synthesis and interest in hybridization of autonomous...

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

[email protected]

Université de Tunis, DGE-ENSIT

Université de Tunis El Manar,

ENIT/LSE, BP 37 le belvédère 1002

Tunis, Tunisia

[email protected]

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)



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



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)



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


Ta(°C

)

0 2000 4000 6000 8000 10000 12000 14000 16000 180000

5

10

15

20

25


Vw

ind(m

²)

0 2000 4000 6000 8000 10000 12000 14000 16000 180000

2

4

6

8


Load p

ow

er(

kW

)

Power

Management

Hybrid

PV/Wind/Battery

source models

Ir Ta Vwind

AWT

APV

Cn

Electrical load

LCA indicators



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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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

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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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