projects, it can be said that:

- The best sites for the solar projects, this is with highest IRR and lowest LCOE results,

were III-1, III-2 and IV-2, being these projects the only ones able to be feasible in all

the scenarios considered in the sensitivity analysis.

- It must be noted that the northern regions of the country showed the best results.

This is mainly because the amount of solar radiation is higher than in the center and

south of the country.

92

- The installed costs of the solar projects were very similar because the solar radiation

density was very homogenous as it was seen in the Solar Explorer colored map layers,

so there were many options for choosing sites near the grid, saving costs related to

the HVT lines.

- The ROI for these three projects indicates that the future flows will recover 1.63 to

1.86 times the cost of the investment, with paybacks of 4 years. The HPR ranged

between 3.9 and 4.3% almost as twice as the interest rates observed in the financial

market.

From the wind projects, it can be concluded that:

- The best sites which for the wind projects, were IV-2, VIII-2 and X-1, being these

projects the ones who withstood all the sensitivity analysis scenarios.

- It can be mentioned that there were better results in the northern and southern

regions, whereas most of the projects of elevated risk were found in the central zone

of the country.

- Besides having more resource availability in the north and south, another

characteristic of the wind resource was that it is less homogeneous compared to the

solar resource, and that higher wind speeds were found in specific places like in hilly

areas, which were frequently far away from the grid, increasing the difference of total

installed cost between the wind projects (due the HVT lines cost).

- The ROI for these three projects indicates that the future flows recover 1.70 to 1.89

times the investment, with paybacks of 3 years. The HPR ranged between 4.1 and

4.3%, a bit better compared to what was obtained for the solar projects.

93

Table 9.1: Feasibility analysis summary – Best solar and wind projects found.

Project

ROI (%)

Project Period

HPR (%)

Annual Rate

Payback (Years) Base

Scenario

LCOE (USD/MWh)

IRR (%)

Base Scenario

+30% Installed

Cost

Base Scenario

Only Energy Sales

-30% Energy

Rate

Solar

III-1 175.5 4.1 4 56.2 70.0 28.3 19.0 11.8

III-2 186.3 4.3 4 54.0 67.1 30.9 21.3 13.3

IV-2 163.3 3.9 4 59.1 73.6 25.4 16.5 10.3

Wind

IV-2 188.7 4.3 3 61.0 64.7 36.3 23.9 12.1

VIII-2 175.2 4.1 3 56.5 67.6 32.6 20.7 10.2

X-1 175.3 4.1 3 56.6 67.7 32.6 20.5 10.3

Table 9.2: Resource and costs details.

Project Site Annual Average Resource

Installed Cost (USD/kW)

Solar

III-1 29.14 S - 70.92 W 6.57 kWh/m2d 1,380

III-2 26.31 S - 69.92 W 6.77 kWh/m2d 1,383

IV-2 30.05 S - 70.72 W 6.20 kWh/m2d 1,374

Wind

IV-2 30.79 S - 71.67 W 8.9 m/s 1,778

VIII-2 36.89 S - 72.65 W 8.0 m/s 1,779

X-1 42.49 S - 73.86 W 8.1 m/s 1,745

Table 9.3: Other considerations summary.

Item Units Input

Incomes Data

Electricity export price $/MWh 78

Price variation (long term)

% 0

Power capacity price USD/kW 127,536

CE certificate price $/MWh 4

CE sales duration Years 6

Technical Data

Solar Panels Model - Mono-Si-TSM-DC05A/275W

Number of Units Modules 145.455

Wind Turbines Model - VESTAS V90-1.8 MW-105m

Number of Units Wind Turbines 22

Solar Project O&M $/MWh 10.4

Wind Project O&M $/MWh 19.7

Financial Evaluation Data

Inflation rate % 3

Discount rate % 10

Project life Years 25

94

Debt ratio % 75

Debt interest rate % 5.1

Debt term Years 15

When analyzing the results of LCOE (Table A.3) it was found that it ranged between 54 to 99

USD/MWh for solar projects, and from 54 to 80 USD/MWh for the wind projects (excepting

V-1, with a non-systematic 146). Comparing these results with the study made by Bloomberg

New Energy Finance (2011) mentioned in the literature review, which predicted LCOE values

in Chile for utility-scale projects, an important decrease of the LCOE values for both

technologies was found as the values predicted by the mentioned study ranged between 92

to 143 USD/MWh for solar projects, and from 60 to 121 USD/MWh. Predicting higher costs

is understandable considering that this study was made years ago with different costs

variables into consideration, in a scenario where the CIS energy average price was predicted

to be around 110 USD/MWh, when in the present the average price for that market is about

80 USD/MWh. Nonetheless, the LCOE values in the mentioned study, along with what Oses

et al. (2016) found, were projected between 50 and 120 USD/MWh for wind projects, very

close to what was found in this work, except for the higher limit.

The obtained LCOE values are also near to the magnitude of the average values observed in

other markets in the world, as seen in Figures 6.5 and 6.7 (Farrell, 2016), for utility-scale solar

and wind energy projects, which were 41 – 65 and 35 – 42 USD/MWh respectively, results

that shows lower results than the ones found in this work mainly because it considers the

values of the European and American market.

95

Not much information is available about IRR and ROI rates for the energy market in Chile, but

the HPR of these projects can be compared with the profitability of mutual funds and long-

term deposits observed in the financial market of Chile, being these tools basically

mechanisms that financial institutions offer to capitalize funds by managing portfolios of

stocks or bonds. In the present, the Bank of Chile (2017) offers mutual funds with annual

interest rates between 0.6 and 1.9%, whereas the most competitive long-term deposits from

different financial institutions offers annual interest rates from 2.16 to 2.65% (Rankia, 2016).

The HPR values found ranged between 3.9 and 4.3%, almost twice of the compound interest

that financial institutions in Chile offer. The limitation of this approach, as it was mentioned

before, is that does not consider the many of the variables present in the cash flow evaluation

such as the inflation (which increase the future cash flows), debt ratio (which decrease the

initial cost for the investor), among others. Also, the payback of these 6 projects resulted to

range between 3 to 4 years, which is expected as the initial cost is drastically reduced to a

25% because of the debt ratio used in this evaluation.

The main limitation and source of error of the prediction of the main economic performance

indicators (LCOE and IRR) calculated in this work comes from the solar and wind explorer

resource availability data, which average mean squared deviation, when compared to

measured data from numerous of monitoring stations in the country, were 10.6 and 14.0%

for solar radiation on horizontal surface and wind speed respectively. Also, the site selection

was not optimal, but a better alternative for further works will be discussed later.

Other sources of error could come from the reliability of the cost data retrieved from

numerous sources, considering for example the initial installed costs which were retrieved

96

mainly from the International Renewable Energy Agency (2016), and which could not always

would reflect the real average prices observed in the electric investments market in Chile in

the present, and especially considering that specific PV module and wind turbine models

were selected and considered to be in relation to the prices found in the general market.

Also, many of the inputs needed for RETScreen to run the feasibility analysis were extracted

from numerous various sources and different dates, which could lead to an error as they do

not represent with perfection the real values that could be observed in the market.

Finally, the initial costs established could lack some factors like the commissioning cost or

any other item that a regular capital expenditure analysis would consider. Hence, the initials

cost found in this study were assumed as the real one.

97

9.2 Conclusions and Further Works

The main goal of this work was to estimate the economic performance indicators of solar

and wind energy projects in the Central Interconnected System of Chile to find the most

profitable projects. For this, a solar and wind resource assessment was done with the help

of online resource map tools (Solar and Wind Explorer), which generate the daily solar

radiation on horizontal surface and wind speed data for the whole country. Sites with high

resource availability were selected for being used later in an economic feasibility analysis

with current technological and cost data. The analysis was done with RETScreen, software

specialized in performing this type of analysis for renewable energy projects.

All the economic performance data, in form of the levelized cost of energy and internal rate

of return, were plotted and compared to the average energy price and a discount rate

observed in the market, which were found to be 78 USD/MWh and 10% in the case of Chile.

These variables were tested again in a sensitivity analysis which considered seven risk

scenarios such as an increase in the initial cost and a decrease of the price of the electricity,

obtaining with this method the sites with best economic performance and lowest risk.

In conclusion, it is highly recommended for solar projects to be evaluated in the north of

Chile, especially in the III, IV and V regions, where 6 of the best results were found, compared

to the other 12 projects evaluated which were found to be non-feasible in most of the

scenarios. Also, 3 of the 18 projects evaluated (sites III-1, III-2 and IV-2) withstood all the risk

scenarios, making them the most profitable ones.

98

For wind projects, 10 of 18 resulted to be feasible in most of the scenarios, placed in the III,

IV, VII, VIII, IV, XIV and X regions. From all the projects evaluated, the sites IV-2, VIII-2 and X-

1 were the most profitable and least exposed to risk scenarios. Also, the wind resource

resulted to be very focalized and not as homogeneous as the solar resource, which influences

the distance from the CIS system and the installed cost.

About the sensitivity analysis, it was found that the price of energy resulted to be the most

important variable to consider due to its important impact on the internal rate of return.

Nevertheless, a drop of 30% was considered, which would mean prices below 50 USD/MWh,

which is very unlikely in the long term according to the electric market historic information.

Addressing the questions which were proposed at the beginning of this work:

- How the solar and wind resource are distributed in Chile and where the resource

availability is higher?

In the resource assessment section, all the results (see Tables A.1 and A.2) of solar radiation

on horizontal surface and wind speed annual profiles of the selected sites were plotted in

Figure 5.13 and 5.14 respectively, where it can be seen a pronounced seasonality in the case

of solar radiation, and high concentration of this resource in the north of Chile with maximum

annual averages 6.77 kWh/m2-day the III region. In the case of the wind resource, the annual

profile between regions did not follow any recognisable pattern compared to the solar

resource seasonality between summer and winter, instead, the annual average wind speed

of all the sites were compared, finding that there was higher wind resource in the far north

and far south, highlighting the X region with an annual average of 9.4 m/s.

99

- Why investing in utility-scale solar and wind power projects and with which

characteristics?

In the project details section, it was found that when it comes to solar and wind energy

projects, it is highly recommended to invest in the utility scale, this is a plant size over 20

MW, because beyond this amount of capacity the drop of LCOE and initial cost due scale

economies does not decrease at a considerable rate. Also, it was found that the power

demand increases around 800 MW in the CIS market, therefore it is not recommended to

consider huge scale projects, and that just a fraction between 20 and 100 MW should be

considered. Finally, for utility-scale projects it was found that mono-Si Trina Solar PV modules

and offshore Vestas wind turbines would be the best choice.

- How economically feasible would these projects be along the country?

In the economic performance section, by plotting the LCOE and the IRR of all the projects,

equivalent results to the resource assessment were spotted. It can be concluded that the

north of Chile has the best economic performance for solar projects, with LCOE values from

54.0 to 59.1 USD/MWh and IRR between 25.4 and 30.9%, and deficient performance in the

south, with values of LCOE over the average price of energy in the CIS market and IRR below

the discount rate. For wind projects, far north and far south were found to be the best places

with LCOE values from 56.6 to 61.0 USD/MWh and IRR between 32.6 and 36.3 %.

100

- Which are the best regions for investing in utility-scale solar and wind power projects?

For utility-scale solar energy projects, it was found that regions III and IV had the best

profitability and resilience to adverse scenarios, while for wind projects regions IV, VIII and X

had the best results. Table 9.1 shows all the details for sites selected in these regions.

- Recommendations

For further works, it is highly recommended to address one of the main limitations stated in

the discussion section, which should consist in improving the site selection methodology in

the Solar and Wind Explorers, by using programming tools to optimize the selection of the

sites in each Energy Resource Map.

For this, programs such as SQL may be used to use the online data generated from the maps,

data that would be used as input for a code that would maximize the resource availability

when selecting the site, while minimizing the distance to the CIS grid, decreasing

transmission initial cost.

Finally, another way to enhance the reach of this study would be to include an economic

feasibility study of the implementation of energy storage systems for each selected site,

which was not included in the scope of this study.

101

10. Annex

10.1 Solar and Wind Explorer Software Project Sites

Figure A.1: Sites – III Region.

102

Figure A.2: Sites – IV Region.

103

Figure A.3: Sites – V Region.

104

Figure A.4: Sites – VI Region.

105

Figure A.5: Sites – VII Region.

106

Figure A.6: Sites – VIII Region.

107

Figure A.7: Sites – IX Region.

108

Figure A.8: Sites – XIV Region.

109

Figure A.9: Sites – X Region.

110

10.2 Costs Section Data

Figure A.10: Transmission cost (Euros).

Reference: Yli-Hannuksela (2011).

Figure A.11: Historic inflation (consumer price index) in Chile.

Reference: Central Bank of Chile (2017).

111

Figure A.12: Marginal Cost of Electricity in the CIS. Reference: National Commission of Energy (2017).

Figure A.13: Average price (Chilean Peso per MWh) of renewable energy certificates and penalty fees.

Reference: Alberto Hurtado University (2015).

Figure A.14: Evolution of the stabilized price of electricity for small renewable generators.

Reference: Systep (2016, April).

112

10.3 Solar and Wind Resource Data

Table A.1: Average daily solar radiation on a horizontal surface (kWh/m2day). Region Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec Year

III-1 9.11 8.35 7.15 5.53 4.06 3.56 3.87 4.81 6.39 7.79 8.92 9.32 6.57

III-2 8.88 8.29 7.31 5.84 4.53 4.00 4.28 5.30 6.73 8.04 8.96 9.13 6.77

IV-1 8.93 8.18 6.75 5.03 3.58 3.03 3.23 4.17 5.77 7.29 8.58 9.17 6.14

IV-2 8.86 8.13 6.79 5.13 3.73 3.21 3.49 4.34 5.87 7.35 8.48 9.01 6.20

V-1 8.79 7.97 6.41 4.58 3.13 2.57 2.73 3.60 5.11 6.57 8.17 8.97 5.72

V-2 8.63 7.77 6.23 4.51 3.10 2.61 2.78 3.62 5.04 6.63 8.09 8.76 5.65

VI-1 8.33 7.32 5.98 4.14 2.72 2.27 2.52 3.30 4.82 6.22 7.77 8.46 5.32

VI-2 8.27 7.29 5.88 4.05 2.56 2.11 2.35 3.15 4.64 6.06 7.61 8.35 5.19

VII-1 8.42 7.40 5.68 3.74 2.23 1.77 1.96 2.82 4.42 5.75 7.51 8.20 4.99

VII-2 8.51 7.44 5.80 3.80 2.27 1.74 1.95 2.80 4.30 5.73 7.51 8.27 5.01

VIII-1 8.27 7.20 5.48 3.61 2.28 1.73 1.89 2.67 4.29 5.61 7.25 8.04 4.86

VIII-2 8.41 7.36 5.68 3.74 2.25 1.78 1.92 2.80 4.43 5.77 7.53 8.16 4.99

IX-1 7.53 6.40 4.72 2.97 1.94 1.56 1.69 2.38 3.85 4.97 6.24 7.15 4.28

IX-2 7.72 6.58 4.94 3.15 2.06 1.58 1.74 2.52 4.04 5.21 6.51 7.35 4.45

XIV-1 6.95 5.83 4.19 2.54 1.70 1.30 1.51 2.16 3.57 4.51 5.65 6.71 3.88

XIV-2 7.18 6.11 4.37 2.84 1.90 1.44 1.58 2.23 3.66 4.59 5.78 6.71 4.03

X-1 6.17 5.27 3.73 2.48 1.66 1.30 1.44 2.06 3.25 4.16 5.24 5.88 3.55

X-2 7.00 5.79 4.16 2.60 1.67 1.31 1.46 2.18 3.51 4.44 5.62 6.56 3.86

Table A.2: Average daily wind speed (m/s) and height.

Region Height

(m) Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec Year

III-1 79 7.6 7.6 7.5 7.3 6.3 7.2 9.1 9.3 9.8 8.4 7.6 8.5 8.0

III-2 74 4 4.7 4.8 8.6 8.6 11.7 10.7 10.3 9.1 8.6 5.9 5 7.7

IV-1 78 6.9 5.7 5.2 5.5 5.5 6.9 7.2 6.1 7.5 7.2 7.7 6.4 6.5

IV-2 77 9.2 8 7.5 7.8 7.1 8.5 9.4 9 10.9 9.7 10 9.2 8.9

V-1 77 2.1 3.3 2.8 3 4.4 7.9 6 7.4 3.3 3.3 2.8 3 4.1

V-2 78 5.8 4.9 4.9 5 5.7 7.4 6.7 5.8 6.6 5.9 5.8 5.5 5.8

VI-1 77 6.5 5.3 5.6 5.3 5 7.8 6.4 5.8 6.1 5.7 6.3 5.4 5.9

VI-2 78 6.7 5.4 5.9 5.8 5.8 7.6 6.8 6.1 6.4 5.9 6.8 5.8 6.3

VII-1 77 8.5 6.1 5.9 6.6 5.9 8.9 8.2 7.5 6.1 5.7 7.3 6 6.9

VII-2 77 8.6 6.8 7.2 4.7 6.1 8.5 6.7 6.6 6.9 6.3 7.8 7 6.9

VIII-1 78 12.8 9 8.6 6.5 8 7.6 8.4 8 6.9 8.1 12.1 10.8 8.9

VIII-2 77 10.3 8.1 7.7 6.9 7.7 8.2 8.5 8.2 6.4 6.9 9.1 8.1 8.0

IX-1 75 4.6 7.5 5.5 6.8 6.8 11.7 12.9 12 5.9 6.8 5.8 5.2 7.6

IX-2 77 11.8 8.9 8.8 6.6 8.3 7.1 7.6 7.5 6.3 7.7 10.9 9.4 8.4

XIV-1 75 7.8 9.4 7.1 5.8 7.1 10 10.1 10 6.5 7.3 8.1 7.1 8.0

XIV-2 77 7.6 9,3 6.9 6.4 6.9 10.7 10.5 10 6.6 7.4 7.9 6.9 8.0

X-1 75 7.5 9 6.5 6.5 7.8 10.4 9.7 10 7.1 7.5 7.6 7.8 8.1

X-2 75 8.7 11 7.7 5.1 9.2 13.3 12 12.6 7.9 8.8 8.1 8.9 9.4

113

10.4 Levelized Cost of Energy and Internal Rate of Return Calculus Sheets Table A.3: Calculation of LCOE for solar projects.

Solar Initial Cost Annual Cost NPC Generation Units of E. LCOE

USD USD/y USD MWh/year MWh-25 years USD/MWh

III-1 55,200,172 1,055,123 67,725,315 101,454 1,204,339 56.2

III-2 55,320,173 1,110,686 68,504,893 106,797 1,267,765 54.0

IV-1 54,960,172 977,871 66,568,273 94,026 1,116,163 59.6

IV-2 54,800,171 986,841 66,514,753 94,889 1,126,407 59.1

V-1 55,160,172 886,973 65,689,242 85,286 1,012,412 64.9

V-2 55,080,172 872,986 65,443,205 83,941 996,446 65.7

VI-1 55,160,172 808,608 64,758,987 77,751 922,966 70.2

VI-2 55,320,173 783,473 64,620,616 75,334 894,274 72.3

VII-1 55,160,172 754,434 64,115,899 72,542 861,131 74.5

VII-2 55,080,172 751,600 64,002,257 72,269 857,890 74.6

VIII-1 55,160,172 745,432 64,009,038 71,676 850,851 75.2

VIII-2 55,000,172 762,095 64,046,841 73,278 869,868 73.6

IX-1 55,480,173 645,875 63,147,219 62,103 737,212 85.7

IX-2 55,360,173 678,593 63,415,608 65,249 774,557 81.9

XIV-1 55,240,173 575,446 62,071,171 55,331 656,823 94.5

XIV-2 54,920,172 603,240 62,081,107 58,004 688,553 90.2

X-1 54,920,172 540,366 61,334,743 51,958 616,782 99.4

X-2 55,160,172 578,090 62,022,557 55,586 659,850 94.0

Table A.4: Sensitivity analysis of the LCOE upon variation of initial cost for solar projects.

Solar (+)30% Project Initial Cost (-)30% Project Initial Cost

Initial Cost USD NPC USD LCOE USD/MWh Initial Cost USD NPC USD LCOE USD/MWh

III-1 71,760,224 84,285,366 70.0 42,461,671 54,986,814 45.7

III-2 71,916,225 85,100,944 67.1 42,553,979 55,738,699 44.0

IV-1 71,448,224 83,056,324 74.4 42,277,055 53,885,156 48.3

IV-2 71,240,222 82,954,804 73.6 42,153,978 53,868,559 47.8

V-1 71,708,224 82,237,293 81.2 42,430,902 52,959,971 52.3

V-2 71,604,224 81,967,257 82.3 42,369,363 52,732,396 52.9

VI-1 71,708,224 81,307,039 88.1 42,430,902 52,029,717 56.4

VI-2 71,916,225 81,216,668 90.8 42,553,979 51,854,422 58.0

VII-1 71,708,224 80,663,951 93.7 42,430,902 51,386,629 59.7

VII-2 71,604,224 80,526,309 93.9 42,369,363 51,291,448 59.8

VIII-1 71,708,224 80,557,090 94.7 42,430,902 51,279,768 60.3

VIII-2 71,500,224 80,546,893 92.6 42,307,825 51,354,494 59.0

IX-1 72,124,225 79,791,271 108.2 42,677,056 50,344,102 68.3

IX-2 71,968,225 80,023,659 103.3 42,584,748 50,640,183 65.4

XIV-1 71,812,225 78,643,223 119.7 42,492,441 49,323,439 75.1

XIV-2 71,396,224 78,557,159 114.1 42,246,286 49,407,221 71.8

X-1 71,396,224 77,810,795 126.2 42,246,286 48,660,857 78.9

X-2 71,708,224 78,570,608 119.1 42,430,902 49,293,286 74.7

114

Table A.5: Calculation of LCOE for wind projects.

Wind Initial Cost Annual Cost NPC Generation Units of E. LCOE

MMUSD USD/y MMUSD MWh 25 years MWh-25 years USD/MWh

III-1 76,586,400 3,078,731 113,133,367 156,281 1,855,179 61.0

III-2 68,904,000 2,713,075 101,110,342 137,720 1,634,845 61.8

IV-1 68,587,200 2,374,180 96,770,591 120,517 1,430,632 67.6

IV-2 70,408,800 3,378,408 110,513,170 171,493 2,035,757 54.3

V-1 69,379,200 912,643 80,212,993 46,327 549,938 145.9

V-2 68,151,600 1,879,090 90,457,882 95,385 1,132,295 79.9

VI-1 69,854,400 1,934,000 92,812,507 98,173 1,165,391 79.6

VI-2 69,022,800 2,105,649 94,018,516 106,886 1,268,821 74.1

VII-1 69,973,200 2,509,617 99,764,335 127,392 1,512,244 66.0

VII-2 69,181,200 2,539,843 99,331,141 128,926 1,530,453 64.9

VIII-1 73,933,200 3,328,900 113,449,871 168,980 2,005,926 56.6

VIII-2 70,329,600 3,168,938 107,947,396 160,860 1,909,535 56.5

IX-1 81,615,600 2,515,534 111,476,974 127,692 1,515,805 73.5

IX-2 69,894,000 3,050,688 106,108,075 154,857 1,838,275 57.7

XIV-1 69,973,200 3,055,522 106,244,658 155,103 1,841,195 57.7

XIV-2 69,418,800 3,043,943 105,552,806 154,515 1,834,215 57.5

X-1 69,102,000 3,105,812 105,970,440 157,655 1,871,489 56.6

X-2 77,378,400 3,327,113 116,873,858 168,889 2,004,846 58.3

Table A.6: Sensitivity analysis of the LCOE upon variation of initial cost for wind projects.

Wind (+)30% Project Initial Cost (-)30% Project Initial Cost

Initial Cost USD NPC USD LCOE USD/MWh Initial Cost USD NPC USD LCOE USD/MWh

III-1 99,562,320 136,109,287 73.4 58,912,615 95,459,583 51.5

III-2 89,575,200 121,781,542 74.5 53,003,077 85,209,419 52.1

IV-1 89,163,360 117,346,751 82.0 52,759,385 80,942,775 56.6

IV-2 91,531,440 131,635,810 64.7 54,160,615 94,264,985 46.3

V-1 90,192,960 101,026,753 183.7 53,368,615 64,202,408 116.7

V-2 88,597,080 110,903,362 97.9 52,424,308 74,730,589 66.0

VI-1 90,810,720 113,768,827 97.6 53,734,154 76,692,260 65.8

VI-2 89,729,640 114,725,356 90.4 53,094,462 78,090,177 61.5

VII-1 90,965,160 120,756,295 79.9 53,825,538 83,616,673 55.3

VII-2 89,935,560 120,085,501 78.5 53,216,308 83,366,249 54.5

VIII-1 96,113,160 135,629,831 67.6 56,871,692 96,388,363 48.1

VIII-2 91,428,480 129,046,276 67.6 54,099,692 91,717,488 48.0

IX-1 106,100,280 135,961,654 89.7 62,781,231 92,642,605 61.1

IX-2 90,8622,00 127,076,275 69.1 53,764,615 89,978,690 48.9

XIV-1 90,965,160 127,236,618 69.1 53,825,538 90,096,997 48.9

XIV-2 90,244,440 126,378,446 68.9 53,399,077 89,533,083 48.8

X-1 89,832,600 126,701,040 67.7 53,155,385 90,023,825 48.1

X-2 100,591,920 140,087,378 69.9 59,521,846 99,017,304 49.4

115

Table A.7: Sensitivity analysis of the IRR upon variation of initial cost of the solar projects.

Solar IRR (%) Initial Cost Variation

(-)30% Base (+)30%

III-1 53.0 28.3 15.4

III-2 56.0 30.9 17.2

IV-1 48.7 24.8 13.1

IV-2 49.6 25.4 13.5

V-1 42.0 20.4 10.2

V-2 41.1 19.8 9.9

VI-1 36.4 16.9 8.0

VI-2 34.5 15.7 7.3

VII-1 32.6 14.6 6.6

VII-2 32.5 14.5 6.6

VIII-1 32.0 14.2 6.4

VIII-2 33.4 15.0 6.9

IX-1 24.8 10.1 3.8

IX-2 27.2 11.4 4.6

XIV-1 20.4 7.7 2.3

XIV-2 22.5 8.8 3.0

X-1 18.4 6.6 1.6

X-2 20.6 7.8 2.4

Table A.8: Sensitivity analysis of the IRR upon variation of initial cost of the wind projects.

Wind IRR (%) Initial Cost Variation

(-)30% Base (+)30%

III-1 50.7 25.8 13.1

III-2 50.6 25.7 13.1

IV-1 42.3 20.0 9.4

IV-2 62.4 36.3 20.4

V-1 7.1 0.3 -3.7

V-2 29.8 12.2 4.7

VI-1 29.7 12.1 4.7

VI-2 34.9 15.2 6.5

VII-1 44.3 21.3 10.2

VII-2 46.0 22.4 10.9

VIII-1 57.8 32.1 17.4

VIII-2 58.4 32.6 17.8

IX-1 33.4 14.3 5.9

IX-2 56.5 30.9 16.6

XIV-1 56.5 30.9 16.6

XIV-2 56.8 31.2 16.8

X-1 58.4 32.6 17.8

X-2 54.6 29.2 15.4

116

Table A.9: Solar projects sensitivity analysis summary.

Site Only Energy Energy-Power Energy-Power-CER Installed Cost Energy Price

MWh Sales MWh-MW Sales Base Scenario (+)30% (-)30%

III-1 19.0 26.0 28.3 15.4 11.8

III-2 21.3 28.4 30.9 17.2 13.3

IV-1 16.0 22.8 24.8 13.1 9.9

IV-2 16.5 23.3 25.4 13.5 10.3

V-1 12.3 18.8 20.4 10.2 7.6

V-2 11.8 18.2 19.8 9.9 7.3

VI-1 9.5 15.6 16.9 8.0 5.8

VI-2 8.5 14.5 15.7 7.3 5.2

VII-1 7.6 13.5 14.6 6.6 4.7

VII-2 7.6 13.4 14.5 6.6 4.6

VIII-1 7.3 13.1 14.2 6.4 4.5

VIII-2 8.0 13.9 15.0 6.9 4.9

IX-1 4.1 9.4 10.1 3.8 2.4

IX-2 5.1 10.6 11.4 4.6 3.1

XIV-1 2.1 7.2 7.7 2.3 1.2

XIV-2 3.0 8.2 8.8 3.0 1.8

X-1 1.3 6.6 6.6 1.6 0.6

X-2 2.2 7.3 7.8 2.4 1.2

Table A.10: Wind projects sensitivity analysis summary.

Wind IRR (%) Only Energy MWh Sales

Energy-Power MWh-MW Sales

Energy-Power-CER Base Scenario

Installed Cost (+)30%

Energy Price (-) 30%

III-1 15.4 23.2 25.8 13.1 6.7

III-2 14.6 23.2 25.7 13.1 7.0

IV-1 9.9 18.1 20.0 9.4 4.6

IV-2 23.9 32.8 36.3 20.4 12.1

V-1 -7.5 0.2 0.3 -3.7 -4.8

V-2 4.0 11.1 12.2 4.7 1.5

VI-1 4.1 11.1 12.1 4.7 1.3

VI-2 6.3 13.8 15.2 6.5 2.6

VII-1 11.1 19.2 21.3 10.2 5.0

VII-2 11.9 20.2 22.4 10.9 5.5

VIII-1 20.6 29.0 32.1 17.4 9.8

VIII-2 20.7 29.5 32.6 17.8 10.2

IX-1 6.6 12.9 14.3 5.9 1.7

IX-2 19.1 27.9 30.9 16.6 9.4

XIV-1 19.1 27.9 30.9 16.6 9.4

XIV-2 19.3 28.2 31.2 16.8 9.6

X-1 20.5 29.5 32.6 17.8 10.3

X-2 18.4 26.3 29.2 15.4 8.2

117

Table A.11: HVT lines distances and costs.

Region/Site Distance (km) Total Cost (USD)

Solar Wind Solar Wind

III-1 2.00 38.47 448,000 8,617,280

III-2 2.61 3.74 584,640 837,760

IV-1 0.87 2.29 194,880 512,960

IV-2 0.23 10.62 51,520 2,378,880

V-1 1.78 5.95 398,720 1,332,800

V-2 1.54 0.41 344,960 91,840

VI-1 1.90 8.12 425,600 1,818,880

VI-2 2.57 4.36 575,680 976,640

VII-1 1.86 8.62 416,640 1,930,880

VII-2 1.41 5.10 315,840 1,142,400

VIII-1 1.76 26.47 394,240 5,929,280

VIII-2 1.16 10.70 259,840 2,396,800

IX-1 3.20 61.11 716,800 13,688,640

IX-2 2.72 8.19 609,280 1,834,560

XIV-1 2.15 8.60 481,600 1,926,400

XIV-2 0.69 6.15 154,560 1,377,600

X-1 0.84 4.68 188,160 1,048,320

X-2 1.88 41.99 421,120 9,405,760

118

References

Note: Some computers may experience problems while opening PDF URLs. This can be fixed

by copying the URL directly into the internet browser.

- Aho, T. & Virtanen, I. (1982, October). On relationships between ROI and IRR under inflation:

A constant real cash-flow case.

Retrieved from: lipas.uwasa.fi/~itv/publicat/EJOR_83.pdf

- Alberto Hurtado University. (2015, June 19). ¿Cómo funciona el Mercado de los certificados

verdes en Chile? [How the clean energy certificate marker works in Chile].

Retrieved from: http://fen.uahurtado.cl/2015/articulos/observatorio-economico/como-

funciona-el-mercado-de-certificados-verdes-en-chile/

- Alibaba. (2017, August). 40 MVA HVT Transformers.

Retrieved from: https://www.alibaba.com/showroom/40-mva-transformer.html

- Araya, F., Ferrada, P., Fuentealba, E., Marzo, Aitor., Parrado, C. & Portillo, C. (2015, February

27). Photovoltaic performance and LCOE comparison at the coast zone of the Atacama

Desert. p. 6.

Retrieved from: www.sciencedirect.com/science/journal/01968904

- Austral University of Chile, Catholic University of Temuco & Pontifical Catholic University of

Chile. (1999). Catastro y Evaluación de los Recursos Vegetacionales [Vegetal Resources

Assessment].

- Bank of Chile. (2017). Fondo Mutuo Ahorro [Mutual Funds Savings].

Retrieved from: http://www.banchileinversiones.cl/web/guest/nuestros-productos

lipas.uwasa.fi/~itv/publicat/EJOR_83.pdf

http://fen.uahurtado.cl/2015/articulos/observatorio-economico/como-funciona-el-mercado-de-certificados-verdes-en-chile/

http://fen.uahurtado.cl/2015/articulos/observatorio-economico/como-funciona-el-mercado-de-certificados-verdes-en-chile/

https://www.alibaba.com/showroom/40-mva-transformer.html

http://www.sciencedirect.com/science/journal/01968904

http://www.banchileinversiones.cl/web/guest/nuestros-productos

119

- Basu, S., Dudhia, J., Swift, A. & Giammanco, I. (2009). Evaluation of the Weather Research

and Forecasting model on forecasting low-level jets: Implications for wind energy. Wind

Energy. p. 12, 81 - 90.

- Bloomberg New Energy Finance. (2011). Chile’s Clean Energy Future.

Retrieved from: https://www.nrdc.org/sites/default/files/chilecostofenergy_eng.pdf

- Bolinger, M. & Seel, J. (2016, August). Utility-Scale Solar 2015. An Empirical Analysis of

Project Cost, Performance and Pricing Trends in the United States.

Retrieved from: https://emp.lbl.gov/sites/all/files/lbnl-1006037_slides.pdf

- Bosch, J., Kleissl, J. & Mejia, F. (2014, June 1). The effect of dust on solar photovoltaic

systems. Retrieved from:

http://www.sciencedirect.com/science/article/pii/S187661021400705X?via%3_Dihub

- Performance and Pricing Trends in United States. p. 20, 39.

Retrieved from: https://emp.lbl.gov/sites/all/files/lbnl-1006037_report.pdf

- Bolinger, M. & Wiser, R. (2016, August). 2015 Wind Technologies Market Report: Summary.

p. 28 - 44, 47.

Retrieved from: https://energy.gov/sites/prod/files/2016/08/f33/2015-Wind-Technologies-

Market-Report-Presentation.pdf

- Bustos, F., Contreras, J., Fuentes. A. & Toledo, A. (2015, October 18). Sensitivity analysis of

a photovoltaic solar plant in Chile.


https://www.nrdc.org/sites/default/files/chilecostofenergy_eng.pdf

https://emp.lbl.gov/sites/all/files/lbnl-1006037_slides.pdf

http://www.sciencedirect.com/science/article/pii/S187661021400705X?via%253_Dihub

https://emp.lbl.gov/sites/all/files/lbnl-1006037_report.pdf

https://energy.gov/sites/prod/files/2016/08/f33/2015-Wind-Technologies-Market-Report-Presentation.pdf

https://energy.gov/sites/prod/files/2016/08/f33/2015-Wind-Technologies-Market-Report-Presentation.pdf


120

- Campiñez, S., Colmenar, A., Enriquez, L. & Perez, C. (2014, October 14). Simplified Analysis

of the Electric Power Losses for On-Shore Wind Farms Considering Weibull Distribution

Parameters. p. 2.

Retrieved from: www.mdpi.com/1996-1073/7/11/6856/pdf

- Central Bank of Chile. (2017, March). Monetary Policy Report.

Retrieved from: http://www.bcentral.cl/en/web/guest/politica-monetaria

- Chilean Renewable Energy Association and Natural Resources Defence Council. (2013).

Beneficios Economicos de Energías Renovables No Convencionales en Chile [Economic

Benefits of Non-Conventional Renewable Energy in Chile]. p. 23.

Retrieved from: http://www.acera.cl/wp-content/uploads/2016/09/11.2013-Beneficios-

Economicos-ERNC.pdf

- Chakraborty, S., Kramer, W., Kroposki, B., & Thomas H. (2008, March). Advanced Power

Electronic Interfaces for Distributed Energy Systems.

Retrieved from: https://www.nrel.gov/docs/fy08osti/42672.pdf

- Chou, M. & Suarez, M. (1999). A solar radiation parameterization for atmospheric studies,

Tech. Rep., NASA/TM‐1999‐104606. p. 40.

- Compton, A., McCoy, T., Rhoads, H., Smith, Brian. & VandenBosche, J. (2000, August).

Comparison of Projections to Actual Performance in the DOE-EPRI Wind Turbine Verification

Program.

Retrieved from: https://www.nrel.gov/docs/fy00osti/28608.pdf

- Darling, S., Velosa, A., Veselka, T. & You, F. (2011, January). Assumptions and the Levelized

Cost of Energy for Photovoltaics.

http://www.mdpi.com/1996-1073/7/11/6856/pdf

http://www.acera.cl/wp-content/uploads/2016/09/11.2013-Beneficios-Economicos-ERNC.pdf

http://www.acera.cl/wp-content/uploads/2016/09/11.2013-Beneficios-Economicos-ERNC.pdf

https://www.nrel.gov/docs/fy08osti/42672.pdf

https://www.nrel.gov/docs/fy00osti/28608.pdf

121

Retrieved from: http://www.mcs.anl.gov/publication/assumptions-and-levelized-cost-

energy-photovoltaics

- Dinero. (2015, June 24). Costo de la Energia Eolica [Costs of Eolic Energy].

Retrieved from: http://www.dinero.com/pais/articulo/el-costo-energia-eolica/209799

- Dvoraka, J., Cristina, L., Mark, Z. & Jacobsona. (2010). California offshore wind energy

potential. Renewable Energy.

- Electricidad. (2016, November 14). Aerogeneradores representan hasta 75% en el costo de

proyectos eólicos [Aerogenerators represents up to 75% of the eolic projects costs].

Retrieved from: http://www.revistaei.cl/reportajes/aerogeneradores-representan-75-

costo-proyectos-eolicos/

- Energy Bulletin. (2016, August). Boletín del Mercado Eléctrico [Energy Market Bulletin].

Retrieved from: http://www.generadoras.cl/documentos

- Energy Informative. (2017, August 5). Which Solar Panel Type is Best? Mono- vs.

Polycrystalline vs. Thin Film. Retrieved from:

http://energyinformative.org/best-solar-panel-monocrystalline-polycrystalline-thin-film/

- Farrell, J. (2016, September). Is Bigger the Best in Renewable Energy?. p. 10, 19 - 20.

Retrieved from: https://ilsr.org/report-is-bigger-best/

- Financetrain. (2017). Holding Period Return.

Retrieved from: http://financetrain.com/holding-period-return-total-return/

- Financial Journal. (2016, January 27). Diferencia de tasas de interes para empresas llega

hasta siete puntos [Seven points of difference in the interest rate for companies]. Retrieved

http://www.mcs.anl.gov/publication/assumptions-and-levelized-cost-energy-photovoltaics

http://www.mcs.anl.gov/publication/assumptions-and-levelized-cost-energy-photovoltaics

http://www.dinero.com/pais/articulo/el-costo-energia-eolica/209799

http://www.revistaei.cl/reportajes/aerogeneradores-representan-75-costo-proyectos-eolicos/

http://www.revistaei.cl/reportajes/aerogeneradores-representan-75-costo-proyectos-eolicos/

http://www.generadoras.cl/documentos

http://energyinformative.org/best-solar-panel-monocrystalline-polycrystalline-thin-film/

https://ilsr.org/report-is-bigger-best/

http://financetrain.com/holding-period-return-total-return/

122

from: https://www.df.cl/noticias/empresas/banca-instituciones-financieras/diferencia-en-

tasas-de-interes-para-empresas-llega-hasta-siete-puntos/2016-01-26/210920.html

- Gajardo. (1994). Clasificación y Descripción Geográfica de la Vegetación de Chile [Geograhic

Classification and Description of the Vegetation of Chile].

- Gipe, P. (2009, March). Wind Energy Basics. A Guide to Home- and Communitiy-Scale Wind

Energy Systems. p. 46.

- Green Tech Media (2012, November 28). Solar Balance-of-System: To Track or Not to Track,

Part I).

Retrieved from: https://www.greentechmedia.com/articles/read/solar-balance-of-system-

to-track-or-not-to-track-part-i

- Hillger, D. & Schmit, T. (2007). The GOES‐13 science test: Imager and sounder radiance and

product validations. US Dept. of Commerce, National Oceanic and Atmospheric

Administration, National Environmental Satellite, Data and Information Service, 2007.

Internal Tax Service. (2017). UTM - UTA - IPC 2017.

Retrieved from: http://www.sii.cl/valores_y_fechas/utm/utm2017.htm

- Internal Tax Service. (2017). Nueva Tabla de Vida Útil de Bienes Físicos del Activo

Inmovilizado [New Table of Working Life of Goods and Inmovilized Assets].

Retrieved from: http://www.sii.cl/pagina/valores/bienes/tabla_vida_enero.htm

- International Renewable Energy Agency. (2012, June). Renewable Energy Technologies:

Cost Analysis Series. Wind Power. p. 19. Retrieved from:

https://www.irena.org/DocumentDownloads/Publications/RE_Technologies_Cost_Analysis-

WIND_POWER.pdf

https://www.df.cl/noticias/empresas/banca-instituciones-financieras/diferencia-en-tasas-de-interes-para-empresas-llega-hasta-siete-puntos/2016-01-26/210920.html

https://www.df.cl/noticias/empresas/banca-instituciones-financieras/diferencia-en-tasas-de-interes-para-empresas-llega-hasta-siete-puntos/2016-01-26/210920.html

https://www.greentechmedia.com/articles/read/solar-balance-of-system-to-track-or-not-to-track-part-i

https://www.greentechmedia.com/articles/read/solar-balance-of-system-to-track-or-not-to-track-part-i

http://www.sii.cl/valores_y_fechas/utm/utm2017.htm

http://www.sii.cl/pagina/valores/bienes/tabla_vida_enero.htm

https://www.irena.org/DocumentDownloads/Publications/RE_Technologies_Cost_Analysis-WIND_POWER.pdf

https://www.irena.org/DocumentDownloads/Publications/RE_Technologies_Cost_Analysis-WIND_POWER.pdf

123

- International Renewable Energy Agency. (2015, January). Renewable Power Generation

Costs in 2014. p. 61, 70. Retrieved from:

https://www.irena.org/DocumentDownloads/Publications/IRENA_RE_Power_Costs_2014_r

eport.pdf

- International Renewable Energy Agency. (2016). The Power to Change: Solar and Wind

Cost Reduction Potential to 2025. p. 32 - 36. Retrieved from:

http://www.irena.org/DocumentDownloads/Publications/IRENA_Power_to_Change_2016.

pdf

- Jordan, B., Ross, S., Westerfield, R. (2003). Fundaments of Corporate Finance. Sixth Edition.

McGraw-Hill Irwin. Chapter 9.

- Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S.,

White, G., Woollen, J., et al. (1996). The NCEP/NCAR reanalysis project Bull. Am. Meteorol.

Soc. p. 77, 437‐471.

- Kopp, G. & Lawrence, G. (2005). The total irradiance monitor (TIM): Instrument Design the

Solar Radiation and Climate Experiment (SORCE). Springer, 2005. P. 91 ‐ 109.

- Luebert & Pliscoff. (2006). Sinopsis Bioclimática y Vegetacional [Bioclimatic and

Vegetational Synopsis].

- Mineria Chilena. (2014, October 6). Parque eólico El Arrayán: Energía del viento para Los

Pelambres [Wind farm El Arrayan: Wind energy for Los Pelambres].

Retrieved from: http://www.mch.cl/reportajes/parque-eolico-el-arrayan-energia-del-

viento-para-los-pelambres/

https://www.irena.org/DocumentDownloads/Publications/IRENA_RE_Power_Costs_2014_report.pdf

https://www.irena.org/DocumentDownloads/Publications/IRENA_RE_Power_Costs_2014_report.pdf

http://www.irena.org/DocumentDownloads/Publications/IRENA_Power_to_Change_2016.pdf

http://www.irena.org/DocumentDownloads/Publications/IRENA_Power_to_Change_2016.pdf

http://www.mch.cl/reportajes/parque-eolico-el-arrayan-energia-del-viento-para-los-pelambres/

http://www.mch.cl/reportajes/parque-eolico-el-arrayan-energia-del-viento-para-los-pelambres/

124

- Ministry of National Properties. (2017, June 16). Inauguran potente proyecto solar

fotovoltaico en terrenos de Bienes Nacionales en la región de Antofagasta [Big Solar

Photovoltaic project inaugurated in National Properties terrains].

Retrieved from: http://www.bienesnacionales.cl/?p=28608

- Mozó, J. (2016, July 28). Precio Estabilizado: El híbrido entre PPA y Spot [Stabilized Price:

Hibrid between PPA and Spot price].

Retrieved from: https://es.linkedin.com/pulse/precio-estabilizado-el-h%C3%ADbrido-entre-

ppa-y-spot-que-vuelve-moz%C3%B3

- Murdoch University. (2017). Renewable Energy System Design Notes. Economic Evaluation

of Renewable Energy Systems.

- National Commission of Energy. (2016). Anuario Estadistico de Energia [Energy Statistics

Yearbook]. p. 16, 20.

Retrieved from: http://www.energia.gob.cl/sites/default/files/anuariocne2016final3.pdf

- Nacional Commission of Energy. (2016, January). Anuario Estadístico de Energía 2016

[Energy Statistics Yearbook]. P 12, 46.

Retrieved from: http://www.energia.gob.cl/sites/default/files/anuariocne2016final3.pdf

- National Commission of Energy. (2017). Energy Maps. Retrieved from Energiamaps.cne.cl/#

National Comission of Energy. (2017). Costo marginal promedio diario SIC – SING [Daily

Average Marginal Price SIC - SING].

Retrieved from: http://energiaabierta.cl/visualizaciones/costo-marginal-promedio-diario/

- New Energy Finance Climascope. (2017).

Retrieved from: http://www.Global-climatescope.org/en/

http://www.bienesnacionales.cl/?p=28608

https://es.linkedin.com/pulse/precio-estabilizado-el-h%C3%ADbrido-entre-ppa-y-spot-que-vuelve-moz%C3%B3

https://es.linkedin.com/pulse/precio-estabilizado-el-h%C3%ADbrido-entre-ppa-y-spot-que-vuelve-moz%C3%B3

http://www.energia.gob.cl/sites/default/files/anuariocne2016final3.pdf

http://www.energia.gob.cl/sites/default/files/anuariocne2016final3.pdf

http://energiaabierta.cl/visualizaciones/costo-marginal-promedio-diario/

http://www.global-climatescope.org/en/

125

National Institute of Statistics. (2014). Población país y regiones - Proyección 2013-2020

[Country and Region’s Population - Projection 2013-2020]. Retrieved from:

http://www.ine.cl/canales/sala_prensa/noticias/noticia.php?opc=news&id=615&lang=esp

- National Library of Chile. (2013, October 22). Law 20.257.

Retrieved from: http://www.Leychile.cl/Navegar?idNorma=270212

- National Library of Chile. (2013, October 22). Law 20.698.


- National Library of Chile. (2016, February 29). Decree 148.


- National Energy Commission (2012). Antecedentes sobre la matriz energética en chile y sus

desafíos para el futuro [Background of the energy grid in Chile and the challenges for the

future].

- Natural Energy Hub. (2017). 20 Top Solar Panel Manufacturers Ruling the World (2016 -

2017).

Retrieved from: http://naturalenergyhub.com/solar-energy/20-top-solar-panel-

manufacturers-ruling-world/

- Natural Resources Canada. (2017, January 13).

Retrieved from: https://www.nrcan.gc.ca/energy/software-tools/7465

- NextTracker. (2017, March 21). On Thinking About Solar Trackers and O&M.

Retrieved from: https://www.nextracker.com/2017/03/best-practices-for-solar-tracker-om-

blog-series-post-1/

http://www.ine.cl/canales/sala_prensa/noticias/noticia.php?opc=news&id=615&lang=esp

http://www.leychile.cl/Navegar?idNorma=270212



http://naturalenergyhub.com/solar-energy/20-top-solar-panel-manufacturers-ruling-world/

http://naturalenergyhub.com/solar-energy/20-top-solar-panel-manufacturers-ruling-world/

https://www.nrcan.gc.ca/energy/software-tools/7465

https://www.nextracker.com/2017/03/best-practices-for-solar-tracker-om-blog-series-post-1/

https://www.nextracker.com/2017/03/best-practices-for-solar-tracker-om-blog-series-post-1/

126

- Oses, N., Perez, R. & Watts, D. (2016): Assessment of wind energy potential in Chile. p. 13.


- Phillips, K. & Phillips, P. (2002, June). How to measure the return on your HR investment.

Retrieved from: https://roiinstitute.net/wp-content/uploads/2017/02/How-to-Measure-

the-Return-on-your-HR-Investment.pdf

- Planprojections. (2017). Return on Investment.

Retrieved from: http://www.planprojections.com/projections/return-on-investment/

- Rankia. (2016, December 17). ¿Cuáles son los mejores depósitos a plazo para 2017? [Which

are the best long-term deposits for 2017?].

Retrieved from: https://www.rankia.cl/blog/mejores-depositos-a-plazo/3257869-cuales-

son-mejores-depositos-plazo-para-2017

- Renewable Energy World. (2016, August 8). Chile: A Hotbed for Renewable Energy

Investment. Retrieved from:

http://www.renewableenergyworld.com/articles/ucg-content/2016/08/08/chile-a-hotbed-

for-renewable-energy-investment.html

- Science and Mathematics Faculty, University of Chile. (2016). Explorador de Energía Solar

[Solar Energy Explorer].

Retrieved from: http://www.minenergia.cl/exploradorsolar/

- Science and Mathematics Faculty, University of Chile. (2016). Explorador de Energía Eólica

[Eolic Energy Explorer].

Retrieved from: Walker.dgf.uchile.cl/Explorador/Eolico2/


https://roiinstitute.net/wp-content/uploads/2017/02/How-to-Measure-the-Return-on-your-HR-Investment.pdf

https://roiinstitute.net/wp-content/uploads/2017/02/How-to-Measure-the-Return-on-your-HR-Investment.pdf

http://www.planprojections.com/projections/return-on-investment/

https://www.rankia.cl/blog/mejores-depositos-a-plazo/3257869-cuales-son-mejores-depositos-plazo-para-2017

https://www.rankia.cl/blog/mejores-depositos-a-plazo/3257869-cuales-son-mejores-depositos-plazo-para-2017

http://www.renewableenergyworld.com/articles/ucg-content/2016/08/08/chile-a-hotbed-for-renewable-energy-investment.html

http://www.renewableenergyworld.com/articles/ucg-content/2016/08/08/chile-a-hotbed-for-renewable-energy-investment.html

http://www.minenergia.cl/exploradorsolar/

walker.dgf.uchile.cl/Explorador/Eolico2/

127

- Science and Mathematics Faculty, University of Chile. (2012, 2016). Explorador de Energía

Solar, Manual de Usuario [Solar Energy Explorer, User Manual].

Retrieved from: http://www.minenergia.cl/exploradorsolar/

- Science and Mathematics Faculty, University of Chile. (2012). Explorador de Energía Eólica,

Manual de Usuario [Eolic Energy Explorer, User Manual].

Retrieved from: Walker.dgf.uchile.cl/Explorador/Eolico2/

- Solar Facts. (2011, December). Carlo Gavazzi Information. Retrieved from:

http://www.solar-facts.com/manufacturers/show-manufacturer.php?id=140

- Solar Power World. (2016, May 9). Advantages and disadvantages of a solar tracker system.

Retrieved from: https://www.solarpowerworldonline.com/2016/05/advantages-

disadvantages-solar-tracker-system/

- Solar Power World. (2016, Julio 8). Solar inverters and clipping: What DC/AC inverter load

ratio is ideal? Retrieved from https://www.solarpowerworldonline.com/2016/07/solar-

inverters-clipping-dcac-inverter-load-ratio-ideal/

- Solar Professional. (2013, May 3). Impacts of Soiling on Utility-Scale PV System

Performance. Retrieved from: http://solarprofessional.com/articles/operations-

maintenance/impacts-of-soiling-on-utility-scale-pv-system-

performance?v=disable_pagination&nopaging=1#.WZe9CqJLc2x

- Stein, O., Schultz, M., F. J¨ulich, J., Flemming, A., Inness, J., Kaiser, L., Jones, A., Benedetti,

J., Morcrette, E., et al. (2011). MACC global air quality services ‐ Technical documentation.

http://www.minenergia.cl/exploradorsolar/

http://www.solar-facts.com/manufacturers/show-manufacturer.php?id=140

https://www.solarpowerworldonline.com/2016/05/advantages-disadvantages-solar-tracker-system/

https://www.solarpowerworldonline.com/2016/05/advantages-disadvantages-solar-tracker-system/

http://solarprofessional.com/articles/operations-maintenance/impacts-of-soiling-on-utility-scale-pv-system-performance?v=disable_pagination&nopaging=1#.WZe9CqJLc2x



128

- Sustainable Earth. (2017, January 24). Impacto y Mitigación de Riesgos de Financiamiento

para proyectos con Energías Renovables No Convencionales [Risk Impact and Mitigation of

Renewable Energy Projects Finance].

Retrieved from: http://www.sustainablearth.com/2017/01/24/impacto-y-mitigacion-de-

riesgos-de-financiamiento-para-proyectos-con-energias-renovables-no-convencionales-

ernc-parte-12/

- Systep. (2016, April). Reporte Mensual del Sector Eléctrico [Monthly Report of the Electric

Sector]. p. 2.

Retrieved from: http://www.emb.cl/electroindustria/informes/201604systep.pdf

- TheEngineeringToolbox. (2017). Wind Shear.

Retrieved from: http://www.engineeringtoolbox.com/wind-shear-d_1215.html

- The Latin American Energy Review. (2017 May 1). How one wind turbine manufacturer

came to dominate the Chilean market.

Retrieved from: http://carlosstjames.com/renewable-energy/how-one-wind-turbine-

manufacturer-came-to-dominate-the-chilean-market/

- Think Solar. (2017). Top Solar Panel Manufacturers - 2017.

Retrieved from: http://thinksolar.co/learn/top-solar-panels

- University of Chile, Faculty of Engineering. (2016, May 6). En Chile 2 de las plantas de

energía solar más grandes del mundo [In Chile 2 of the biggest solar energy plants in the

world].

Retrieved from: http://ecodie.cl/en-chile-2-de-las-plantas-de-energia-solar-mas-grandes-

del-mundo/

http://www.sustainablearth.com/2017/01/24/impacto-y-mitigacion-de-riesgos-de-financiamiento-para-proyectos-con-energias-renovables-no-convencionales-ernc-parte-12/



http://www.emb.cl/electroindustria/informes/201604systep.pdf

http://www.engineeringtoolbox.com/wind-shear-d_1215.html

http://carlosstjames.com/renewable-energy/how-one-wind-turbine-manufacturer-came-to-dominate-the-chilean-market/

http://carlosstjames.com/renewable-energy/how-one-wind-turbine-manufacturer-came-to-dominate-the-chilean-market/

http://thinksolar.co/learn/top-solar-panels

http://ecodie.cl/en-chile-2-de-las-plantas-de-energia-solar-mas-grandes-del-mundo/

http://ecodie.cl/en-chile-2-de-las-plantas-de-energia-solar-mas-grandes-del-mundo/

129

- Williams, A. (2014). Improving wind farm availability.

Retrieved from: www.windenergyupdate.com/operation-maintenance-onshore-

report/pdf/WEUSciemusWP.pdf

- Yli-Hannuksela J. (2011). The Transmission Line Cost Calculation. p. 43.

Retrieved from: www.theseus.fi/bitstream/handle/10024/29401/Yli-Hannuksela_Juho.pdf

http://www.windenergyupdate.com/operation-maintenance-onshore-report/pdf/WEUSciemusWP.pdf

http://www.windenergyupdate.com/operation-maintenance-onshore-report/pdf/WEUSciemusWP.pdf

http://www.theseus.fi/bitstream/handle/10024/29401/Yli-Hannuksela_Juho.pdf

Documents

FEASIBILITY ANALYSIS OF SOLAR AND WIND ENERGY PROJECTS ...€¦ · approaches. To do this, Chile has signed different laws in favor of the development of renewable energy technologies