International Conference on Applied Energy

ICAE 2013, Jul 1-4, 2013, Pretoria, South Africa

Paper ID: ICAE2013-182

INVESTIGATING THE ROLE OF HYBRID PHOTOVOLTAIC-DIESEL STAND-ALONE

SYSTEMS FOR THE ELECTRIFICATION OF REMOTE TELECOMMUNICATION

STATIONS IN GREECE

Papadopoulos G, Zafirakis D, Kavadias K, Kaldellis JK

Lab of Soft Energy Applications & Environmental Protection, TEI of Piraeus

P.O. Box 41046, Athens 12201, Greece, www.sealab.gr

ABSTRACT

Due to the rapid development of the telecommunication

(T/C) sector during the recent years, expansion of T/C

networks to remote areas as well requires installation of

T/C stations that normally are not suited for grid-

connection. To this end, such T/C stations comprise stand-

alone systems that usually operate on the basis of diesel-

power engines. Considering the above, the solution of a

hybrid, PV-based energy solution with the contribution of

diesel power and battery storage is currently examined. In

this context, an optimization algorithm is developed for the

sizing of the proposed solution that allows examination of

numerous configurations at different levels of fuel

consumption (i.e. diesel engine contribution) and that is

based on the optimization criterion of minimum long-term

electricity production cost. The developed algorithm is

accordingly applied to numerous areas of the Greek

territory, that also suggest regions of different quality solar

potential, with results obtained indicating the cost-

effectiveness of the proposed solution in comparison to

both stand-alone PV-based systems, employing extreme

battery capacity, and the diesel-only solution entailing

increased fuel consumption.

Keywords: telecommunication station; solar energy; lead-

acid batteries; diesel engine; electricity production cost

1. INTRODUCTION

Due to the rapid development of the telecommunication

(T/C) sector during the recent years [1], expansion of T/C

networks to remote areas as well requires installation of

T/C stations that normally are not suited for grid-

connection, owed also to the fact that antennas need to be

installed at considerably high elevations. To this end, such

T/C stations comprise stand-alone systems that usually

operate on the basis of diesel-power engines, which in turn

imply considerable fuel consumption and increased

maintenance and operation (M&O) costs.

At the same time, the mobile T/C sector in Greece has

presented rapid growth during the last years [2], reflected

by the operation of more than 5000 stations, similar to the

situation encountered in the rest of the developed world

[3]. The majority of these stations cover their electrification

needs through connection to the main grid. On the other

hand, there are several cases of T/C stations located far

away from the electrical grid, in order to cover the needs of

remote areas with their antennas. Actually, in Greece alone,

there are more than 500 non-interconnected T/C stations

placed in rural areas, small islands and mountainous

regions, covering their needs on the basis of small diesel-

electric generators; otherwise one should invest on

expensive grid-extensions, wherever possible.

The result of this solution is as already seen the high

operational cost of the remote T/C stations, mainly due to

the necessary fuel required by the operating internal

combustion engines. Alternatively, there are limited cases

where small PV generators along with an appropriate

energy storage system (usually lead-acid batteries) are

utilized, often in an experimental/pilot mode. Note at this

point that the entire Greek territory appreciates high-

quality solar potential [4,5] that ranges from 1300kWh/m2.a

to 1800kWh/m2.a (see also Figure 1). On the other hand, to

ensure 100% energy autonomy of a representative T/C

station during the entire year (i.e. no load rejection

encountered) both the PV generator and the system

batteries should be oversized. As a result, the respective

investments become capital intensive, although low M&O

needs of such systems should be noted [6].

Considering the above, the solution of a hybrid, PV-based

energy solution with the contribution of diesel power and

battery storage is currently examined, in terms of both

Paper ID: ICAE2013-182

2 Copyright © 2013 by ICAE2013

energy and economic performance [7-9]. In this context, an

optimization algorithm is developed for the sizing of the

proposed solution that allows examination of numerous

configurations at different levels of fuel consumption (i.e.

diesel engine contribution) and that is based on the

optimization criterion of minimum long-term electricity

production cost. The developed algorithm is accordingly

applied to numerous areas of the Greek territory, that also

suggest regions of different quality solar potential, with

results obtained indicating the cost-effectiveness of the

proposed solution in comparison to both stand-alone PV-

based systems employing extreme battery capacity and the

diesel-only solution entailing increased fuel consumption

[10].

Figure 1 Solar potential of the Greek territory

2. PROBLEM STATEMENT

2.1 Problem description

The problem to be solved in the current study concerns

the determination of optimum hybrid PV-based

configurations that also incorporate an appropriate lead-

acid battery bank in order to improve the system reliability.

The configurations examined should be able to cover the

energy requirements of a remote T/C station without load

rejections during an entire year period, while the proposed

system may also include a small diesel-electric generator,

used as a back up solution for long periods of low solar

irradiance. As a result, there are two extreme cases for the

proposed solution; the first corresponding to the diesel-only

system (with no PV modules) and the second considering a

stand-alone PV generator with zero diesel-oil contribution.

Accordingly, in an attempt to determine the optimum tilt

angle of the PV panels, one may also investigate the impact

of the PV panels’ tilt angle on the system dimensions, while

the proposed analysis is also focused on presenting the

influence of the annual diesel-oil consumption. In this

context, the main system parameters to be estimated are

the PV generator peak power, the lead-acid batteries’

storage capacity and the annual diesel-oil consumption.

Finally, as already mentioned, the main directions for

obtaining the optimum hybrid power station dimensions are

provided, taking also into consideration the energy

performance of the installations under investigation and a

specific optimization criterion set, currently being the long-

term electricity production cost of the system.

2.2 Input data

Initially, for the investigation of a typical T/C station one

needs the energy consumption time distribution of the

installation (see also Figure 2). More precisely, a

representative, small size T/C station includes the following:

• The air conditioning machines which are usually the

main energy consumers (up to 60%), especially during

summer, in order to maintain the T/C station

temperature within safe operation limits (14oC and

24oC).

• The T/C equipment which operates continuously, with

its contribution to the total energy demand of the T/C

station varying between 40% during the hot summer

days and 85% for the spring months of the year.

• The safety lights (e.g. 2 x 100W), operating at the top of

the antenna tower during the night for safety reasons;

average contribution 5-10%.

• Additional lighting used during the inspection and the

service of the T/C station along with the additional

auxiliary equipment.

To this end, the hourly average load demand of the

typical remote T/C station is provided in Figure 2, on a

monthly basis.

LOAD DEMAND OF THE REMOTE T/C STATION

2,0

2,5

3,0

3,5

4,0

4,5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hours of Day

Pow

er

Dem

and (

KW

)

January FebruaryMarch AprilMay JuneJuly AugustSeptember OctoberNovember December

Figure 2 Load demand variation of the remote T/C station

Paper ID: ICAE2013-182

3 Copyright © 2013 by ICAE2013

According to the data available, the T/C station under

investigation presents a peak load demand "Nmax" of 4.2kW

(appearing during July) and a minimum load demand "Nmin"

equal to 2.4kW (actually appearing during April), while the

corresponding annual electricity consumption "Etot"

approaches 27.1MWh. Note that by adopting the diesel-

only solution one needs approximately 12 tons of diesel oil

(annual fuel consumption Mf=12185kg/year for diesel

calorific value Hu=40MJ/kg) in order to cover the above

mentioned electrical load demand, using a diesel-electric

generator of rated power "Nd" equal to 7.5kW (10PS) (total

electricity generation efficiency ηd=20%).

Accordingly, the solar radiation profile and the ambient

temperature at the PV station location are also required.

Taking into consideration the available solar potential

measurements, one may find in Figure 3 the solar potential

values for the Greek areas currently examined. More

precisely, to capture the solar potential variation across the

entire Greek territory, five different areas are considered

including Rhodes (1758kWh/m2), Sparti (1586kWh/m

2),

Zakynthos (1552kWh/m2), Larissa (1432kWh/m

2) and Kavala

(1363kWh/m2), for which hourly temperature data is also

used.

Daily Solar Energy on an Annual Basis for the Five Areas under Examination

0

1

2

3

4

5

6

7

8

9

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380

Day of the Year

Daily

Sola

r E

nerg

y (

kW

h/m

2)

Rhodes

Sparti

Zakynthos

Larissa

Kavala

Figure 3 Solar potential of the areas examined

3. PROPOSED SYSTEM AND SIZING

METHODOLOGY

3.1 The proposed configuration

In more detail, the system configuration (see also Figure

4) comprises of the following components:

• A PV system of "z" panels ("No" being the max power of

every panel) connected appropriately ("z1" in parallel

and "z2" in series) to provide the charge controller with

the desired voltage.

• A lead acid battery storage system for "ho" hours of

autonomy, or equivalently with total capacity of "Qmax",

operation voltage "Ub" and minimum permitted

capacity "Qmin" (or otherwise maximum depth of

discharge "DODL").

• A DC/DC charge controller of "Nc" nominal power,

charge rate "Rch" and charging voltage "Ucc".

• A DC/AC inverter of maximum power "Np", able to

meet the consumption peak load demand "Nmax".

• A small internal combustion engine of "Nd" (kW) power,

able to match the consumption peak load demand

"Nmax", therefore Nd≥Nmax (including a safety margin of

30%).

Figure 4 The proposed hybrid configuration

3.2 The sizing algorithm

For estimating the appropriate configuration of the

proposed PV-diesel hybrid system, three governing

parameters should be determined: the peak power "NPV" of

the PV generator used (or equivalently the number "z" of

the panels required, NPV=z.No), the maximum necessary

capacity of the battery "Qmax" and the annual diesel-oil

consumption "Mf".

To confront similar problems, a computational algorithm

"PV-DIESEL III" (see also Figure 5) is developed. This specific

numerical code is the extension of the already presented

"PHOTOV-III" numerical code [11,12] and is used to carry

out the necessary energy balance analysis on a given time

step (e.g. on an hourly basis). More precisely, given the "Mf"

value for each "z" and "Qmax" pair, the "PV-DIESEL III"

algorithm is executed for the entire time-period selected

(e.g. one month, six-months, one year or even for three

years), while emphasis is laid on obtaining zero-load

rejection operation. After calculating the appropriate (Mf, z,

Qmax) combinations that guarantee the stand-alone system

energy autonomy, one may proceed to analyze the

proposed PV-diesel hybrid installation energy balance in

detail.

Paper ID: ICAE2013-182

4 Copyright © 2013 by ICAE2013

Following the integration of the energy balance analysis, a

(z-Q*) curve is predicted under a given diesel-oil quantity

"Mf". To get an unambiguous picture, keep in mind that for

every pair of (z-Q*) the stand-alone hybrid PV-based system

is energy autonomous for the period investigated, using

however a predefined diesel-oil quantity "Mf". Finally, the

optimum pair may be selected from each (z-Q*) curve, on

the basis of minimum long-term operational cost for the

installation.

START

z o=z in

t=0

M eteorological Data, i.e. SolarR adiation, Am bient Tem perature

Rem ote Consum er Energy Dem and, N D(t)

PV Generator Power Curve NPV =NPV (t)

N PV >ND NPV =0

∆N=NPV -N D

∆N= ND -NPV

B attery Em pty?

Battery Empty?

ND is covered by Battery via Charge

Controller and Inverter

∆N is covered by B attery via C harge

Controller and Inverter

B attery Full?

Energy is S tored to the B attery via

C harge C ontroller

t >∆t

Q*=Q

z ≥

z in, Q in, M fin, δz, δQ, δM f , ∆t, δ t, z FIN, Q FIN, M f FIN

≥fM

FINfM

in

≥Q

inf

FINQ

FINz

YES

NO

NO

YES

N

o o

o

o

END

M f = M f +δM f

Q=Q+δQ

t= t+δt

V ia Inverter

PV

ND

YES

YES

Y ES

NO

YES

NO

NO

YES

NOYES

NO

Energy Storage

YES

To Low Priority Loads

NO

PH O TOV -D IESEL III Algorithm

(z -Q*) curve

NO

=

Q=Q

z =z +δz

fM M

Figure 5 The developed sizing algorithm

3.3 Long-term electricity production cost estimation

The present value of the entire investment cost of a

hybrid PV-diesel power system (after -n years of operation)

shall take into account the initial investment cost as well as

the respective M&O cost, considering at the same time the

residual value of the investment, with all above mentioned

quantities being expressed in present (constant) values. In

this context, the initial investment cost "ICo" takes into

account the market (ex-works) price of the system main

components (i.e. PV-panels, "ICPV"; battery, "ICbat"; diesel-

electric generator, "ICd" and electronic devices "ICel",

including inverter, rectifier and charge controller cost) as

well as the balance of the plant cost, expressed as a fraction

"f" of the PV panels’ market price. As a result, one may

write:

PVelecbatdPVo ICfICICICICIC ⋅++++= (1)

Using the market analysis data [10], equation (1) finally

reads:

)()1(11

max opdoro NzbNNQfNzPIC ⋅⋅+⋅+⋅+⋅++⋅⋅⋅⋅= −− τω λφξζ (2)

where "ζ" is a function of "z" (i.e. ζ=ζ(z)), expressing the

scale economies for increased number of PV panels utilized.

In the present case (z≈100), "ζ" is taken equal to one.

Subsequently "Pr" is the specific buy-cost of a PV panel

(generally Pr=Pr(z.No)) expressed in €/kWp (3000€/kW),

while "φ" is the respective diesel generator cost (250€/kW).

Additionally, the parameters "λ" (483€/kW), "τ" (0.083) and

"b" (380€/kW) are used for the determination of the major

electronic devices’ cost, being normally a function of the

appearing peak load demand of the consumer (e.g. for the

inverter) and the rated power of the PV modules (e.g. for

the charge controller) respectively, while the parameters

"ξ" and "ω" describe the battery bank purchase cost

(5.04€/Ah and 0.078 respectively).

Furthermore, during the long-term operation of the

system one may divide the M&O cost into the respective

fixed "FCn" and variable "VCn" M&O cost. More precisely,

the fixed M&O cost is currently also considering the cost of

fuel consumed by the diesel-electric generator, while it

must be noted that normally the annual fixed M&O cost is

expressed as a fraction "m" of the initial capital invested

(e.g. 1.5%), further including an annual inflation rate equal

to "gm" (e.g. 2%). In fact, this inflation rate is used so as to

describe the annual changes of both labor costs and spare

parts of the system, taking also into account the need for

lubricants. Subsequently, the fuel consumption cost results

by the annual diesel-oil quantity consumed "Mf", the

current fuel price "cf" (1.1€/kg) and the oil price annual

escalation rate "ef" (e.g. 5%). Thus one gets:

1

1

)()1(

)1(1)1()1(

)()1(

)1(1)1()1(

−

−

−⋅

+

+−⋅+⋅+⋅⋅+

+−⋅

++

−⋅+⋅+⋅⋅=

fn

n

fn

fff

mn

n

mn

mon

eii

eieMc

gii

gigICmFC

(3)

where "i" corresponds to the return on investment index

(e.g. 8%). Furthermore, the variable M&O cost "VCn" is

configured by the replacement of "ko" system major parts,

which have a shorter service period "nk" than the one of the

entire installation. Using the symbol "rk" for the

replacement cost coefficient of each "ko" major part

Paper ID: ICAE2013-182

5 Copyright © 2013 by ICAE2013

(battery, diesel-electric generator, inverter, charger, etc.)

the "VCn" term can be expressed as:

[ ] },)1()1()1({ )(

11

k

kko

nl

nlll

l

kk

kk

k

kon igrICVC⋅−

⋅=

=

=

=

+⋅−⋅+⋅⋅= ∑∑ ρ (4)

where "lk" is the integer part of [(n-1)/nk], while "gk" and

"ρk" describe the mean annual change of the price and the

corresponding technological improvement level for the k-th

major component of the system. In the present analysis one

may take into account the diesel-electric generator, the

battery bank and the inverter and charger replacement

every "nd" "nb" and "ne" years respectively (e.g. nd≈4÷6,

nb≈5÷7 and ne≈10 years). By applying the above set of

equations, taking also into account that the hybrid PV-diesel

system currently proposed has a service period of "n" years,

the corresponding total operational cost "Cn" of the

installation may be estimated, using both the initial cost and

the fixed and variable M&O cost, i.e.

nnnon YVCFCICC −++−⋅= )1( γ (5)

Note also that in equation (5), the symbol "Yn" stands for

the residual value of the investment, corresponding to

investment value that is recovered at the "n-th" year of the

system service period (e.g. value of buildings and land,

value of scrap or second hand equipment, etc.), considering

also any experience and technological know-how gained

from the system operation. Finally, symbol "γ" is used to

introduce State subsidy provided by the Greek State (e.g.

30%-50% of the initial investment), according to the

development law in force (e.g. 3522/2006) or the

corresponding National Operational Competitiveness

Program. Finally, using the analysis presented in [13]

regarding estimation of the current electricity marginal

production cost "ce", the following equation may be

applied, under the condition that the net present value

"NPV" of the investment becomes equal to the respective

residual value of the investment (NPV=Yn, where "Yn" may

be equal to zero) after -n years of operation, i.e.

( ) 1)1()1(−

−⋅⋅⋅−⋅= n

totne wwEwCc (6)

where w=(1+p)/1+i) and "p" is the produced electricity

mean annual escalation rate, e.g. p=3%. Besides, one should

also bear in mind that the proposed model also includes the

diesel-only solution (i.e. ICo=φ.Nd, z=0, Mf=Mfmax) as well as

the zero-diesel configuration (i.e. ICd=0, Mf=0).

4. APPLICATION RESULTS

The developed algorithm is accordingly applied in the five

areas of investigation, considering at the same time the

energy consumption characteristics of the typical remote

T/C station described earlier. Furthermore, application

results obtained first investigate the impact of different

panel tilt angle. Accordingly, energy autonomous

configurations are presented using the optimum tilt angle,

with energy results then used in order to evaluate the

proposed solution under economic terms. For this purpose,

optimum configurations are designated based on the

criterion of minimum, long-term electricity production cost

(see also [8,14] for the analysis of the long-term electricity

production cost estimation and the cost values adopted). In

this context, the PV panels’ cost is estimated at 3000€/kW

and the cost of fuel oil equal to 1.1€/kg, while State subsidy

is considered to be zero At the same time, different levels of

fuel contribution are also considered, allowing maximum

fuel participation of 3000kg per year.

4.1 Investigation of the optimum angle

According to the results of a previous study [8],

considering the island of Rhodes, it has been proved that

the optimum PV tilt angle providing the optimum solution

was equal to β=52.5ο. In the current research, Rhodes is the

area with the highest solar potential (1758 kWh/m2.a),

which also belongs to the highest solar potential zone of

Greece. Therefore, in order to verify and examine any

possible difference in terms of optimum panel inclination,

the area of Kavala is currently examined. Note that Kavala is

the area with the lowest solar potential (1363 kWh/m2.a)

and belongs to the lowest solar potential zone of the

country. So, if the optimum PV tilt angle is the same or

similar (+/- 5o) with the one of Rhodes, then it can be

assumed that the same is valid for all other areas found

between the minimum and maximum solar potential cases.

To this end, investigation of the optimum PV panel tilt angle

is based on the criterion of minimum electricity production

cost for a period of 20 years, with results obtained given in

Figure 6.

Electricity Production Cost for 20 Years of Operation in Kavala

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

1,1

1,2

1,3

1,4

350 550 750 950 1150 1350 1550 1750

Number of PV Panels

Pro

duction C

ost

(€/k

Wh)

β=0; Mf=0 β=15; Mf=0

β=30; Mf=0 β=45; Mf=0

β=47.5; Mf=0 β=52.5; Mf=0

β=57.5; Mf=0 β=60; Mf=0

β=75; Mf=0 β=0; Mf=250

β=15; Mf=250 β=30; Mf=250

β=45; Mf=250 β=47.5; Mf=250

β=52.5; Mf=250 β=57.5; Mf=250

β=60; Mf=250 β=75; Mf=250

β=0; Mf=1000 β=15; Mf=1000

β=30; Mf=1000 β=45; Mf=1000

β=47.5; Mf=1000 β=52.5; Mf=1000

β=57.5; Mf=1000 β=60; Mf=1000

β=75; Mf=1000 β=0; Mf=2000

β=15; Mf=2000 β=30; Mf=2000

β=45; Mf=2000 β=47.5; Mf=2000

β=52.5; Mf=2000 β=57.5; Mf=2000

β=60; Mf=2000 β=75; Mf=2000

β=0; Mf=3000 β=15; Mf=3000

β=30; Mf=3000 β=45; Mf=3000

β=47.5; Mf=3000 β=52.5; Mf=3000

β=57.5; Mf=3000 β=60; Mf=3000

β=75; Mf=3000 Diesel Only

Figure 6 Electricity cost of various configurations for the

area of Kavala

Paper ID: ICAE2013-182

6 Copyright © 2013 by ICAE2013

As it may be concluded, for every different diesel quantity

there is a different optimum tilt angle solution. The cost

effective solutions are those found below the cost of the

Mf=Mfmax line, which denotes the electricity production cost

corresponding to the diesel-only solution. Based on the

figures’ results, the optimum solution corresponds to the

combination of Mf=2000kg and β=52.5ο.

In order to have a more clear view of the optimum

solution, Figure 7 is next provided, including all optimum

solutions for various diesel quantities. In this context, taking

also into account the fact that cost uncertainties should be

considered, it can be seen that the most cost-effective

solution is the one denoted by the orange line.

For this solution the diesel quantity "Mf" is equal to

2000kg, the number of PV-panels is 550 and the tilt angle

"β" is equal to 52.5o. Finally, another way of presenting the

optimum solution is to have a comparison between the

electricity cost and the tilt angle, as shown in the next

Figure 8.

20-Year Minimum El. Production Cost Solutions - Case Study: Kavala

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

1,1

1,2

1,3

350 550 750 950 1150 1350 1550 1750

Number of Panels (z)

Ele

ctr

icity C

ost

(€/k

Wh)

Μf=0,β=52.5

Μf=250,β=57.5

Mf=1000,β=57.5

Mf=2000,β=52.5

Mf=3000,β=52.5

Mf=Mfmax

Figure 7 Minimum cost curves for Kavala in relation to the

number of PV panels used

20-Year Minimum El. Production Cost Solutions for Kavala in Relation to Panel Titl Angle

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

1,1

1,2

0 15 30 45 60 75

Panel Tilt Angle "β" (degrees)

Ele

ctr

icity C

ost

(€/k

Wh)

Mf=0kg Mf=250kg Mf=1000kg Mf=2000kg Mf=3000kg

Figure 8 Minimum cost curves for Kavala in relation to the

examined PV panel tilt angle

Similar to the previous figure, the above graphical

representation indicates that the 2000kg line (orange line)

provides the optimum solutions when the tilt angle is

between 45o-75

o. More specifically the lowest and optimum

point is as already seen at 52.5o.

Thus, based on the initial assumption that areas found

between the two extremes will also present the same

optimum tilt angle (provided that the two extremes present

similar optimum angles) the optimum tilt angle adopted for

the entire Greek territory is β=52.5o.

4.2 Energy autonomous configurations

By applying the developed algorithm for all five areas

examined, energy related results are first presented for the

group of solutions appreciating zero fuel contribution (see

also Figure 9). Moreover, for comparison purposes, panel

tilt angle variation (i.e. from β=15ο to β=75

ο) is also included

in order to determine its impact on the system size, while

for the rest of parameters involved, battery capacity is

allowed to reach a maximum of 70,000Ah and number of PV

panels is set not to exceed 1700 (each panel being of 51Wp).

Stand-Alone Hybrid PV-based Configurations (Mf=0)

7000

14000

21000

28000

35000

42000

49000

56000

63000

70000

400 500 600 700 800 9001000

11001200

13001400

15001600

17001800

Number of Panels (z)

Ba

ttery

Ca

pacity (

Ah

)

Rhodos-1758 kWh/m2,β=15 Rhodos-1758 kWh/m2,β=30 Rhodes-1758 kWh/m2,β=45Rhodos-1758 kWh/m2,β=52.5 Rhodes-1758 kWh/m2,β=60 Rhodos-1758 kWh/m2,β=75Sparti-1586 kWh/m2,β=15 Sparti-1586 kWh/m2,β=30 Sparti-1586 kWh/m2,β=45Sparti-1586 kWh/m2,β=52.5 Sparti-1586 kWh/m2,β=60 Sparti-1586 kWh/m2,β=75Zakynthos-1492 kWh/m2,β=15 Zakynthos-1492 kWh/m2,β=30 Zakynthos-1492 kWh/m2,β=45Zakynthos-1492 kWh/m2,β=52.5 Zakynthos-1492 kWh/m2,β=60 Zakynthos-1492 kWh/m2,β=75Larissa-1432 kWh/m2,β=15 Larissa-1432 kWh/m2,β=30 Larissa-1432 kWh/m2,β=45Larissa-1432 kWh/m2,β=52.5 Larissa-1432 kWh/m2,β=60 Larissa-1432 kWh/m2,β=75

Kavala-1363 kWh/m2,β=15 Kavala-1363 kWh/m2,β=30 Kavala-1363 kWh/m2,β=45Kavala-1363 kWh/m2,β=52.5 Kavala-1363 kWh/m2,β=60 Kavala-1363 kWh/m2,β=75

Figure 9 Energy autonomous PV-battery configurations for

the areas examined

From the graph, it can be seen that according to the solar

potential of the areas, there are differences in the

proportion between the battery capacity and the number of

PV panels used. For example, in the case of Kavala, which

has a low solar potential, the minimum number of 1200 PV

panels requires battery capacity of 65,000Ah in order to

cover the energy needs of the T/C station, while in the case

of Rhodes, the respective number of panels drops to almost

500.

At the same time, what is common among the different

curves is the vast reduction of battery capacity noted –up to

Paper ID: ICAE2013-182

7 Copyright © 2013 by ICAE2013

a point- with the increase of the installed PV panels.

Furthermore, as the tilt angle decreases the battery

capacity drop becomes steeper with the increase of PV

power, although the tilt angle impact seems to fade out for

angles that are greater than 45o.

Apart from the complete autonomous solution where

Mf=0kg, there are more solutions which suggest a

combination of a certain diesel quantity with different

number of PV panels. For example for a specific diesel

quantity of Mf=1000kg the possible energy autonomous

solutions can be seen in the next graph (Figure 10),

considering also the panel tilt angle variation as well. Based

on the figure’s results, it can be clearly seen that the region

of Kavala (which has the lowest solar irradiance – azure set

of lines) provides the worst solutions in comparison with

the other areas.

The area of Rhodes (with the biggest solar irradiance –

dark blue set of lines) provides the best energy solutions,

while the other four areas are overlapping each other (since

their solar potentials are not too different) depending on

the tilt angle "β" of the panels. At the same time, what may

also be noted is that use of even 1000kg of fuel contributes

considerably in the reduction of the required battery

capacity.

Stand Alone Hybrid PV-based Configurations (Mf=1000kg)

0

10000

20000

30000

40000

50000

60000

70000

400 500 600 700 800 900 1000 1100

Number of panels (z)

Battery

Capacity (

Ah)

Rhodes-1758 kWh/m2-β=15 Rhodes-1758 kWh/m2-β=30 Rhodes-1758 kWh/m2-β=45

Rhodes-1758 kWh/m2-β=52.5 Rhodes-1758 kWh/m2-β=60 Rhodes-1758 kWh/m2-β=75

Sparti-1586 kWh/m2-β=15 Sparti-1586 kWh/m2-β=30 Sparti-1586 kWh/m2-β=45

Sparti-1586 kWh/m2-β=52.5 Sparti-1586 kWh/m2-β=60 Sparti-1586 kWh/m2-β=75

Zakynthos-1492 kWh/m2-β=15 Zakynthos-1492 kWh/m2-β=30 Zakynthos-1492 kWh/m2-β=45Zakynthos-1492 kWh/m2-β=52.5 Zakynthos-1492 kWh/m2-β=60 Zakynthos-1492 kWh/m2-β=75

Larissa-1432 kWh/m2-β=15 Larissa-1432 kWh/m2-β=30 Larissa-1432 kWh/m2-β=45

Larissa-1432 kWh/m2-β=52.5 Larissa-1432 kWh/m2-β=60 Larissa-1432 kWh/m2-β=75

Kavala-1363 kWh/m2-β=15 Kavala-1363 kWh/m2-β=30 Kavala-1363 kWh/m2-β=45

Kavala-1363 kWh/m2-β=52.5 Kavala-1363 kWh/m2-β=60 Kavala-1363 kWh/m2-β=75

Figure 10 Hybrid energy autonomous configurations for the

areas examined (Mf=1000kg)

Accordingly, influence of fuel consumption variation

(from 250kg to 3000kg) is also studied, this time for the

optimum panel tilt angle of β=52.5ο, with results obtained

given in Figure 11. Based on the figure, there is a

considerable decrease of PV panels used (for Qmax=ct) as the

Mf value increases.

Stand alone Hybrid PV-based Configurations (β=52.5o)

0

10000

20000

30000

40000

50000

60000

70000

300 400 500 600 700 800 9001000

11001200

13001400

1500

Number of panels (z)

Batt

ery

Capacity (

Ah)

Rhodes-1758 kWh/m2-Mf=250 Rhodes-1758 kWh/m2-Mf=1000 Rhodes-1758 kWh/m2-Mf=2000

Rhodes-1758 kWh/m2-Mf=3000 Sparti-1586 kWh/m2-Mf=250 Sparti-1586 kWh/m22-Mf=1000

Sparti-1586 kWh/m2-Mf=2000 Sparti-1586 kWh/m2-Mf=3000 Zakynthos-1492 kWh/m2-Mf=250

Zakynthos-1492 kWh/m2-Mf=1000 Zakynthos-1492 kWh/m2-Mf=2000 Zakynthos-1492 kWh/m2-Mf=3000

Larissa-1432 kWh/m2-Mf=250 Larissa-1432 kWh/m2-Mf=1000 Larissa-1432 kWh/m2-Mf=2000

Larissa-1432 kWh/m2-Mf=3000 Kavala-1363 kWh/m2-Mf=250 Kavala-1363 kWh/m2-Mf=1000

Kavala-1363 kWh/m2-Mf=2000 Kavala-1363 kWh/m2-Mf=3000

Figure 11 Hybrid energy autonomous configurations for the

areas examined (β=52.5ο)

For example, this can be seen clearly for the Rhodes area

(dark blue lines), where at Mf=250kg the minimum battery

capacity solution needs about 550 panels, at Mf=1000kg

panels are reduced to 450, at Mf=2000kg they are further

reduced to 400 panels and finally for Mf=3000kg panels

required drop to 350 panels. In addition, what is also worth

mentioning is that as expected, considerable increase of

fuel consumption eliminates the impact of the solar

potential and produces similar results for all areas

examined.

4.3 Economic evaluation results

Next, energy-related results obtained are used in order to

determine the economic performance of various energy

autonomous hybrid configurations. In this context, as

already mentioned, the criterion of optimization currently

considered corresponds to the minimum long-term (20-

year) electricity production cost, considering purchase cost

of PV panels equal to 3000€/kW and fuel oil cost of 1.1€/kg

as well as the methodology analyzed in [8,14]. In this

context, minimum cost configurations are gathered in

Figure 12 for all areas examined in relation to the fuel

consumption variation and a fixed panel tilt angle of 52.5o.

According to the results obtained, Rhodes, Sparti and

Zakynthos pesent their optimum solutions when fuel

quantity of 1000kg is used, while Kavala and Larissa present

a minimum cost solution when 2000kg of fuel are used. At

the same time, Rhodes is as expected giving the overall

optimum solution (since it is the area with the highest solar

potential) and Kavala provides the overall "less optimum"

solution (since it is the area with the lowest solar potential).

Paper ID: ICAE2013-182

8 Copyright © 2013 by ICAE2013

Optimum Stand-Alone Configurations in Relation to Annual

Fuel Consumption (β=52.5ο)

0,3

0,4

0,5

0,6

0,7

0,8

0,9

0 500 1000 1500 2000 2500 3000

Annual Fuel Consumption (kg)

Ele

ctr

icity C

ost (€

/kW

h)

Kavala Larissa Zakynthos Sparti Rhodes Diesel-only

Figure 12 Minimum cost curves for all areas examined in

relation to fuel consumption (β=52.5ο)

Finally, as one may see, the diesel-only solution appears

to be more cost-effective only when compared to zero-fuel

configurations and only for the areas of Kavala and Larissa.

On the other hand, in the rest of areas, both PV-battery only

and hybrid configurations present a lower production cost

than the diesel-only option, while the same is valid for

Kavala and Larissa in case that fuel consumption is allowed

for the PV-based solution, even at a minimum level of

250kg.

5. CONCLUSIONS

Based on the application of a sizing algorithm for PV-

based hybrid stand-alone systems, electrification of typical

remote T/C stations was studied, for five different areas of

the Greek territory. In this context, areas selected captured

variation of the solar potential across the entire Greek

region. Given the constraint of zero load rejections during

the entire year for the T/C station examined, various

energy-autonomous configurations were produced by using

different quantities of fuel used on an annual basis.

Accordingly, such hybrid configurations were compared to

both the PV-only and the diesel-only solution, validating the

hypothesis that minimum cost is achieved if reducing the

diesel-only fuel consumption and at the same time

downsizing the battery component. More precisely, the

optimum fuel consumption for all cases examined was

found in the area of 1000kg to 2000kg per year, with higher

oil quantity required corresponding to the lower-quality

solar potential cases studied. Finally, it was only for the

cases of Kavala and Larissa that the diesel-only solution

proved to be slightly less expensive than the corresponding

zero-fuel option, with the rest of configurations however

presenting sound difference of electricity production cost,

in favor of PV-based solutions.

REFERENCES

[1] Lam PL, Shiu A. Economic growth, telecommunications

development and productivity growth of the

telecommunications sector: Evidence around the world.

Telecommun Policy 2010;34:185-99.

[2] Koufomichalis C, Bargotakis C, Kaldellis JK, Tsoutsos T.

Examples of RES applications in the telecommunications. In:

Proceedings of the 7th National Conference on the Soft

Energy Resources, Patras, Greece; 2002.

[3] Kenny Ch, Keremane R. Toward universal telephone

access: market progress and progress beyond the market.

Telecommun Policy 2007;31:155–63.

[4] Santamouris M, Katsoulis BD. Solar radiation over the

northwest part of Greece. Sol Wind Tech 1989;6:79-84.

[5] Zabara K. Estimation of the global solar radiation in

Greece. Sol Wind Tech 1986;3:267-72.

[6] Kaldellis JK, Zafirakis D, Stavropoulou V, Kaldelli El.

Optimum wind- and photovoltaic-based stand-alone

systems on the basis of life cycle energy analysis. Energ

Policy 2012;50:345-57.

[7] Kaldellis JK. Optimum hybrid photovoltaic-based solution

for remote telecommunication stations. Renew Energ

2010;35:2307-15.

[8] Kaldellis JK, Ninou I, Zafirakis D. Minimum long-term cost

solution for remote telecommunication stations on the

basis of photovoltaic-based hybrid power systems. Energ

Policy 2011;39:2512-27.

[9] Kaldellis JK, Ninou I. Energy balance analysis of combined

photovoltaic–diesel powered telecommunication stations.

Int J Elec Power 2011;33:1739-49.

[10] Kaldellis JK. Optimum technoeconomic energy

autonomous photovoltaic solution for remote consumers

throughout Greece. Energ Convers Manage 2004;45:2745-

60.

[11] Kaldellis JK, Kavadias KA, Koronakis PS. Comparing wind

and photovoltaic stand-alone power systems used for the

electrification of remote consumers. Renew Sust Energ Rev

2007;11:57–77.

[12] Kaldellis JK, Koronakis P, Kavadias K. Energy balance

analysis of a stand-alone photovoltaic system, including

variable system reliability impact. Renew Energ

2004;29:1161-80.

[13] Vlachos GTh, Kaldellis JK. Application of gas-turbine

exhaust gases for brackish water desalination: a techno-

economic evaluation. Appl Therm Eng 2004;24:2487-500.

[14] Kaldellis JK, Zafirakis D, Kavadias K, Kondili E. Optimum

PV-diesel hybrid systems for remote consumers of the

Greek territory. Appl Energ 2012;97:61-7.