Analysis of Levelized Cost of Energy (LCOE) and Grid Parity for Utility-Scale Photovoltaic...
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Transcript of Analysis of Levelized Cost of Energy (LCOE) and Grid Parity for Utility-Scale Photovoltaic...
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
[email protected]; [email protected]. 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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