Hybrid Solar-Wind Renewable Energy Systems in Remote Australia Project

This project report examines hybrid solar-wind renewable energy systems, specifically focusing on their application in remote areas of Australia. The study begins with a comprehensive literature review, covering photovoltaic solar systems, wind energy technologies, and the integration of wind-solar hybrid power systems. The methodology section details the component modeling of the system, including solar PV arrays, wind turbine generators, and battery storage, along with the modeling of energy demand. An optimal sizing algorithm is developed, considering both reliability and cost objectives, and boundary conditions. The results of the modeling and optimization are presented, followed by an analysis of the system's performance and the factors influencing its effectiveness. The project concludes with a discussion of the potential of hybrid renewable energy systems to provide sustainable and cost-effective power solutions in remote Australian communities, addressing the challenges of electricity supply in sparsely populated regions and the benefits of reducing reliance on conventional energy sources.

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HYBRID (SOLAR-WIND) RENEWABLE ENERGY SYSTEMS IN REMOTE AUSTRALIA
By (Student’s Name)
Tutor’s Name
Institution
City
Date

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Table of Contents
2. LITERATURE REVIEW............................................................................................................3
2.1. Photovoltaic Solar Electrical System....................................................................................3
2.2. Wind energy..........................................................................................................................4
2.3. Wind–Solar Hybrid Power Systems.....................................................................................5
2. Method....................................................................................................................................12
2.1. Component Modelling of the System.............................................................................12
2.1.1. Hybrid System Modelling........................................................................................12
2.1.2. Modelling the Solar PV array..................................................................................12
2.1.3. Modelling a Wing Turbine generator......................................................................14
2.1.4. Modelling the Battery..............................................................................................15
2.1.5. Modelling Demand..................................................................................................17
2.2. Optimal Sizing Algorithm...............................................................................................18
2.2.1. Objective Function...................................................................................................18
2.2.2. The Reliability Objective.........................................................................................18
2.2.3. Cost Objective.........................................................................................................20
2.2.4. Boundary Conditions...............................................................................................24
3. Results....................................................................................................................................28
4. Analysis..............................................................................................................................of 29
5. Conclusion..............................................................................................................................32

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6. References..............................................................................................................................35

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2. LITERATURE REVIEW
2.1. Photovoltaic Solar Electrical System
Wolfe (2013) reported that the global photovoltaic installation capacity has greatly increased in
the last decade. The PV capacity installed worldwide has undergone a yearly
growth of about 45% for the last 15 years. For instance in Asia, 22.7 GW was added with
approximately 42 GW comprising of operating photovoltaic solar till the completion of 2013.
Additionally, obtaining a record of 12.9GW to about 20GW this is approximately triple its
potential. Individually, China’s installations amounted to a third of the installations worldwide. It
is clear that the capacity addition has been on the rise compared to grid incorporation and
curtailment are now the problems. The solar PV capacity of China depends on the western
provinces that are sunny and far away from the main centres that comprise of projects on a large
scale. This capacity renders the three utilities owned the state the largest owner of the solar asset
in the world. However, the existing PV spread on small scales are also interesting, thus the
government intends to concentrate on rooftop marketing. There are numerous factors that impact
the application of PV including pollution to the environment, technological aspects related to PV
and challenges in setting up new transmission lines.
Letcher and Fthenakis (2018) focused on the effects of cloud coverage on evaluating reliability
and radiation of solar PV. Apart from applying DGs as the renewable sources of energy, there
has been a lot of sensitization and uncertainties related to the system operation and planning.
However, in a number of developing countries, because of calibration demands of equipment
measurement, equipment costs and expensive maintenance, there is a lack of existing data on
solar radiations. Substitute mechanism of resolving these challenges is through the estimation of
solar radiations by modelling technique. Hence, the PV model which is significant in the

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estimation of short term power production of cloud coverage PV changes is recommended as
among the most significant power utility requirements. While operating Photovoltaics, the data
available in regards to power output in each hour in all seasons is more important. The reliability
evaluation challenges of Oktas-scale PVs in new power systems are arranged in steps as follows;
 Moving clouds affects power output fluctuations of PV arrays.
 When cloud blanks-out frequencies, the PV system and voltage interferences happen in
the power systems.
 Load stability issues, uncontrollable production and stability of voltage.
 Utilities should integrate rapid ramping production of power so as to mitigate fluctuations
of power based on moving clouds.
 Consider the application of islanding mode in operating PVs
 Put into account the summary of recommended Photovoltaic penetrations. Cloud cover
plays a major part in eliminating power system limitations.
2.2. Wind energy
Jain (2016) recently in his research studied the sensitivity of availability of wind farms to
numerous parameter including years of operation, manufacturers of turbines, capacity aspects
and size wind farm. The outcome revealed that the size of the turbine does not have a relevant
statistical impact on availability, however, this could be as a result of the dataset quality applied
which is popularly dominated by large scale wind turbines. On the other hand, the size of the
wind farm, for instance, total wind turbines on each wind farm indicated that a linear reduction in
the turbine loss availability with 52% R2 but the correlation was not trustworthy. The factor of
capacity should be put into account since the valid variability proxy causing 0.4% low
availability for all of its 10% variation. In relation to manufacturers, the mentioned factors

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determine the various maintenance and operation contracts which in most cases outline the
downtime maintenance schedule. For instance, availability loss may differ between two and five
per cent. Operational years or age should be the more interesting proximal variable, however, the
evidence of the analysis reveals decreased availability for the first year effect. Availability was
very high in second and third years that followed a linear rise in availability loss amounting to
89% R2. Therefore, the operational year should be taken into account while finding out
availability against the economic evaluation of installed wind farm performances.
As per Wizelius (2015), the wind-turbine system reliability relies on the workability of its
components under the given environment, stress, the process of manufacture, the ageing process
and handling. The study on expert-based methodology of maintenance has the capabilities of
enhancing the system's reliability apart from the conventional monitoring techniques. Analysis of
the causes of failure in electronic systems and contributing factors to failure relied on various
aspects whose predictions are never trustworthy. The impacts of the aspects have the ability to
vary the long-duration of wind farm services.
Ng and Ran (2016) performed simulated statistics to achieve the availability of wind farm near
an offshore comprising of wind turbines totalling to 121 all of them consisting of 8MW. Thus,
the installed wind turbines summed up to 968 MW capacity. The results achieved through
simulation revealed availability attained fewer values approximately 50% and 82% average
values consistently less compared to onshore installed wind farms.
2.3. Wind–Solar Hybrid Power Systems
EARNEST (2013) opinioned that renewable sources of energy such as biomass, solar,
geothermal, ocean, wind and hydropower sources are taken in to account as technological clean
energy generator options. Wind and solar generate low power compared to the energy produced

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by fossil fuels. However, generating electricity through the utilization of wind turbines and PV
cells have rapidly increased these days. In this work, the hybrid wind-solar system of power that
harvests renewable energies in wind and solar to produce electricity has been discussed. System
control mainly depends on micro-controller. The micro-controller makes sure that resources are
optimally utilized, thus, enhancing efficiency than using the individual generation mode. Micro-
controller also minimizes dependence on an individual resource and enhances reliability. Solar-
wind hybrid system power generator is well applicable for domestic purposes and industries.
Nersesian (2016) the world agree that among other many challenges currently experienced by the
world is the emission of greenhouse gases which have enormous environmental effects. Green-
houses gases are as follow; methane, Sulphur hexafluoride, carbon dioxide, perfluorocarbons and
fluorocarbons. The gases aid in retaining the planet's temperature at levels that are favourable for
living organisms. The reduction in the level of these gases leads to unfavourable temperatures
that lower the survival of organisms. It is believed by a number of scientists that the escalation in
natural calamities is greatly influenced by changes in climate due to the shifting patterns of
oceans and atmosphere whilst the temperature of the planet increases.
Breeze (2016) reported that renewable sources of energy currently play a bigger part in power
systems worldwide. Renewable sources of energy are very complex to be incorporated into
power grids. Incorporation of renewable sources of energy applies the systems of communication
as the major technology. Communication systems to a larger extent are very crucial in operation,
monitoring and protection of both power systems and renewable energy generators.
Incorporation of renewable sources of energy especially solar and wind to the grid are presented
in this article.

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Towler (2014) even though wind and solar are very viable renewable sources of energy, much
study has not been conducted on the operation of both sources together with the other so as to
exploit the complementary benefits of both sources. An optimized design is created in this work
for a solar-wind hybrid energy plant. The optimized variable includes; the height of the wind
turbine, the diameter of the turbine rotor, photovoltaic module units, number of wind turbines
with the goal of minimizing costs. The analysis of sensitivity and simulated research have shown
the hybrid plants are capable of taking advantage of the complementary features of the two
sources of energy. Hybrid plants also have the potential to offer energy reliability for the whole
year.
Ratan (2018) hybrid systems of power comprising of wind turbines and PV-arrays comprising of
devices that store energy also known as batter banks. These hybrid systems that also have
electronic power devices were developed in this work. This system focus on the utilization and
production of electrical energy resulting from several sources so long as amongst them there is a
renewable source. The efficacy of the electronic power devices designed have approximately
respective resistive and capacitive loads of 73% and 95%. The Incorporation of a hybrid plant
was to bring electricity in dwelling houses and its environment so as to minimize the fossil fuels
demand. Electrifying dwelling homes and the surroundings lead to growth in the sustainable
supply of power. The mechanism is economically and technologically viable for the
electrification of rural areas.
AHMED (2011) suggests that electricity is currently the most required facility for mankind.
Many of the conventional sources of energy are depleting each day. Therefore, there is a need to
transform to non-conventional from conventional sources of energy. The process of shifting
exploits the sustainability of sources of energy causing no harm to the natural environments. The

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hybrid systems of energy can deliver uninterrupted electricity. The hybrid system generally,
entails the incorporation of two systems of energy which can provide continuous energy. Wind
turbines are applied in the conversion of wind energy to electricity whilst solar panels are applied
in the conversion of solar energy. The electrical energy produced is very useful in various ways
and electricity production is going to be very cheap. The article looks into the application of
combined two sources of energy in the production of electricity which results in the production
of energy that is very cheap and does no harm to the natural environment.
Jenkins (2016) is of the view that because wind and solar power are naturally uncertain and
intermittent, very high preparation hybrid systems into the power grid systems can lead to very
complex problems. The individual systems or fragile grids more specifically experience technical
problems due to lack of sufficient and appropriate capacities of storage. Through combined
optimization of incorporating two renewable sources of energy, the effects of changing
properties of wind and solar power may be partially addressed. The whole system shall have
been rendered more economical and reliable to operate. A review of the opportunities, problems
and solutions of the wind power and hybrid solar energy incorporation has been provided in this
article. Frequencies and voltages harmonics and fluctuations are the main issues in relation to the
quality of power for both individual and grid linked systems. The effect much worse when the
grid is weak. This issues can be greatly solved through appropriate designing, properly
optimizing hybrid systems and quick control response infrastructures. This paper presents a
review in relation to power control, optimized sizing designs and electronic power topologies as
well as the nature of the art of both individual and grid incorporated hybrid wind and solar
systems.

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According to Apostol et al. (2016) systems of renewing energy will most probably become very
common in future because of enormous effects on the environment and increase in the costs of
energy. These costs and environmental issues are connected with using the sources of energy
already established. Wind and solar power are substitutes to one another which creates the real
ability to some extent meet the load dilemma. However, any moment these solutions are studied,
they are not individually in their entirety to be relied upon due to their impact of natural
instability. Hybrid wind systems of energy and autonomous photovoltaic have been revealed to
be very viable economic substitutes to meet the demands of the energy of various scarce user in
the world. This paper aims at offering the hybrid configuration system concept, renewable
sources of energy and modelling, mechanisms of optimizing hybrid systems, application of
software applied in optimized sizing and strategies of control. A comparative study of numerous
combined individual hybrid systems for Barwani a rural in India discussed. The finding was that
the hybrid DG photovoltaic wind battery system is the most suitable optimized solution in
relation to emission and costs among other combination of hybrid systems. Future enhancements
with the actual potentials to boost the real monetary appeal that has a link with the given
techniques and consumer endorsement have been featured in this article.
As per Sumathi et al. (2015) currently energy is the 21st demand. Therefore, renewable sources of
energy are consumed in huge quantities due to their easy accessibility and affordability.
However, renewable energy in their independent nature comprises of negatives including
uncertainty and unreliability at any given time. These challenges can be addressed through
hybrid systems of energy. Basically, renewable sources comprise of various combination of
sources of energy. These energy resources deliver efficient feedback against frequency and
voltage fluctuations, power problems and harmonic measures in independent systems. Power

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hybrid systems offer minimized complexities, energy fluctuations because of DPSP, retain very
low unit costs, better feasibility and control, enhanced optimization and good quick response
with the aid of better designs. A review of wind and solar hybrid systems of power are laid down
in this article. The hybrid photovoltaic wind system complex feasibility in various load
requirements was examined and evaluation of the economy of individual hybrid photovoltaic
wind systems have been created by the use of models.
Morris and Jungjohann (2016) state the energy is very vital for social growth and economic
growth of every nation. The planet is experiencing the power production challenges due to the
scarcity of fossil fuel sources of energy are required to be appropriately used. This energy
produced escalates the impact of the greenhouse. The combined application of wind and solar
systems of power can be exploited so as to produce energy for the whole year. This study has
reviewed and outlined the various types of the solar and wind-related hybrid system in the
development of the suggested research work.
Sovacool and Drupady (2016) presented decision support mechanisms to aid in making decisions
to research the factors affecting the design of solar-wind hybrid power systems applied in power
grids. These factors are social-political related as well as economic and technical advances
conditions. Analytical Hierarchy Process is applied in the quantification of a variety of
divergences practices, opinions and events which cause planning uncertainties and confusion in
HSWPS. The trade-off technique is applied in the production of many plans under varied 16
futures in order to achieve trade-off curves correspondences. As opposed to the traditional
simulation of 2-D, trade-off surfaces being novel modelled in 3-D space is outlined. The
modelling is presented in a way that the set knees are determined through the application of the
reduced distance technique. Inferior and robust planning is divided considering their frequency

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in happening within the set decisions of conditions. This segregation is conducted for hedging
analysis and all futures to minimize is done so as to designate substituted options in scenarios
where risks happen in future.
Reynolds (2016) reviewed a number of technological communication systems in existence for
grid incorporation of renewable sources of energy. Given that many of the renewable sources of
energy are uncertain and intermittent naturally, incorporation is a very crucial task. Most of the
renewable sources of energy integration into the power grid infrastructures the flow of electricity
will majorly occur in a similar direction to the users from the main plant. In comparison to huge
power plants, renewable sources of energy comprise of very low capacity. However, it is
significant to put into consideration the emerging renewable sources of energy.

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2. Method
2.1. Component Modelling of the System
2.1.1. Hybrid System Modelling
This section consists of a modelling process for a solar-wind hybrid power system. The system is
made up of solar photovoltaic array generator array, battery banks and wind turbine generator
together with associated power conversion and regulation accessories. Also, there is switching
and protection equipment (Hager & Stefes, 2016). In this study, only the generation components
will be modelled since they are the key components within the remote plant.
The figure below displays the simplified single line diagram of the project’s hybrid power
system. In this system, hybridization is achieved at the DC and should be frequency and phase
independent, contents that should be overcome on the AC bus.
The figure above shows the hybrid system model.
2.1.2. Modelling the Solar PV array
With the assumption that the used PV arrays are equipped with MPPT tracking, a simplified
model for outputting an array power can be produced using the equation below.

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1+αt ( T ( t ) −Tstc )
Ppv=fpvPpvr G ( t )
Gstc
(1)
Equation (2) is produced from equation (1) and is applied in modelling the overall instantaneous
power generated from the solar array model in total NPV number of units (SenGupta et al., 2017).
1+ αT ( T ( t ) −Tstc )
Ppv ( t ) =NpvfpvPpvr G ( t )
Gstc
(2)
In equation (2) above, t represents the solar array’s output power in instantaneous time. At this
instance, the output power is represented as PPV (t). The solar array’s rated power is the PVR while
fPV represents the derating factor considered by wire losses and shading (Sayigh, 2016). The
model’s inputs are made up of the temperature at an instant time t and are represented by T (t)
while the radiation from the solar is represented by G (t). In standard conditions for testing, the
TSTC, as well as the G STC, represent the temperature and the solar radiation of the panel. The
symbol α T represents the coefficient of temperature that is availed in the manufacturer’s
datasheet.
Using solar PV’s modelling best practice, the fPV derating factor can be identified. There is an
assumption that shading is negligible (Boxwell, 2017). Hence, shading was not accounted for in
determining the fPV. The element of this assumption is somehow for the application utility scale
purposes given that the layout was such that possible shading for adjacent solar panels was
minimized close to elimination through proper spacing. Additionally, the factors within the
surrounding’s topography were selected properly during site selection. Other relevant factors that
were considered have been listed in the table below.

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Parameters Values
Array soiling 95.000%
Connections and Diodes 99.50%
AC wiring 99.00%
Mismatch 98.00%
Transformer and Inverter 92.00%
DC Wiring 98.00%
Derating factor 82.68%
2.1.3. Modelling a Wing Turbine generator
The criterion for modelling the output power from a desired wind turbine generator used the
equation (3) three below that has the ability to govern the device’s output power as well as using
equation (4) below. However, wake losses were neglected to maintain simplicity.
In the above equations, the rated output power from the wind turbine is Pr while CP (vu)
represents the turbine’ coefficient of performance making it a function of wind speed. These
factors allowed simple modelling of the turbine using additional information from the datasheet
(Misak & Prokop, 2016). Vu, on the other hand, represents the prevailing wind speed adjustment
incident to mast weight. Vin represents the wind turbine’s cut-in speed gotten from the turbine’s

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datasheet. The turbine’s rated speed is represented by the vr also taken from the datasheet. The
out speed/ cut-off of the turbine is represented by vout obtained from the datasheet.
2.1.4. Modelling the Battery
This project used an advanced lead-acid battery. The State of Charge of the battery was one
important parameter. It represented the ratio of the quantity of stored energy within the battery to
the battery’s capacity at any point within use (Vasant, 2018). The SOC can be determined using
the equation shown below.
SOC ( t )=SOC ( t−1 ) (1− σΔt
24 )+ Pwtg (t ) + Ppvg ( t ) −Pl ( t )
CbessVbess (5)
Equation 5 above is used in updating the BESS charging state in every complete cycle in a time
step. The battery’s rate of self-charging is represented by σ while Δt represents the time step
length. Cbess, as well as Vbess, are functions representing ampere-hours BESS capacity and the
terminal voltage of the chosen BESS battery.
The SOC is an energy ratio, therefore, could not be directly plugged into an equation
representing a power flow. The SOC was necessarily multiplied with the energy rating of the
BESS. To produce a simple calculation, at this point the research makes an assumption that the
BESS can manage to deliver constant power over the time step duration of an hour. Additionally,
the rate of internal self-discharge was neglected since it had negligible relativity to all other
values (Carriveau, 2011). Practically, the self-discharge rate depends on the technology used in
making the battery and is about 0.2% in a day in generic models. In reality, it makes the system
complex but is quite simple when used during computation without greatly influencing the
results. Therefore, the energy rating and the power rating are assumed to be equal in every time
step. In this regard, the corresponding power flowing from the BESS can be derived as:

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Pbess ( t )= ( SOC ( t ) +DODmax−1 ) × Pbessyated (6)
Looking at the above expression, the maximum Depth of Discharge is represented by the
DODmax and this value is similar to the minimum BESS’ SOC during appropriate functioning.
The DOD varies with the used technology (Yan, 2015). The algorithm deriving the BESS charge-
discharge was written in pseudo code as shown below;
Table 1 - a table showing the charge-discharge BESS algorithm

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2.1.5. Modelling Demand
The project involved using a hypothetical load model. The load model was derived from the data
existing in the country's national power website. This data was covering a month of May for the
years 2018 to 2019. The data was used in deriving the typical load curve in every day in the
metropolitan area. In improving the accuracy, there was a need to develop two load curves in that
one was representing the weekday analysis while the other represented the weekend and holiday
analysis (Rusu & Venugopal, 2019). The figure deriving the base consumption was gotten from the
data above for the aim of reflecting the growth.
Figure 1 - Demand Curve for the Weekday

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Figure 2 - Demand Curve for the Weekend
2.2. Optimal Sizing Algorithm
2.2.1. Objective Function
Using the generated models above, the cost function had to be derived. Id the process of
deriving, it is necessary that the system’s technical feasibility is sized at its minimum cost. The
objective is thus, in this case, a function of the economic cost which is restricted by the set
technical boundary conditions (Zobaa, et al., 2016). These set boundary conditions will be
discussed in the section that follows. A technical index is used in modelling the optimization
problem, this is the Loss of Power Supply Probability. This LPSP is used in modelling the
system’s reliability as well as another economic index known as the Levelized Cost of Energy.
This in return allows the modelling of energy cost gotten from the system. Henceforth, they are
referred to as the cost objective and the reliability objective.
2.2.2. The Reliability Objective
The LPSP concept is important in this section since it shows the probability of failure to meet the
power demand over a specific period within the study. In a mathematical sense, the equation
below can be used.

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Pl ( t 1 ) −Pc ( t 1 ) }
¿ Pl ( t 1 )
¿
∑
i=1
N
❑
¿ ∑
i=1
N
❑
LPSP=¿
¿
(7)
In the model, the consideration period is a year in an hourly time step. Thus N = 8760
Looking at equation 7 above, load in a specific hourly time step is represented by Pl(t1). Using a
similar definition, Pc(t1) is a representation of the generated energy from the hybrid-powered
system. In a practical sense, Pl(t1) is gotten from the daily load curves as picked during the
weekend and weekdays with an additional factoring of load growth anticipated monthly. This
ensures cover or the load model above.
Case1: this is used when the summed generated power from the solar PV cells and wind turbines
is lower than what load demands. The drop in power can, therefore, be solved by using up energy
from the battery (Gujarathi & Babu, 2016).
Case2: is used when the summed generated power from the solar PV cells and the wind turbines
is similar to the demanded power at the load.

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Case 3: in this scenario, the summed generated power from the solar PV cells and the wind
turbines much higher than the amount demanded. In this case, there is surplus production of
power that is directed towards battery charging (Carlos & Castilla, 2018).
The wind turbine will be generating power represented as PWTG (ti) in the t time step.
Alternatively, this power can be derived as PPVG(ti) as seen in equation 4 whereby there is the
generated power from the solar PV in at time step. The expression PBESS(t) is it’s the flowing
power to or from the battery energy storage system in the t time step. The expression for this can
be seen in equation 18.
There is further simplification of equation 8 with the use of the Heaviside step function to
produce equation 9.
H ( Pi−Pwtg−Ppvg )− 1
2
Pg ( ti )=Pwtg ( t )+Ppvg ( ti )=+2. Press ( ti )
(9)
Taking from the LPSP description, there exists clarity in that a 1 value in LPSP is an indication
of not meeting the load while a zero reading would mean fully meeting the load. The passing of
the reliability objective is in the inequality constraint cost objective minimization. The chosen
LPSP is 5.0% to approximate about 500 hours within a year for unmet load demand. This is a
low threshold to work with (Gasch & Twele, 2011).
2.2.3. The Cost Objective
One convenient metric for measuring the summative cost competitiveness of a used generation
technology is LCOE. LCOE is a representation of overnight operation, capex and maintenance
cost and incurred negative flow of cash making an inter alia across the project’s life (Park et al.,

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2019). This is then divided by the summative generated energy from the project in its entire life
given that the following considerations are put to notice:
 Incentives and Rebates: incentives, accelerated depreciation and tax credits.
 Costs: Initial maintenance and operating costs, investment capital, insurance costs,
financial costs, decommissioning costs.
 Energy: annual degradation, system availability and annual production of energy.
Therefore, the LCOE can be described as shown below;
LCOE=∑ LifeCycleCost ( USD ) −∑ LifeCycleRebates ( USD )
∑ LifeCycleEnergy (10)
Furthermore, the LCOE could be presented as either a real LCOE or a nominal LCOE in that real
LCOE represent the adjusted inflation catering for macroeconomic factors. In equation 10, the
used LCOE lacks the adjusted inflations as its major purpose is showing the fitness comparison
function for multiple options. The multiple options occur in one similar setting that shows how
macroeconomic functions to remain with the initial value. Therefore no need for having
adjustments. In increasing the efficiency of executing the algorithm, a simple LCOE version is
used in place of the objective function. This simple LCOE will not factor in insurance cost,
financing cost, degradation and future repel cement since in this project, such differences will be
considered marginal yet there are savings quantified by the computation proving to be
substantial. The used LCOE is, therefore;
LCOE=CC × CRF+ FOM
8760 ×CF (11)

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In equation 11 above, the calculated capital cost is represented as CC in USD/kW which has to
be derived from;
LCOE= Npv
Ntic CCpv + Nwt
Ntic CCwt + Nbess
Ntic CCbess (12)
In the above equation, Npv represents the installed PV capacity in kW, the summed installed
capacity is represented as Ntic while wind turbine’s capacity is represented by Nwt.the battery
systems are installed and their power rating is represented as Nbess and is given in storage units
(Anon., 2014). The capital recovery factor is denoted as CRF in definition, the capital recovery
ratio factor is a ratio ensuring constant an annuity for presenting a constant annuity value for a
given duration. In this equation, the use of the CRF is on a nominally rated discount which has a
settled feature for LCOE evaluation compared to a real rated discount. CRF is obtained using the
equation shown below;
In the above equation, t is the rate of nominal annual discount while n represents the life of the
project in years.
Another important consideration is the maintenance and operation costs. O&M is divided into
variable and fixed components. In it, the maintenance costs as well as the fixed operations, FOM,
in equation (11) refer to the O&M cost that is related to the plant’s installed capacity using the
USD/kW units.
FOM = Npv
Ntic FOMpv + Nwt
Ntic FOMwt + Nbess
Ntic FOMbess (14)

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In the above equation, Ntic, Nwt, Npv and Nbess maintain their definitions as seen in equation
(12). The associated cost for the fixed O&M with regards to wind turbines is shown as FOMwtg.
Also, the associated fixed O&M cost with regards to the stored battery energy’s technology is
shown as FOMbess.
The other operational components affecting the operation and maintenance costs, VOM variable
O&M as shown in equation (11) is the relative O&M component to the energy amount generated
from the plant. The VOM costs are also simply defined as shown in equation (14). Hence;
VOM = Epv
Etic VOMpv + Ewt
Etic VOMwt (15)
Looking at equation (15), the summative generated energy through the plant’s life in kWh is
represented as Etic (Hossain & Mahmud, 2014).
Etic=Epv+ Ewt (16)
The above equation has the generated PV energy represented as Epv kWh while the wind tribune
energy generation being represented by Ewt in kWh.
The O&M costs that relate to the battery energy storage are all formed as FOM. This technique
was to head for simplicity when evaluating the objective function. Additionally, the paper’s
postulation was that the various elements within O&M costs of a battery energy storage
negligible when compared to the fixed components (Ketan, et al., 2011).
The cost of fuel is shown as FC. Looking this procedure, given that the generated energy is from
wind and solar, whereby the energy source is abundant and free, the cost of fuel was then

Hybrid Systems 25
perceived taken as zero hence removed from being applied in the objective function’ cots
implementation.
Looking at equation (11) the capacity of the plant can be evaluated as;
CF= AnnualtotalgeneratedenergybytheHRES ∈ kWh
Summativepowerunitsfortheinstalledcapacitor ∈ kWh× 8760 (17)
CF= Energywt +Energypv+ Energybess
8760 × Ntic (18)
The full representation of the objective function’s costs therefore shown below as;
Minimum LCOE, in that LCOE, is given by;
As the subject to the boundary conditions shown below.
2.2.4. Boundary Conditions
The boundary conditions are set to produce an optimal solution in the project thereby ensuring
physical feasibility limit as well as safe during operations adherence within the power plant.
Hence the conditions below were considered (Fathima et al., 2018).
The reliability objective was reached, the inequality constraints are defined as,

Hybrid Systems 26
PSP < 0.05 (19)
The i configuration is the possible candidate solution in case the LPSP is below 5%. This
information will be corresponding to the 500 annual unmet demand hours, which is better than
the distribution grid nationally present (Roy & Bandyopadhyay, 2019).
The highest installed capacity is considered according to demand. A 2 MW peak demand one
through factoring the growth of load over a 20 year period being considered. A suitably
configured system could then be sought for supplying the peak demand using the least cost.
Constraint sizing could then be applied within the individual technological generation
(Kamalakannan et al., 2014).
Option 1 – Constraints relating to the land size
The wind turbine unit number is restricted by considering the loss coming from the wake effect.
Therefore, as seen in equation (10), the region’s length L and the region’s are Si together with its
width W, are used in determining the maximum wind turbine number that could possibly be
installed. This was evaluated as in the equation below;
Nwt ≤ [ L
Lmd +1 ]× [ W
Wmd +1 ] (20)
In the above equation, the 6 and 10 multipliers are represented as Lm while the 3 and 5
multipliers are represented by Wm. On the other hand, a 14 unit evaluation assuming a 10-acre
land size is represented as Ntw.

Hybrid Systems 27
The highest number in PV panels is also restricted by the land size that the project can acquire.
Hence, using the assumption that the panel PV size represented by Spv while the land area is
represented by Sa;
Npv ≤ Sa ( 1−αbop )
Spv (21)
The land size ratio requirements for the plant’s balance to the entire plant is represented as factor
αbop. This was evaluated at around 0.276. Hence, the value leads to a 117,430 based on units of
a 10-acre land size (Walker, 2013).
The battery charge/discharge constraints are handled through the SOC. The SOC Is seen o
correlate with the health state of the battery. The higher the least SOC present, the higher the
battery’s cycles for proper operating conditions. The battery charge/discharge is then modelled to
from the performance of the objective function.
The battery unit number Nbat and the number of panels installed for the given wind turbines Nwt
are taken to be positive integers. The reason for this is to ensure consideration of only the hybrid
system that uses battery storage.
Option 2 – Evaluated Algorithm Constraints
These are the second constraints applied in the project. These constraints help in constraining the
optimization algorithm to a minimum less duration (Dincer et al., 2017).
A Matlab code is written to achieve the same and is in the appendix. Additionally, the
pseudocode is listed as shown below;

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The matrix for the upper and lower bounds determined using this method is shown below in a
table.
Table 2 - Shows the boundary conditions
Case Trk DSM Upper Bounds Lower Bounds
W S B W S B
D No No 40 19,750 7,890 4 1,975 7,879
E No Yes 40 24,380 32,300 4 2,438 3,230

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F Yes Yes 30 30,410 21,030 3 3,041 2,103
W- wind, S- solar, B – BESS
Since the solution would be involving scaling storage and generation sources, for matching the
load and its margin, the upper constraints were on every installed solar PV generation unit
capacities, generation units for a wind turbine as well as the battery system used for storage (Leal
et al., 2013).
The highest wind turbine number is set to 1.2 times the highest possible load demand divider by
the capacity rating of an as single unit. This leads to 10 units.
The highest PV panel number was set to be 2 times the highest demand divided by the capacity
rating of one unit. This produced a total of 16,000 units.
The highest battery unit number was calculated to be 1 time the highest demand divided by the
delivered power rating in an hour of every unit. This produced a total of 23,809 units.
Table 3 - Shows the configuration used in the base case
Assumptions for the Base Case (Unit Numbers)
Wind Solar BESS
10 16,000 23,809

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3. Results
Numerous scenarios were simulated in this project in an attempt to get an optimal solution in
sizing the solar PV hybrid RES. To avoid confusion, the results for sizing and obtaining the
sizing values have been respectively documented in every subtopic in the previous chapter. In
this chapter, there is a generation of summarized results before the work analyzed and concluded
(Kaldellis, 2010). There were three scenarios set up; these were the base cases B, A and C which
were found suboptimal as predicted. In these mentioned control cases, the PV modules’
configuration was 16,000, 10 for the wind turbines and the battery units’ was 23,809 being used.
Additionally, scenario B involved simulating the demand side management schemes other than
the mounted PV units in one axis and the racking system used to track the sun. the results from
these optimal configurations of the HRES were E, F and D based on the scenarios B, C and A
respectively.
Table 4 – shows the optimized results
Solar (kW) Wind (kW) LCOE
($/kWh)
BESS (kWh)
Scenarios A 2,500 4,000 NE* 2,000
B 2,500 4,000 NE 2,000
C 2,500 4,000 31.9 2,000
D.1 8,250 1,115 21.51 6,601
D.2 3,500 5,708 30.03 5,614

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E.1 6,250 1,708 28.36 2,708
E.2 3,250 494 17.76 9,879
F.1 3,750 2,762 28.02 1,763
F.2 3,250 494 17.62 7,968
4. Analysis
The results obtained were analyzed and compared with the TCD database’s information. The
TCD is an initiated move within the US that provides a public and transparent database of
program costs as well as performance estimations regarding programs in renewable energy and
their efficiencies (Machrafi, 2012). This information has been provided in the open literature. Its
website shows the collated information on cost for about 500 variety of sources within the last
decade. Hence, the site was a good spot for gauging and benchmarking them in case one decides
to come up with project relating to investment, developments, policies and regulations.
Table 5 - Shows the Transparent Cost Database comparison with the results
Research
Results
Worst Case of
and adjusted
TCD
Nominal Case
of an adjusted
TCD
Scenarios A - 41.54 21.75
B - 41.54 21.75

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C - 41.54 21.75
D.1 21.51 25.03 13.96
D.2 30.03 42.90 22.49
E.1 28.26 25.33 13.83
E.2 17.76 31.04 17.25
F.1 28.02 33.16 17.61
F.2 17.62 30.22 16.80
Table 5 above shows the documented Levelized cost taken from the simulated options laid out.
There are three layouts; cases B, C and A presented as base cases. The following cases, cases E,
D and F are the optimized configurations. In the case of A, it's the base case is similar to the one
in table 4. Case B shows an improvement of the base cases through simulation of implemented
Demand Side Management schemes (Abdelaziz & Mohamed, 2017). The actual scheme
implementation is not covered in this project, however, the desired effect in aligning the demand
to the supplied power has been simulated. In the case of C, there is a further improvement in the
yielded specific PV units’ energy through simulating an axis tracker mounting technique. The
tracker increases the energy for a CAPEX and OPEX marginal increase. Scenarios F. E and D
are the optimal B, C and A configurations respectively.

Hybrid Systems 33
A further look into the E, F and D scenarios reveals the use of two approaches. The first
approach constraints the algorithm used for simulation by using a different algorithm for
working out the potential of the resource and strictly use the LPSP<0.05 reliability requirement.
The second approach constraints the algorithm used for simulating the size of land which was
41,000 m2. However, due to the trade-off that lowers LPSP, the requirement is set at LPSP<0.1.
Approach 1 is headed towards determining the long term solution with a reliable pertinent over
the cost while approaching 2 points towards finding the medium-termed solution. The medium-
term solution anticipates the national grid being expanded to the project’s location in the future,
hence, cost becomes pertinent over reliability. Thereafter, the LCOE can be calculated in
accordance with equation (11). This is the crucial index used in the optimization process (Hossain
& Leung, 2007).
It is not easy deciding to directly compare the obtained results with the values of LCOE in the
TCD due to various reasons. To begin with, there lack HRES records in the TCD. Therefore. A
comparative method should be derived from the documented and measured data in the TCD.
Secondly, energy storage concept is different from energy generation and taking out its
contribution to LCOE from generated sources is difficult. This is the reason other researchers are
developing and taking in the new term levelized storage cost for adequate measurement of the
storage cost (Gabbar, 2016).
Nonetheless, TCD comes out the best found database that can freely be found and this research
has had an attempt od determining the results’ quality through comparison with TCD values. The
TCD nominal case is derived from averaging the values of TCD solar and wind generation and
the weight of a similar proportion as the scenario under comparison (Del Socorro, 2014). The

Hybrid Systems 34
worst case TCD is gotten from averaging the maximum LCOE values of TCD solar and wind
generation proportion to the scenario being compared to. weighted with a similar with an
exception of scenario E.1, all results were lying within the bounds of their nominal as well as
worst-case LCOE as seen in TCD. These findings were satisfactorily invalidating the obtained
results.
5. Conclusion
The three simulated instances resulted in the achievement of 3 optimal configurations. The
components of the system were modelled and an objective purpose successfully established. The
developed objective purpose is known as a two-fold objective. The objective is to achieve LCOE
and LPSP. The objective parameters are costs and reliability. The objective purpose was later
reduced through the process of a genetic parallel algorithm from which the plant's sizing optimal
configurations were attained. Analysis of numerous scenarios was conducted to find out the
highest optimal configuration. The evaluated scenarios were nine in total. Relying on the
outcome of this research, it can be concluded that, hybrid renewable sources of energy can
practically be developed in some specific areas. The findings show that, in areas where solar and
wind complement each other, it is optimally feasible to size every plant component in order to
fulfil specific demands of reliability (Ashton, 2013).
As seen from the simulated constraints of land size results, it is possible to attain a very high
requirement for reliability at very costs. A number of the cases consisting of very low
requirements of reliability led to very minimal LCOE. On the other hand, better cases with
higher requirements for reliability led to LCOE (Zerriffi, 2010).

Hybrid Systems 35
An interesting observation made is that the optimal configuration of resources does not have to
be similar to the optimally configured costs in case the utilization costs for varied resources are
not similar. A conclusion was drawn that upon simulation of the management side demands, in
order to obtain optimized utilization of solar as well as mounting the modules of solar PV on the
racking sun tracker system, the resultant optimized configurations optimize the utilization of
solar energy (Sciubba, et al., 2012). However, this did not result in LCOE due to systems of solar
PV being more costly compared to the wind turbines. A way forward in regards to the results is
that optimized cost systems are the ones lead to the optimal utilization of the most affordable
resources to be exploited. This finding is supported by the observation that intensive wind
configurations led to very low LCOE compared to intensive solar configuration.
Upon simulation and implementation of algorithms, it was often observed and conclusion made
that, the implementation of genetic multi-deme algorithm most suitable compared to the same
control experiment conducted in GA generic serial. The GA generic serial was conducted in
various singles in the exploration of space and its search. The measurements were obtained by
examining the mean distance between individual and was very high within parallel GA multi-
deme than in the GA generic serial. However, in regards to the final solution's quality, there were
no precise conclusions made on the most suitable solution because both of them converged to
almost the same solutions (Morales et al., 2013).
As per the obtained results, the recommended scenario is F.2. The outcome of optimization relies
on solar and wind data resources obtained from the meteorological stations available in the area.
The proposed plant shall be situated in a remote area with a semi-arid climate that is hot and dry
(Khatib & Elmenreich, 2016). The plant suggested will consist of solar PV module generation with
PV module amounting to 1976 resulting in the installation to of 494kWp peak power. A module

Hybrid Systems 36
for wind turbine generation consisting of thirteen wind turbine generators comprising 3250kW
installation capacity with storage battery array units of 94, 856. The battery storage consists of
batteries enhanced with lead acids comprising of 7968kWh total capacity for storing energy
(Khare, et al., 2018). The installation of PV arrays is done in one axis racking sun tracker system
and the presence of a side management demand scheme. The plant at large shall cover a land
area totalling to 41,000 m2. Considering the huge impact of putting in place solar arrays as well
as the effects of wind turbines in practical, it is possible that the adjacent parcels of land in the
area will be enough (Zingoni, 2013). The impacts of siting solar arrays and wind turbines reflect
current challenges and way forward for future study of sitting optimized solar arrays and wind
turbines in the generation of wind solar energy. Although this work contains 10% LPSP it a
great deal and the cheapest leading to 17.62 kWh of LCOE. This work can be enhanced further
by using grid back-up or grid storage, however, the option can be applied a thesis for future
research.

Hybrid Systems 37
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