Wind Power Integration and Challenges in Low Wind Zones.
A Study Case: Albania
ANDI HIDA1, LORENC MALKA2,*, RAJMONDA BUALOTI1
1Faculty of Electrical Engineering,
Department of Electrical Power Systems,
Polytechnic University of Tirana,
ALBANIA
2Faculty of Mechanical Engineering,
Department of Energy,
Polytechnic University of Tirana,
ALBANIA
*Corresponding Author
Abstract: - High wind performance systems are influenced by many factors such as site wind resources and
configuration, technical wind turbine features and many financial conditions. Scenario planning and modelling
activities often focus on restricted parameters and numbers to justify wind power plant performance. To better
understand possible pathways to scaling up the distributed wind market in Albania, a deep and
multidimensional calculations based on Monte Carlo analysis, using RETScreen and wind JEDI model, to
assess socio-economic impact as a function of turbine output power, operating and maintenance cost and many
other financial inputs by testing different WT (i.e., VESTAS, GAMESA, W2E and NORDEX) with rated
power from 3.45 MW up to 4.5MW applied on LCOE, NPV, SPP, equity payback, B-C, after-tax IRR on
equity and effects of GHG credits extended at a sensitivity range of ±35% is scientifically performed. From the
simulation results LCOE reaches a minimal value of €43.48/MWh, if the debt rate is 99 % and a debt interest
rate of 5.0%, a TotCapEx of €828/MW (-35 % less expenditures) indexed as the best scenario. For the base
case scenario LCOE results €62.79/MWh, when applying a debt rate of 80% and a TotCapEx of (€1274/MW),
while in the worst-case scenario LCOE impart a maximal value of €87.63/MWh if a TotCapEx of €1720/MW
(+35 % more expenditures) and a share of 52 % debt rate is applied. Local annual economic impact (m€) during
construction period and operating period evaluated in the wind JEDI model result around m€ 89.92 and m€
23.54, respectively. As a conclusion, wind power plants (WPP), installed in low wind zones (Albania and many
other EU countries) would be of interest if an electricity export rate of 110€/MWh, and a GHG credit rate of
€50/tCO2 were accepted.
Key–Words: - Wind power, LCOE, Energy Modelling and Sustainability, NPV, SPP, equity payback, B-C,
after-tax IRR on equity and GHG credit rate.
1 Introduction
The pressure exerted on environmental protection
issues due to GHG released from existing
energy systems is calling the exigency for immediate
global actions. The initiator treaty’s UNFCCC
countries accepted the fact that uncontrollable
increase in energy demand influenced by economic
growth and many other factors such as social factors
and low-efficiency processes of various branches of
the economy are the main reason for negative
environmental concerns that brought countries into
collaboration under the Paris Agreement in
2015. The focus of the (COP) was to design
uncompromising GHG emission policies to reduce
the negative effects of global warming and to keep
the temperature below 2°C, even to limit the
temperature increase to 1.5°C compared to pre-
industrial times, [1], [2]. On the other hand,
technological progress, [3] and large penetration of
different RES for smart, flexible, and diversified
energy systems should be supported by wind
technologies. The Ukrainian crisis brought a lot of
trajectories in the way different countries are
supporting the progress of RES exploitations
Received: April 17, 2023. Revised: February 19, 2024. Accepted: April 21, 2024. Published: May 14, 2024.
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including, incentives, and financing mechanisms
toward a competitive and affordable power sector,
[4]. On the other hand, renewable power generation
technologies, charting the falling costs of the energy
transition beyond most commentators’ expectations,
[5] supporting the future energy transition through
revised renewable energy directive EU/2023/2413
which aims the binding renewable target for the EU
in 2030 to a minimum of 42.5%, [6]. Increases in the
prices of imported oil, higher electricity import rates,
and prices influenced by weather conditions have
compelled various countries globally to pursue low-
cost and clean energy sources. The total final energy
consumption (TFEC) in Albania is estimated at 24
TWh, [7], while electricity covers 7.5 TWh, equal to
25% of the total energy demand, fully generated
from domestic hydropower plants (HPP), [8], [9]. In
the case of the Albanian power sector, an averagely
(60-65) % of the country's electricity demand is
provided by domestic HPP, and the rest is imported
from the regional energy market (250.66 ktoe)
usually with higher prices. The Albanian energy
roadmap toward 2030 goals aims to reach a RES
share of 54.9% of the total final energy consumption
in the country, reducing energy consumption and
CO2 levels by 8.4% and 18.7%, respectively. The
goals can be met by applying different energy
efficiency measures (EEM) and large-scale
integration of RES coupled with ESS, especially in
the T&D section, [10]. Such policies that seek to
foster wind power plants must consider local
interests such as socio-economic aspects, especially
when installed near rural and remote zones. The total
capacity of all wind turbines installed around the
globe by the end of 2018 amounted to 597 GW, with
a potential of 50,1 GW added in 2018, [5]. The focus
of this research work is to provide a systematic
framework for the techno-economic and socio-
economic dimensions, giving a clear response to
policy debate when comparing different supportive
schemes that promote wind power exploitations in
Albania.
2 Wind Speed Prediction and
Forecasting
Wind turbines (WT) belong to machines that convert
kinetic energy (KE) of air in motion that hits the
blades of the rotor into mechanical energy (rotational
energy) which is transferred by a co-axial shaft to
the generator producing electrical energy categorized
as a secondary, transportable, and tradable energy
form. The most striking problem of the wind
resource is its variability in time (temporally) and
geographically even more in space. On a large scale,
spatial variability and fluctuations evoke the fact that
there are many different climatic regions worldwide,
which are identified as windier due to latitude, which
affects the amount of insolation.
At a given location, temporal variability on a
large scale means that the amount of wind may vary
from one year to the next, with even larger scale
variations over periods (decades or more),
accentuating the fact that energy from wind will be
imperishable or not, [6]. On time scales shorter than
a year, seasonal variations are much more
predictable, but still not with a very high accuracy if
a few days ahead information is required. Short-term
forecasts necessarily rely on statistical techniques for
extrapolating the recent past, whereas longer-term
forecasts can carry out studies based on
meteorological methods. A combination of
meteorological and statistical forecast models and
in-site surveys can carry out very useful information
on future wind farm projects, [11].
2.1 Wind Power
Nowadays utility-scale wind turbines use airfoils
like an aircraft wing as shown in Figure 1 to exercise
the kinetic energy contained in the wind stream. Two
wind-coercing forces act on the airfoil; known as lift
and drag forces. The angle of attack (AOA), which
represents the angle between the wing’s chord line
and the relative wind, is the lift-to-drag ratio (often
denoted as L/D ratio).
Fig. 1: Cross section of wind turbine blade airfoil (left) and relevant angles (right). Modified after, [12]
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AOA is a critical parameter in aerodynamics as
it significantly influences the lift and drag forces
experienced by an airfoil that defines the overall
aerodynamic performance of an aircraft. Turbines
depend predominantly on lift force to apply torque to
rotor blades which is perpendicular to effective
airflow direction. The lift force is primarily
responsible for the torque that rotates the rotor and
creates mechanical energy, but some torque is
caused by the drag force as well. The idea of
constructing the tips of the blades, being farthest
from the hub, is responsible for a major part of the
torque. In the case of pitch-adjusting variable-speed
wind turbines, the angle of attack (α) decreases,
while the pitch angle (βp0), increases. In the cases
when the wind speed results in less than the rated
value, the pitch angle changes its value, normally it
is reduced. On the other hand, when wind speed
exceeds its projected value, the pitch angle (βp0) is
increased, and therefore, the angle of attack (α) is
reduced. The blades will be rotated according to
pitch angle value, and the required lift and drag force
is applied to the rotor blades by the wind (as given in
Figure 1). Other angles of interest in the
aerodynamics of the turbine rotor are section pitch
angle (βp), angle of relative wind (), and section
twist angle ().
2.1.1 Wind Speed Distribution
Wind speed distribution is calculated in the energy
tool as a Weibull probability density function, which
is commonly used in wind energy due the fact that it
agrees well with the observed long-term distribution
of mean wind speeds for various sites, [13], [14]. In
some cases, the chosen model also uses the Rayleigh
wind speed distribution, a specific case of the
Weibull distribution where the form factor equals 2.
The Weibull probability density function expresses
the probability p(x) of having a wind speed x during
the year, as given in Equation 1, [15]. The two-
parameter Weibull distribution is expressed
mathematically as given in Eq.1.
(1)
p(x) is the frequency of occurrence of wind speed x,
the two Weibull parameters defined in equation (1)
are usually referred to as the scale parameter A given
in equation (2) and the shape parameter (factor) k,
which typically ranges from 1 to 3. A lower shape
factor produces higher energy for a given average
wind speed. The scale factor (A) is given in
Equation 2, [15].
(2)
where represents the average wind speed value,
and is the gamma function:
The relationship between the wind power
density (WPD) and the average wind speed are,
Eqs. 3 and 4:
(3)
The average wind speed can be calculated:
(4)
Where is the air density and is the probability
of having a wind speed of during the year.
2.2 Wind Energy Curve
The wind turbine power curve, for a wind speed
range from up to 25 m/s, generates a set of points
given as the energy curve , and can be calculated
from expression in Equation 5:
(5)
Px - Turbine power at speed and – represents
the Weibull probability density function for wind
speed , calculated for an average wind speed .
2.3 Unadjusted Wind Energy Production
The model calculates the unadjusted energy
production from the wind equipment for one (proxy)
wind turbine at standard conditions of temperature
and atmospheric pressure, and respectively.
Mathematically, the wind speed at hub height is
usually much higher than that measured at
anemometer height due to the wind shear effect. The
following power law in Eq.6 to calculate the average
wind speed at hub height, [16], is used:
(6)
is the velocity (m/s) measured at the hub
height, is the velocity (m/s) at the
anemometer height, represents the
geometric height of the anemometer installation and
is the hub height given in (m), and
represents the wind shear exponent.
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2.4 Wind Gross Energy Production
Gross energy production represents the total annual
energy that can be delivered by the wind turbine
before losses in wind speed (free stream),
atmospheric pressure, and temperature conditions at
the supposed hub height.
(7)
is the unadjusted energy production, and
are the pressure and temperature adjustment
coefficients calculated by the following Equation 8:
and
(8)
where P is the annual average atmospheric pressure
at the site while P0 and T0 refer to standard
atmospheric pressure and temperature of 101.3 kPa
and 228.1K, respectively. The perfect gas law and
the stepwise linear temperature variation
assumption, the hydrostatic equation yield (Eq. 9):
(9)
The renewable energy collected is equal to the
net amount of energy produced and can be
calculated from expression in Eq.10:
(10)
represents the gross energy production, and -
loss coefficient and is given by Eq. 11:
(11)
where specify array losses, soil and
icing losses, downtime, and miscellaneous losses,
respectively, are applied to calculate the net energy
production. The hour wind plant capacity factor CF
represents the ratio of the average power produced
by the plant over a year to its rated power capacity,
[17], calculated using Eq.12.
(12)
where is the renewable energy collected,
expressed in kWh, is hourly capacity for each
turbine with air density adjusted wind speeds at a given
height. Full Load Hours (FLH) for a given WPP can
be calculated by using the expression in Eq. 13:
(13)
FLH is a sum of CF for each hour. FLH is a sum
of CF for each hour. While FLH is of limited value
as a standalone number, it is an important part of the
LCOE calculation. FLH is of limited value as a
standalone number, it is an important part of the
LCOE calculation. According to Betz’s Law, no
wind turbine can convert more than 59.3% of the
kinetic energy of the wind into mechanical energy
transformed at the rotor (=59.3%), [18].
3 Albanian Wind Potential
In the context of our country, there is a preliminary
perception that certain areas such as that Lezha,
Korça, the area of Karaburun in Vlora, certain areas
in the district of Puka, the area of Kryevidh, part of
Rrogozhina municipality, Torovica, and Vau i Dejes
part of Shkodra district, etc., have a noticeable flow
of wind that can be exploited to produce future
electricity demand. A series of international policies
are increasingly channeling the Albanian
government to diversify more power sources from
renewable energy sources (RES), [19], especially
exploiting wind potential for electricity generation.
Fig. 2: Distribution of wind potential in Albania as a
function of wind power density at 100m height
(W/m2), [20]
Potential wind power density (W/m2) is shown
in the seven classes used by NREL, measured at a
height of 100m. The distribution of the country's
land area in each of these classes compared to the
global distribution of wind resources by wind power
density is given in Figure 2. Albanian territory can
be a shelter of at least 7500 MW of wind potential.
3.1 Proposed Wind Power Plant
The proposed land-based wind project is situated in
the south-eastern part of Albania, near the cross
border with Greece, and comprises 40 wind turbines
with a rated power of 4.5 MW equating to a capacity
of 180 MW and covers an area of 4905 ha part of
Korça District.
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Fig. 3: Distribution of wind turbines on the map for the proposed wind project (Pretusha sub-area and
Kapshtica sub-area)
The topographic has identified and provided 40
possible points to settle wind turbines in Pretushe
Subzone and in the Kapshtica Subzone as given in
Figure 3, respectively. The mast meters installed in
the region have provided the wind speed regime and
its direction for a period of one year (from February
24. 2008 to February 5.2009), [21]. The highest
wind velocity of 6.2 m/s is reached in March, while
the lowest value of 3.8 m/s falls in July.
The annual average wind speed chosen for the
reference project analysis, which is consistent with
prior reports, is 5.8 meters per second (m/s) at 105
meters (m) (hub height). The representative
elevation is used to carry out pressure at hub height
that impacts AEP (Annual Energy Production).
4 Materials and Methods
In this study, three different nature energy modeling
tools are used, as given in the methodology
flowchart in Figure 4. The RETScreen energy
model, reliable software to estimate power
generation, life cycle costs, and mitigation of
GHG, [22] and for different RES energy projects is
considered the primary tool. A high accuracy level,
on the annual electricity generated by the proposed
wind power plants (WPP), requires a set of data,
including technical features (Wind Turbine type and
model, power, and energy curve, and other
influencing factors such as climate data, wind mean
velocity/or power density at hub height information
and wind shear exponent. To re-evaluate the wind
speed data, the combination of recordings with
automated equipment was analyzed (new wind
monitoring technology, providing 10-minute
information to average 15-second measurements for
both speed and direction). Daily variations of mean
wind speed (m/s) are provided by RETScreen
Climate Database (CanmetENERGY) which has an
integrable Energy Resource Maps (Such as global
wind map and the Global Wind Atlas) that do not
differ from in site wind values. While the socio-
economic assessment of the proposed wind power
plant is executed in the wind JEDI energy model.
The validation of the energy production from the
proposed wind energy system is performed and
executed using an advanced energy modeling tool,
EnergyPLAN, which is a deterministic model as
opposed to a stochastic model or models using
Monte Carlo methods such as RETScreen model,
[23]. Both technical and economic context, tower
height, rotor diameter rated power and specific
yields are evaluated for a set of wind turbines (WT).
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Fig. 4: Methodology flowchart of proposed wind power plant (WPP)
4.1 Tested Wind Turbine Parameters
In In this case study four different wind turbines
with specific technical data (Gamesa G128-45 MW,
Vestas V126-3.45MW, W2E-151/4.5MW, and
Nordex N149/4.5-105m) with rated power from 3.45
up to 4.5 MW are considered. As a first step, the
assessment of AEP per each WT assumed to operate
in equal conditions is performed (Figure 5).
Different types of losses such as array losses (5%),
airfoil losses 1%, and miscellaneous losses are
accepted 2% due to losses of energy production due
to starts and stops, off-yaw operation, high wind, and
cut-outs from wind gusts are included in the
model. The energy model has included any parasitic
power requirements and any transmission line losses
assumed for the proposed wind energy project site to
the connection point of the selected region, [13],
[14] and [21] too.
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Fig. 5: Power (MW) and energy curve (MWh) delivered by the selected wind turbine measured at a range of
wind speeds (m/s), [24]
To subjugate faster and with a high accuracy
level, especially at the initial feasibility stage, the
latest version of the RETScreen Expert model added
the ability to rapidly analyze the feasibility of
multiple wind turbines at real site conditions. Based
on this strong feature, such evaluation and
assessment are performed comparing four different
turbine types (i.e., VESTAS, GAMESA, W2E, and
NORDEX) with rated power from 3.45 MW to
4.5MW, different tower heights and rotor diameters
and results are given in Figure 5. In Figure 5, the
power and energy curve delivered by each of the
tested wind turbines measured at a range of wind
speeds (m/s) is depicted. The power curve for each
wind turbine is provided from the model database,
and further for the chosen region and real data
measurements, the energy curve is depicted as a
function of wind velocity. From the simulation
results, the selected energy tool calculates the
capacity factor (CF) and energy production per year
(AEP). This comparison is based on equal
simulation conditions.
Fig. 6: Result of the Annual Energy Production (AEP) for tested wind turbines
0
5000
10000
15000
20000
25000
30000
0
1000
2000
3000
4000
5000
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Energy curve (MWh)
Power curve (MW)
Wind velocity (m/s)
Gamesa G128-4.5MW W2E Wind to Energy W2E-151/4.5
Vestas V126-3.45 Nordex Nordex N149 / 4500 - 105m
Gamesa G128-4.5MW-Energy curve W2E Wind to Energy W2E-151/4.5-Energy curve
Vestas V126-3.45--Energy curve Nordex Nordex N149 / 4500 - 105m Energy curve
23
23,5
24
24,5
25
25,5
26
26,5
360000
370000
380000
390000
400000
410000
420000
GamesaG128-4.5MW W2E Wind to Energy
W2E-151/4.5
Nordex N149/4500 -
105m
Vestas V126-3.45
Capacity Factor (%)
Energy Generation (MWH)
TESTED WIND TURBINES
CF (%) E (MWh)
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From simulation results given in Figure 6 per
each of WT selected, Nordex WT model N149/4.5-
105m, performs better and yields a net annual energy
production of 414 384 MWh or equivalent to 2,302
MWh/MW/year, which corresponds to a 26.3 % of
capacity factor (CF) assuming 98 % of wind turbine
availability throughout the year. As a conclusion,
based on preliminary simulation results it is
reasonable that the extended analyses will be
performed based on the Nordex N149/4.5 wind
turbine, with a rated power of 4.5 MW and hub
height of 105m, as CF and AEP are higher than the
other three wind turbines tested.
4.2 Economic Aspects of Wind Power Plants
The capital costs of wind energy projects are
dominated by the cost of the wind turbine itself. In
Figure 7 cost structure and breakdown for a typical
4.5 MW turbine are given. The average turbine costs
vary by brand, model, and other technical
indicators. In our analyses, a total investment cost of
m€1.274/million/MW, [5], is assumed. The turbine
itself shares around 70.4% of the total (WT) cost,
while BoS accounts for around 22.1% (such as grid
connection electrical infrastructure; assembly
installation; site access and staging; foundation;
engineering and management development) and the
rest finance (contingency, risks etc.) share around
7.5%. Although the cost of wind energy has dropped
dramatically in the last 10 years, technology requires
a higher initial investment than traditional fossil fuel
generators. The investment distribution costs and
other costs such as contingencies during construction
and other financial parameters that impact the
overall efficiency of any wind power plant (WPP)
are used based on assumptions.
According to [25], (65-75) % of the cost goes to
equipment purchase and the rest to construction
costs. In our case study to better assess and map a
clear picture of the impact when a set of financial
parameters and combinations (inflation rate, fuel
escalation rate, discount rate, reinvestment rate, debt
ratio, debt interest rate, debt term, equity, and
incentives and grants) of the proposed wind power
plant (WPP), on the main financial indicators
(financial viability) such as Net Present Value
(NPV), Simple payback period (SPP), equity
payback, benefit to cost ratio (B-C), pre/after-tax
IRR-equity or assets, debt service coverage, GHG
reduction cost, annual life cycle savings, after-tax
modified internal rate of return (MIRR) on equity or
assets and electricity production cost (LCOE)
several scenarios are designed.
Fig. 7: Cost breakdown (%) for the proposed wind power plant (WPP)
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Fig. 8: Breakdown of O&M costs (%) per components for the selected wind turbine with a rated power of 4.5
MW
The operation and maintenance (O&M) costs of
Wind Power Plants (WPP) fall between (1.5-1.7) %
of the total initial cost, a value which is expected to
be spent during the operation phase of the proposed
project (such as insurance; regular maintenance;
repair; spare parts and administration). In our case
study calculations are carried out based on specific
costs of electricity generation during a year and
assumed (10-15) €/MWh or equivalent to (30-40)
€/kW per year, [21].
Maintenance is the largest component of O&M
costs, accounting for 75% of the total or €3,107,877
annually, and covers regular servicing to ensure the
turbines and critical infrastructure are operating at
peak efficiency, as well as any repairs or
replacement of parts as required, salaries share 7%
of O&M costs, accounting an annual cost of
€290,069. Materials are set as 8% of the O&M
budget, which is €331,507 per year. The last
component refers to “to others” equal to 10% of
O&M expenditures (other indirect costs associated
with operations), which equates to €414.38 (Figure
8).
All the above results are based on assumptions
and techno-economic inputs for the chosen wind
turbine as given in Table 1.
Table 1. Technical and economic indicators of the tested wind turbine: Nordex (N149/4.5-105m)
Selected value
Unit
Information
Turbine capacity
4.5
MW
Onshore Wind Turbine NORDEX
Number of turbines
40
Units
Total installed capacity 180 MW
Capacity factor (CF)
26.3
%
Swept area
m2
22,697
Mean velocity value
5.8
m/s
At hub height 105 m
Annual Electricity Production
434800
MWh/yr.
Electricity export rate
100
€/MW
Total investment cost
1274
€/kW
[5]
Discount rate
10
%/yr.
5%-8%-10%
Inflation rate
3
%/yr.
Debt rate
80
%
Debt interest rate
5
%
Debt term
15
years
GHG reduction credit rate
50
€/tCO2
Effective income tax rate
15
%
Depreciation method
Linear
Depreciation period
15
Years
Depreciation tax basis
100
%
Turbine lifetime
20-25
Years
(O&M)
10
€/MWh
[21]
Land lease
NA
€/yr.
€3.107,88
€290,07 €331,51 €414,38 € 100,00
€ 600,00
€ 1.100,00
€ 1.600,00
€ 2.100,00
€ 2.600,00
€ 3.100,00
€ 3.600,00
0%
10%
20%
30%
40%
50%
60%
70%
80%
Maintenance Salaries Materials Others
O&M costs (€) Χιλιάδες
Distribution of O&M costs (%)
O&M components for the selected wind turbine with a rated power of 4.5 MW
Annual O&M Cost (€)
Selected share (%)
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5 Simulation and Results
5.1 Sensitivity Analyses for the Selected Wind
Turbine: NORDEX N149/4.5
The input parameters described in Table 1 reflect the
proposed land-based wind project; however, input
parameters for a near-term wind energy project are
subject to considerable uncertainty. As a result, it is
beneficial to investigate how this variability may
impact the LCOE and other indicators such as NPV,
After-tax IRR, SPP, equity payback, debt coverage
service, and other economic indicators. The
sensitivity analysis shown in Table 2 focuses on the
basic inputs: CapEx, OpEx, and electricity export
rate (€/MWh). Sensitivity analyses are executed
based on constant assumptions and changing the
other set of financial variables. Based on the above
analysis of the investment cost and the references of
various international agencies such as IRENA.
Scenarios are raised on assumptions and supposing a
TotCapEx of 1274 €/kW, [5] is assumed in the range
of (±35%), exactly from 828 €/kW up to 1,720 €/kW
for three different discount rates, 5 %, 8 % and 10
%.
All the indicators are promising and what makes
the difference is the investment cost and the discount
rate. If an investor has a lower installation price per
€828/kW or -35% to reference TotCapEx
(€1274/kW), as depicted in Table 2, all economic
indicators improve significantly. The sensitivity
analysis is carried out considering a change in
installation price referring to the base case scenario
that assumes a total unit cost of €1274/kW, discount
rate of 10%, and sensitivity range of financial
parameters (±35) %. In the result given in Table 2
LCOE for the base case (discount rate 10%) results
€62.79/MWh, while changing the total unit cost in
the range (±35) % then LCOE reaches a minimum
and maximum value of €49.97/MWh and
€85.21/MWh.
Table 2. Simulation results for sensitivity analyses applied on TotCapEx for three different discount rates, 5 %,
8 %, and 10 %, and impact on after-tax IRR (%); B-C; SPP (yrs.); LCOE (€/MWh) and NPV (€)
AEP (
)
414,384
Electricity
export rate
(
100
Discount rate
(%)
5
8
10
TotCapEx
1,720
1,497
1274
1,051
828
1,720
1,497
1274
1,051
828
1,720
1,497
1274
1,051
828
After-tax IRR
(%)
16.9
23
37.2
16.9
23
16.9
23
37.2
16.9
23
16.9
23
37.2
44.5
64
B-C
3.1
4.1
6.0
7.0
9.6
2.2
2.9
4.4
5.1
7.1
1.7
2.3
3.6
4.3
6.0
Equity payback
(yrs.)
6.4
4.3
2.7
2.2
1.5
6.4
4.4
2.7
2.2
1.5
6.4
4.4
2.7
2.2
1.5
LCOE
73.89
65.77
55.29
51.35
43.84
80.78
72.32
59.84
55.40
46.95
85.21
76.15
62.79
58.0
49.97
NPV ()
138491125
177465006
227759101
2466800309
282742998
76736107
107884635
153839191
170181692
201330221
49766239
78346261
120511404
135506303
164086324
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Fig. 9: NPV variation at a sensitivity range ±35% of TotCapEx (€) and electricity export rate €/MWh for a
discount rate of 10%
Figure 9 shows the variation in NPV for a 10%
discount rate, as a function of the total investment
cost (TotCapEx in €) and electricity export rate
(€/MWh). The region determined by high NPV
values over the black dotted line area is called the
feasibility region and is highly impacted by the
electricity export rate generated by the wind farm.
Also, all NPV values would have a positive increase
if applying higher electricity export rates (values in
blue bars) and total unit investment cost is reduced
by 828€/kW (-35% referring base case scenario
1274€/kW).
NPV calculated for investment cost (+35%) over
the reference value of €1,274/kW leading to a total
investment cost (€309,184,533), while moving
selling price of electricity from minimal value
€65/MWh to €76.76/MWh, €88.33/MWh, and
€100/MWh the NPV becomes negative € (-
23019429) and € (-54467844) if installation price
survives an increase or 17.5% and 35% and the
electricity export rate €65/MWh. In the case of
electricity export rate is reduced by 123.3% reaching
a value of €76.76/MWh then NPV becomes negative
value of € (-11962501) if installation price
experiences an increase of 35%, the point of total
capital expenditure reaches a value of 309,194,533€.
In all other cases, NPV becomes positive. The
feasibility region is depicted in Figure 9. Under these
conditions, the sensitivity analysis provides accurate
information about the influencing factors in the cost
of energy production by the wind power farm. From
the analysis, the selling price should be at least
above €100/MWh based on the reference scenario
applying a total unit cost of €1,274/kW. In the
design calculations of the proposed wind farm, the
selling price is assumed €100/MWh, and the detailed
financial analysis highlights the fact that the plant is
not efficient under certain financial conditions,
clearly expressing the need for a fixed price
agreement. This price must be adjusted respecting
the legal framework that supports the production of
electricity from wind power plants (WPP), [19] and
[26] in Albania.
-200000
0
200000
400000
600000
800000
1000000
1200000
1400000
€ 148.866.627 € 188.946.104 € 229.025.580 € 269.105.057 € 309.184.533
NPV (€)Χιλιάδες
TotCapEx (€)
NPV variation at a sensitivity range ±35% of total investment cost (€) and electricity export rate
€/MWh and 10% discount rate €135/MWh
€123.33/MWh
€111.67/MWh
€100/MWh
€88.33/MWh
€76.67/MWh
€65/MWh
Feasible region
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Fig. 10: The variation of LCOE(€/MWh) as a function of total capital expenditures and debt rates (%) at a
sensitivity range of ±35% range and electricity export rate €100/MWh
Figure 10 depicts the variation of
LCOE(€/MWh) as a function of total capital
expenditures and debt rates (%) at a sensitivity range
of ±35% range. Changes in LCOE for a set of
variables are better than a single variable function
and one can be understood by moving to the left or
right along a set of specific variables. Values on the
y-axis indicate how the LCOE will change as a debt
rate and total investment cost (TotCapEx) in the x-
axis are altered and all others are assumed constant
(i.e., remain reflective of the reference project given
in Table 1). The higher the share of debt rate, %), the
lower the cost of electricity production, LCOE
(€/MWh). From the results of the simulation, it is
clearly shown that LCOE varies from 43.48
(€/MWh) in the best case of financial parameters
(99% debt rate and assuming -35% less
expenditures), 62.79 (€/MWh) referring to the base
case scenario (80% debt rate as given in Table 1) up
to highest value of LCOE, 87.63 (€/MWh) given the
worst-case scenario (52% debt rate and assuming
+35% more expenditures). This fact shows that wind
power plants (WPP) are highly exposed to risks
(financial), leading to the need to determine a more
accurate electricity export rate, €/MWh (electricity
selling price).
Figure 11, sensitivity analyses of the equity
payback as a function of TotCapEx and electricity
export rate (€/MWh) at a range of ±35% are given.
In our analyses, the equity payback, which
represents the length of time that it takes for the
owner of a facility to recoup its initial investment
(equity) out of the project cash flows generated is
calculated. The equity payback considers project
cash flows from its inception as well as the leverage
(level of debt) of the project, which makes it a better
time indicator of the project merits than the simple
payback. The model uses the year number and the
cumulative after-tax cash flows to calculate this
value.
49,31 59,89 68,47 78,05 87,63
47,98 57,2 66,42 75,64 84,86
46,67
55,57
64,48
73,39
82,3
45,56
54,17
62,79
71,4
80,02
44,51
52,84
61,17
69,5
77,83
43,48
51,53
59,58
67,63
75,68
40
90
140
190
240
290
340
390
440
490
540
€148.866.627 €188.946.104 €229.025.580 €269.105.057 €309.184.533
LCOE (€/MWh)
TotCapEx (€)
D. ratio 52%
D. ratio 61%
D. ratio 71%
D. ratio 80%
D. ratio 89%
D. ratio 99%
Best scenarios
(+) Worst scenarios
(-)
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Fig. 11: Sensitivity analyses of the equity payback as a function of TotCapEx and electricity export rate
(€/MWh) at a range of ±35%
Referring to results carried out from the
simulation and based on the accepted TotCapEx of
1274 €/kW and if the electricity export rate
(€/MWh) both varies in the range ±35%, concretely
from 65€/MWh to 135€/MWh, then the simple
payback period (SPP) would results 10.1 years and
decreases to 8.4 years, 7.1 years, 6.2 years and 4.4
years if the electricity export rate is changed within
the range €65/MWh, €76.67/MWh €88.33/MWh,
€100/MWh, €135/MWh. While equity payback
results in 11.6, 5.2, 3.5, 2.7, 2.1, 1.8, and 1.5 years,
respectively (in the base case scenario, debt rate
80%, total unit cost 1274€/kW, debt interest 5% and
inflation rate 3%). If the electricity export rate
(recommended price by the Albanian government)
[19] of €76/MWh is assumed, then a simple payback
period (SPP) of 8.4 years and an equity payback of
5.3 years is achieved. These numbers clearly show
that the wind power plant (WPP) with a capacity of
180MW would be of interest if the electricity export
rate would be at least 110€/MWh (refer to other
financial indicators below)
The model calculates the after-tax internal rate
of return (IRR) on equity (%), which represents the
true interest yield provided by the project equity
over its life after income tax (Table 1). The after-tax
internal rate of return (IRR) on equity (%), is
calculated using the after-tax yearly cash flows and
the project life, as given in Figure 12.
€148,866,627
€188,946,104
€229,025,580
€269,105,057
€309,184,533
0,00
3,00
6,00
9,00
12,00
15,00
18,00
21,00
TotCapEx (€)
Equity payback (yrs)
Electricity export rate (€/MWh)
Sensitivity analyses of the equity payback as a function of TotCapEx and electricity export rate
(€/MWh) at a range of ±35%.
0,00-3,00
3,00-6,00
6,00-9,00
9,00-12,00
12,00-15,00
15,00-18,00
18,00-21,00
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Fig.12: Sensitivity analyses of After-tax-IRR as a function of TotCapEx and electricity export rate (€/MWh) at
a range of ±35%
Figure 11 shows the sensitivity analyses of
After-tax-IRR as a function of TotCapEx and
electricity export rate (€/MWh) at a range of ±35%.
From Figure 12 it is observed that for fixed
installation cost (1274 €/kW) as well as selling price
of electricity 135€/MWh, 76.67€/MWh and
minimum price 65€/MWh after-tax IRR results
64.3%, 19.60% and 11.5%, respectively. The
designer has evaluated all the possibilities of the
variability of TotCapEx and the selling price of the
electricity generated by the plant. This dependence
of the IRR was performed for the whole range of the
accepted sensitivity analysis. Referring to the
electricity export rate of (76.67-100) €/MWh, it is
noted that the after-tax IRR on equity has increased
from 11.5% to 37.2 %, setting the conditions of a
reliable investment from intermittent energy sources
such as wind power. Once again, the wind power
plant (WPP) would be of interest if the electricity
export rate were at least 110€/MWh (refer to other
financial indicators such as B-C, equity payback,
NPV, etc.)
6 Risk Analyses
A risk analysis, that provides a risk level of 5% by
specifying the uncertainty associated with several
key input parameters for the is given in Table 3. The
evaluation of the impact of this uncertainty can be
executed on Net Present Value (NPV), after-tax IRR
- equity, equity payback, and levelized cost of
energy (LCOE) is performed. The impact of each
input parameter on a financial indicator is obtained
by applying a standardized multiple linear regression
on the financial indicator using a Monte Carlo
simulation and several combinations of 2000. The
risk analysis empowers to assess if the variability of
the financial indicator is admissible, or not, by
looking at the distribution of the possible outcomes.
The risk analysis for the proposed wind power plant
(WPP) is conducted by changing values in the range
(±) 35 % of total investment cost (€), O&M
(€/MWh), electricity export rate (€/MWh), and the
electricity that would be exported to the network,
while debt rate (%) and interest of the debt in the
estimated time frame is assumed in the range of (±)
25% as given in Table 3.
32,20
19,20
11,50
6,40 2,90
46,30
29,80
19,60
13,10
8,40
60,30
40,70
28,20
19,90
14,20
74,20
51,70
37,20
27,20
20,10
88,20
62,70
46,20
34,70
26,40
102,00
73,70
55,30
42,40
32,90
115,90
84,70
64,30
50,00
39,50
y = 2,5214x2- 28,399x + 99,54
R² = 0,9986
0,00
20,00
40,00
60,00
80,00
100,00
120,00
140,00
€148.866.627 €188.946.104 €229.025.580 €269.105.057 €309.184.533
Afterr tax-IRR-equity (%)
Total CAPEX (€)
€65/MWh
€76.67/MWh
€88.33/MWh
€100/MWh
€116.7/MWh
€123.33/MWh
€135/MWh
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Table 3. Simulation results for risk analyses using a
Monte Carlo simulation and accepted risk level of
5%
Mean
€
115 814 085
Risk level
%
5.0
Minimum level of
confidence
€
8622159
Maximum level of
confidence
€
235042617
Fig. 13: Impact graph on NPV
Figure 13 shows the impact graph on NPV based
on the parameters impacting the economy of wind
power plants (WPP). From the Monte Carlo
simulation, it is observed that the electricity export
rate (€/MWh) and energy exported to the network
rate (MWh) have a positive impact (0.64 and 0.66),
while initial costs, (O&M) and debt interest rate
have a negative impact (-0.31, -0.09 and -0.07),
respectively.
The prediction of electricity generation from the
proposed wind power plant has a major contribution
to the stability of the future economy of wind power
plants mainly impacted by weather and the method
used to calculate it. The electricity export rate should
be carefully addressed based on the above sensitivity
analyses.
Fig.14: Impact graph on equity payback
Figure 14 shows the impact graph for the equity
payback period based on the parameters impacting
the economy of wind power plants (WPP). The
electricity export rate, energy exported to the grid,
debt ratio, and debt term impact negatively the WPP
weighting, -0.48, and -0.44, -0.27 and – 0.22,
respectively, while initial costs, (O&M) costs, and
debt interest rate have a positive impact weighting
0.4, 0.12 and 0.08, respectively.
Fig. 15: Impact graph on LCOE
Figure 15 depicts the impact graph on LCOE
based on the parameters that drive the economy of
the proposed wind power plants (WPP). The
electricity exported to the grid, debt ratio, and debt
term have a negative impact on the proposed WPP,
weighting -0.76, -0.12, and -0.1, respectively, while
initial costs, (O&M) costs, debt interest rate, and
electricity export rate have a positive impact
weighting 0.59, 0.16, 0.16 and 0.008, respectively.
Fig. 16: Distribution of the possible LCOE values in
%
The histogram given in Figure 16 provides a
distribution of the possible values for the financial
indicator (LCOE) resulting from the Monte Carlo
simulation. The height of each bar represents the
frequency (%) of values that fall in the range defined
by the width of each bar, which in most cases (75%)
corresponds to values between 53.76 and 76.67
€/MWh. This graph highlights the fact that
electricity generation cost (LCOE) is influenced by
the financial parameters and electricity exported to
the grid, hence we can rapidly assess its variability,
supporting again that this price should be at least 110
€/MWh.
7 Cash Flow Analyses
The analysis also shows the annual and cumulative
cash flows presented in Figure 17, which were
calculated during the lifetime of the wind power
plant (WPP). One simple method to evaluate the
feasibility of WPP is the simple payback period
(SSP) method, which represents the length of time
needed from WPP to recoup its own initial cost, out
of the revenue or savings it generates during the
operation stage.
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Fig. 17: Cash flow analyses: Simple payback period (SPP), LCOE, and equity payback for the base case
scenario (stable parametres given in Table 1)
The simple payback method should not be used
as the primary indicator to evaluate a project but can
be used as a secondary indicator to assess the level
of risk of the investment. Based on based case
scenario results (financial parameters as given in
Table 1) simple payback results 6.2 years, and equity
payback results 2.7 years, which represents the
length of time that is needed for the owner of the
WPP to recoup its initial investment (equity) out of
the project cash flows generated calculated in the
year number and the cumulative after-tax cash flows.
The equity payback considers project cash flows
from its inception as well as the leverage (level of
debt) of the proposed wind power plant (WPP)
project, which makes it a better time indicator of the
project merits than the simple payback method.
8 Emission Analysis
In RES projects, especially wind power plants, all
kinds of mitigating policies that lead to a decrease in
the cost of electricity production (LCOE) should be
considered. However, other analyses are needful to
accurately determine the risk and the feasibility
region, leading to the determination of the true
electricity export rate (€/MWh). Assuming that the
amount of electricity produced of 414 384MWh/year
from would be produced through the use of simple
Rankine cycle burning diesel 2 as fuel (D#2), with
an emission factor of 70 kg CO2/GJ, as well as
accepting a level of losses in the transmission and
distribution network (T&D) of 7%, then the annual
CO2 level for the base case scenario would be
375401tCO2/year. In the case of the proposed WPP,
would avoid an amount of 342140tCO2/year, which
is equivalent to 150 008 320 liters of petrol not used
32 110 hectares of forest absorbing CO2, as shown in
Figure 18.
-100
-80
-60
-40
-20
0
20
40
60
80
-100
0
100
200
300
400
500
600
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Cumulative (€) Εκατομμύρια
Cash flow pre/after tax (€)
Εκατομμύρια
YEARS
Cumulative(€)
Pre-tax
After-tax(€)
SPP (6.2 yrs.)
Equity payback (2.7 yrs.)
LCOE (€62.79/MWh)
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Fig. 18: Emission analysis and differences between base case scenario and proposed energy system (WPP)
8.1 Carbon Shadow Price and GHG
Reduction Revenue
The simulation results on the effect coming from the
application of carbon credits on the main economic
indicators of the proposed wind power plant are
given in Table 4. The escalation rate of the "carbon
shadow" price was considered (3%), which is the
estimated average annual rate of price increase
during the life of the energy project, which enables
to apply inflation rates to the carbon shadow price
value but can be different from general inflation in
the cases where carbon prices or other schemes, such
as carbon taxes, increase over time. For Clean
Development Mechanism (CDM) projects, two
options are currently available for the length of the
crediting period (i) a fixed crediting period of 10
years or (ii) a renewable crediting period of 7 years
that can be renewed twice (for a maximum credit
duration of 21 years) as given in Table 4.
The model calculates annual GHG reduction
revenue that represents revenue generated from the
sale or exchange of GHG reductions. In the model,
the percentage of loans that will have to be paid
every year as a transaction fee is indicated, which is
accepted at the level of 2%. To obtain credits for a
GHG project, a portion of the credits must be
deducted as a transaction fee, which will be paid
annually to the lending agency and/or host country.
Benefits from GHG reduction revenues are given in
Figure 19, Figure 20, Figure 21 and Figure 22.
Table 4. Simulation results on the effect of carbon credits (€/tCO2) for a 10% discount rate in the ±35% range
of the sensitivity analysis of the total investment cost concerning the fixed cost (€1274/kW)
375401
26278
349123 342140
0
50000
100000
150000
200000
250000
300000
350000
400000
Base case (tCO2) Proposed case (tCO2) Gross GHG reduction
(tCO2)
Net GHG reduction
(tCO2)
tCO2
Scenario comparision
Base case (tCO2) Proposed case (tCO2) Gross GHG reduction (tCO2) Net GHG reduction (tCO2)
150 008 320 litres of pertol not used
32 110 hectares of forest absorbing CO2
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From the Figure 19, equity payback is improved
by 1.8 times or reduced from 2.7 years to 1.5 years
in comparison to the base case scenario.
Fig. 19: Effect of carbon credits (€/tCO2) on equity payback as a function of total investment cost at a
sensitivity range of ±35% for fixed other financial parameters
Fig. 20: Effect of carbon credits (€/tCO2) on equity payback as a function of total investment cost and
electricity export rate for a sensitivity range of ±35% (other financial parameters given in Table 1 are kept
unchanged)
1,00 0,90 0,90 0,90 0,80 0,80 0,70
1,40 1,30 1,20 1,10 1,10 1,00 1,00
1,80 1,70 1,60 1,50 1,40 1,30 1,30
2,30 2,20 2,00 1,90 1,80 1,70 1,60
3,00
2,70
2,50 2,40 2,20 2,10 2,00
0,00
1,00
2,00
3,00
4,00
5,00
6,00
7,00
8,00
9,00
10,00
€32.50/tCO2 €38.33/tCO2 €44.17/tCO2 €50/tCO2 €55.83/tCO2 €61.67/tCO2 €67.50/tCO2
Equity payback period (yrs)
GHG credit rate (€/tCO2)
€148,866,627 €188,946,104
€229,025,580 €269,105,057
€309,184,533
1,00 0,90 0,90 0,90 0,80 0,80 0,70
1,40 1,30 1,20 1,10 1,10 1,00 1,00
1,80 1,70 1,60 1,50 1,40 1,30 1,30
2,30 2,20 2,00 1,90 1,80 1,70 1,60
3,00
2,70
2,50 2,40 2,20 2,10 2,00
0,00
1,00
2,00
3,00
4,00
5,00
6,00
7,00
8,00
9,00
10,00
€32.50/tCO2 €38.33/tCO2 €44.17/tCO2 €50/tCO2 €55.83/tCO2 €61.67/tCO2 €67.50/tCO2
Equity payback period (yrs)
GHG credit rate (€/tCO2)
Effect of carbon credits (€/tCO2) on equity payback as a function of total investment cost at as
sensitivity range of ±35% for fixed other financial parameters (refer table 1.0)
€ 148,866,627 € 188,946,104
€ 229,025,580 € 269,105,057
€ 309,184,533
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Fig. 21: Effect of carbon credits (€/tCO2) on NPV as a function of total investment cost and electricity export
rate at a sensitivity range of ±35% (other financial parameters given in Table 1 are assumed fixed)
Fig. 22: Effect of carbon credits (€/tCO2) on after-tax-IRR on equity as a function of total investment cost and
electricity export rate at a sensitivity range of ±35% (other financial parameters given in Table 1 are assumed
fixed)
-2000000
498000000
998000000
1498000000
1998000000
2498000000
NPV (€)
TotCAPEX (€)
€135/MWh
€123.33/MWh
€116.7/MWh
€100/MWh
€88.33/MWh
€76.66/MWh
€65/MWh
91,80
66,98
50,67
39,20
30,79
y = 4,102x2- 47,969x + 200,45
0,00
20,00
40,00
60,00
80,00
100,00
120,00
140,00
160,00
180,00
€ 148.866.627 € 188.946.104 € 229.025.580 € 269.105.057 € 309.184.533
Afterr tax-IRR-equity (%)
TotCapEx (€)
€65/MWh
€76.66/MWh
€88.33/MWh
€100/MWh
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As expected, in Figure 20 and Figure 21, the
equity period is highly influenced by initial cost,
electricity export rate, and GHG credits. If we apply
a carbon credit value of (€50/tCO2) extended to the
range of the sensitivity analysis ±35% of the total
investment cost and electricity export rate, then the
simple payback period (SPP) is decreased from 8.4
years to 5.7 years.
From the simulation results of the proposed
wind power plant as given in Figure 19, Figure 20
and Figure 21, it is observed that the impact of the
"ETS" emission trading schemes will bring
significant benefits to the economy of the wind
proposed wind farm project. If a carbon price of
€50/tCO2 is assumed, then the equity payback period
will be reduced from 2.7 years to 1.5 years for a
fixed electricity export rate of €100/MWh, discount
rate 10% and total unit installation cost of
(€1274/kW). Other factors that have an impact on
the price may include voluntary or required
reduction of emissions; private or public purchase of
credits; tradable credits (Trading Schemes such as
EU ETS), and many other national or regional
schemes and technologies they use.
Figure 22 shows the function of "after tax IRR"
for different levels of capital investment and
electricity export rate extended at a sensitivity range
of ±35% (other financial parameters given in Table 1
are assumed unchanged). From Figure 22, it was
observed that for the chosen cost of installation
(1274 €/kW) as well as electricity export rates of
€65/MWh and €135/MWh, after-tax IRR results
11.5% and 64.3%, and if carbon credits rates are
applied, this indicator increases to 42.24% and
93.71%, respectively. The analysis showed that
"after-tax IRR" increases with the reduction of
capital investment (TotCapEx) and with the increase
of both electricity export rates and carbon credits.
Referring to the electricity export rate in the
range (76-100) €/MWh, it is concluded that the after-
tax IRR on equity varies from 50.67 % up to 67.79
%. These values are acceptable and encouraging
especially for projects with high financial risk such
as energy projects from renewable generation
sources (RES) and again leading to the conclusion
that the electricity export rate should be adjusted at
least to €110/MWh.
9 Socio-economic Impact
The economic impacts of wind energy project
development can be significant to both the rural
counties and the state in which the project is located.
The benefits that are generated by the expenditures,
both during the construction and the operations
phases of wind plants, depend on the extent to which
those expenditures are spent locally, as well as the
structure of the local and state economies. The Land-
Based Wind Jobs and Economic Development
Impact model (LBW JEDI model) is an easy-to-use
tool that can be used by county and state decision-
makers, public utility commissions, potential project
owners, developers, and others interested in
analyzing the economic impacts associated with new
or existing power plants, fuel production facilities, or
other projects. The model provides an approximation
of the economic impacts to the local society and the
state that can be generated from wind project
development, during the construction phase of the
project and throughout the 20 to 25-year life, or
operating years, of the project, [27]. The wind JEDI
model has limitations in the point of view as it does
not consider potential electricity price impact or
alternative investment. These benefits arising from
the proposed wind power plant can be used for
future reference wind energy systems in Albania.
Accurate forecasting of renewable energy production
is extremely important to ensure that supply meets
the demand path as deviations have an impact on the
system's stability and could potentially cause a
blackout in some situations, [28].
As can be seen from the simulation results in
Figure 23(a, b, c, d) the wind JEDI model easily
calculates jobs, earnings, and output distributed
across three categories including project
development and on-site labor impacts, local
revenue and supply chain impacts, and induced
impacts for the proposed wind power plant of 180
MW capacity. The number of jobs during the
construction period and operating period exceeds 32
and 7 on-site jobs respectively, 74 and 17 induced
impacts and 111 and 74 local revenue and supply
chain impacts, respectively as depicted in Figure 23.
Local annual economic impact (m€) during
construction period and operating period are m€
89.92 and m€ 23.54, respectively.
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Fig. 23: Socio-economic impact from a 180 MW
wind power plant: a) Number of jobs created during
the construction period. b) Number of jobs created
during operating years. c) Local annual economic
impact (m€) during the construction period, and d)
Local annual economic impact (m€) during
operating years
32
111
74
0
50
100
150
200
250
Jobs
Number of jobs created during construction
period (FTE equivalents)
Local annual economic impact: Number
of jobs created during construction
period.
Induced Impacts
Local Revenue and Supply Chain Impacts
Onsite Labor Impacts
a)
7
13
17
0
5
10
15
20
25
30
35
40
Jobs
Number of jobs created during operating
years (FTE equivalents)
Local annual economic impact: Number
of jobs created during operating years.
Induced Impacts
Local Revenue and Supply Chain Impacts
Onsite Labor Impacts
b)
€ 4,72 € 4,89 € 4,75
€ 9,27
€ 24,85
€ 12,19
€ 5,59
€ 14,45
€ 9,21
€ -
€ 5,00
€ 10,00
€ 15,00
€ 20,00
€ 25,00
€ 30,00
€ 35,00
€ 40,00
€ 45,00
€ 50,00
Earnings Output Value
Added
Economic impact (m€)
Local annual economic impact (m€)
during construction period
Induced Impacts
Local Revenue and Supply Chain Impacts
Onsite Labor Impacts
c)
€ 0,52 € 0,52 € 0,52
€ 1,06
€ 7,77
€ 6,15
€ 1,34
€ 3,45
€ 2,20
€-
€2,00
€4,00
€6,00
€8,00
€10,00
€12,00
€14,00
Earnings Output Value
Added
Local annual economic impact (m€)
Local annual economic impact (m€) during
operating years
Induced Impacts
Local Revenue and Supply Chain Impacts
Onsite Labor Impacts
d)
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10 Conclusion
This study presents a snapshot of the levelized cost
of energy (LCOE) of a land-based wind power plant
with a total capacity of 180 MW, based on real
market condition data as given in Table 1, especially
for low wind zones such as Albania. To better
understand possible pathways to scaling up the
distributed wind market in Albania, deep and
multidimensional calculations based on Monte Carlo
analysis using the RETScreen model and wind JEDI
model, to assess socio-economic impact as a
function of turbine output power, operating cost, and
maintenance cost and other financial conditions are
included. From the simulations it is proved that
LCOE becomes minimal €43.48/MWh, if the bank
provides a debt rate of 99 % and a debt interest rate
of 5.0%. In the scenario with €828/MW (-35 % less
expenditures) the LCOE results €62.79/MWh
considering 80 % debt rate, inflation rate of 3 % up
to a maximal LCOE value of €87.63/MWh called as
the worst-case scenario (+35 % more expenditures)
€1720/MW with a share of 52 % debt rate (Figure
10). Local annual economic impact (m€) during
construction period and operating period are
evaluated around m€ 89.92 and m€ 23.54,
respectively.
In conclusion, the promotion of a wind power
plant (WPP), located in Albania can be feasible if
the electricity export rate (selling price) is at least
110€/MWh, a GHG credit rate of €50/tCO2 and the
application of banking supporting schemes/monetary
policies enabling to faster meet NECP goals in 2030
[21], especially when reducing dependence on or
abandoning fossil fuels, considering large-scale
integration of RES is required, [29]. On the other
hand, the cost of installation of the wind turbine at a
given location does not depend only on the wind
resource, but also on the structure of the turbine and
the energy conversion technology, [30].
11 Future Work
Our future work will be focused on identifying an
"optimized wind rating point" (OWRP) considering
low wind class regions that employ different rated
wind power turbines (MW), hub height, etc. being a
pivotal starting point in sheltering the identified
Albanian's potential of 7400 MW wind capacity,
fully in line with sustainability and decarbonization
(electrification of transportation and industry sector)
ambition program in 2050.
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Contribution of Individual Authors to the
Creation of a Scientific Article
- Andi Hida has carried out the simulation and
numerical model, got the idea of the simulation,
conceptualization and wrote the research article.
- Lorenc Malka has developed the mathematical
model and has carried out the computer
simulations in selected energy tools. He has
participated in the conception of the system
topology. He has collaborated with the writing and
revision of the manuscript and supervised the
numerical study made the figures and editing.
- Rajmonda Bualoti has contributed to reading,
advice, and formal suggestions at the first stage of
the problem conceptualization.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itsel
This publication was made possible with the
financial support of AKKSHI. Its content is the
responsibility of the author, the opinion expressed in
it is not necessarily the opinion of AKKSHI.
Conflict of Interest
The authors have no conflicts of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
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