Assessing The Impact of Renewable Energy In Trinidad and Tobago
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Transcript of Assessing The Impact of Renewable Energy In Trinidad and Tobago
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
1 | P a g e
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
2 | P a g e
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.
3 | P a g e
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.
6 | P a g e
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
7 | P a g e
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
8 | P a g e
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.
9 | P a g e
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
10 | P a g e
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]
11 | P a g e
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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qo=investopediaSiteSearch [Accessed 27 Apr. 2016].
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Tropical Islands and Relevance to Wind Power. ISRN Renewable Energy, 2013,
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65 | P a g e
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
66 | P a g e
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
67 | P a g e
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
68 | P a g e
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
69 | P a g e
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)
70 | P a g e
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)')
71 | P a g e
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 ($)')
ylabel('Probability')
set(gca,'YTick',[])
saveas(gcf,'solarLCOE100','epsc')
coefST=corr(St,LCOE,'type','Spearman','rows','complete')
coefr=corr(LCOE,r,'type','Spearman')
coefMO=corr(MO,LCOE,'type','Spearman')
coefIt=corr(It,LCOE,'type','Spearman')
coefFt=corr(Ft,LCOE,'type','Spearman')
coefcf=corr(cf,LCOE,'type','Spearman')
coefdr=corr(dr,LCOE,'type','Spearman')
coefir=corr(ir,LCOE,'type','Spearman')
quantile(LCOE,0.25)
quantile(LCOE,0.75)
quantile(LCOE,0.5)
mean(LCOE)
72 | P a g e
APPENDIX F: MATLAB CODE FOR LCOE OF WIND syms t;
n=100000;
hn=200;
capacity=100000;
dterm=normrnd(25,2.5,n,1);
figure('Name','Debt term','NumberTitle','off')
histogram(dterm,hn,'FaceColor','green')
xlabel('Debt term (yr)')
ylabel('Probability')
set(gca,'YTick',[])
saveas(gcf,'windDebtTerm100','epsc')
hn=200;
figure('Name','Interest rate','NumberTitle','off')
edges=[0:0.3/hn:0.3];
ir=normrnd(0.13,0.04,n,1);
plotir=histogram(ir,edges,'FaceColor','green')
xlabel('Interest rate (%/yr)')
ylabel('Probability')
set(gca,'YTick',[])
saveas(gcf,'windInterestRate100','epsc')
figure('Name','Initial cost per kW','NumberTitle','off')
edges=[0:5000/hn:5000];
I=normrnd(2346,770,n,1);
plotI=histogram(I,edges,'FaceColor','green')
xlabel('Initial cost per capacity (%/kw)')
ylabel('Probability')
set(gca,'YTick',[])
saveas(gcf,'windInitialCostPerCap100','epsc')
figure('Name','Total Initial cost','NumberTitle','off')
It=capacity.*I;
edges=[0:500000000/hn:500000000];
plotIt=histogram(It,edges,'FaceColor','green')
xlabel('Total initial cost ($)')
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')
73 | P a g e
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)')
74 | P a g e
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)