Analysis of Levelized Cost of Energy (LCOE) and Grid

Parity for Utility-Scale Photovoltaic Generation Systems

Mohamed EL-Shimy

IEEE member, Associate Prof., Electrical Power and Machines Department

Ain Shams University

Abbassia, Cairo 11517, Egypt

Mohamed_bekhet@eng.asu.edu.eg; shimymb@gmail.com. Tel. +2 01005639589

Abstract - This paper highlights the Levelised Cost of Energy

(LCOE) and the opportunity for grid parity of utility-scale

photovoltaic (PV) generating systems in Egypt. Various technical

and financial assumptions required for estimating the PV-LCOE

are discussed and inspected. In addition, the sensitivity of LCOE

to various input parameters is performed. Measures for PV-

LCOE reduction are discussed. For standardized modelling of

the LCOE, the internationally verified System Advisor Model

(SAM) is used in this study. Detailed results are available in the

associated sections in the paper; however, the main decision-aid

related results show that the PV-LCOE values in Egypt are far

away from being comparable with the actual retail electricity

prices and the estimated LCOE of conventional power

generation. Achieving grid parity through grid-connected PV-

generation requires huge reduction in the current costs

associated with PV-plants. Probable economic use of PV-plants is

still possible in off-grid applications in remote and arid areas

where grid-connection is neither economical nor possible. Index Terms - LCOE; grid parity; breakeven; photovoltaic

(PV); energy production; techno-economic analysis; sensitivity and

parametric analysis.

I. INTRODUCTION

Solar photovoltaic (PV) technology, which converts

sunlight directly into electricity, is one of the fastest growing

Renewable Energy Technologies (RETs) in the world [1].

Photovoltaic (PV) capacity has exhibited an average annual

growth rate of more than 40% over the last decade [1-3]. The

installed capacity almost increased by 50% between 2008 and

2009 from 15.7GW to 22.9 GW [2, 3]. This is due to both technological innovations that have reduced manufacturing

costs in the last decade by approximately 100 times and

various government incentives for consumers and producers

[1, 4].

The cost of solar generated electricity is consistently

coming down, while the cost of conventional electricity is

increasing. Advances in solar cell technology, conversion

efficiency and system installation have allowed utility-scale

photovoltaic (PV) to achieve cost structures that are

competitive with other peaking power sources [5, 6].

Governments subsidize the deployment of solar photovoltaics (PV) because PV is deployed for societal purposes [7].

There are many different types of PV cells. Single

crystalline silicon and multi-crystalline silicon represent 85-

90% of the PV market. Thin film PV cells represent 10%-15%

of the PV market, and have many several different categories.

Thin film cells are less efficient yet cheaper, whereas

crystalline silicon cells are more expensive [2]. Egypt is endowed with high intensity direct solar radiation

[8-10]; Egypt is one of the Sun Belt countries, which have

highest potential for solar energy projects [9, 10].

In the past, dollars per Watt serve as an index to estimate

the cost of solar PV systems. However, the $/Watt evaluation

method does not consider the effects of the lifetime,

performances of the solar equipments, and financial

policies. Therefore, The U.S. Department of Energy (DOE)

has chosen levelized cost of energy (LCOE) as a key

parameter to evaluate PV systems [11]. With the LCOE

method, the $/Watt which is traditionally used in solar industry can be transformed into $/kWh, which is a more

decisive parameter in power industry.

Generally, the levelised cost of energy (LCOE) is a cost

of generating energy (usually electricity) for a particular

system. It is an economic assessment of the cost of the energy-

generating system, including all the costs over its lifetime:

initial investment, operations and maintenance (O&M), cost of

fuel, and cost of capital [12, 13]. The LCOE is a measure

of the marginal cost (the cost of producing one extra

unit) of electricity over an extended period [2]. The LCOE is

also known as Levelised Energy Cost (LEC), Levelised Unit

Energy Cost (LUEC), and Long-Run Marginal Cost (LRMC) [2, 12-14]. Therefore, the LCOE is the constant unit cost (per

kWh or MWh) of a payment stream that has the same present

value as the total cost of building and operating a generating

plant over its life. Simply, the LCOE converts unequal annual

costs to a constant cost and allows a single cost value to

characterize resource cost [15].

The breakeven cost for photovoltaic (PV) technology is

defined as the point where the cost of PV-generated electricity

equals the cost of electricity purchased from the grid [4, 16].

This target has also been referred to as grid parity [1, 4, 16].

Grid parity is considered when the LCOE of solar PV is comparable with grid electricity prices of conventional

technologies and is the industry target for cost-effectiveness

[1, 4]. Given the state of the art in the technology and

favorable financing terms, clearly PV has already obtained

grid parity in specific locations and as installed costs continue

to decline, grid electricity prices continue to escalate, and

industry experience increases, PV will become an increasingly

economically advantageous source of electricity over

expanding geographical regions [1].

The LCOE is most often used metric for evaluating the

economic feasibility of energy generation projects when

comparing electricity generation technologies or considering

grid parity for emerging technologies such as PV [1, 4, 5, 15].

Since most studies involving new generation or transmission

require an assessment of costs, accurate and readily available

costs of generation estimates are very essential for electric

utilities [15]. One use for LCOE calculations is to compare

costs without incentives. If incentives such as the U.S.

Investment Tax Credit (ITC) are assumed in an LCOE

calculation, they should be specifically referenced to make clear the basis for comparison between technologies [5, 6].

The LCOE is highly sensitive to small changes in the

input variables and assumptions. For this reason, Careful

assessment and validation of assumptions used for different

technologies when comparing the LCOE are very important

[1, 5, 6, 15-24]. The main assumptions made in the LCOE

calculation is the choice of the discount rate, average system

cost, financing method and incentives, average system

lifetime, and degradation of energy generation over the

lifetime. References [1, 5, 6, 15, 23, 25] provide guidelines for

correctly setting the input assumptions for estimation of the LCOE. Sensitivity curves to study the change in the LCOE as

various input variables are changed. In addition, the sensitivity

analysis can be used to overcome the ample uncertainty in the

input variables and assumptions.

The key parameters that govern the cost of PV power are

the capital costs and the discount rate. Other costs are the

variable costs, including operations and maintenance. Of these

parameters, the capital cost is the most significant and

provides the largest opportunity for cost reduction [2].

The capital costs themselves fall into one of two broad

categories: the module and the Balance of System (BOS) [2, 18]. The module is the interconnected array of PV cells and

incorporates feedstock silicon prices, cell processing and

module assembly costs. The BOS includes structural

system costs (structural installation, racks, site preparation

and other attachments) and electrical system costs (the

inverter, wiring and transformer and electrical installation

costs). Breakdowns of the capital costs for a ground-mounted

system as suggested by the Rocky Mountain Institute are 40%

module and 60% BOS.

Cost reduction can be achieved by numerous alternatives,

but all possible opportunities are based on the technological

improvements and the economics of scale and volume. Both the module and BOS cost components have experienced, or

have the potential to experience reductions because of both

factors [2]. Cost reductions are mainly associated with

increasing capacity [2], inverter development for utility-scale

PV systems [2, 18, 19, 24], downsizing of the structural

components [18, 21], increased installation efficiency [18, 21],

and use of trackers [2, 5, 6].

This paper highlights the Levelised Cost of Energy

(LCOE) and the opportunity for grid parity of utility-scale

photovoltaic (PV) generating systems in Egypt. Various

technical and financial assumptions required for estimating the PV-LCOE are discussed and inspected. In addition, the

sensitivity of LCOE to various input parameters is performed.

Measures for PV-LCOE reduction are discussed. For

standardized modelling of the LCOE, the internationally

verified System Advisor Model (SAM) is used in this study.

II. MODELING OF THE LCOE AND GRID PARITY

A. The LCOE

The nomenclature used in this paper is shown in Table 1.

Utility scale PV systems are generally built directly on the

ground, and are typically between 1MW and 50MW in size.

The module is combined with a ground based, mounting

system, to create the solar collector array. The modules within the array are connected to an inverter, which converts the DC

power into AC power, which is then transformed at a

substation for distribution in a high-voltage transmission-line

[2]. With time, the efficiency of power plants is reduced. In

PV systems, the time-dependent reduction in the efficiency is

called output degradation [17]. PV systems are often financed

based on an assumed 0.5 to 1.0% per year degradation rate,

although 1% per year is used based on warranty. In general, a

degradation rate of 0.2% - 0.5% per year is considered

reasonable given technological advances [1]. The energy

generated in a given year (Et) is equal to the rated energy output per year (St) multiplied by the degradation factor (1 - d)

which decreases the energy production with time [1].

The economies of scale inherent in utility-scale solar

systems is similar to those found with other power options, but

PV has the benefit of being completely modular – PV works at

a 2 KW residential scale, at a 2 MW commercial scale or at a

250 MW utility scale. PV has the unique advantage among

renewable resources of being able to produce power anywhere:

deserts, cities, or suburbs [5, 15].

A LCOE model is an evaluation of the life-cycle energy

cost and life-cycle energy production [5]. It allows alternative technologies to be compared when different scales of

operation, investment, or operating time periods exist. It

captures capital costs, ongoing system-related costs and fuel

costs – along with the amount of electricity produced – and

converts them into a common metric: $/kWh. Generally, the

LCOE can be represented by [5, 6],

𝐿𝐶𝑂𝐸 = Total Life Cycle Cost

Total Lifetime Energy Production (1)

TABLE 1

NOMENCLATURE

T Life of the project [years]

t Year number i.e. 0, 1, 2 … T

Ct Net annual cost of the project for year t [$]

Et Energy produced in year t [kWh]

It Initial investment and cost of the system for year t [$]

Mt Maintenance cost for year t [$]

Ot Operation cost for year t [$]

Ft Interest expenditure for year t [$]

r Discount rate [%]

St Rated energy output for year t [kWh/year]

d Degradation rate [%]

Rt Revenue for year t [$]

Dep Depreciation [%]

TR Tax rate

RV Residual Value

From economic point of view, the LCOE is

representative of the electricity price that would equalize

cash flows (inflows and outflows) over the economic life

time of an energy generating asset. It is the average electricity

price needed for a Net Present Value (NPV) of zero when

performing a discounted cash flow (DCF) analysis. The LCOE

is determined by the point where the present value of the sum-

discounted revenues is equivalent to the discounted value of

the sum of costs [1, 2, 22] i.e.

𝑅𝑡 (1 + 𝑟)𝑡 = 𝐶𝑡 (1 + 𝑟)𝑡

𝑇

𝑡=0

𝑇

𝑡=1

(2)

One of the most important assumptions and input

parameters is the discount rate (r). This input represents an

appraisal of the time value of the money used in the

investment [2]. Therefore, the discount rate can be used to

convert future costs to present value. The discount rate is

particularly important in the context of renewable energy

generating assets, due to their inherent capital intensity. This

can be contrasted with technologies with higher operating

costs such as open cycle gas turbines. Whilst the LCOE for these technologies is affected by the choice of the discount

rate, the impact is less pronounced, and they are not as much

sensitive to variations in the discount rate.

Considering that 𝑅𝑡 = 𝐸𝑡 ∗ 𝐿𝐶𝑂𝐸𝑡 . In addition, the sum of

the present value LCOE multiplied by the energy generated

should be equal to the present valued net costs, and the LCOE

is a constant value [1, 2, 22], then equation (2) results in

equation (3).

𝐿𝐶𝑂𝐸 = 𝐶𝑡 (1 + 𝑟)𝑡

𝑇

𝑡=0

𝐸𝑡 (1 + 𝑟)𝑡

𝑇

𝑡=1

(3)

There are multiple ways to calculate LCOE, depending on

the level of financial detail. For example, the model presented

in [5, 6] included in the LCOE inputs the initial investment,

total depreciation tax benefit, total annual cost, total system

residual value, and lifetime energy production. The LCOE

equation is then represented by equation (4). In [1], another

model is given as shown in equation (5); in this model, no

incentives are considered.

𝐿𝐶𝑂𝐸

= 𝐶0 −

𝐷𝑒𝑝𝑡

1 + 𝑟 𝑡 𝑇𝑅 𝑛

𝑡=1 + 𝐶𝑡

1 + 𝑟 𝑡 1 − 𝑇𝑅 −

𝑅𝑉 1 + 𝑟 𝑡

𝑛𝑡=1

𝑆𝑡 1 − 𝑑 𝑡

1 + 𝑟 𝑡 𝑛

𝑡=1

(4)

𝐿𝐶𝑂𝐸 = 𝐼𝑡 + 𝑂𝑡 + 𝑀𝑡 + 𝐹𝑡 (1 + 𝑟)𝑡 𝑇

𝑡=0

𝑆𝑡 1 − 𝑑 𝑡

(1 + 𝑟)𝑡 𝑛𝑡=1

(5)

Several international organizations and institutions such

as [15, 23] attempt to standardized modeling of the LCOE.

One of the most clear recent LCOE reports was by the

California Energy Commission in 2010 [15]. In the Energy

Commission’s Model [15], 25 separate cost-of-generation

models are combined into one model with drop-down menus.

In addition, the Model has been completely reorganized to

make it more flexible and more transparent. The model

includes analytical functions for screening and sensitivity

curves to allow users to evaluate the effect of the various cost

factors used in developing the levelised costs.

The System Advisor Model (SAM) [23, 24] is a

performance and economic model designed to facilitate decision making for people involved in the renewable-energy

industry. The National Renewable Energy Laboratory (NREL)

in collaboration with Sandia National Laboratories and in

partnership with the U.S. Department of Energy (DOE) Solar

Energy Technologies Program (SETP) develops SAM. The

SAM makes performance predictions for grid-connected solar

and non-solar generation projects. The model is very flexible

and provides several functions for sensitivity analysis and

other techno-economic analysis.

The SAM is used in many previous studies for techno-

economic analysis of grid-connected PV systems [11, 20, 25, 26], and it will be used for the study in this paper as a standard

tool for determining the LCOE and associated techno-

economic analysis. In contrast with the typical or average

capacity factor methods [1, 2, 4, 7, 17] for determining the

annual energy production of PV plants, the SAM provides an

accurate tool for determination of the produced energy. Actual

long-term meteorological and accurate models for the PV

system components are available in the SAM for

determination of the annual energy production.

B. Grid Parity Despite increased incentives and the demand for more

sustainable forms of energy, PV has still not become a major

energy supply contributor [1]. The tipping point for solar PV

adoption is considered to be when the technology achieves

grid parity [1, 4] given that conventional-powered electricity

prices are rising whilst PV installed prices are falling.

The concept of grid parity for solar PV represents a

complex relationship between local prices of electricity, solar

PV system price that depends on size and supplier, and

geographical attributes. However, depending on the location,

the cost of solar PV has already dropped below that of

conventional sources achieving grid parity [1, 4, 28]. The grid parity is often graphically given as the industry

average for solar PV electricity generation against the average

electricity price for a given country [1, 4, 27]. Whilst this is a

useful benchmark, its validity depends on the completeness

and accuracy of the method used to calculate the PV-LCOE.

In addition, claims of grid parity at manufacturing cost instead

of retail price have contributed to confusion [1, 4].

III. DEMONSTRATION EXAMPLE

A. PV PLANT AND LOCATION

In [10], the viability analysis of building a 10 MW PV-

grid connected power plant in Egypt is given. Both techno-

economical and environmental conditions are taken into

account. The results show that placement of the proposed 10

MW PV-grid connected power plant Kharga site (Lat. 25o 27’

N, Long. 30o 32’ E, Elev. 77.8 m) offers the highest

profitability, energy production, and GHG emission reduction.

The Sanyo mono-Si-HIP-205BA3 205 Wp PV-module is

selected from a large list of modules from many

manufacturers. The selection criterion was a minimum

efficiency of 15% and the highest capacity/area ratio. The

required number of modules is 48781. Two-axis trackers are

selected for maximization of the electric energy production. The DC system is connected to the AC system via two 4750

kW inverters. The inverter efficiency is assumed to be 95%.

The initial and periodic costs of the PV plant as well as the

financial parameters are given in [10].

B. Objectives

The objective of this example is to determine the LCOE

and to perform a sensitivity analysis for the mentioned PV

plant in the considered location. The sensitivity and parametric

analysis are performed to overcome the uncertainties in the

input parameters and to take into account the time-dependent

changes of the costs. In addition, the sensitivity analysis is also valuable in assessing the impact of various technologies

on the LCOE. For example, in [1], the cost of PV modules

varies from technology to technology, from country to

country, and according to the project scale. The sensitivity

analysis is also valuable in determining the significant

directions for reducing the LCOE. The PV-LCOE in

comparison with the actual retail price of electricity and the

estimated LCOE of conventional power generation is

considered to evaluate the grid parity.

The System Advisor Model (SAM) [23, 24] is used for

this study. No incentives are considered in this example. The degradation is assumed to be 0.5% [1]. The availability factor

accounts for downtimes due to forced and scheduled outages

[23, 24]. The availability of PV systems is largely driven by

inverter downtime [5, 6]. The availability of the PV power

plant is assumed to be 99%.

IV. RESULTS AND DISCUSSIONS

A. BASE-CASE ANALYSIS

The base case is the situation where the considered 10

MW PV power plant is placed at the Kharga site in Egypt. The

total incident solar radiation on Kharga and the expected

monthly energy production from the PV plant are shown in Fig. 1. It is depicted from Fig. 1 that high-energy production

levels can be achieved during the summer period. This

production pattern is an agreement with the annual load curve

of Egypt. Therefore, the considered PV power plant can

support well the power grid in supplying the peak loading. The

maximum energy production is 2.5 GWh associated with May.

The effect of the PV system degradation is shown in Fig.

2. The system is capable of producing 24.8 GWh in the first

year; however, due to the system degradation, its production

capability is limited to 22 GWH by the 25th year. Therefore,

11.33% of the production capability is lost by the end of the

lifetime which is considered 25 years in the base case.

Fig. 1 Incident radiation and monthly energy production at the Kharga site

Fig. 2 Impact of system degradation on the annual energy production.

Fig. 3 Stacked cost/watt and LCOE for the base case

The costs per Watt and the LCOE as well as the cost components for each of them are shown in Fig. 3. The cost Per

Watt is 11.83 US$/Watt where the modules present the

dominant cost (60.55% of the cost per Watt). The BOS is the

second major cost component (21.05%) followed by O&M

costs (11.92%). The LCOE is 37.74 cent/kWh where the

major cost components are the same as in the cost per Watt

but with different percentage values.

In the LCOE, the Modules present 65.95% followed by

the BOS (22.93%) then the O&M costs (4.06%). Therefore,

reducing the cost per Watt and the LCOE can be achieved by

reducing the costs associated with the modules, BOS, and O&M. This is can be achieved, for example, by increasing the

scale, and volume of PV power plants. In addition,

technological improvements can provide an opportunity for

cost reduction [2, 5, 6, 18, 19, 21, 24].

B. SENSITIVITY AND PARAMETRIC ANALYSIS

The sensitivity analysis is used in this section to

investigate how sensitive an output metric is to variations in

the values of input variables. The parametric analysis involves

assigning multiple values to one or more input variables to

explore the relationship between the input variables and

Fig. 4 Base case LCOE sensitivity to input values

Fig. 6 Impact of various variables on the LCOE

resulting metrics [23]. Fig. 4, 5, and 6 show the results of

the sensitivity analysis while Fig. 7, and 8 show the results

of the parametric analysis.

Based on Fig. 4, where the LCOE sensitivity to various

input variables is shown, the system availability is the most

affective variable on the LCOE. The availability of PV

systems is largely driven by inverter downtime [5, 6].

Therefore, improving the availability of components such as inverters, electrical connections, and structures is associated

with reduction in the LCOE. The second influential variable

on the LCOE is the cost of modules followed by loan

interest rate then the analysis period (lifetime). BOS,

inflation rate, debt fraction, loan term, and insurance costs

are of significant effect on the LCOE value.

The impact of solar tracking options on the LCOE is

shown in Fig. 5. Three options are considered; the fixed

system, one-axis trackers, and two-axis trackers. Although

the use of tracking systems adds to the costs of the PV

power plants, their beneficial impacts are demonstrated in

Fig. 5. The use of tracking systems results in increasing the solar-energy capture capability of the PV modules as a

result the electrical energy production increases in

comparison with fixed PV systems. The economic

consequence is a reduction in the LCOE. Therefore, the

costs required for providing solar tracking are recovered by

the economic gains.

As shown in Fig. 5, the two-axis trackers provide more

energy capture and LCOE reduction in comparison with the

one-axis trackers. In comparison with the fixed PV system,

the use of one-axis tracker increases the annual energy

production by 23.3% and decreases the LCOE by 18.87%

while these values are respectively 27.2% and 21.37% for

the systems with two-axis trackers.

Fig. 6 shows the impact of some other variables on the

LCOE. It is clear that reducing the LCOE can be achieved

by reducing the loan interest rate, reducing the discount rate,

increasing the lifetime, increasing the debt fraction,

reducing the degradation, increasing the efficiency, or

increasing the availability. As previously stated that all possible opportunities for reducing the LCOE are based on

the technological improvements and the economics of scale

and volume.

Fig. 5 Impact of solar tacking type

Fig. 7 LCOE and annual energy production as affected by the location

Fig. 8 Stacked LCOE as affected by the location

TABLE 2

ABBREVIATED FORM OF THE TARIFF STRUCTURE IN EGYPT

Power service voltage level /

consumer class

Tariff

Min. Max.

Very high voltage1 12.9 Pt/kWh 20.2 Pt/kWh

High voltage1 15.7 Pt/kWh 24.5 Pt/kWh

Medium and low

voltage

> 500

kW1

9.5

LE/kW/month +

21.4 Pt/kWh

10.4

LE/kW/month +

33.4 Pt/kWh

< 500

kW 11.2 Pt/kWh 25.0 Pt/kWh

Residential2 5 Pt/kWh 48 Pt/kWh

Commercial2 24 Pt/kWh 60 Pt/kWh

1. Power factor dependent tariff

2. Block rate tariff

In order to study the sensitivity of the results to the

geographical location, i.e. the meteorological conditions, the

considered PV power plant is placed at AL-Arish site (Lat.

31o 16’ N, Long. 33o 45’ E, Elev. 15.0 m), and the results are

compared with those obtained with Kharga site. The

locations of the considered site can be identified on the map

that is available at [29]. With the PV plant placed at AL-

Arish, the LCOE is 48.07 cent/kWh and the annual energy

production is 17.9764 GWh. This shown in Fig. 7. In

addition, the results for the Kharga site are included for

comparison. The results shown in Fig. 7 show the surpass of the Kharga site to provide higher-energy production

(+38.4%) and lower LCOE (-27.75%) in comparison with

AL-Arish. This proves the site-dependency of the LCOE.

The breakdown of the LCOE for the considered sites is

shown in Fig. 8. Higher values of all the cost components

are associated with AL-Arish site in comparison with the

Kharga site. It is worthy to be mentioned that the

determined values for the LCOE for the considered sites in

Egypt are within the range of LCOE values estimated from

various sources in North America and other locations [1].

C. GRID PARITY In Egypt, a tiered retail electricity-pricing scheme is

used [30]. A low tariff is offered for low-energy consuming

customers such as residential and commercial customers.

These consumers receive subsidies for their electricity. An

abbreviated form of the electricity-tariff structure in Egypt

is shown in Table 2.

Based on the 2012 exchange rate [31], one US$ is equal

to 6.03130 Egyptian Pounds (EGP). Therefore, the LCOE at

Kharga site is 143.183 Pt/kWh while its value at AL-Arish

is 289.925 Pt/kWh. These LCOE values are far away from

being comparable to the retail electricity prices shown in

Table 2 i.e. the grid parity with PV systems is impossible

with the current costs. However, neither incentives nor

subsidies are considered in the determined values for the

LCOE.

The LCOE for conventional power generation (Conv-

LCOE) and its forecast up to 2050 has been estimated in

[32]. The determined average Conv-LCOE values in

cent/kWh for years 2010, 2015, and 2050 were 2.39, 2.54,

and 4.01 respectively. Therefore, achieving grid parity

through grid-connected PV-generation requires huge reduction in the current costs associated with PV-plants.

Based on Figures 3 and 8, the costs associated with the

modules, BOS, and O&M are the main cost items that

should be highly reduced for grid parity to be realized in

Egypt. Probable economic use of PV-plants is still possible

in off-grid applications in remote and arid areas where grid-

connection is neither economical nor possible. However,

detailed techno-economic analysis is essential for the

optimal choice of an alternative power production

technology. Available renewable power generation

technologies include photovoltaics (PV), concentrated solar power (CSP), wind, wave/tidal, geothermal, biomass,

hydropower … etc.

V. CONCLUSIONS

Overview and Standardized evaluation and analysis

using the System Advisor Model (SAM) of the LCOE of

grid-connected PV generating systems are presented in this paper. In addition, the results included detailed sensitivity

and parametric analysis to investigate the effects of

variations of the input variables on the LCOE and the cost

per Watt. The grid parity is also investigated where both the

actual retail prices of electricity, and the estimated LCOE of

conventional power are considered. The demonstration site

is the Kharga site in Egypt. This site was previously found

to be the optimal location for placing utility-scale PV power

plants in Egypt. The Arish site is also considered in the

geographical location sensitivity.

The results show that the main cost portions in the cost

per watt, and the LCOE are the Modules present 65.95% followed by the BOS (22.93%) then the O&M costs

(4.06%). Therefore, LCOE reduction increasing the scale of

PV power plants as well as technological improvements.

The LCOE sensitivity analysis shows that the main affective

variables are the system availability followed module cost,

then loan rate, then lifetime, then BOS, then inflation rate,

then debt fraction, then the loan term. Although it adds to

the system costs, the impact of solar tracking options shows

that the use of trackers results in reduction in the LCOE.

Highest LCOE reduction is achieved using two-axis

trackers. In comparison with fixed PV systems, the use of two-axis trackers results in an increase in the annual energy

production by 27.2% and reduction in the LCOE by

21.37%. The LCOE sensitivity to the geographical site

shows the surpass of the Kharga site to provide higher-

energy production (+38.4%) and lower LCOE (-27.75%) in

comparison with AL-Arish. However, the determined values

for the LCOE for the considered sites in Egypt are within

the range of LCOE values estimated from various sources in

North America and other locations.

Evaluation of grid parity shows that the grid parity with

PV systems is impossible with the current costs. Both the

actual retail electricity prices and estimated LCOE for

conventional power generation are considered in the

evaluation. The results show that achieving grid parity

through grid-connected PV-generation requires huge

reduction in the current costs associated with PV-plants.

Probable economic use of PV-plants is still possible in off-grid applications in remote and arid areas where grid-

connection is neither economical nor possible. However,

detailed techno-economic analysis is required for proper

decision making. In addition, various alternatives for

renewable power generation should be considered in

achieving optimal decisions.

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