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978-1-7281-6746-6/20/$31.00 ©2020 IEEE
2020 IEEE PES/IAS PowerAfrica
Analysis of the Levelized Cost of Electricity
(LCOE) of Solar PV Systems Considering their
Environmental Impacts on Biodiversity
S Kibaara
Electrical Engineering Department
Jomo Kenyatta University of Science
and Technonology
Nairobi, Kenya
samuelkariuki@tum.ac.ke
M Karweru
EEE Department
Technical University of Mombasa
Mombasa, Kenya
PXULXNLPDU#JPDLOFRP
D K Murage
EEE Department
Jomo Kenyatta University of Science
and Technonology
Nairobi, Kenya
dkmurage25@yahoo.com
Petermoses Musau EEE
Department University
of Nairobi Nairobi,
Kenya
pemosmusa@gmail.com
Abstract—Large scale solar photo voltaic plants are being
developed and implemented at rapid rates and others are being set
up to occupy large tracts of land running to millions of acres across
the globe. The cascaded environmental impacts of such huge
installations are not well addressed in both literature and in the
famous techno-economic modelling tools such as HOMER, SAM,
INSEL and TRNSYS. This study provides a full cost approach for
determining the Levelized cost of Electricity (LCOE). The study
incorporates all the costs incurred during generation and operation
including the externality costs that have been traditionally omitted
by other models. This has been aided by the use of a new software
called the ECOS model developed by students of the Jomo Kenyatta
University of Science and Technology. The study carries out sizing
of Solar PV for Lodwar and the resultant metrics such as LCOE
when externalities are included. The novel contribution of this
paper is the incorporation of the environmental impacts of Solar PV
which has not done by other software tools like HOMER.
Keywords—ECOS Model, HOMER, Externalities, HRES
I. INTRODUCTION
Solar Photovoltaic power is experiencing high growth having an
installed capacity of 22GWp and growing at a rapid rate of about
40% annually[1]. In countries like the US the government has
been forcing the utility companies that power generated must
contain a certain renewable energy fraction. This has lead to
wide scale land occupation and ecosystem damage. For instance,
New Jersy has set a target of 22.5% renewable energy by 2021,
New York has completed a 37MWp solar plant at Long Island,
while Canadian Ontario has a complete solar Plant of about
80MWp [1][2].
Most of the published literature focusing on the impacts of solar
energy mainly look into the life cycle assessment , majorly
focusing on the Greenhouse Gases (GHG) and energy payback
time (EPBT)[3][4]. A small number of published work consider
other impacts such as hazardous materials[5], land use and land
use efficiency[6][7], wild animals habitat
fragmentation[2][4][1]. It is further reported that the installation
and operation phases of solar photovoltaic have received little
scientific attention[1]. Most of these studies contains no
quantitative information on the wider impacts of solar
photovoltaic. In the most recent up-to date LCA, it is reported
that about 16-40gm/Kwh of Carbon Dioxide is emitted [8].
However, this value does not account for the carbon dioxide
where the solar photo-voltaic are mounted in forested regions
where vegetation must be removed to pave way. Turney et al [1]
reports that there is only one published report that collected raw
data on the impacts of solar to the environment. Despite lack of
enough studies addressing the wider impacts of Solar
photovoltaic there is a significant need to address these impacts.
This paper focuses on the impact identification and monetary
valuation. Once the negative impacts of solar monetized, they
are lumped up together LCOE equation using the ECOS
modelling tool. The main contribution of this paper is the
development and use of the ECOS model, a tool which is able to
integrate the environmental impacts in the LCOE metric. In the
following section the methodology on the use of ECOS model as
a sizing and techno-economic tool is discussed.
II. METHODOLOGY
The methodology followed in this paper starts with the
mathematical description of the ECOS model and the
incorporation of the environmental impacts of solar energy in the
cost modelling analysis. The ECOS software development
involves the use of the lifecycle analysis methods and other life
cycle cost of Solar PV such as initial cost, replacement cost,
operations and maintenance cost and the salvage value. The key
impact categories identified in the development of the ECOS
software are land use, human health, wildlife and the Greenhouse
gases (GHG) emissions. Each of the impact category is described
in subsequent sections.
A. ECOS Model System Architecture
The ECOS modelling tool was developed to overcome the
inability to include the environmental impacts of Solar PV in the
determination of the system metrics. The traditional tools which
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2020 IEEE PES/IAS PowerAfrica
includes HOMER, IHOGA, SAM uses the capital cost,
operations and maintenance costs, residual value and life time
energy generated to calculate the LCOE as shown by Equation 1
below.
cos
Total life cycle ts
LCOE Total life time energy production
(1)
Where the LCOE represents the cost of electricity that would
match the cash inflows and the cash outflows normalized over
the lifespan of the plant as shown by Equation 2. This important
metric allows the independent power producers (IPPs) to fully
recover all the costs of the plant over a predetermined period of
time [9][10] . The LCOE of an energy generating unit is usually
determined at the point where the sum of all the discounted
revenues equalizes with the sum of all the discounted cost as
described by equation (2).
10
(1 ) (1 )
TT
tt
tt
tt
RC
rr
¦¦
(2)
Where
t
R
:the revenue generated for period
t
,
t
C
is the sum
total of costs incurred for period
t
and
r
is the discount rate.
Unlike the modeling done by HOMER, the LCOE equation (3)
adopted in the ECOS model has included the externalities
k
ik
EC
¦
(social, environmental and economic) of solar PV in
the computation of
LCOE
and other metrics such as energy
generated, cash flows among others.
k
ik
111
1
EC
&(1 )
(1 ) (1 ) (1 ) (1 ) (1 )
*
(1 )
(1 )
TTT
ntt tt
ttt
Nt
t
n
t
DEP INT LP O M RV
IC TR ROI RC
DR DR DR DR DR
LCOE
S
DR
SDR
¦
¦¦¦
¦
(3)
IC
: Initial capital cost of the plant,
DEP
: Depreciation rate (%)
INT
: Interest rate (%),
LP
: Loan repayment (USD),
&OM
:
Operation and maintenance cost (USD),
ROI
: Return on
investment (%),
RV
: Residual value (USD),
DR
: Discount
rate (%),
RC
: Replacement cost (USD),
SDR
: System
degradation rate (%),
k
ik
EC
¦
: Represents the aggregated
environmental impacts cost of the USSE. The impacts of USSE
are discussed and modelled in the following section. The ECOS
model is developed using visual basic programming. A graphical
user interface (GUI) provides user interactive platform. SQL has
been used for database development. The system has the user
interface and the database. The GUI is window based that
provides functions to manipulate the data according to the
requirements. The interface calls stored procedures and views
heavily for data processing and data retrieval. Finally the
database stores all system data and none is held outside the
database enhancing data integrity. The process flow diagram of
the ECOS model is described by Fig 1 below. The database used
is a relational database management system which is a Microsoft
SQL server. The database stores the tabular files of DNI, cost of
equipment’s used for solar photovoltaic and their types, different
environmental aspects of the different regions in Kenya,
batteries, inverters etc.
Fig 1: ECOS Model system Architecture
A. Land use
The land use intensity is a very important impact category as it
is used as a proxy for other impacts. In the ECOS model land use
is quantified by use of land area directly occupied by the panels
which results to transformation of land use. The ECOS model
defines the land occupation as the occupation of land for a
number of years defined as the area occupied multiplied by the
length of time the land is under occupation of the Solar PV. The
measurement of land use metrics in this paper followed the work
done by [1] and is shown by Equations (4-7) below.
daysCFdayDNIPyrED
siteac
365**/*/
(4)
(5)
efficiencyinverterdirtMismatch
P
Pac
dc **
(6)
efficiencycollectoryearDNI
P
occu piedArea
site
dc
*/
(7)
Where ED/yr is the energy demand per year,
ac
P
=actual ac
power delivered,
STCdc
P,
=rated dc power output under standard
test conditions and DNI is the direct normal irradiance.
A 30% balance of plant (BOP) was included in the area occupied
which caters for the auxiliary systems such as access roads,
storage facilities, offices etc.
B. Human Health
The health impacts of solar energy in this paper were modelled
as the external effects of the different pollutants emitted. They
represent the damage done to the human population. In order to
calculate the external cost (XC) associated with the emissions of
CO2,SO2, NOx, PM, VOC and the other air pollutants, the
quantities released per unit of electricity generated from USSE
was determined. This was done using the damage cost ($/ton of
pollutant emission) which is ascribed to the emissions and an
emission factor
EF
, defined as the tonnage of the pollutant
emitted per unit of energy generated from solar PV. The cost of
User inputs
Data stored in the
Database
Data processing
Simulation Output
daysCFdayDNI
yrED
P
site
ac
365**/
/
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2020 IEEE PES/IAS PowerAfrica
per unit of electricity produced is determined using these factors
and is incorporated directly into the LCOE equation above. The
equation for calculating the externality cost XC ($/kWh) of the
pollutant emission is as shown by equation (8)[11].
EFDCXC *
(8)
Where
DC
is the damage cost ($/ton of pollutant),
EF
Emission factor per Kg pollutant per kwh of energy produced.
The damage cost and emission factor values used in this paper
were obtained from [11].
C. Morbidity and Mortality submodel
The health impacts of solar energy in this paper were modelled
as the external effects of the different pollutants emitted. The
work-related and non-work related accidents considered in this
paper are for the non-Organization for Economic Corporation
and Development countries where Kenya is classified into[12].
The per unit prices for treating persons suffering injuries or
mortalities while working with USSE are based on the studies
done by [13][14]. This sub-model consists of two variables viz.
unit morbidity value and the unit mortality value. The per unit
morbidity value (
mod
UV
$/person) is estimated using Equation
(9) below.
)()1804()(
modmodmod
tUVUVtUV '
(9)
Where
)(
mod tUV'
is the discounted change in morbidity
value. The unit mortality values (
mot
UV
,$/person) in this paper
were obtained from [12] and are described by Equation (10)
below
)()17413()( tUVUVtUV
motmotmot
'
(10)
The unit mortality value and the unit morbidity values derive
their costs from three phases that is during the construction of
the USSE, operation phase and the decommissioning phase. The
parameters used for the fatalities/mortality and morbidity were
adapted from [12].
D. Ecosystems goods and services submodel
This sub model is concerned with evaluating the opportunity cost
of the lost ecosystem goods and services resulting from the
installation of the PV system. The value of the different
ecosystem goods and services per biome in the regions
considered in this paper were adopted from Groot et al [15] and
are divided into four different ecosystems services i.e
provisioning services, regulating services, habitat services and
the cultural services. The monetary value of the ecosystem
service value for each of the region is estimated by Equation (11)
below.
¦
kk
VCAESV *
(11)
Where
ESV
= ecosystem service value estimate
k
A
= Area in hectares
k
VC
= value coefficient of ecosystem ($/ha/year)
The different values of
k
VC
used as proxies for the different
biomes were obtained from the Ecosystem service value
database (EVSD).
III. SIMULATION SETUP FOR THE ECOS MODEL
The HRES is designed considering solar PV and batteries with
2 hours of autonomy. The financial parameters used for the
design are described by Table 1 below.
Table 1: ECOS Model Economic Inputs
Component
% Amount
Discount rate
7.5%
Expected inflation rate
7%
Project lifespan
25 years
land cost/acre (for ECOS model)
Area dependent variable
Residual value (ECOS model)
4.5% of CAPEX
It should be noted that some aspects like the land cost,
environmental cost, social cost are treated as sunk cost in
HOMER because they are not included in the user inputs nor are
they displayed in the simulation results and analysis. The LCOE
equation in most models includes the anticipated residual value
after decommissioning the plant [9][10], which has not been
included in the traditional tools architecture.
IV. CRITERION OF SIZING SOLAR PV USING ECOS MODEL
The economic criteria used in the sizing of the solar PV depends
on the load demand. In this paper the load demand of a typical
village in Turkana district which was used as an input to the
ECOS model to determine the number of solar panels required
and the batteries. Solar PV system includes different components
that should be selected according to the system type, site location
and applications. The major components for solar PV system are
the PV module, inverter and the battery bank.
A. Sizing of a standalone PV system
For convenience and accurate sizing of a PV system, the
specific area, Direct Normal Irradiance (DNI) data and the
anticipated load are defined. The size of the PV system, total
number of PV panels and the number of batteries are then
calculated. As such several factors considered are the amount of
energy (kWh) that can be generated by the solar PV to meet the
load demand, the Ah of the batteries required and the area
occupied. There are several sizing techniques used previously in
literature such as intuitive, numerical, analytical, commercial
computer tools, artificial intelligence and the hybrid
methods[16]. The numerical technique has been used in this
paper for sizing the PV system because of its known accuracy
and ability to easily use the linear functions unlike other tools
[16].The energy delivered by a PV is given by equation (12)
K
*
,STCdcac
PP
(5) (12)
where
ac
P
=actual ac power delivered,
STCdc
P
,
=rated dc power
output under standard test conditions,
K
=conversion efficiency
which accounts for inverter efficiency, dirt, PV collectors
efficiency and mismatch factor.
B. Steps followed in Sizing the PV Array
The insolation data (kWh/m2) for the different sites used in the
ECOS model are obtained from the NASA websites. The worst
month (month with the lowest solar irradiance) of the year is
used for design. As shown by Equation (13) identification of a
PV module and using its rated current IR together with its
coulomb efficiency of about 0.9 and a derating factor (DR) of
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2020 IEEE PES/IAS PowerAfrica
0.9 and the Direct Normal Irradiance (DNI) of the design month
, the Ah/day produced by each solar PV string is determined.
DRImkWhDNIstringdayAh
R
**)/(/
2
(13)
The number of parallel strings is given by equation (14) below
monthdesigninuleperdayAh
dayAhloadmonthdesign
parallelinStrings mod/
)/(
(14)
The number of PV modules in series is determined by equation
(15) below
)(modmin
)(
mod VvoltageulealNo
Vvoltagesystem
seriesinules
(15)
C. Determination of collector Area
The size of area occupied and the number of PV cells varies
according to type, as each has different parameters.
Amount of energy delivered by a cell PV is described by
Equations (16) and (17) below
STC
av
ambientcell
DNI
TNOCT
TT *)..
8.0
(
(16)
Where
STC
DNI
=insolation under standard test conditions (kWh/m2),
NOCT
=Nominal Operating Cell Temperature,
av
T
=average
maximum daily temperature,
)](1[ ovcel llratingdc TTPPVP
(17)
Where
dc
P
=solar PV DC output power,
rating
PV
=rating of the
solar PV,
l
P
=power loss per degree above
ov
T
Including the dirt, mismatch and inverter efficiencies will result
in an estimated ac rated power of the solar photo voltaic (
ac
P
)
shown by Equation (18).
K
inverterdirtmismatchPP
dcac
***
(18)
The collector area is governed by the yearly energy yield and the
yearly energy demand as described by Equations (19)-(22)
below.
daysCFdayDNIPyrED
siteac
365**/*/
(19)
(20)
efficiencyinverterdirtMismatch
P
Pac
dc **
(21)
efficiencycollectoryearDNI
P
occu piedArea
site
dc
*/
(22)
D. Battery Storage
The battery storage capacity is determined by Equation (23)
below.
DRMDOM
autonomyofdaysdayAh
cap aci tystoragebattery *
*/
(23)
Where
MDOM
=maximum depth of discharge
DR
=% discharge rate
V. RESULTS AND ANALYSIS
In this section the simulation results obtained from ECOS model
for Turkana District are discussed. The software calculates the
output based on the procedure mentioned in the methodology
and the results are described. The ECOS model displayed results
of yearly energy generated from 1992-2016 as shown in Fig 2
below. The yearly energy delivered varies according to the DNI
estimated at 1800kWh/m2/yr. The random variability of the solar
resource leads to the uneven energy production in the different
years. The area required for installation to meet the electricity
demand was estimated to be 5130 acres of land that required
about 4008 solar photovoltaic panels and 394 batteries. The
cascaded impacts on land as a result of this land occupation
includes diseases like Cancer which results from emission of
some hazardous gases such as particulate matter , lead, VOC
among others. The ECOS model estimated the NPC including
the externalities (environmental and health costs) to a tune of
$2.07 billion for a period of 25 years The environmental cost
included were the cost of land and the various function of land
in this particular region.
Fig 2: Yearly Energy Generated
The ECOS determines the cost of a disease using two functions
described above, that is, unit morbidity value and unit mortality
values. The cash inflow and cash outflow for the whole period is
shown in Fig 3 and Fig 4. The cash flow is highest at the
beginning of the project and minimum near the end of the
lifespan. ECOS model further determines the LCOE to be about
$3.81when externalities are included and $4.12 when
externalities are excluded. As discussed earlier LCOE is a
function of the Life cycle costs (LCC) and the energy generated.
The ECOS model is among the first tools to accommodate the
external costs of energy generation which in this case are the
environmental costs and the health costs.
Fig 3: ECOS Model Cash Inflow
daysCFdayDNI
yrED
P
site
ac
365**/
/
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2020 IEEE PES/IAS PowerAfrica
Fig 4:ECOS Model Cash outflow
VI. CONCLUSIONS AND RECOMMENDATIONS
In this paper the ECOS modelling tool has been used to size solar
photovoltaic system for Turkana District. The ECOS model
calculates the LCOE based on the full cost life cycle costing over
the entire life of the plant. The result shows a levelized cost of
electricity of about $3.81cents and 5130 acres of land occupied
inclusive of the balance of plant. The environmental costs
amounts to about 573 Million USD. If no externalities are
considered the LCOE is about 8% lower which indicates that the
incorporation of the environmental impacts tends to increase the
overall cost of energy. The highest contribution of the added cost
(externality cost) comes from the land use occupancy. The
pollutant emissions from the solar panels are highest for
cadmium and nitrogen monoxide. Research and development
should be geared towards improving the ECOS model software
to accommodate more than one energy resource type to enhance
hybridization of renewable energy systems.
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