SCHOOL OF ELECTRICAL AND ELECTRONIC ENGINEERING

Final Report

Assessing The Impact of Renewable Energy In Trinidad and Tobago

Jerel Mohammed 8450058

Supervised by: Dr Joseph Mutale

ABSTRACT

The twin island state of Trinidad and Tobago (T&T) has prospered from the

extraction and processing of fossil fuels since the discovery of significant reserves

within its borders. At the same time, renewable energy sources for electricity (RES-

E) have increasingly become a focus worldwide among countries striving for energy

security and environmental sustainability. As a small island economy the country is

vulnerable to both global shocks in the oil and gas industry, and to the adverse

effects of climate change.

To date, no major initiative has been undertaken to improve the implications of RES-

E in T&T. The country has outlined a general framework for renewable energy, but

key steps are yet to be undertaken in order achieve a significant implementation of

RES-E. The allure of locally available natural gas has convinced many people that

RES-E are low on the agenda, and so the ensuing emphasis on fossil fuels places

the country in a precarious position in terms of its energy independence and

environmental stability.

This paper seeks to assess the viability of using renewable energy to power some of

the load requirement of the country. Much of the analysis employed discounted cash

flow analysis, thereby considering the ―time-value‖ of money. The levelised cost of

energy was found to determine the cost of generation considering wind and solar PV

technology. The financial attractiveness of RES-E projects was also considered

using the Net Present Value (NPV) and Equity Payback Period (EPP).

Environmental analysis allowed the reduction in Greenhouse Gas (GHG) emissions

to be found. Finally, policies that could incentivise RES-E implementation were

reviewed to give the above context.

T&T can offer lucrative RES-E energy projects to investors for both wind and solar

PV technologies. The increase in energy tariffs associated with RES-E was mitigated

by a levelised cost of energy for both technologies that was comparable to that in

developed nations with extensive RES-E experience. Reductions in greenhouse gas

emissions were significant and can help the country meet the obligations of the

Kyoto Protocol. Although both technologies were found to be attractive in all

measures, wind technology showed greater potential.

ACKNOWLEDGEMENTS

The author wishes to thank his supervisor Dr Joseph Mutale for the expertise he has

shared, for the constructive comments he has provided, and for the guidance he has

offered throughout the course of this project.

TABLE OF CONTENTS

LIST OF FIGURES ...................................................................................................... i

LIST OF TABLES ........................................................................................................ ii

LIST OF EQUATIONS ................................................................................................ iii

1 INTRODUCTION ................................................................................................. 1

1.1 AIM ................................................................................................................ 1

1.2 MOTIVATION ................................................................................................ 1

1.3 OBJECTIVES ................................................................................................ 2

1.4 PROJECT HISTORY .................................................................................... 2

2 BACKGROUND .................................................................................................. 3

2.1 POLICIES AND THEIR EFFECT ON RES-E ................................................ 3

2.2 LEVELISED COST OF ENERGY .................................................................. 6

2.3 FINANCIAL ATTRACTIVENESS AND SUSTAINABILITY ASSESSMENT ... 7

2.4 ENERGY SCENARIO OF T&T ...................................................................... 9

3 METHODOLOGY .............................................................................................. 11

3.1 RE PERFORMANCE USING T & T CLIMATE DATA ................................. 11

3.2 FINANCIAL ANALYSIS ............................................................................... 13

3.2.1 Levelised Cost of Energy ...................................................................... 13

3.2.2 Monte Carlo Simulation ........................................................................ 14

3.2.3 The Inter-quartile Range ....................................................................... 15

3.2.4 Correlation between parameters and LCOE ......................................... 15

3.2.5 Discounted Cash Flow Analysis ........................................................... 16

3.2.6 Project Cash Flow ................................................................................ 17

3.2.7 Project Costs ........................................................................................ 17

3.2.8 Project income ...................................................................................... 18

3.3 Environmental Analysis ............................................................................... 19

4 PROJECT STRUCTURE .................................................................................. 21

4.1 DATA .......................................................................................................... 21

4.2 TOOLS ........................................................................................................ 21

4.3 CASE STUDY ............................................................................................. 21

5 CASE STUDY ................................................................................................... 22

5.1 PERFORMANCE OF RE TECHNOLOGY .................................................. 22

5.1.1 Solar PV ............................................................................................... 22

5.1.2 Wind ..................................................................................................... 23

5.2 LEVELISED COST OF ENERGY ................................................................ 24

5.2.1 Global Input Parameters ....................................................................... 25

5.2.2 Solar PV ............................................................................................... 26

5.2.3 Wind ..................................................................................................... 28

5.3 PROJECT FEASIBILITY ............................................................................. 31

5.3.1 Solar PV ............................................................................................... 31

5.3.2 WIND .................................................................................................... 34

5.4 GREENHOUSE GAS EMISSIONS ANALYSIS ........................................... 36

5.5 COMPARISON ............................................................................................ 37

6 DISCUSSION .................................................................................................... 41

6.1 PROJECT ACHIEVEMENTS ...................................................................... 42

6.2 PROPOSED FUTURE WORK .................................................................... 44

6.3 REFLECTIVE COMMENTS ........................................................................ 45

6.4 CONCLUSION ............................................................................................ 45

REFERENCES ......................................................................................................... 46

APPENDIX A: PROGRESS REPORT...................................................................... 52

APPENDIX B: PROJECT PLAN ............................................................................... 65

APPENDIX C: TECHNICAL RISK ANALYSIS ......................................................... 66

APPENDIX D: HEALTH AND SAFETY RISK ASSESSMENT ................................. 67

APPENDIX E: MATLAB CODE FOR LCOE OF SOLAR PV .................................... 69

APPENDIX F: MATLAB CODE FOR LCOE OF WIND ............................................. 72

i

LIST OF FIGURES

Figure 2.1 - Chart showing predicted peak demand growth in T&T[3] ..................... 10

Figure 2.2 - Projected energy sales in T&T [34] ....................................................... 10

Figure 3.1 - Diagram illustrating interquartile range on a normal probability

distribution [32] ......................................................................................................... 15

Figure 5.1 – Probability distribution of debt ratio ...................................................... 25

Figure 5.2 - Probability distribution of discount rate ................................................. 25

Figure 5.3 - Probability distribution of debt interest rate ........................................... 25

Figure 5.4 - Probability distribution of debt term ....................................................... 26

Figure 5.5 - Probability distribution of debt payment ................................................ 26

Figure 5.6 - Probability distribution of O&M .............................................................. 26

Figure 5.7 – Probability distribution of capacity factor .............................................. 27

Figure 5.8 – Probability distribution of total installed costs ....................................... 27

Figure 5.9 - Probability distribution of LCOE per energy produced .......................... 27

Figure 5.10 - Tornado chart showing impact of input parameters on LCOE ............. 28

Figure 5.11 - Probability distribution of debt payment .............................................. 28

Figure 5.12 - Probability distribution of O&M ............................................................ 29

Figure 5.13 - Probability distribution of capacity factor ............................................. 29

Figure 5.14 - Probability distribution of total installed costs ...................................... 29

Figure 5.15 - Probability distribution of LCOE per energy produced ........................ 30

Figure 5.16 - Tornado chart showing impact of input parameters on LCOE ............. 30

Figure 5.17 - Spiderplot showing the impact of each variable on NPV ..................... 32

Figure 5.18 – Probability distribution of NPV for Solar PV at 100MW installed

capacity .................................................................................................................... 32

Figure 5.19 - Spiderplot showing the impact of each variable on Equity Payback

Period ....................................................................................................................... 33

Figure 5.20 - Probability distribution of Equity Payback for Solar PV at 100MW

installed capacity ...................................................................................................... 33

Figure 5.21 - Spiderplot showing the impact of each variable on NPV ..................... 35

Figure 5.22 - Probability distribution of Net Present Value for Solar PV at 100MW

installed capacity ...................................................................................................... 34

Figure 5.23 - Spiderplot showing the impact of each variable on Equity Payback

Period ....................................................................................................................... 35

Figure 5.24 - Probability distribution of Equity Payback Period for Wind at 100MW

installed capacity ...................................................................................................... 36

Figure 5.25 - Comparison of NPV vs installed capacity for proposed technologies . 39

Figure 5.26 - Comparison of equity payback vs installed capacity for proposed

technologies ............................................................................................................. 40

Figure 5.27 - Graph comparing net annual GHG emission reduction across proposed

technologies ............................................................................................................. 41

ii

LIST OF TABLES

Table 2.1 -Significance of energy sector to the economy of T&T [3] .......................... 9

Table 2.2 - Time left before reserve of natural gas may finish .................................... 9

Table 3.1 - Solar Capacity Factor Calculation Nomenclature ................................... 11

Table 3.2 - Wind Capacity Factor Calculation Nomenclature ................................... 12

Table 3.3 - LCOE Calculation Nomenclature ........................................................... 14

Table 3.4 – Present Value Calculation Nomenclature .............................................. 16

Table 3.5 - Debt Payment and O&M Nomenclature ................................................. 18

Table 3.6 - Project income Nomenclature ................................................................ 18

Table 3.7 - Nomenclature for finding methodology used to find GHG emissions

reduction .................................................................................................................. 19

Table 5.1 - Climate data for Crown Point, Tobago [50] ............................................ 22

Table 5.2 - Relevant specifications of proposed solar PV module [54]..................... 22

Table 5.3 - Relevant specifications of proposed [30] ................................................ 23

Table 5.4 - Input parameters with a defined range of values .................................... 31

Table 5.5 - Input parameters with a defined range of values .................................... 34

Table 5.6 - GHG emission reduction for Solar PV .................................................... 37

Table 5.7 - GHG emission reduction for Wind .......................................................... 37

iii

LIST OF EQUATIONS

Eqn. 3.1 .................................................................................................................... 11

Eqn. 3.2 .................................................................................................................... 11

Eqn. 3.3 .................................................................................................................... 11

Eqn. 3.4 .................................................................................................................... 12

Eqn. 3.5 .................................................................................................................... 12

Eqn. 3.6 .................................................................................................................... 12

Eqn. 3.7 .................................................................................................................... 13

Eqn. 3.8 .................................................................................................................... 13

Eqn. 3.9 .................................................................................................................... 13

Eqn. 3.10 .................................................................................................................. 14

Eqn. 3.11 .................................................................................................................. 14

Eqn. 3.12 .................................................................................................................. 14

Eqn. 3.13 .................................................................................................................. 16

Eqn. 3.14 .................................................................................................................. 16

Eqn. 3.15 .................................................................................................................. 17

Eqn. 3.16 .................................................................................................................. 17

Eqn. 3.17 .................................................................................................................. 17

Eqn. 3.18 .................................................................................................................. 18

Eqn. 3.19 .................................................................................................................. 18

Eqn. 3.20 .................................................................................................................. 18

Eqn. 3.21 .................................................................................................................. 19

Eqn. 3.22 .................................................................................................................. 19

Eqn. 3.23 .................................................................................................................. 20

Eqn. 3.24 .................................................................................................................. 20

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1 INTRODUCTION

1.1 AIM

The aim of this project is to model the viability of supplying renewable energy to the

load in Trinidad and Tobago in terms of cost in energy generation, project

attractiveness, and the cost to the environment.

1.2 MOTIVATION

Natural gas and oil has been of tremendous importance to the growth and

development of the twin island state of Trinidad and Tobago (T&T). At present,

almost all the load is powered by natural gas generators [1]. This is of great

significance since T&T is a signatory of the Kyoto Protocol which means it committed

to reducing 1997 levels of greenhouse gas emissions by around 5% [2]. Worldwide,

countries are paying more attention to the looming threat of climate change. Climate

change has led to increased frequency in drought and famine, more severe storms,

salinization of fresh water aquifers due to rising sea levels, erosion of coastal zones

and the destruction of marine resources. Being a small island state, T&T is

particularly vulnerable to the adverse effects of rising sea levels and rising sea

temperatures. Many in the region rely heavily on the coast to make a livelihood from

agriculture, tourism and fishing. Furthermore, the majority of infrastructure like

refineries, highways and factories lie near the coast to be in close proximity to ports

[3]. Therefore, it should be driven to take any measures it can to help mitigate the

impacts of climate change.

In addition to the above, recent fluctuations in global oil and natural gas prices have

demonstrated the impact it can have on a small singular economy. More and more

countries around the world have incorporated some level of Renewable Energy

Sources for Electricity (RES-E), with trends suggesting this is likely to increase in the

future Therefore, it would be in the best interest of the country to position itself where

it has started to reduce its reliance on these finite sources of energy while it is still in

a relatively good fiscal position. The country is also in a prime position for attracting

major investors and financiers due to its extensive experience in the energy sector

[3]. All these factors suggest that T&T ought to make a modest effort into reducing

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dependence on fossil fuels through gradual introduction and increases in RE

generation.

1.3 OBJECTIVES

In order to accomplish the aim of this project, certain objectives were fulfilled:

1. Assess the energy scenario of T &T keeping in mind the load requirements

2. In the absence of any long-term studies of the availability of RE resources

in Trinidad and Tobago, use solar (irradiance) and wind (speed) generic

data from longitude and latitude T & T to determine RE performance

3. Model the cost of generation of the proposed RE technologies

4. Compare the financial attractiveness of RE projects

5. Determine the benefits to the environment in terms of reduced

Greenhouse Gas (GHG) emissions from use of renewable energy

sources

6. Suggest the appropriate incentive to promote RE (including feed-in tariffs)

7. Determine the true cost of power generated using natural gas in T&T

1.4 PROJECT HISTORY

The project undertaken was a unique study within the school. Therefore, there were

no projects against which the results of this study could be compared internally.

Furthermore, few published studies have been done to assess the feasibility of

introducing RES-E in T&T, and those which have been completed did not engage in

in-depth economic analysis.

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2 BACKGROUND

Many new concepts were introduced in this Final Report. This section analyses the

literature that were applied to either model the objectives stated or to give these

models context.

2.1 POLICIES AND THEIR EFFECT ON RES-E

Several strategies exist for promoting RES for electricity generation. Different

regulatory strategies focus on different perspectives of the RE generating project.

Generally speaking, the investment focussed strategies rely on incentives such as

rebates, tax incentives or competitive bidding, whereas generation based strategies

rely on incentives such as feed in tariffs, rate based incentives and quotas [4].

The quota based system, also called the Renewable Portfolio Standard (RPS), was

widely considered in literature written in the United States [5][6]. In essence, this type

of quota mandates a gradual increase in energy generated by suppliers from RES

within a certain timeframe. A similar incentive widely employed is the Mandatory

Utility Green Power Option [5]. In this case, utilities need to give customers the

choice to buy electricity generated by RES. Both Palmer et al and Menz et al

concluded that from all the regulatory incentives considered, the RPS and MGPO

were most effective in promote RE development [5][6]. This is not a surprising

conclusion, since the government directly mandates the increase in RE deployment

in the electricity sector by often by allowing power suppliers to trade so called ―green

certificates.‖ However, unike Palmer et al, Eichhammer also addresses the financial

implication for consumers. Due to typical higher generation costs extracting energy

from RES, consumers therefore ultimately pay more for electricity [4].

The above can likely be enhanced by adopting a generation disclosure policy. Menz

et al described the policy as one where utilities are required to continuously disclose

relevant information about the fuel and emissions involved in the generation of the

electricity which they are being sold [5]. However, the effectiveness of this can be

questioned, as Winther et al found that ―customers tend to disregard information

coming from their supplier…focus group participants found the presented terms and

figures to be incomprehensible to the extent that the information can be said to have

produced ignorance in them [7].‖ Therefore, this underscores the importance of

4 | P a g e

making generation disclosure both accessible and comprehendible by the general

public so that public support for RE could be improved in T&T.

Financial incentives have been shown to promote RE generation. The production

tax credit is a popular implantation of this whereby utilities using RES for power

generation are granted tax credits. Menz et al highlighted how this has been

implemented in the US, whereby a public fund is set up and maintained by taxes on

electricity customers [5]. Clearly, this is disadvantageous to customers who do not

use or even support RES-E since they will effectively be subsidising the cost of

energy to sustainable energy customers. Developing upon this concept, the

government of T&T can incentivise an RE industry into existence by guaranteeing a

steady income to RE utilities by paying them the avoided cost of fuel if conventional

generation was used. Frondel et al [8] shared a pessimistic outlook on the viability of

financial incentives used to make RE generation competitive with conventional

energy when considering the impact on employment. In their view, the over-reliance

on government funded incentives threatens negative repercussions on the economy

with their removal and in terms of unstable employment and increased conventional

generation [8].

Feed in Tariffs (FITs) are the most prevalent form of support scheme in Europe for

RES-E [9]. FITs work by setting the price of RES-E for a guaranteed period of time.

This is useful because it accounts for the higher costs of generation associated with

certain RE technologies when setting the price of electricity. Proponents of FIT, like

Couture and Gagnon, argued that the FIT system enables small RES-E suppliers

such as home owners and small businesses to enter into the energy supply chain

[10]. Like Couture and Gagnon, Ringel confirmed that FITs reduce the risk of

investment by guaranteeing prices so that cash flows can be accurately predicted

[10][11]. Furthermore, the author attempted to give some context to the FIT

discussion; there exists further problems when trying to integrate separate FIT

systems under competing markets as illustrated in Europe. Fortunately T&T

possesses a regulated monopoly electric utility that is powered by Independent

Power Producers (IPPs) which are majority state owned [1]. Finally, because FITs

incorporate anticipated expenses, RES-E projects that implement this strategy are

more likely to have the finances to consider factors such as the impact upon the

environment and site integration [12]. These factors are undoubtedly important in the

5 | P a g e

context of a twin-island state that has encouraging tourism potential in addition to RE

resources.

However, Ringel and Madlener et al then made the case against FITs. The price at

which RES-E is to be fixed is fraught with bias from national interests and other

lobbying interests [11][13]. This therefore makes the system highly political with a

susceptibility to corruption. Furthermore, both papers concluded that the

inappropriate price set for the FIT has adverse effects in two ways: if the price was

set too low then not enough money was generated to pay for the energy, and if the

price was set too high then RES-E suppliers benefit unfairly at the expense of

electricity consumers[11][13]. The resulting drain on the economy can be extended

to FITs that subsidize technologies with an unreasonably high generation cost. The

authors have justified how FITs can be abused and if used improperly, how they can

be detrimental to the economy, and by extension, the RES-E industry. The potential

for manipulation because FITs have this inherent vulnerability should be considered

given T&T‘s perceived management risks [14].

An alternative to the FIT system is the competitive bidding system. Madlener and

Stagl drew comparisons across the FIT system and Quota based system to

incorporate elements of both into the discussion around bidding systems [13].

According to them, RES-E quantities are set and then bid on by interested suppliers,

effectively determining the price at which the energy would be sold. Menanteau et al

clearly distinguished between FITs and competitive bidding systems to give a clearer

context: bidding systems fix the amount of energy to be bid on while FITs do not

determine this [12]. The authors then went one step further to criticise the

shortcomings of competitive bid systems in that the lower price agreements equate

to a lower risk appetite for investors, ultimately resulting in reduced installed

capacities for RES-E projects. Since T&T lacks any RE infrastructure, a competitive

bidding process will near useless at the initial stages of RE development.

Furthermore, at present, almost all IPPs are publicly owned so an optimistic

realisation of the competitive bidding system could occur if privately owned IPPs can

be established [1]. Jiang et al [15] inferred that competition encourages RES-E:

―..the monopoly situation of power grids, the main barrier for developing renewable

power..‖ Given T&T‘s limited land space as small islands and limited load demand

projection, this is unlikely to change.

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2.2 LEVELISED COST OF ENERGY

The Levelised Cost of Energy (LCOE) is defined as the ratio of the total lifecycle cost

of the energy generating power plant to the total lifecycle cost of engineering,

procuring of resources, construction and consequently operating the plant over its

lifetime [16][17][18][19]. After reviewing much of the material and analysing the

methodology used to calculate the LCOE, it became apparent that different authors

interpreted the definition of the LCOE to suit their own individual needs. The ensuing

confusion for finding LCOE was attempted to be clarified by Branker [20] et al and

Hernández-Moro et al [21] in their reviews, however these papers were not reviewed

to prevent unintentional bias on the part of these authors from being introduced into

the project. The LCOE methodology applied in this study needed to be well suited to

any constraints identified and to fall within the scope of the project.

Reichelstein et al [17] and Gökçek et al [18] used different methodologies based on

the above definition. However, in both cases, the general definition was refined to

such a detailed level that it could not be applied to a case study of RES-E in T&T.

The former author incorporated the cost of taxes into the LCOE, while the latter

broke down the costs used to determine the LCOE into much more detail than could

be contained within the scope of the project. Ouyang and Lin [22] opted to utilise the

EGC Spreadsheet model created by the IEA [19]. This model omitted any

parameters beyond the ―raw, technical costs‖ as it would be applied to variety

countries [19]. Although the LCOE in this study was country specific, the factor of

data reliability still remained prevalent in this study for how much input could be

provided for the accurate calculation of the LCOE, thereby making a similar

approach attractive.

Darling et al [16] approached the question of accuracy in the LCOE calculation not

from the data quality perspective, but instead emphasised the importance of

accounting for uncertainty in the input data. At the same time, the quality of the data

that could be found would indeed determine the accuracy of the LCOE calculation,

but in this way the certainty of the LCOE calculation could also be determined.

Uniquely, they proposed the Monte Carlo simulation as the preferred method for

modelling the uncertainty in the input parameter to correspondingly produce a range

of probabilities for possible values that the LCOE could take. As a consequence the

analysis performed was superior to that done by Riechelstein et al, Gökçek et al and

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the model created by the IEA, since in these analyses only single point estimations

were done. Furthermore, correlation sensitivity analysis could be performed to link

the LCOE to each of the input parameters in terms of strength of associated.

Therefore the most suited approach chosen for guiding the relevant stakeholders

was to employ the Monte Carlo simulation to find the LCOE.

Interestingly, Hernandez-Moro and Martinez-Duart [21] suggested that selling

electricity at the LCOE sets the Net Present Value of the RES-E project to zero, but

interestingly, Short et al. [23], defined the LCOE with respect to NPV as ―The cost

per unit of energy that, if held constant through the analysis period, would provide

the same net present revenue value as the net present value cost of the system.‖

These were incoherent definitions and hence the correct definition was evidenced at

the end of this report.

2.3 FINANCIAL ATTRACTIVENESS AND SUSTAINABILITY ASSESSMENT

Mathematical models have been widely employed to solve policy and planning

challenges in the energy sector [24][25]. Energy models are typically classified

according to the analytical approach: either the ―top-down‖ model or the ―bottom-up‖

model [26]. Urban et al [25] described the top-down models as using aggregated

data to create forecasts for energy demand and other indices, while Pandey [24]

elaborated on this with the view that it has the ability to solve energy policy

challenges that are related to ―macroeconomic indicators‖ and ―economy-wide

emissions‖ because the energy sector is modelled as connected to the entire

economy. In the case of the bottom-up model, Urban et al [25] posited that it uses

data separated into its component parts, and calculated costs of various

technologies with an energy sector considered in isolation. Pandey [24] criticised the

model for assuming a government regulated monopoly of energy technologies to find

the costs and emissions associated with these technologies. This is well suited to the

case of T&T.

Both Pandey [24] and Urban et al [25] raised the issue of the suitability of models in

the context of the developing country, especially when the models were created

using the framework of developed countries. While Pandey considered which model

was better from the analytic approach as explained above, the analysis done was

limited to the tools available at the time of writing making it slightly out-dated. Urban

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et al updated the analysis of available tools by employing a rigorous methodology to

test several qualifying models. They concluded that the best approach is the bottom-

up model because it addressed the most unique characteristics of the developing

country. From this refined list of bottom-up models, a selection could therefore be

made. Qualifying models were further shortlisted for this study according to

availability and ease of use.

Harder and Gibson [27] successfully utilised one of the model software called

RETScreen to predict the energy production, financial feasibility and GHG reductions

for Saudi Arabia. This was useful because the authors cited its user-friendliness as a

major advantage, and demonstrated some analysis on a developing country [28].

This success was repeated by Su et al who used the software for assessing the

feasibility of building a RES-E plant [29]. Further to the above, RETScreen was

made available free of charge by the Canadian Government [30], and was

immediately available for use in the project. This contrasts with the other potential

models identified in which were either unavailable at the time of writing or required

the user to request a licence resulting in a lengthy delay.

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2.4 ENERGY SCENARIO OF T&T

The energy scenario was largely captured in the progress report, found in Appendix

A, and briefly expanded upon in this section.

T&T is the biggest producer of oil and natural gas in the Caribbean. The local

economy is based heavily around revenue and foreign exchange earned from the

energy sector. The energy balance found indicated that power generation is

generated almost exclusively from natural gas [3]. Since natural gas supports much

of the economy and power generation, it would be detrimental to the country if the

reserves were depleted. It would be difficult to suddenly acquire new and costly

technology if there was no revenue to spend. Table 2.1 reflects this in terms of

government revenue and GDP.

2010/2011 2011/2012 2012/2013 2013/2014 %Government revenue

57.6 54.0 50.4 48.1

% GDP 18.1 16.9 15.5 15.7 Table 2.1 -Significance of energy sector to the economy of T&T [3]

Furthermore, Table 2.2 shows the calculated length of time remaining that T&T can

continue drilling at its current rate of 1,962 MMSCF/d [31] based on its reserves [3].

Natural gas extracted (MMSCF/d) 1,962 Natural gas extracted (MMSCF/y) 716, 130 Proved reserve [3] (TCF) 15.37 Probable reserve [3] (TCF) 7.88 Possible reserve [3] (TCF) 5.88 Time before proven reserve runs out (years)

22

Table 2.2 - Time left before reserve of natural gas may finish

The ‗proved‘ reserve is the category of relevance since it reflects a high certainty

(>90%) of being recovered. Commercial aspects promote the recovery of this type of

reserve, but technical problems separate it from the other categories. The ‗probably‘

and ‗possible‘ reserves are unproven and have a probability of being extracted of

>50% and >10% respectively [32]. These require more analysis and complex

engineering techniques [32]; the cost of power generation could be expected to

increase once T&T‘s proved reserves have been depleted since unproven reserves

cost more to work. This scenario needed to finally be adjusted for the date on which

the figures for the reserves were referenced. Since the document was published in

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2011, the actual time left with which T&T has a reliable supply of natural gas is

approximately 17 years. Based on this, T&T may run out of a reliable supply of gas

by 2037.

In this report, solar photovoltaic and wind technology were focussed on because

these are the most readily available sources [3]:

1. T&T is geographically located between 10° 2‘ and 11° 12‘N latitude and 60°

30‘ and 61° 56‘W longitude. This means it has good exposure to the sun

throughout the year (see solar map for region in appendix G).

2. The Northeast Trade Winds blow through T&T. The generally predictable

characteristics of these winds [33] make it a reasonable choice for RES-E.

It was noted that retail consumers pay US$0.04 per kWh for power generated by

natural gas [3].‖

Fig. 2.1 shows that the 2016 peak

demand was forecasted at about

2400MW. The government of T&T

previously indicated that it was

interested in a low level of RE

penetration for the initial stages: 5% of

2011 levels [3] of 60MW. This figure

was approximated to 100MW out of

consideration for the datedness of that

commitment and therein referred to as

the ―proposed case‖. Increased

installed capacities considered were

called the ―extrapolated cases‖.

Fig. 2.2 shows a general projection

based on the energy sales from the

country‘s only distribution and

transmission company [34] from 2008-

2012. In 2016, the load was predicted to

draw about 9,200MWh.

0

2000000

4000000

6000000

8000000

10000000

2005 2010 2015 2020

Ene

rgy

(MW

h/y

r)

Year

Annual Energy Sales

Figure 2.2 - Projected energy sales in T&T [34]

Figure 2.1 - Chart showing predicted peak demand growth in T&T[3]

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3 METHODOLOGY

After reviewing the literature, the relevant theories needed to be well understood to

perform the required analysis as outlined in the objectives. This section presents the

required theories as applied in the study.

3.1 RE PERFORMANCE USING T&T CLIMATE DATA

Finding the solar capacity factor allowed for total actual energy, Eactual, to be found in

the cases modelled in Section 5. This value was central to the financial and emission

analysis.

3.1.1.1 Solar PV

The capacity factor was modelled using climate and technology information to give a

general idea of technology performance based on site specific data. The below table

summarizes the abbreviations used for key terms when deducing solar PV

performance in T&T.

Capacity factor is defined as the ratio of actual amount of energy produced by the

plant over a given period, to the energy produced at full capacity over the same

period [35]:

Eqn. 3.1

where

Eqn. 3.2

Eqn. 3.3

From the above it can be seen that the capacity factor determined the energy output

from proposed generating systems, where a higher capacity factor results in superior

plant performance and vice versa. Table 3.1 below summarizes the nomenclature.

Eactual Actual energy output (kWh/year) Erated Rated energy output (kWh/year)

s Average daily solar insolation (kWh/day/m2)

n Module efficiency (%) A Module area (m2)

Prated Rated power of module (W) Cf Capacity factor (%)

Table 3.1 - Solar Capacity Factor Calculation Nomenclature

12 | P a g e

3.1.1.2 Wind

The Weibull PDF is well suited for modelling wind speed profiles in the Caribbean

[36][37]. and so Weibull parameters can then be derived to find the performance of

wind technology in T&T. The probability density function for wind speed can be

expressed as:

( )

(

)

( (

)

* Eqn. 3.4

The below table summarizes the abbreviations used for key terms when measuring

energy extraction from wind in T&T.

k Shape parameter c Scale parameter

Annual average wind speed at a measured height(m/s)

Average wind speed at height, xm (m/s) Measured wind speed (m/s) Rated speed (m/s) Cut in speed (m/s) Cut out speed (m/s)

Cf Capacity factor (%) Proposed wind turbine hub height (m) Roughness length (m)

Height at which measured wind speed taken (m)

Table 3.2 - Wind Capacity Factor Calculation Nomenclature

The measured wind speed could be measured at any elevation. It is prudent to

model the wind speed at the height at which a proposed wind turbine would be

installed i.e. at the wind turbine hub height. This can be found as shown below [38]:

(

) Eqn. 3.5

The shape parameter k is calculated [35]:

Eqn. 3.6

13 | P a g e

The scale parameter c is approximated by [35]:

( ( )) Eqn. 3.7

Where the gamma function of a variable, z, is found according to the following:

( ) ∫

Eqn. 3.8

The capacity factor can then be found using proposed turbine characteristics

combined with the parameters derived from the modelled distribution of wind as

shown below [39]:

(

)

(

)

(

⁄ )

(

⁄ ) (

⁄ )

Eqn. 3.9

3.2 FINANCIAL ANALYSIS

The financial viability of the RE project is deduced using certain key measures:

1. Levelised Cost of Energy

2. Net Present Value

3. Equity Payback Period

3.2.1 Levelised Cost of Energy

The levelized cost of energy is defined across the literature as the ratio of total

lifecycle cost to total lifecycle energy output. It is often used as a measure to

determine the minimum price that a power generating plant must sell each kWh of

energy to be able to break even with all investment costs associated with the project

[40].

Moreover, grid parity is the ability to sell power from non-conventional power

generation at comparable prices to conventional generation, which has already

defined the price of electricity in the grid [40]. The LCOE is an important measure

14 | P a g e

when considering the introduction of renewable energy powered generation since it

suggests whether or not the RE source is capable of achieving grid parity.

Table 3.3 summarizes the abbreviations used for key terms when calculating the

LCOE of a proposed RE project.

] Debt payment period (years)

( ) ( )

Yearly rated energy output for t [kWh/year] Total cost of project for t ($)

Table 3.3 - LCOE Calculation Nomenclature

In general, the LCOE could be found by comparing total lifecycle cost to lifecycle

energy output:

Eqn. 3.10

It could then be expressed as [20]:

∑

( )

∑

( )

Eqn. 3.11

This can be further refined into the following equation:

∑ ( ) ⁄ ∑ ( ) ( )

∑ ( )

Eqn. 3.12

3.2.2 Monte Carlo Simulation

This type of simulation mathematically computes a probability distribution, as

opposed to a single value, while allowing for risk in the analysis and decision making

[16]. All the input parameters were defined as a range of values thereby creating the

required probability distribution by defining the shape or the trend of the input

parameter. The probability distribution therefore provided all outcomes with the

15 | P a g e

likelihood of each occurring. This was useful since in the financial analyses done,

rarely were inputs assigned a single input value.

The simulation runs over a defined number of iterations. These values can be varied

uniformly between limits, or they could be randomised such that the resulting

distribution for the parameter in question takes the shape of a desired probability

distribution.

From the results obtained from the Monte Carlo Simulation, further analyses could

be performed. The correlation between defined input distributions and the output

distribution could also be determined thereby indicating how sensitive the outcome

was to the defined variations in the input.

3.2.3 The Inter-quartile Range

The Inter-quartile range is used to

measure the spread of values within the

central 50% of values of the probability

distribution being analysed, as shown in

Fig. 3. The interquartile range is taken as

the difference of the upper quartile, and

the lower quartile. These represented the

variable at 25% along the dataset and at

75% along the dataset respectively. This

was chosen to compliment other methods

of average in distributions that were

skewed, in order to reduce the effect of outlier values [41] in the analysis.

3.2.4 Correlation between parameters and LCOE

The Pearson correlation coefficient determines the strength of an association

between two variables. It is measured between -1 and 1 where 0 indicates no

association, a negative value indicates that when one value increases the other is

reduced, and a positive value indicates that both values vary linearly. This was used

to plot Tornado charts to give a visual impression of the sensitivity of the LCOE to

variation in each input parameter.

Figure 3.1 - Diagram illustrating interquartile range on a normal probability distribution [32]

16 | P a g e

3.2.5 Discounted Cash Flow Analysis

The feasibility of implementing a RE power project was investigated by using

RETScreen to perform a discounted cash flow (DCF) analysis. This could be used to

output several key financial parameters:

1. Net Present Value (NPV)

2. Equity payback period

($) ($) (%)

Table 3.4 – Present Value Calculation Nomenclature

Like the LCOE, the NPV takes into account the ‗time value of money‘. DCF analysis

can be used to determine the value of cash flows, in today‘s terms by discounting the

future value, , to a present value [42]:

( ) Eqn. 3.13

3.2.5.1 Net Present Value

The Net Present Value (NPV) determines the difference between the present value

of net cash inflow and outflow over a project lifetime. The NPV is given by [43]:

∑ ∑

( )

Eqn. 3.14

Therefore, a NPV value greater than zero means a project is financially attractive

since it would generate profit. NPV values of zero or less found at the feasibility

assessment stage indicate a project will not be profitable and should therefore be

avoided [43]. Hence, in this analysis only positive NPV values were acceptable for

consideration of a renewable energy project. In addition, the NPV was used to

compare projects to determine which is worth the most over the lifetime of the

generating plant.

17 | P a g e

3.2.5.2 Payback Period

The payback period captured the length of time required for the initial investment

made to implement the chosen RES-E. Therefore, it could be referred to as ―years to

positive cash flow‖ of the project, and is given by [44]:

⁄

Eqn. 3.15

Clearly, this method does not capture the ―time-value of money‖ as the NPV does.

Furthermore, it only focuses on the initial phase of the project and does not measure

profitability [45]. This was acknowledged. Nonetheless in this project, it was used

carefully to compare investments in different RES-E.

3.2.6 Project Cash Flow

The cash flow per year of a RE project was calculated as shown below:

Eqn. 3.16

(

)

Eqn. 3.17

From the above, the cash flows over the project life were discounted as described in

Section 3.2.5.

3.2.7 Project Costs

3.2.7.1 Total initial cost

The total initial cost, sometimes referred to as total installed system cost, comprised

of several sub-costs, and therefore can be considered a ‗turn-key‘ cost for a power

project [46]. This means that Balance of System costs, installation costs, warranties

etc. were included in the total installed system cost [47]. This parameter was

benchmarked based on the literature at an average value.

3.2.7.2 Debt Payments and O&M

The annual debt payment was the most significant cost contributing to the total

annual costs of operating a RE generating power plant. This underscored the need

to borrow from one or more debt financiers offering optimal interest rates and debt

payment periods. Table 3.5 summarized the nomenclature used.

18 | P a g e

( ) ( ) ( )

( ) Table 3.5 - Debt Payment and O&M Nomenclature

The formula used to calculate annual debt payments, DP, is shown below [48]:

( (

)

)

Eqn. 3.18

The Operations and Maintenance Cost (O&M) includes the cost of replacing

components such as module inverters or wind turbines, the cost of labour, and other

expenses related to the running of the RE power generating plant [47]. It was

benchmarked based on the literature at an average value.

3.2.8 Project income

3.2.8.1 Avoided cost of fuel, Electricity export income, GHG reduction income

The avoided cost of fuel using conventional generation was modelled as a Clean

Energy (CE) production income. Table 3.6 summarized the nomenclature used.

Fuel cost ($/Million Btu) ( )⁄

( )⁄

Clean energy income ($) ( ) ( ) ( ) ( )

( ) Table 3.6 - Project income Nomenclature

If a single cycle gas turbine plant of known heat rate, then this can be used together

with the cost of fuel to determine the cost per unit of energy produced:

Eqn. 3.19

The proposed RE generating plants do not use conventional fuel and therefore the

proposed CE production income, , could be modelled as the product of the total

energy produced per year, and the :

Eqn. 3.20

19 | P a g e

The electricity export income, , was modelled simply as the product of the set

electricity price, , and total energy produce per year:

Eqn. 3.21

Similarly, the GHG reduction income, , was modelled as the product of the net

annual GHG reduction and the GHG reduction credit rate, , to incorporate

income from the Clean Development Mechanism:

Eqn. 3.22

3.3 Environmental Analysis

The methodology used to analyse the impact on the environment calculated the

yearly GHG emission reduction by comparing emissions from proposed technologies

to that of the baseline technology. Therefore, it compared the emissions from a

hypothetical wind or solar power plant, to the emissions output by a project

producing the same amount of energy using conventional generation.

Key input parameters were assumed to take certain ideal values based on

benchmark figures available in the literature. The model identified , and

as the Greenhouse Gases to be included in the net annual GHG emission reduction.

The model associated an emission factor with each GHG gas of 54.5kg/GJ,

0.004kg/GJ and 0.001kg/GJ.

For the two renewable energy technologies compared, the model assumed the GHG

emission would be nil. However, since it is envisioned that any form of RE

generation would utilise the pre-existing transmission and distribution infrastructure,

then it would also experience losses in these areas. Consequently, the actual GHG

emission value from these RE technologies would take a non-zero value as it is

implied that conventional generation would be required to compensate for the energy

lost in the transmission and distribution (T&D) network. Table 3.7 summarized the

nomenclature used.

n=1 n=2 n=3

CO2 emission factor

CH4 emission factor

N2O emission factor

Emission factor Losses due to T&D

Table 3.7 - Nomenclature for finding methodology used to find GHG emissions reduction

20 | P a g e

( )

∑ ( ( )

) ( )⁄

Eqn. 3.23

Using the above parameters, the GHG emission factor, in tCO2/MWh, was

determined. The GHG emission was then found by implementing the potential

energy gained from a RES-E project as shown below:

( )

( ) ( )

Eqn. 3.24

21 | P a g e

4 PROJECT STRUCTURE

The approach, data and tools used in Section 5 to fulfil the objectives were

introduced here.

4.1 DATA

Numerous data inputs were required for modelling to be done in this study. In all

cases, these values were benchmarked using the most relevant data available.

There were instances where single values were used to determine the output, but

also cases where the certainty of the benchmarked input was considered also

thereby producing a range for the input. Nevertheless in both cases the data was

first presented and then justified.

4.2 TOOLS

Two different instances of modelling software were used to implement the models

outlined in the aim and described in the methodology of this project. The numerical

computation and visualisation tool, Matlab [49] was used to perform the LCOE

analysis by coding the chosen methodology and modelling the input parameters as

probability distribution from scratch. The second tool used was the Excel-based

clean energy project analysis software tool, RETScreen [50]. This tool was used to

find the financial attractiveness of the project and emissions reduction.

4.3 CASE STUDY

The case study was broken into four sections, each considering two RES-E: wind

and solar PV. In Section 5.1, the RES-E Performance using climate data relevant to

T&T was assessed. In Section 5.2 the LCOE was found and analysed in depth at the

proposed installed capacity, as other considered below did not reveal significant

reductions in LCOE. Section 5.3 dealt with the prospective project feasibility using

discounted cash flow analysis, and Section 5.4 summarised the greenhouse gas

reduction; both cases being analysed at the proposed capacity, and extrapolated

installed capacities to glean further information. Finally, a comparison for analysis of

the results was delivered in Section 5.5.

22 | P a g e

5 CASE STUDY

5.1 PERFORMANCE OF RE TECHNOLOGY

5.1.1 Solar PV

The capacity factor for a solar PV project sited in T&T was determined using the

annual solar insolation described in Table 5.1.

Month

Daily solar radiation - horizontal

(kWh/m²/d)

January 5.64

February 6.24

March 6.81

April 6.95

May 6.64

June 5.94

July 6.33

August 6.41

September 6.19

October 5.70

November 5.16

December 5.23

Annual mean 6.10

Table 5.1 - Climate data for Crown Point, Tobago [50]

This site demonstrated better solar resources of the two sites where weather stations

were located in T&T [51]. The primary function of these weather stations are to

forecast weather for aviation purposes as both locations have an airport [52], and

hence are not ideally situated within the country for a solar PV project. However, a

benchmark capacity factor could be gleaned.

Maximum power (W) Length (mm) Width (mm) Efficiency (%)

200 1482 992 13%

Table 5.2 - Relevant specifications of proposed solar PV module [54]

23 | P a g e

For this study, the Suntech Power STP200-18/Ud photovoltaic module was selected

as a typical module choice [53]. Module specific parameters, shown in Table 5.2

were then used to find the actual energy output of a proposed solar PV system [54]:

( )

If a PV module has a known rated power output, , the rated energy

output, , can be found:

5.1.2 Wind

In the absence of reliable historic wind speed data, near-surface reanalysis data was

used to conclude that the wind speed in T&T ranged from a low of 5m/s in the rainy

season to 8m/s in the dry season [55]. From this range the mean wind speed could

be approximated assuming that the dry and rainy seasons were of equal lengths.

The wind speed distribution used to arrive to the above conclusion was assumed to

follow a Weibull distribution.

Rated

power (kW)

Cut off wind

speed (m/s)

Rated wind

speed (m/s)

Cut out wind

speed (m/s)

Hub height

(m)

1500 3.5 15 25 80

Table 5.3 - Relevant specifications of proposed [30]

The GEA14954C 1.5 MW design, with parameters as seen in Table 5.3, was chosen

as a suitable wind turbine for utility scale energy production [51], with the chosen

design using a hub height of 80m. However, the above wind speed was calculated at

a height of 10m. Therefore, the mean wind speed at a proposed wind turbine hub

height was extrapolated using the power exponent law to a hub height of 80m, as

shown below:

24 | P a g e

(

)

The shape parameter k was calculated:

The scale parameter c was approximated by:

( (

))

Capacity factor was found using the proposed turbine characteristics, combined with

the parameters derived from the modelled distribution of wind as shown below:

(

)

(

)

( ⁄ )

( ⁄ )

( ⁄ )

5.2 LEVELISED COST OF ENERGY

The LCOE was found to determine the approximate ‗break-even‘ price that should be

applied to electricity exported from these RE generating power stations. The input

parameters were modelled to fit differing distributions according to a range of values

found in the literature, or values found in the literature and compared to the author‘s

own findings. In each case, an explanation was given to outline the reasoning used

to create the distributions and any assumptions made. It was therefore prudent that

the uncertainty was modelled to ensure good accuracy of the resulting LCOE

distribution. This was achieved in Matlab using a Monte Carlo simulation iterated

over 100,000 iterations for each case.

In this section, the costs, financing and energy output associated with two RE

technologies, solar PV and wind, were modelled to find the LCOE. A 100MW utility-

scale system of each type of RE technology was investigated translating the

previously calculated capacity factors to further reflect cost of RES-E in T&T.

25 | P a g e

5.2.1 Global Input Parameters

5.2.1.1 Debt Ratio

The debt ratio was likely to be very high for such an intensive capital project. It was

found that the total installed costs for a 100MW

solar PV project, the RE technology with lower

total installed costs, exceeded US$0.2 billion.

This project would be unlikely to be 100% equity

funded by T&T in the near future [56], so it was

prudent to model a high level of borrowing at an

average of 70% as shown in Fig. 5.1.

5.2.1.2 Discount Rate

The risk perception of the proposed technology

affects the relative stability and magnitude of the

discount rate [57]. Both Solar PV and Wind were

considered low risk RE technologies. It was

found that the discount rate can range from 6%-

9% for Solar PV, and 6%-8% for Wind.

Therefore, a normal distribution was used to

model the discount rate centred at

approximately 7.5% as seen in Fig. 5.2.

5.2.1.3 Debt Interest Rate

The interest rate was modelled with a normal

distribution and based on interest rates given to

utility scale projects in the past. However, since

the interest rate calculated by debt financiers

depends on the debtor‘s credit rating, the

distribution was therefore shifted up to an

average of about 13% per annum [58].

Figure 5.1 – Probability distribution of debt ratio

Figure 5.2 - Probability distribution of discount rate

Figure 5.3 - Probability distribution of debt interest rate

26 | P a g e

5.2.1.4 Debt Term

The debt term ranges from 20 to 30 years for

most utility sized RE projects [59][60][58].

Therefore a normal distribution could be

applied with the distribution centred on an

average debt term of 25 years as shown in

Fig. 5.4.

5.2.2 Solar PV

5.2.2.1 Debt Payment

The annual debt payment was calculated, as

shown in Eqn. 3.18, by using the debt

interest rate, debt term, debt ratio and initial

costs parameter distributions. It was found

from the distribution that an annual debt

payment of almost $20 million was the most

likely case as shown in Fig. 5.5.

5.2.2.2 O&M

The O&M cost associated with the upkeep of

a utility scale PV system depended on site

specific conditions such as having to

maintain panels near coastal locations due to

corrosive sea breeze or having to clean

panels due to dust. The O&M cost above was

centred at a cost of $16/kW and standard

deviation of $9 [61], and then extrapolated

using the proposed capacity to produce the

probability distribution shown in Fig. 5.6.

Figure 5.4 - Probability distribution of debt term

Figure 5.5 - Probability distribution of debt payment

Figure 5.6 - Probability distribution of O&M

27 | P a g e

5.2.2.3 Capacity Factor

The capacity factor was derived in the

previous section to be about 25%. The

average values for Africa and India

respectively were 20% and 21%

respectively, while South America was

shown to be favoured with an average

capacity factor of 27% [47]. Therefore, a left

skewed distribution centred on 25% was

created to model the performance of the

proposed PV module when used to take

advantage of T&T‘s favourable solar

insolation as shown in Fig 5.7.

5.2.2.4 Total Initial Cost

The distribution for the total installed costs

for this project was extrapolated from the

distribution derived for the total initial costs

per capacity. Each kW of installed capacity

was estimated to cost on average $2025

with a standard deviation of $694 [61]. This

was in keeping with 2016 figures, since the costs associated with solar PV projects

have been significantly reducing each

year. A normal distribution was

therefore utilised to model this cost. The

median total initial cost was found to

about $220 million.

5.2.2.5 LCOE

The derived probability distribution of

possible values of LCOE of a 100MW

Solar PV project is shown in Fig. 5.9.

The distribution suggests that the median Figure 5.9 - Probability distribution of LCOE per energy produced

Figure 5.7 – Probability distribution of capacity factor

Figure 5.8 – Probability distribution of total installed costs

28 | P a g e

LCOE was about 17 cents per kWh with an interquartile range of approximately

$0.12/KWh- $0.22/KWh.

5.2.2.6 Sensitivity Analysis

Fig. 5.10 shows the

sensitivity of the

calculated LCOE to each

of the input parameters.

As described in the

Methodology, a higher

magnitude of correlation

relates to a stronger

association between the

variables in question. A

positive correlation

indicated that when one

variable increased the

LCOE increased, and

vice versa for a negative

correlation.

5.2.3 Wind

5.2.3.1 Debt Payment

The annual debt payment was calculated by

using the debt interest rate, debt term, debt ratio

and initial costs parameter distributions using

Eqn. 3.18. An annual debt payment of more than

$20 million was likely as seen in Fig. 5.11.

Figure 5.10 - Tornado chart showing impact of input parameters on LCOE

Figure 5.11 - Probability distribution of debt payment

-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

1

Debt payments 0.8057

Total installed costs 0.7798

Capacity factor -0.478

Interest rate 0.2893

Debt ratio 0.0917

Discount rate 0.0872

O&M 0.0532

Debt term -0.0148

Impact of parameters on LCOE

29 | P a g e

O&M

The O&M cost above was centred at a cost of

$32/kW and standard deviation of $10 [61], and

then extrapolated using a capacity of 100MW. In

this study, wind power therefore costs almost

twice as much as solar PV to finance O&M as

seen in Fig. 5.12.

5.2.3.2 Capacity factor

The capacity factor was derived in the previous

section to be about 41%. Previous studies have

suggested that there is great wind power

potential, with other authors calculating power

factors of 28.09% and 73.29 % in T&T [38].

Therefore, a left skewed distribution centred on

41%, as shown in Fig. 5.13, was created to

capture the possibility of sub-optimal wind

technology performance in addition to

considering optimal locations and weather.

5.2.3.3 Total initial cost

The distribution for the total installed costs for

this project was extrapolated from the distribution

derived for the total initial costs per capacity, as

seen in Fig. 5.14. Each kW of installed capacity

was on average estimated to cost $2,346 with a

standard deviation of $770 [61]. This was in

keeping with the most updated figures released

in 2016, since the costs associated with solar PV

projects have been reducing each year. A normal

Figure 5.12 - Probability distribution of O&M

Figure 5.13 - Probability distribution of capacity factor

Figure 5.14 - Probability distribution of total installed costs

30 | P a g e

distribution is therefore utilised to model this cost. The median total initial cost was

found to about $250 million.

5.2.3.4 LCOE

The derived probability distribution of

possible values of LCOE of a 100MW Wind

project is show in Fig. 5.15. The distribution

suggests that the median LCOE was about

12 cents per kWh and with an interquartile

range of approximately $0.10/KWh-

$0.17/KWh.

5.2.3.5 Sensitivity Analysis

Fig. 5.16 shows the sensitivity

of the calculated LCOE for a

potential Wind power project

to each of the input

parameters. A positive

correlation indicated that

when one variable increased

the LCOE increased, and vice

versa for a negative

correlation.

Figure 5.15 - Probability distribution of LCOE per energy produced

Figure 5.16 - Tornado chart showing impact of input parameters on LCOE

-1 -0.5 0 0.5 1

1

Debt payments 0.7969

Total installed costs 0.7621

Capacity factor -0.4916

Interest rate 0.3041

Debt ratio 0.1005

Discount rate 0.0794

O&M 0.0528

Debt term -0.0139

Impact of parameters on LCOE

31 | P a g e

5.3 PROJECT FEASIBILITY

In this section the attractiveness of proposed RE generating projects was

investigated to determine what technology would be better suited to debt financiers

and other investors in the context of RES-E project in T&T. The energy price was

fixed at the upper limit of 16 cents per kWh in keeping with the findings of the LCOE

calculations. However, in this section, the LCOE calculated above was less relevant

since other income streams were considered in addition to the revenue earned from

the sale of electricity.

A sensitivity analysis was performed to determine the sensitivity of the calculated

financial attractiveness indicators to variations in input parameters. The base case

values for these parameters were chosen to be the mean values used to define the

distributions in Section 2. This was followed by a Monte Carlo simulation iterated

over 500 iterations to determine the probability distributions for Net Present Value

and Equity Payback Period. Using the median values from these distributions as a

measure of average allowed for comparison of the above financial attractiveness

findings. This was achieved in RETScreen.

5.3.1 Solar PV

5.3.1.1 NPV

Table 5.4 breaks down each parameter according to the variation given to it as a

measure of uncertainty.

Parameter

Unit Value Range (+/-) Minimum Maximum

Initial costs

$ 200,000,000 70% 100,000,000 300,000,000

O&M

$ 1,600,000 50% 800,000 2,400,000

GHG reduction credit rate

$/tCO2 1.00 50% 0.50 1.50

CE production credit rate

$/kWh 0.02 100% 0.00 0.04

Debt ratio

% 70% 29% 50 90%

Debt interest rate

% 13.00% 30% 9.1% 16%

Debt term

yr 25 20% 20 30 Table 5.4 - Input parameters with a defined range of values

32 | P a g e

Figure 5.17 – Probability distribution of NPV for Solar PV at 100MW installed capacity

The NPV distribution shown in Fig. 5.18 suggests that the NPV will likely be positive.

The spread of the distribution is within a positive net present value since the

interquartile range was found to be bounded by a lower and upper value of

$102,863,696 and $192,279,818. The median NPV for the simulated variation in the

input parameters was found to be $145,056,873.

Figure 5.18 - Spiderplot showing the impact of each variable on NPV

Fre

qu

en

cy

Distribution - Net Present Value (NPV)

0%

2%

4%

6%

8%

10%

12%

14%

16%

-115,148,156 -65,147,642 -15,147,127 34,853,387 84,853,901 134,854,415 184,854,929 234,855,443 284,855,957 334,856,471

-40 -20 0 20 40

NP

V (

$)

+/- %

Impact of each variable on NPV

CE production credit rate

Electricity export rate

Debt ratio

Debt interest rate

O&M

Initial costs

Discount rate

Debt term

GHG reduction credit rate

33 | P a g e

5.3.1.2 Equity Payback

Figure 5.19 - Probability distribution of Equity Payback for Solar PV at 100MW installed capacity

The equity payback period distribution shown in Fig. 5.20 suggested that the equity

payback will very likely be less than one third of the project life. The spread of the

distribution reinforced this as the interquartile range was found to be bounded by a

lower and upper value of 0.1 year and 4.5 years respectively. The median equity

payback for the simulated variation in the input parameters was found to be 3.1

years.

Figure 5.20 - Spiderplot showing the impact of each variable on Equity Payback Period

-40 -20 0 20 40

Equ

ity

Pay

bac

k P

eri

od

(ye

ars)

+/- %

Impact of each variable on Equity Payback Period

CE production credit rate

Electricity export rate

Debt ratio

Debt interest rate

O&M

Initial costs

Debt term

GHG reduction credit rate

34 | P a g e

5.3.2 WIND

Table 5.5 breaks down each parameter, according to the variation of the values

found as a range to be used in the below Monte Carlo simulation.

Parameter

Unit Value Range (+/-) Minimum Maximum

Initial costs

$ 234,600,000 60% 93,840,000 375,360,000

O&M

$ 3,100,000 65% 1,085,000 5,115,000

Electricity export rate

$/MWh 160.00 0% 160.00 160.00

GHG reduction credit rate

$/tCO2 1.00 50% 0.50 1.50

CE production credit rate

$/kWh 0.02 100% 0.00 0.04

Debt ratio

% 70% 29% 50% 90%

Debt interest rate

% 13.00% 30% 9.10% 16.90%

Debt term

yr 25 20% 20 30 Table 5.5 - Input parameters with a defined range of values

5.3.2.1 Net Present Value

Figure 5.21 - Probability distribution of Net Present Value for Solar PV at 100MW installed capacity

The NPV distribution shown in Fig. 5.22 suggested that the NPV will likely be

positive. The spread of the distribution is within a positive net present value as the

interquartile range was found to be bounded by lower and upper quartiles of

$301,218,538 and $411,807,348 respectively. The median NPV for the simulated

variation in the input parameters was found to be $354,917,960.

Fre

qu

en

cy

Distribution - Net Present Value (NPV)

0%

2%

4%

6%

8%

10%

12%

14%

16%

56,716,280 117,066,671 177,417,062 237,767,453 298,117,844 358,468,235 418,818,626 479,169,016 539,519,407 599,869,798

35 | P a g e

Figure 5.22 - Spiderplot showing the impact of each variable on NPV

5.3.2.2 Equity Payback

Figure 5.23 - Spiderplot showing the impact of each variable on Equity Payback Period

-40 -20 0 20 40

NP

V (

$)

+/- %

Impact of each variable on NPV

CE production credit rate

Electricity export rate

Debt ratio

Debt interest rate

O&M

Initial costs

Discount rate

Debt term

GHG reduction credit rate

-40 -20 0 20 40

Equ

ity

Pay

bac

k P

eri

od

(ye

ars)

+/- %

Impact of each variable on Equity Payback Period

CE production credit rate

Electricity export rate

Debt ratio

Debt interest rate

O&M

Initial costs

Debt term

GHG reduction credit rate

36 | P a g e

Figure 5.24 - Probability distribution of Equity Payback Period for Wind at 100MW installed capacity

The equity payback period distribution shown in Fig. 5.24 suggests that the equity

payback will very likely be less than one sixth of the project life. The spread of the

distribution reinforces this as the interquartile range was found to be bounded by a

lower and upper value of 0.1 year and 2.3 years respectively. The median equity

payback for the simulated variation in the input parameters was found to be 1.7

years.

5.4 GREENHOUSE GAS EMISSIONS ANALYSIS

Since the energy mix in T&T comprises almost 100% natural gas fuel, this was

selected as the fuel type for the baseline technology. The model associated an

emission factor with each GHG gas of 54.5kg/GJ, 0.004kg/GJ and 0.001kg/GJ. The

global warming potential was then measured by converting and terms of

at equivalent rates of 25 tonnes of per tonnes of , and 290 tonnes of

per tonne of . Given the total GHG emission, the efficiency of conventional

generation could be modelled: the efficiency was found to be 20%. In addition, in

T&T the T&D network has an approximate loss of 6%. The model then used Eqn.

3.23 and Eqn. 3.24, calculating the GHG Emissions Factor to be about

1.05tCO2/MWh.

Table 5.6 and Table 5.7 summarise the annual reduction in GHG emissions for the

proposed cases of installed capacities. The mean average actual energy produced

by the plant was included to allow for better comparisons between the two

technologies studied.

Fre

qu

en

cy

Distribution - Equity payback

0%

5%

10%

15%

20%

25%

0.2 0.9 1.6 2.3 3.0 3.7 4.4 5.1 5.8 6.5

37 | P a g e

Capacity (MW)

Actual energy produced (MWh)

Base case GHG emissions (tCO2/yr)

Proposed case GHG emissions (tCO2/yr)

Net GHG emission reduction (tCO2/yr)

% Reduction of 2015 conventional generation emissions level

GHG emission

GHG emission

100 219,000 230,053 13,803 230,053 2.22

200 438,000 460,107 27,606 432,500 4.17

300 657,000 690,160 41,410 648,751 6.26

400 876,000 920,214 55,213 865,001 8.35

500 1,095,000 1,150,267 69,016 1,081,251 10.44 Table 5.6 - GHG emission reduction for Solar PV

Capacity (MW)

Actual energy produced (MWh)

GHG emission factor (kg/KJ)

GHG emissions (tCO2/yr)

Net GHG emission reduction (tCO2/yr)

% Reduction of 2015 Conventional Generation Emissions Level

100 359,160 377,288 22,637 354,650 3.42

200 718,320 754,575 45,275 709,301 6.85

300 1,077,480 1,131,863 67,912 1,063,951 10.27

400 1,436,640 1,509,151 90,549 1,418,602 13.69

500 1,795,800 1,886,438 113,186 1,773,252 17.12 Table 5.7 - GHG emission reduction for Wind

5.5 COMPARISON

The results obtained after performing the exercises in the previous sections has

implications for the implementation of a potential RES-E project in T&T. Therefore

this section aims to clarify the above by giving context to the findings.

The capacity factor of the proposed technologies using generic climate data of T&T

was found to be 25% and 41% for solar PV and wind technology respectively. The

assessed performance of solar PV technology could be described as excellent, given

that the average capacity factor in Africa and India was 21% and 20% respectively

[47]. Furthermore, this is in keeping with an average capacity factor of 27% in South

America [47] which T&T can be geographically considered to be a part of. Likewise,

the assessed performance of wind technology was also in keeping with the findings

of previous studies done on wind resource in T&T. Capacity factors for wind

technology were concluded to be as high as 73% and low as 28%, which therefore

suggests that there is still room for improvement in siting the proposed wind RES-E

project.

38 | P a g e

The probability distributions gained from the Monte Carlo simulation enabled multiple

observations to be made on the LCOE. Firstly, the median LCOE for wind

technology was found to be cheaper than the median LCOE for solar PV technology,

at $0.13/KWh and $0.17/KWh respectively. The average cost of electricity in T&T is

$0.04 using conventional generation [3]. If these technologies were to be adopted, it

can therefore be seen that the cost of generation from these RES would increase by

at least 3 times in the case of wind, and by at least 4 times in the case of solar PV.

The LCOE found for wind was fair after considering that large developed economies

such as China, North America and Europe have average LCOEs ranging from

$0.06/kWh - $0.08/kWh, while large developing economies like Central America,

South America and Africa have LCOEs ranging from $0.09/kWh - $0.095/kWh [47].

Similarly, in the case of solar PV, developed economies have average LCOEs

ranging from $0.09/kWh - $0.30/kWh, with developing economies boasting a

comparable range [47]. The difference in ranges can be attributed to the rapid

decline in solar PV cost over the last few years, the time over which the above

ranges were based on. It was anticipated that the LCOE for RES-E in T&T would be

higher since the lending terms were deliberately modelled at above international

norms in an attempt to account for sub-optimal debt financing conditions given T&T‘s

position as a twin-island state developing economy. Therefore, the LCOE for both

technologies were fair in light of better optimized cases in larger and more developed

economies.

Moreover, the LCOE probability distribution allowed the correlation between the input

parameters and the LCOE to be found. In the case of solar PV, the result showed

that the debt payment, total installed cost and capacity factor were the most

influential input parameters when their distributions were considered. The debt

interest rate distribution considered impacted less significantly on the LCOE. In this

case, the debt ratio, discount rate, O&M and debt term affected the LCOE the least.

It was noted that the discount rate, which was used to determine the time-value of

the cash flows of the project, had little influence on the LCOE. This was expected

since ―despite capital costs accounting for a large share of total LCOE in renewables

plants, given their short lead times, these technologies are, among the capital-

intensive technologies, the least sensitive to variations in discount rates.[57]‖

39 | P a g e

A similar relationship was found for wind; the result shows that the debt payment,

total installed cost and capacity factor are the most influential input parameters when

their distributions have been considered. The debt interest rate distribution

considered impacts less significantly on the LCOE. In the case of utilizing wind

energy, the debt ratio, discount rate, O&M and debt term affect the LCOE the least.

Fig. 5.25 and Fig. 2.26 compare the NPV and Equity Payback Period respectively

across the two technologies in varying degrees of installed capacity. The choice of

price of $0.16/kWh, in between the LCOE calculated for both technologies, was

chosen as it allowed for an important observation to be made, as discussed later in

this section.

It can be seen from

Fig. 5.25 that at the

proposed installed

capacity, the two

technologies were

found to have a

difference in NPV of

approximately $210

million. This

difference became

more pronounced as the extrapolated cases were considered, with the difference in

NPV increasing to approximately $1.25 billion at 500MW installed capacity. Based

on this trend, the RES-E with a high rate of return on investment can be clearly

identified as wind technology. Nevertheless, the findings indicate that both

technologies could be economically viable in T&T. This was considered since there

could possibly be factors that work against the introduction of wind technology, such

as lobby action from groups concerned that wind turbines could be detrimental to

flying animals or that the un-anaesthetic appearance of wind turbines could hamper

tourism on the islands.

-500,000,000

0

500,000,000

1,000,000,000

1,500,000,000

2,000,000,000

2,500,000,000

0 100 200 300 400 500 600Ne

t P

rese

nt

Val

ue

($

)

Capacity (MW)

NPV vs Installed Capacity

Linear (Solar PV) Linear (Wind)

Figure 5.25 - Comparison of NPV vs installed capacity for proposed technologies

40 | P a g e

After cross-checking the calculated LCOE values, with the above net present values

and corresponding net present cost of the system as expressed by Short et al [23], it

could be concluded that this definition was evidenced in the findings of this study.

Indeed, the net present value was roughly equal to the net present cost of the

system when the price of electricity was modelled to be sold at the LCOE [23].

The equity payback was determined to be very short in both cases, at a maximum of

3 years for solar PV

technology and 1.7

years for wind

technology. The gradient

of the plots of median

equity payback period as

shown above in Fig. 5.26

suggested that the

equity payback was not

significantly reduced by

increasing the total

installed capacity

represented by the

extrapolated cases. The

Monte Carlo simulation at the proposed case revealed that the output distribution of

Equity Payback Period would likely increase or decrease as the price varies with a

reasonable spread: the interquartile range was found to be approximately 2.2 years

and 4.4 years for wind and solar PV respectively, as seen in the distribution in Fig.

5.19 and Fig. 5.24. Furthermore, the Equity Payback Period was most likely to take a

lower than higher value given that the distributions were right skewed.

The findings of both financial viability studies reiterated the expected outcome that

the NPV and debt equity payback period could be attractive to investors especially

since the price has been set higher than the LCOE for the proposed technologies

calculated in this study. The sensitivity analysis performed on both measures

gleaned the same information across technologies; Fig. 5.18 and Fig. 5.22 illustrate

that the electricity rate, initial cost and debt ratio as being most influential on the

NPV, followed by the CE production credit rate, debt term and GHG reduction rate.

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

100 200 300 400 500 600

Equ

ity

pay

bac

k P

eri

od

(ye

ars)

Capacity (MW)

Equity Payback Period vs Installed Capacity

Linear (Solar PV) Linear (Wind)

Figure 5.26 - Comparison of equity payback vs installed capacity for proposed technologies

41 | P a g e

In the case of debt equity, discount rate had no effect since this measure did not

consider the present value of cash flows.

Fig. 5.27 succinctly

summarises the potential

of each technology for

GHG emission reduction.

It can be deduced that

the proposed case

cannot realistically be

used to differentiate

between the proposed

technologies since the

GHG emissions

reduction was almost the

same in both cases. On the other hand, the extrapolated cases illustrate a key trend

that wind technology promises a greater reduction of GHG emissions than solar PV

with increasing installed RES-E capacity. This was expected since the performance

of wind technology was found to be better than that of solar PV by approximately

160%, and ultimately influenced the amount of energy generated from each

technology. The modelled energy was then applied to the baseline analysis using

conventional generation so that the baseline emission could be calculated. Since

both RE technologies were considered to be near emission-less, it therefore became

clear why the measure of technology performance in T&T almost exclusively

determined the potential for GHG emission reduction.

6 DISCUSSION

In this section the achievements accomplished in the project were discussed,

including what worked as expected and what limitations could have impacted in the

accuracy of results. As a consequence of limitations identified, future work to

enhance the study was then discussed.

0

500,000

1,000,000

1,500,000

2,000,000

100 200 300 400 500 600

GH

G e

mis

sio

n r

ed

uct

ion

(tC

O2/y

r)

Capacity (MW)

Net Annual GHG emission reduction

Solar PV Wind

Figure 5.27 - Graph comparing net annual GHG emission reduction across proposed technologies

42 | P a g e

6.1 PROJECT ACHIEVEMENTS

The project plan outlined in the Progress Report was adhered to for most of the

objectives. However, upon embarking upon further research, the scope of the project

was expanded to determine the financial attractiveness of a proposed RES-E project

considering the viewpoint of relevant stakeholders. In light of this, assessing

methods of grid integration did not fit into the new project direction. Furthermore,

after much research it was determined that no existing hybrid RES-E and

conventional generating system could be transferred to T&T; indeed a solution

tailored to T&T‘s specific circumstances was required and so this objective was not

progressed further.

The first objective was achieved to the extent that was necessary for setting the

energy context of the rest of the objectives to be completed. Data was limited;

neither an hourly nor seasonal load profile could be found. This had implications for

some of the objectives originally proposed in the Progress Report, as discussed at

the end of this section.

Finding good reliable data to model the proposed technologies required a creative

approach since only two measurement sites existed in the country, and therefore

may not have been optimal sites for RES-E generation. As established previously,

climate data in T&T was primarily measured in line with services provided to the

aviation industry. Nevertheless, the second objective was achieved. RETScreen

contained a climate database, and this was used to benchmark the solar insolation.

However, the wind speed data present at these sites were so sub-optimal that

project viability was unfeasible. To compound the problem, published papers aiming

to address wind-related investigations in T&T did not extrinsically present the data or

source of data to the reader for an independent review. Nevertheless, the chosen

methodology found that the performance of wind technology compared better to

solar PV. This had implications for comparisons made between the assessments

given to both technologies in keeping with the other objectives.

In addition, a sound understanding of what costs comprised the input parameters of

the LCOE i.e. the costs associated with the execution phase and running of RE

plants was developed. This required extensive background reading to understand

what was required for establishing wind and solar PV projects. Once this was

43 | P a g e

understood, the best implementation of the concept was determined. The chosen

method of using a Monte Carlo simulation required some understanding of

probability distributions. This area could have benefitted from more accurate input

models by using triangle distributions for those input parameters that took a range of

values, with a most likely value.

Extensive research into discounted cash flow analysis was done to carry out the

fourth objective of the project. As this objective relied on the use of RETScreen,

there were many limitations that impacted upon the results. Firstly, the input

distributions for the Monte Carlo simulation were assumed to take a random

distribution, unlike the tailored distributions modelled in order to calculate the LCOE

in this study. Additionally, the number of iterations used for the Monte Carlo

simulation was indeed only limited to 500, thereby reducing the accuracy of results.

The fifth objective was met as the GHG reduction was found to be superior if wind

technology was used, with the potential for reduction increasingly becoming

distinguished between the two technologies at higher installed capacities.

A review of the policies that incentivise RES-E was undertaken to be able to suggest

what could be done to promote RES-E in T&T. Several policies that could help were

identified. The FIT was identified as the policy most likely to encourage investors to

value RES-E projects in the country, since they would effectively be guaranteed a

price above the LCOE of technologies considered in this study. Competitive bidding

was deemed to be unsuitable given the extremely limited framework available in T&T

for RES-E. Policies such as the Renewable Portfolio Standard, financial incentives

and generation disclosure could possibly promote RE interest in T&T. Although the

actual effectiveness of each policy can only be determined through a detailed

financial analysis developed on a sound energy model, this initial assessment was

based on RES-E systems elsewhere.

This final objective could not be achieved. After extensive research was done to

understand the financial parameters influencing the costs of power generation for

RES, it became clear that such accuracy could not be achieved with determining the

true cost of conventional generation used at present in T&T especially given

challenging data accessibility. Since this was a low priority objective, as the project

44 | P a g e

focussed more on the RES-E potential than existing infrastructure, it ended up not

being achieved within the time constraint.

6.2 PROPOSED FUTURE WORK

Several directions for future work were identified after carrying out the relevant

research and then implementing the chosen methodologies in line with the aim.

Clearly, there ought to be weather monitoring stations in T&T so that historical

weather data is recorded for research purposes. This would identify areas that are

resource rich in particular RES, and allow for accurate conclusions to be drawn in

this area.

The discount rate should have ideally been calculated experimentally given the

economic climate in T&T, however all indicators suggested that this could be a study

in itself because deducing the parameters required to find discount rate in

developing countries is extremely complex [63]. This can be compared with

challenges faced to find discount rate even in developed economies with better

integrated markets.

An in depth analysis of the tax system in T&T could also be performed to consider

the effect of taxation on the costs associated with and investor confidence in RES-E.

Furthermore, costs can be broken down even further than was quoted in this study to

avoid errors accumulated from using one value to describe many costs. By

amortizing costs as best as possible, the accuracy of results could be enhanced.

[40].

Although RETScreen proved to be useful in this study, at times the work-sheet

based tool was clearly limited. This aspect could be improved by implementing one‘s

own model in a tool such as Matlab so that data could be manipulated, processed

and then presented with greater control.

45 | P a g e

6.3 REFLECTIVE COMMENTS

I believe that although I had initially opted for another standard project, this bespoke

study enabled me to learn about, and achieve objectives, that are more pressing in

the context of energy sustainability in my home country of Trinidad and Tobago. The

challenges faced did not detract from the achievements of the project, and I have

learned a great deal about renewable energy and the energy sector in general.

Nevertheless, a vast amount of work remains to be done in the field of renewable

energy in T&T.

6.4 CONCLUSION

The objectives of the study were mostly achieved with a detailed level of analysis

attempted on each. The energy scenario in T&T was analysed, local albeit non-

optimal climate data was used to determine the performance of wind and solar PV

technologies. Consequently, the LCOE that could inform a FIT system was found for

each technology, with findings favouring wind. The financial attractiveness and the

reduction in GHG emission suggested that wind technology had more potential in

these areas also, although both technologies‘ overall assessment for implementation

could be rated as fair. Finally, the policies that could incentivise RE implementation

were studied. The aim of the project, which sought to assess the impact of RES-E in

T&T, was met and so the project was successful.

46 | P a g e

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technical assessment integrating wind energy into an island power system.

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flow/introduction.aspx [Accessed 27 Apr. 2016].

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qo=investopediaSiteSearch [Accessed 27 Apr. 2016].

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budgeting techniques. 1st ed. [ebook] Florida Atlantic University. Available at:

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2016].

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assessment modeling: EPCCM application (Engineer Procure Construct Contract

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formula, algorithms and equations. [online] Available at:

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Tropical Islands and Relevance to Wind Power. ISRN Renewable Energy, 2013,

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Apr. 2016].

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APPENDIX A: PROGRESS REPORT

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APPENDIX B: PROJECT PLAN

01/10/2015 20/11/2015 09/01/2016 28/02/2016 18/04/2016

Assess energy scenario in T&T

Model costs of introducing different sources of RE using generic pricingdata

Deduce cost of power generation using natural gas

Review existing methods of RE generation

Review existing methods of grid integration

Revision and exams

Gather generic data on availability of RES

Improve accuracy of pricing model using generic resource data and energybalance

Suggest incentives to promote renewable energy

Optimize system using best method of RE generation and grid integration

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APPENDIX C: TECHNICAL RISK ANALYSIS

WORK ACTIVITY/ WORKPLACE (WHAT PART OF THE ACTIVITY POSES TECHNICAL RISK)

TECHNICAL RISK (S) (SOMETHING THAT COULD CAUSE HARM, ILLNESS OR INJURY)

LIKELY CONSEQUENCES (WHAT WOULD BE THE RESULT OF THE HAZARD)

WHO OR WHAT IS AT RISK (INCLUDE NUMBERS AND GROUPS)

EXISTING CONTROL MEASURES IN USE (WHAT PROTECTS PEOPLE FROM THESE HAZARDS)

WITH EXISTING CONTROLS

SEV

ERIT

Y

LIK

ELIH

OO

D

RIS

K R

ATI

NG

RIS

K

AC

CEP

TAB

LE

Understanding and implementing complex methodologies of renewable energy capture in terms of grid integration

Not understanding the theories sufficiently in timespan of project

Overall quality of project will be reduced

Jerel Mohammed

Become well-read to determine the best methodologies and then using a shortlisting approach find the most appropriate one 2 2 8 Yes

Referencing to empirical data and literature

Little data and literature available on subject matter

Calculations and comparisons will be less accurate

Jerel Mohammed

Use similar types of data as benchmarks for own deductions and calculations 2 5 10 Yes

Saving project state on computer

Computer malfunction Loss of data

Jerel Mohammed

Backup work regularly on multiple cloud servers 1 2 12 Yes

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APPENDIX D: HEALTH AND SAFETY RISK ASSESSMENT

WORK ACTIVITY/

WORKPLACE

(WHAT PART OF THE

ACTIVITY POSES RISK

OF INJURY OR

ILLNESS)

HAZARD (S)

(SOMETHING

THAT COULD

CAUSE HARM,

ILLNESS OR

INJURY)

LIKELY

CONSEQUENCES

(WHAT WOULD BE

THE RESULT OF

THE HAZARD)

WHO OR

WHAT IS AT

RISK

(INCLUDE

NUMBERS

AND

GROUPS)

EXISTING CONTROL

MEASURES

IN USE

(WHAT PROTECTS

PEOPLE FROM THESE

HAZARDS)

WITH EXISTING CONTROLS

SEV

ERIT

Y

LIK

ELIH

OO

D

RIS

K R

ATI

NG

RIS

K A

CC

EPTA

BLE

Sitting for extensive

periods of time Lumbar pains

Moderate Injury /

illness of 3 days or

more absence

(reportable

category) /

Moderate loss

Jerel

Mohammed

Go for regular breaks

Sit properly in chair at

appropriate height

Ensure there is

sufficient space in

workspace to allow for

a variation in posture

Use ergonomic office

chair for extra lumbar

3 2 5 Yes

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support

Working with display

screen equipment Eye problems

Moderate Injury /

illness of 3 days or

more absence

(reportable

category) /

Moderate

Jerel

Mohammed

Go for regular breaks

Position screen at

comfortable angle

Ensure proper lighting

in workspace

3 2 3 yes

Sitting for extensive

periods of time

Limb disorders

Moderate Injury /

illness of 3 days or

more absence

(reportable

category) /

Moderate

Jerel

Mohammed

Go for regular breaks Sit properly Do physical activity each day

3 2 3 Yes

Sitting for extensive

periods of time

Muscle

degeneration

Slight Minor injury

/ illness –

immediate 1st Aid

only / slight loss

Jerel

Mohammed

Go for regular breaks Sit properly Do physical activity

each day

2 2 4 Yes

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APPENDIX E: MATLAB CODE FOR LCOE OF SOLAR PV

syms t;

capacity=100000;

hn=200;

n=100000;

dterm=normrnd(25,2.5,n,1);

figure('Name','Debt term','NumberTitle','off')

histogram(dterm,hn)

xlabel('Debt term (yr)')

ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'solarDebtTerm100','epsc')

figure('Name','Interest rate','NumberTitle','off')

edges=[0:0.3/hn:0.3];

ir=normrnd(0.13,0.04,n,1);

plotir=histogram(ir,edges)

xlabel('Interest rate (%/yr)')

ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'solarInterestRate100','epsc')

figure('Name','Initial cost per kW','NumberTitle','off')

edges=[0:4000/hn:4000];

I=normrnd(2025,694,n,1);

plotI=histogram(I,edges)

xlabel('Initial cost per capacity ($/kw)')

ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'solarInitialCostPerCap100','epsc')

figure('Name','Total Initial cost','NumberTitle','off')

It=capacity.*I;

edges=[0:500000000/hn:500000000];

plotIt=histogram(It,edges)

xlabel('Total initial cost ($)')

ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'solarTotInitialCost100','epsc')

figure('Name','Capacity factor','NumberTitle','off')

cf=pearsrnd(0.25,0.05,-0.2,3,n,1);

plotcf=histogram(cf,hn)

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xlabel('Capacity factor (%)')

ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'solarCapFactor100','epsc')

figure('Name','Discount rate','NumberTitle','off')

r=normrnd(0.075,0.0075,n,1);

plotr=histogram(r,100)

xlabel('Discount rate (%)')

ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'solarDiscountRate100','epsc')

figure('Name','Debt ratio','NumberTitle','off')

dr= normrnd(0.7,0.05,n,1);

plotdr=histogram(dr,100)

xlabel('Debt ratio')

ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'solarDebtratio100','epsc')

figure('Name','Principle','NumberTitle','off')

p=dr.*It;

figure('Name','Debt payment','NumberTitle','off')

Ft=(p.*(ir))./(1-((1+ir).^(-dterm)));

edges=[0:60000000/hn:60000000];

plotFt=histogram(Ft,edges)

xlabel('Debt payment ($/yr)')

ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'solarDebtPayment100','epsc')

figure('Name','O&M','NumberTitle','off')

MOav=pearsrnd(16,8,0.4,3,n,1);

edges=[0:5000000/hn:5000000];

MO=MOav.*capacity;

histogram(MO,edges)

xlabel('O&M ($/yr)')

ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'solarO&M100','epsc')

figure('Name','Energy output','NumberTitle','off')

St=capacity.*cf.*8760;

plotSt=histogram(St,100)

xlabel('Energy output (kWh/yr)')

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ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'solarEnergyoutput100','epsc')

figure('Name','LCOE','NumberTitle','off')

LCOE=double((It+symsum(((Ft)./((1+r).^t)),t,1,int64(media

n(dterm)))+symsum(((MO)./((1+r).^t)),t,1,30))./(symsum((S

t./((1+r).^t)),t,1,30)))

edges=[0:0.5/hn:0.5];

plotLCOE=histogram(LCOE,edges)

std(LCOE)

xlabel('LCOE ($)')

ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'solarLCOE100','epsc')

coefST=corr(St,LCOE,'type','Spearman','rows','complete')

coefr=corr(LCOE,r,'type','Spearman')

coefMO=corr(MO,LCOE,'type','Spearman')

coefIt=corr(It,LCOE,'type','Spearman')

coefFt=corr(Ft,LCOE,'type','Spearman')

coefcf=corr(cf,LCOE,'type','Spearman')

coefdr=corr(dr,LCOE,'type','Spearman')

coefir=corr(ir,LCOE,'type','Spearman')

quantile(LCOE,0.25)

quantile(LCOE,0.75)

quantile(LCOE,0.5)

mean(LCOE)

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APPENDIX F: MATLAB CODE FOR LCOE OF WIND syms t;

n=100000;

hn=200;

capacity=100000;

dterm=normrnd(25,2.5,n,1);

figure('Name','Debt term','NumberTitle','off')

histogram(dterm,hn,'FaceColor','green')

xlabel('Debt term (yr)')

ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'windDebtTerm100','epsc')

hn=200;

figure('Name','Interest rate','NumberTitle','off')

edges=[0:0.3/hn:0.3];

ir=normrnd(0.13,0.04,n,1);

plotir=histogram(ir,edges,'FaceColor','green')

xlabel('Interest rate (%/yr)')

ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'windInterestRate100','epsc')

figure('Name','Initial cost per kW','NumberTitle','off')

edges=[0:5000/hn:5000];

I=normrnd(2346,770,n,1);

plotI=histogram(I,edges,'FaceColor','green')

xlabel('Initial cost per capacity (%/kw)')

ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'windInitialCostPerCap100','epsc')

figure('Name','Total Initial cost','NumberTitle','off')

It=capacity.*I;

edges=[0:500000000/hn:500000000];

plotIt=histogram(It,edges,'FaceColor','green')

xlabel('Total initial cost ($)')

ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'windTotInitialCost100','epsc')

figure('Name','Capacity factor','NumberTitle','off')

cf=pearsrnd(0.41,0.075,-0.2,3,n,1);

plotcf=histogram(cf,100,'FaceColor','green')

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xlabel('Capacity factor (%)')

ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'windCapFactor100','epsc')

figure('Name','Discount rate','NumberTitle','off')

r=normrnd(0.075,0.0075,n,1);

plotr=histogram(r,100,'FaceColor','green')

xlabel('Discount rate (%)')

ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'windDiscountRate100','epsc')

figure('Name','Debt ratio','NumberTitle','off')

dr= normrnd(0.7,0.05,n,1);

plotdr=histogram(dr,100,'FaceColor','green')

xlabel('Debt ratio')

ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'windDebtratio100','epsc')

figure('Name','Principle','NumberTitle','off')

p=dr.*It;

figure('Name','Debt payment','NumberTitle','off')

Ft=(p.*(ir))./(1-((1+ir).^(-dterm)));

edges=[0:70000000/hn:70000000];

plotFt=histogram(Ft,edges,'FaceColor','green')

xlabel('Debt payment ($/yr)')

ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'windDebtPayment100','epsc')

figure('Name','O&M','NumberTitle','off')

MOav=pearsrnd(31,10,0.4,3,n,1);

edges=[0:8000000/hn:8000000];

MO=MOav.*capacity;

histogram(MO,edges,'FaceColor','green')

xlabel('O&M ($/yr)')

ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'windO&M100','epsc')

figure('Name','Energy output','NumberTitle','off')

St=capacity.*cf.*8760;

plotSt=histogram(St,100,'FaceColor','green')

xlabel('Energy output (kWh/yr)')

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ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'windEnergyoutput100','epsc')

figure('Name','LCOE','NumberTitle','off')

LCOE=double((It+symsum(((MO+Ft)./((1+r).^t)),t,1,25))./(s

ymsum((St./((1+r).^t)),t,1,30)))

edges=[0:0.4/hn:0.4];

plotLCOE=histogram(LCOE,edges,'FaceColor','green')

xlabel('LCOE ($)')

ylabel('Probability')

set(gca,'YTick',[])

saveas(gcf,'windLCOE100','epsc')

coefST=corr(St,LCOE,'type','Spearman','rows','complete')

coefr=corr(LCOE,r,'type','Spearman')

coefMO=corr(MO,LCOE,'type','Spearman')

coefIt=corr(It,LCOE,'type','Spearman')

coefFt=corr(Ft,LCOE,'type','Spearman')

coefcf=corr(cf,LCOE,'type','Spearman')

coefdr=corr(dr,LCOE,'type','Spearman')

coefir=corr(ir,LCOE,'type','Spearman')

coefdterm=corr(dterm,LCOE,'type','Spearman')

quantile(LCOE,0.25)

quantile(LCOE,0.75)

quantile(LCOE,0.5)

mean(LCOE)

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APPENDIX F: MATLAB CODE FOR LCOE OF WIND

[62]