Synthesis and interest in hybridization of autonomous renewable
multi-source systems under constraints of embodied energy GHG
emissions life cycle cost and loss of power supply probability
H. Cherif J. Belhadj
Université de Tunis El Manar,
ENIT/LSE, BP 37 le belvédère 1002
Tunis, Tunisia
Université de Tunis, DGE-ENSIT
Université de Tunis El Manar,
ENIT/LSE, BP 37 le belvédère 1002
Tunis, Tunisia
Abstract—In this paper the authors investigated a
hybridization study based on embodied energy, GHG
emissions, life cycle cost and loss of power probability of a
hybrid photovoltaic-wind system with battery storage of a
residential house. Based on the life cycle assessment (a period
of 25 years), economic and environmental models are
developed for a photovoltaic subsystem, a wind turbine
subsystem and a battery bank. The performance of
parametric sensitivity technique is evaluated for sizing a
hybrid PV/WT/Battery system to continuously satisfy the
load demand with the minimum of economic and
environmental requirements. The sizing is performed for a
PV/Wind/Battery system, PV/Battery system and
Wind/Battery system and the results are compared in order
to select the best hybrid system configuration which meets
both economic and environmental requirements.
Index Terms—Hybrid systems, dynamic simulator, life
cycle assessment, parametric sensitivity, environmental
impacts.
1. INTRODUCTION
Renewable energy sources as Photovoltaic (PV) Panels
and Wind Turbines (WT) are combined with each other in
order to provide more continuous electrical power
compared generating electricity with single-renewable
energy systems [1], [2] and [3]. The energy storage device
as a battery bank in PV/wind hybrid system is necessary
especially to feed electrical loads due to the intermittency
of weather conditions (solar radiation and wind speed) [4].
In hybrid systems sizing, the designers integrate the
economic aspects into systems design in order to reduce
the total annual cost [5] and [6]. In fact, today it is
necessary to integrate the environmental consideration
with the economic aspects into systems design (eco-design
and eco-optimization) to participate of reducing the
environmental impacts (embodied energy and GHG
emissions) [7], [8] and [9].
In this context, we focus on hybridization interest of a
standalone hybrid PV/WT/Battery system for the
residential house under constraints of Embodied Energy
(EE), GHG emissions, Life Cycle Cost (LCC) and Loss of
Power Supply Probability (LPSP). First, based on Life
Cycle Assessment (LCA) methodology, EE, GHG
emission and LCC models are developed for the studied
hybrid system. Next, a dynamic simulator which includes
PV/Wind renewable generators, storage system, AC load,
battery energy management and LCA indicators (EE,
GHG, LCC and LPSP) will be presented. Then, a
parametric sensitivity algorithm will be developed for size
the hybrid system under constraints of EE, GHG, LCC
and LPSP. Hybridization interest of the studied sources
will be investigated in terms of these LCA indicators in
order to reduce both economic and environmental
requirements.
The remainder of the paper is organized as follows: the
studied system architecture and the modeling of the hybrid
system components are presented in section 2. Next,
section 3 describes the LCA investigation of the hybrid
PV/WT/Battery system in terms of embodied energy,
GHG emissions and life cycle cost for a life period of 25
years. Then, section 3 presents the dynamic simulator of
the proposed system. Finally, in sections 4 and 5
respectively, the results analysis and conclusion are
presented.
2. MODELING THE HYBRID SYSTEM COMPONENTS
The hybrid PV/Wind energy source is composed of
wind turbine and PV module with their appropriate power
electronics converters, battery storage and AC load
(residential house). As shown in Fig. 1, these components
are connected through a DC bus (48V).
Fig. 1. Schematic of the hybrid Wind-PV system with battery
storage.
G
Wind turbine
+
AC/DC
PV panels
DC/DC -
Battery
ControllerLoad
DC Bus
DC/AC
Proceedings of the International Conference on Recent Advances in Electrical Systems, Tunisia, 2016
ISBN: 978-9938-14-953-1 (107) Editors: Tarek Bouktir & Rafik Neji
A. Wind turbine
The produced power of wind turbine is obtained as
follows:
pGWTWT CVAP ×××××= 3
2
1ηρ (1)
where V is the wind speed (m/s), ρ is the air density
(kg/m3), AWT is the wind turbine swept area (m²),Cp is the
turbine efficiency and Gη is the generator efficiency.
For sizing procedure, wind turbine swept area (AWT)
and wind speed (V) are defined as input variables. The
wind turbine operates between the cut-in value and the
cut-out value. If the wind speed exceeds the rated speed,
the generator generates constant output power.
B. PV System
The output power of PV system is obtained from the
solar radiation by the following expression [10]:
PVPVrPV AIP η××= (2)
where Ir is the solar radiation, APV is the PV area and PVη
is the power conversion efficiency of PV panels expressed
by:
( )[ ]NOCTccSTCmpPV TT ,, 1 −×−×= βηη (3)
where STCmp ,η is the maximum power point efficiency
under standard test conditions, � is the generator
efficiency temperature coefficient, ranging from 0.004 to
0.006 per °C, Tc,NOCT is the normal operating cell
temperature when cells operate under standard operating
conditions and Tc is the cell temperature which is
expressed by:
( ) ( )2514.13000175.030 −×+−×+= arc TIT (4)
with Ta is the ambient temperature.
For sizing procedure, PV area (APV), solar radiation (Ir)
and ambient temperature (Ta) are defined as input
variables.
C. Battery storage
An ideal lead-acid battery model is used in this paper.
The lead-acid type of battery bank is usually used in the
field of renewable energy to store the surplus of electric
energy from the wind turbine and PV sources and to feed
the loads in case of low wind speed and/or low solar
radiation [10]. The State of Charge (SOC) of the battery is
defined as follows:
- The battery bank is in charging state if the total output
of wind turbine and PV sources is greater than the load
energy. In charging sate, SOC can be obtained by
( ) ( )
( ) ( )t
tPPtP
UttSOCtSOC
ACDC
LoadDCACWTDCDCPV
bus
cha
∆×���
����
�−×+××
+∆−=
///
ηηη
η
(5)
- The battery bank is in discharging state if the total
output of wind turbine and PV sources is less than the load
demand. In discharging sate, SOC can be obtained by
( ) ( )
( ) ( )t
tPPtP
UttSOCtSOC
ACDC
LoadDCACWTDCDCPV
busdis
∆×���
����
�−×+××
×+∆−=
///
1
ηηη
η (6)
where �DC/DC, �AC/DC and �DC/AC are respectively DC/DC,
AC/DC and DC/AC converter efficiencies, �cha and �dis
are battery efficiencies during charging and discharging
phase, Ubus is the DCbus voltage (48V), PLoad(t) is the
power consumed by the load and �t is the simulation time
step (�t = half an hour).
The SOC limits are determined by:
maxmin SOCSOCSOC ≤≤ (7)
where SOCmin and SOCmax are the minimum and the
maximum allowable states for the battery safety.
The battery operates either in charging or discharging
mode based on the energy management system. The
battery management is determined according to the
operation power (Poper) and the energy constraints of the
battery as shown in Fig. 2. In case of the power generated
by the hybrid system (Phy) is insufficient for the load
power (Pload) and battery SOC is greater than the
minimum value (SOCmin), the power from the battery is
transferred to the load (discharge), otherwise load
shedding is required to maintain power balance. In case of
hybrid power exceeding the load power and battery SOC
is below SOCmax, excess of energy is stored in batteries,
however, if the battery SOC exceeds its maximum
(SOCmax), the battery charging mode stops and the
generated hybrid power is lost.
Fig. 2. Batteries energy management system.
3. LIFE CYCLE ASSESSMENT INDICATORS
The Life Cycle Assessment (LCA) methodology
evaluates the potential impacts of a system or product for
a wide set of impact categories across the whole life cycle
[9] and [11]. LCA indicators used in this paper for
Power generated
by PV system Power generated
by WT system
Power demand
of the user
Calculate Phy = (PPV+PWT), SOC,
Pload, Ploss
Poper = Pload – (Phy - Ploss)
Poper > 0
SOC > SOCmax SOC > SOCmin
Battery charge
until Pbat
(SOCmax)
Lost energy
(off-MPPT) Load
Shedding
Battery
discharge until
Pbat (SOCmin)
Proceedings of the International Conference on Recent Advances in Electrical Systems, Tunisia, 2016
ISBN: 978-9938-14-953-1 (108) Editors: Tarek Bouktir & Rafik Neji
evaluate the LCA of the proposed system are embodied
energy, GHG emissions and life cycle cost.
A. Embodied energy
The Embodied Energy (EE) represents the non-
renewable energy consumed for components
manufacturing of the hybrid system including the
acquisition of raw materials, processing, transportation to
site, and construction. EE is expressed as mega Joules
(MJ) per unit of weight or area.
In order to obtain total EE model of the hybrid system
over a period of 25 years, the amount of EE for different
industrial solar panels (poly-Si), wind turbines and
batteries are investigated. Note that, the static converters
are replaced every ten years and batteries are replaced
every five years. The EE evolution of the PV system
versus PV area (APV) for poly-Si is presented in Fig. 3.
Fig. 4 and 5 present the EE evolution of the wind turbines
versus WT swept area (AWT) and the EE evolution of
batteries versus nominal capacity (Cn), respectively.
Total EE model of the hybrid system is given by
( ) BatWTPV EEEEEEMJEE ++= (8)
where,
EEPV is the embodied energy model of PV system (poly-
Si) expressed as a function of PV surface area (APV):
26.473863 −×= PVPV AEE (9)
EEWT is the embodied energy model of wind turbine
system expressed as a function of wind turbine swept area
(AWT):
43.497.2359 +×= WTWT AEE (10)
EEBat is the embodied energy model of batteries expressed
as a function of nominal capacity (Cn):
nBat CEE ×= 62.31 (11)
Fig. 3. Embodied energy of various poly-Si PV modules and EE
model.
Fig. 4. Embodied energy of various wind turbines and EE model.
Fig. 5. Embodied energy of various batteries and EE model.
B. GHG emissions
A Greenhouse Gas (GHG) emission is composed of
carbon dioxide (CO2), methane (CH4), nitrous oxide
(N2O) and fluorinated gases. GHG emissions are often
measured in carbon dioxide (CO2) equivalent (CO2eq).
The increase of GHG emissions was due to a number of
factors: fuel demand especially in residential and
commercial sectors, transportation and industrial
production.
In our case, to develop a GHG emissions model of the
studied system, the amount of GHG emissions for
different industrial solar panels (poly-Si), wind turbines
and batteries are elaborated. The GHG evolution of the
PV system versus PV area (APV) is presented in Fig. 6.
The GHG emissions evolution of the wind turbines versus
WT swept area (AWT) and the GHG emissions evolution of
batteries versus nominal capacity (Cn) are presented in
Fig. 7 and 8, respectively. Total GHG model of the hybrid
system is given by
( ) BatWTPV GHGGHGGHGeqkgCOGHG ++=2 (12)
where,
GHGPV is the GHG emission model of PV system (poly-
Si) expressed as a function of PV surface area (APV):
54.23.138 −×= PVPV AGHG (13)
GHGWT is the GHG emission model of wind turbine
system expressed as a function of wind turbine swept area
(AWT):
WTWT AGHG ×=156 (14)
GHGBat is the GHG emission model of batteries expressed
as a function of nominal capacity (Cn):
2.2799.1 +×= nBat CGHG (15)
Fig. 6. GHG emission of various poly-Si PV modules and GHG
model.
0
2000
4000
6000
8000
10000
0 1 2 3
EE
(M
J)
APV (m²)
KyoceraSharpSolar WJ. RundaSungoldSopray EQXPVRisen EERA SModel
0
50000
100000
150000
200000
0 20 40 60 80
EE
(M
J)
AWT (m²)
Bergery
Proven
Nera
PWPY
Whisper
Kingspan
Kestrel
Black
Model
0
2000
4000
6000
8000
10000
0 100 200 300
EE
(M
J)
Cn (Ah)
Hoppecke
Solar DL
Crown CR
Dyno BT
Sonnenschein S
Model
0
100
200
300
400
0 1 2 3
GH
G (
kgC
O2eq
)
APV (m²)
Solar World
Sharp
Trina S
Kyocera
QXPV
Sungold S
Sunwize
Model
Proceedings of the International Conference on Recent Advances in Electrical Systems, Tunisia, 2016
ISBN: 978-9938-14-953-1 (109) Editors: Tarek Bouktir & Rafik Neji
Fig. 7. GHG emission of various wind turbines and GHG model.
Fig. 8. GHG emission of various batteries and GHG model.
C. Life Cycle Cost
Life Cycle Cost (LCC) of the hybrid system (expressed
in €) over a period of 25 years integrates initial cost of the
system, installation cost, replacement and maintenance.
LCC of the wind turbine system includes turbine and
tower cost, installation cost, Balance of System (BOS)
costs (cables, connectors, protections) and converters
(replaced every 10 years). LCC of the PV system is
divided between PV modules cost, installation, BOS and
converters.
The LCC evolution of the PV system (for poly-Si),
wind turbine system and batteries are presented in Fig. 9,
10 and 11, respectively. The obtained economic model
(LCC) of the hybrid system is given by
( ) BatWTPV LCCLCCLCCLCC ++=€ (16)
where,
LCCPV is the economic model of PV system (poly-Si)
expressed as a function of PV surface area (APV):
11.9613.440 +×= PVPV ALCC (17)
LCCWT is the economic model of wind turbine system
expressed as a function of wind turbine swept area (AWT):
WTWT ALCC ×= 2.1844 (18)
LCCBat is the economic model of batteries expressed as a
function of nominal capacity (Cn):
8.14611 +×= nBat CLCC (19)
Fig. 9. Life cycle cost of various poly-Si PV modules and LCC
model.
Fig. 10. Life cycle cost of various wind turbines and LCC model.
Fig. 11. Life cycle cost of various batteries and LCC model.
D. Loss of power supply probability
The power system reliability assessment is measured in
terms of Loss of Power Supply Probability (LPSP)
especially with loads powered by intermittent renewable
energy sources. LPSP can be defined as
( )
( )�
�
=∆
=∆
∆×∆
∆×∆∆
=T
t
load
T
t
ttP
ttP
LPSP
1
1 (20)
with
� ≤−
=∆otherwise
SOCSOCPPP
hyload
,0
, min (21)
where Phy is the hybrid source power (PV/Wind) and Pload
is the electrical load power.
4. DYNAMIC SIMULATOR
A dynamic simulator of the hybrid wind–photovoltaic
system with battery storage as shown in Fig. 1 is
developed under annual data of weather conditions (wind
speed, solar irradiance and temperature) [12]. Real
consumption data of electricity energy for a residential
home are used and programmed with half an hour
(sampling step). The average half-hourly insolation,
temperature and wind speed profiles are illustrated in Figs.
12, 13 and 14, respectively. The average half-hourly load
demand considered in this paper is shown in Fig. 15.The
hybrid simulator as shown in Fig. 16 includes all hybrid
system components (PV/Wind renewable generators,
storage system and AC load), battery energy management
and LCA indicators (EE, GHG, LCC and LPSP).
Fig. 12. Average half-hourly profile of PV system solar radiation.
0
5000
10000
15000
20000
0 50 100 150
GH
G (
kgC
O2eq
)
AWT (m²)
Bergey
Proven
Kestrel
Bornay
Fortis
Kingspan W
Aeolos
Wipo
Model
0
100
200
300
400
500
600
0 100 200 300
�����������
Cn (Ah)
Hoppecke
Solar T
Crown
Dyno E
Sonnenschein
Model
0
250
500
750
1000
1250
0 1 2 3
LC
C (
€)
APV (m²)
Astro E
Victron E
Solar land
Kyocera
T. Solar
Model
0
10000
20000
30000
40000
50000
0 10 20 30
LC
C (
€)
AWT (m²)
Enersud
Sonkyo E
Kestrel
Bergey
Southwest
Proven
Ruis E
Model
0
500
1000
1500
2000
2500
3000
0 100 200 300
LC
C (
€)
Cn (Ah)
Sonnenschein S
Hoppecke
Victron E
Ultracell
Model
0 2000 4000 6000 8000 10000 12000 14000 16000 180000
500
1000
Time(half an hour of year)
Ir(W
/m²)
Proceedings of the International Conference on Recent Advances in Electrical Systems, Tunisia, 2016
ISBN: 978-9938-14-953-1 (110) Editors: Tarek Bouktir & Rafik Neji
Fig. 13. Average half-hourly profile of PV system temperature.
Fig. 14. Average half-hourly profile of wind speed.
Fig. 15. Average half-hourly profile of load demand (residential house).
Fig. 16. Dynamic simulator of the hybrid PV/Wind/Battery
system.
5. RESULTS ANALYSIS AND DISCUSSIONS
Based on the three sensitivity criteria (APV (m²), AWT
(m²) and Cn (Ah)), a parametric sensitivity algorithm
seeks to find the best compromise between four
objectives: EE, GHG, LCC and LPSP, in order to reduce
environmental impacts with the objective of minimizing
loss of power supply probability.
The bounds of decision variables are: APV_min=0 m²,
APV_max=120 m², AWT_min=0 m² AWT_max=120 m² and
Cn_min=50 Ah, Cn_max=600 Ah.
LPSP criterion is limited to 5% (LPSP < 5%) with the
aim to provide more than 95% of the electric needs. From
the parametric sensitivity, three configurations are
retained according to the objectives of users/consumers:
- PV/Wind/Battery system,
- PV/Battery system,
- Wind/Battery system.
The studied hybrid systems are summarized in Table 1.
Table 1 shows the optimal parameters of each component
(APV, AWT and Cn) and in detail the system costs, the GHG
emissions, the embodied energy and the LPSP for the
hybrid systems.
The hybrid PV/Wind/Battery system configuration is
composed of APV=60 m², AWT=25 m² and Cn=100Ah. The
energy produced from the hybrid system is about 26540
kWh. Total EE (240406 MJ) is composed of 71% for PV
system components, 27% for wind turbine parts and 2%
for batteries. Total GHG is composed of 65% for PV
system components, 33% for wind turbine parts and 2%
for batteries. LCC is shared between the different
components as follows: 36% for PV subsystem, 61.3% for
wind turbine and 2.7% for batteries.
The embodied energy, greenhouse gas emission and
life cycle cost for a 20-year life time of 1m² of WT, 1m²
of PV panel and 1Ah of battery storage capacity are
summarized in Table 2. It should be noted that the GHG
emission and the life cycle cost of 1m² WT are higher than
the GHG emission and the life cycle cost of 1m² PV panel.
But, the embodied energy of 1m² PV panel is higher than
the embodied energy of 1m² WT.
The optimal sizes of the other systems are as follows:
PV/Battery: APV=115 m², Cn=100Ah; WT/Battery: AWT=58
m², Cn=100Ah. Figs. 17, 18, 19, and 20 represent the
break down of the total embodied energy, the total GHG
emission, the total life cycle cost and the LPSP for the
hybrid systems, respectively. From this figures, it can be
concluded that the embodied energy and GHG emissions
of the hybrid WT/Battery system are lower than that of the
other hybrid systems. Also, the LCC of the hybrid
WT/Battery system is more than that of PV/WT/Battery
system and PV/Battery system. In addition, in terms of
LCC, the cost of PV/Battery system is less than that of the
other hybrid systems. From Fig. 20, it is seen that the
LPSP indicator of the hybrid PV/WT/Battery system is
less than that of the other systems meaning that the hybrid
systems can meet the load demand.
Therefore, hybridization of the sources has a significant
impact on the design of the system in terms of these LCA
indicators. The hybrid system is located in the middle of
these indicators with the lowest LPSP. The choice of the
hybrid system configuration according the specific LCA
indicators (EE, GHG, LCC and LPSP) is returned to the
user or designer. For example, the objective of the user or
designer is to reduce environmental impacts or annual
costs of hybrid systems. The priority should be given to
the use of renewable energy sources that meet
sustainability and environmental requirements.
TABLE I
Summary of the Results over the Hybrid Systems.
PV/WT/Battery PV/Battery WT/Battery
APV (m²) 60 115 -
AWT (m²) 25 - 58
Cn (Ah) 100 100 100 EE (MJ) 240406 329533 155053
GHG(kgCO2eq) 12546 15766 9833
LCC (€) 70907 50211 103073 LPSP (%) 1.64 4.8 4.06
0 2000 4000 6000 8000 10000 12000 14000 16000 180000
20
40
60
Time(half an hour of year)
Ta(°C
)
0 2000 4000 6000 8000 10000 12000 14000 16000 180000
5
10
15
20
25
Time(half an hour of year)
Vw
ind(m
²)
0 2000 4000 6000 8000 10000 12000 14000 16000 180000
2
4
6
8
Time(half an hour of year)
Load p
ow
er(
kW
)
Power
Management
Hybrid
PV/Wind/Battery
source models
Ir Ta Vwind
AWT
APV
Cn
Electrical load
LCA indicators
Proceedings of the International Conference on Recent Advances in Electrical Systems, Tunisia, 2016
ISBN: 978-9938-14-953-1 (111) Editors: Tarek Bouktir & Rafik Neji
TABLE II
LCA indicators of 1m² of poly-Si PV panel, 1m² of
and 1Ah of battery storage capacity.
1m² WT 1m² PV
EE (MJ) 2616 2863.3 GHG(kgCO2eq) 165.8 136.4
LCC (€) 1756 428
Fig. 17. Break down of the total embodied energy
Fig. 18. Break down of the total GHG emission for the hybrid systems
Fig. 19. Break down of the total life cycle cost for the hybrid systems
Fig. 20. Break down of the LPSP for the hybrid systems
(PV/WT/Battery; PV/Battery and WT/Battery).
6. CONCLUSION
This paper studies the economic and environmental
aspects of PV/WT/Battery, PV/Battery
systems and performance of parametric sensitivity
technique to size these systems for a
Firstly, LCA methodology is investigated in terms of
embodied energy, GHG emission and life cycle cost
EE
33%
EE
46%
EE
21%
GHG
33%
GHG
41%
GHG
26%
LCC
32%
LCC
22%
LCC
46%
012345
LPSP(%)
PV/WT/Battery
LPSP(%)
WT/Battery
Si PV panel, 1m² of wind turbine
and 1Ah of battery storage capacity.
1m² PV 1Ah Cn
31.51 2.16
12.25
odied energy.
for the hybrid systems.
for the hybrid systems.
for the hybrid systems
and environmental
/Battery and WT/Battery
parametric sensitivity
for a residential house.
LCA methodology is investigated in terms of
GHG emission and life cycle cost on a
life cycle period of 25 years. Therefore, nine
represent the economic and environmental
EE models, three GHG emission
models) are developed for a photovoltaic subsystem, a
wind turbine subsystem and battery
Secondly, a dynamic simulator
developed under annual data of weather conditions
electric load data. As results, it is found that
hybrid systems configuration depends
which make in priority the economic or environmenta
aspects. The hybrid WT/Battery is the
better choice for producing elec
systems and the hybrid PV/Battery is the economical
better choice for producing electrical power than h
systems. But, the hybrid PV/WT/Battery
designed to meet both the economic and environmental
requirements.
ACKNOWLEDGMENT
This work was supported by the Tunisian Ministry ofHigh Education and Research under the ERANETMEDNEXUS “Energy and Water Systems Integration and Management” ID Number 14support.
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PV/WT/Battery
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PV/Battery
WT/Battery
PV/WT/Battery
PV/Battery
WT/Battery
LPSP(%)
PV/Battery
Therefore, nine models that
and environmental aspects (three
GHG emission models and three LCC
photovoltaic subsystem, a
wind turbine subsystem and battery bank storage.
of the proposed system is
nnual data of weather conditions and
it is found that the choice of
configuration depends of the designers
which make in priority the economic or environmental
The hybrid WT/Battery is the environmentally
better choice for producing electrical power than hybrid
systems and the hybrid PV/Battery is the economically
better choice for producing electrical power than hybrid
PV/WT/Battery system is
the economic and environmental
OWLEDGMENT
This work was supported by the Tunisian Ministry ofHigh Education and Research under the ERANETMED-NEXUS “Energy and Water Systems Integration and Management” ID Number 14-044 for the financial
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Proceedings of the International Conference on Recent Advances in Electrical Systems, Tunisia, 2016
ISBN: 978-9938-14-953-1 (112) Editors: Tarek Bouktir & Rafik Neji