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I
OPTIMAL DESIGN OF A MINI-GRID FOR SUSTAINABLE INTEGRATED COASTAL DEVELOPMENT USING
GENETIC ALGORITHMS
A THESIS
BY
SAIFUL AZAM SIDDIQUE Examination Roll No. 06 Ag. Engg. FPM-JD 01M
Reg. No. 28474 Session: 2000-2001
Semester: January-June, 2008
MASTER OF SCIENCE (M.S.) IN
FARM POWER AND MACHINERY (AGRICULTURAL ENGINEERING)
DEPARTMENT OF FARM POWER AND MACHINERY BANGLADESH AGRICULTURAL UNIVERSITY
MYMENSINGH-2202
May 2008
II
OPTIMAL DESIGN OF A MINI-GRID FOR SUSTAINABLE INTEGRATED COASTAL DEVELOPMENT USING
GENETIC ALGORITHMS
A Thesis
By
SAIFUL AZAM SIDDIQUE Examination Roll No. 06 Ag. Engg. FPM-JD 01M
Reg. No. 28474 Session: 2000-2001
Semester: January-June, 2008
Submitted to the Department of Farm Power and Machinery
Faculty of Agricultural Engineering and Technology Bangladesh Agricultural University, Mymensingh
In partial fulfillment of the requirements for the degree of
MASTER OF SCIENCE (M.S) IN
FARM POWER AND MACHINERY (AGRICULTURAL ENGINEERING)
DEP ARTMENT OF FARM POWER AND MACHINERY
BANGLADESH AGRICULTURAL UNIVERSITY MYMENSINGH-2202
May 2008
III
OPTIMAL DESIGN OF A MINI-GRID FOR SUSTAINABLE INTEGRATED COASTAL DEVELOPMENT USING
GENETIC ALGORITHMS
A Thesis
By
SAIFUL AZAM SIDDIQUE Examination Roll No. 06 Ag. Engg. FPM-JD 01M
Reg. No. 28474 Session: 2000-2001
Semester: January-June, 2008
Approved as to style and content by:
(Professor Dr. B. K. Bala) Supervisor
(Professor Dr. Md. Ashraful Haque) Co-supervisor
(Professor Dr. Ashraful Haque) Chairman
Examination Committee and
Head Department of Farm Power and Machinery
Bangladesh Agricultural University Mymensingh
May 2008
IV
ABSTRACT
Power grid cannot reach everywhere. Yet there are alternatives. Renewable
energy can offer an ideal source of electricity for the communities far from a
grid- on an island, or other isolated situations. Such alternatives are hybrid
photovoltaic systems.
This research presents an optimal design of a solar-diesel hybrid mini-grid
system for 132 families in an isolated island-Sandwip in Bangladesh. The
electrical load is considered based on the local needs and the electrical load
demand is 20 kWh. The system is optimized using genetic algorithms. If the
renewable energy source produces more than the one required by the loads,
they excess energy can be used to charge the battery while if the amount of
energy demand is higher than the one produced by the renewable energy
source, the control strategy determines the most economical way to meet the
energy deficit.
V
ACKNOWLEDGEMENT
First and foremost I would like to express my deepest sense of gratitude to the lord
of the universe “Almighty Allah" and His holy prophet Hazrat Mohammad (SM)
who have been constantly guiding throughout troubled journey of my life. Without
the help and shepherding love of “Almighty Allah” nothing is possible, let alone this
simple research work.
I warmly recognize my continuing heartiest gratitude, sincere appreciation and
profound regards to my revered teacher and research supervisor Professor
Dr. B. K. Bala, Department of Farm power and Machinery, Bangladesh
Agricultural University, Mymensingh who in spite of his busy schedule in time and
abroad, always had time to spare for creative suggestions, constructive criticism,
helpful comments and encouragement all the time. His unparalleled magnanimity,
immense guidance, constant encouragement and constructive advice are also deeply
appreciated.
I also like to express my grateful appreciation and deep sense of respect to my co-
supervisor Professor Dr. Md. Ashraful Haque, Head, Department of Farm power
and Machinery, Bangladesh Agricultural University, Mymensingh for providing
necessary facilities and helpful suggestions during the course of research work.
I would like to take this opportunity to express grateful appreciation indebtedness
to all respected teachers in the Department of farm Power and Machinery,
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Bangladesh Agricultural University, Mymensingh for their help extended in this
research.
Special thanks are due to the staff of REB, Dhaka, for extending all possible helps
in related information. Special thanks to General Manager of Sylhet PBS-2 to let
me free for data collection.
Last but not the least, I profoundly acknowledge gratitude of Md. Ashik-E-
Rabbani, Assistant Professor Department of Farm power and Machinery,
Bangladesh Agricultural University, Mymensingh who helped me very much, and
other well-wishers who helped in many ways and sacrificed a lot during completion
of the research work.
The author
VII
CHAPTER
CONTENTS
TITLE PAGE NO.
ABSTRACT iv
ACKNOWLEDGEMENT v
LIST OF CONTENTS vii
LIST OF TABLES x
LIST OF FIGURES x
GLOSSARY xiii
CHAPTER 1 INTRODUCTION 1
1.1 Introduction 1
1.2 Positive impact of solar-diesel hybrid system
4
1.3 Objectives 6
CHAPTER 2 REVIEW OF LITERATURE 7
CHAPTER 3 SOLAR PHOTOVOLTAIC SYSTEM 20
3.1 Introduction 20
3.2 Systems 21
3.2.1 Stand-alone systems 23
3.2.2 Grid linked systems 24
VIII
CHAPTER
CONTENTS (Contd.)
TITLE PAGE NO.
3.3 The Solar Cell 26
3.3.1 Doping 27
3.3.2 The p-n junction 28
3.3.3 The volt-ampere characteristics 31
3.3.4 Generation of electron-hole pair by photon absorption 33
3.4 Technical Aspects of Solar Cells 34
3.4.1 I-V characteristic of solar cells 34
3.4.2 Short- circuit current and open-circuit voltage 35
3.5 Effect of irradiation 36
3.6 Effect of Fill Factor 37
3.7 Effect of Temperature 37
3.8 Radiation Absorption and Material Selection 38
3.9 Maximizing of Solar Cell Performance 40
3.10 The PV Module and Array 40
3.11 Energy Storage 42
3.12 Associate Electronic Components 43
3.12.1 Charge controller 43
3.12.2 Inverter 43
3.13 Balance of System Components 44
IX
CHAPTER
CONTENTS (Contd.)
TITLE PAGE NO.
CHAPTER 4 Methodology 45
4.1 Optimal Design Using Genetic Algorithm 45
4.1.1 Cycle charging strategy 48
4.1.2 Combined strategy 48
4.2 Develop genetic algorithm 49
4.2.1 Main algorithm 50
4.2.2 Secondary algorithm 51
4.2.3 Implementation of the GA 52
4.3 Case study: Sandwip-an Isolated Island 55
4.3.1 Characteristics of the island-Sandwip 55
4.3.2 Energy requirements in the community 58
4.4 Design Layout 59
CHAPTER 5 RESULT AND DISCUSSION 60
5.1 The optimized system configurations 60
5.2 The control strategies 61
CHAPTER 6 CONCLUSION 65
REFERENCES 66
X
LIST OF TABLES TABLE
TITLE PAGE NO
3.1 Properties of lead acid storage battery 43
3.2 Summary of inverter performance 44
LIST OF FIGURES
FIGURE PAGE NO
1.1 Growing energy demand of the World 1
3.1 Schematic diagram of a photovoltaic system 21
3.2(a) System operation during day time 22
3.2(b) System operation during night time 22
3.2(c) System operation during shortfall 22
3.3 PV system directly connected to load 23
3.4 Basic stand–alone PV system 23
3.5 Hybrid stand-alone PV system 24
3.6 Grid back-up PV system 25
XI
FIGURE LIST OF FIGURES (Contd.)
PAGE NO
3.7 Grid connected PV system 26
3.8 Illustration of the effects of doping 27
3.9 Schematic diagram of p-n junction including the charge density and electric intensity
29
3.10 Volt- ampere characteristics of an ideal p-n diode 32
3.11 Volt- ampere characteristic of an ideal p-n diode with expanded scale for reverse current
33
3.12 Illuminated I-V characteristics 35
3.13 I-V characteristics of solar cell under different illumination levels 37
3.14 Temperature dependence of PV curve of a solar cell 38
3.15 Variation of ∞ with photon energy for different semiconductors 39
3.16 Photovoltaic module 41
4.1 Flowchart of HOGA 54
4.2 The location map of Sandwip, Bangladesh 56
4.3 Monthly average daily load profile 58
4.4 Average daily solar radiations 59
4.5 Schematic diagram of batteries in series and parallel connection 59
5.1 Total net present cost and emissions a function of the main
Algorithm generations. 62
5.2 Power generation capacity of the PV generator, AC generator and
inverter
63
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FIGURE LIST OF FIGURES (Contd.)
PAGE NO
5.3 Annual energy balances the hybrid system 64
5.4 Cost of the different elements of the hybrid system as percentage of
the total net present cost 64
List of Appendix
TABLE
TITLE PAGE NO
A.1 PV modules specifications and costs 78
B.1 Batteries specifications and costs 78
C.1 Inverter specifications and costs 79
D.1 Generator specifications and costs 79
E.1 Monthly Average daily solar insolation Horizontal Surface 80
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1.1 Introduction:
CHAPTER 1 INTRODUCTION
In most of the developing countries, the portions of the population living in the
remote and isolated locations do not have access to the electrical grid line. Yet
there are alternatives. Renewable energy can offer an ideal source of electricity
for an island or other isolated places far from national grid.
Figure 1.1 shows the growing demand of energy for the world (Sarma, 2002).
The dreadful scarcity of energy has made the nation conscious to harness new
renewable sources and to take measure of conservation. Besides, electricity
generation from conventional sources (natural gas, oil, etc.) releases incredible
amount of pollutants (CO2, SO=2, Nitrogen-oxide, etc.). To combat this
adverse situation, alternative and environment-friendly energy sources are
being considered highly. In this respect, solar-hybrid mini-grid technology
stands out to be one of the prospective candidates. The world’s petroleum
production increases exponentially and will reach tits peak in nearly 2010 and
after that it will decrease (Sarma, 2002).
Fig. 1. Growing energy demand of the world
0
100
200
300
400
500
600
700
800
900
1972 1985 2000 2020
Year
Ene
rgy
dem
and
(hex
jule
s)
Fig. 1.1
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In some cases, there is very small local electric power generating plants
running mostly by diesel sets and mainly to give supply to government offices
and to some public consumers. It may be mentioned that the potential demands
of electricity are very much higher compared to the actual demand, which are
higher, compared to the demand which are currently being served with such
limited generation. Diesel sets operate very inefficiently at less than full
capacity, so sometimes the addition of some storage can save not only on
capacity but also on fuel and operating expenses. In some remote areas and
isolated coastal locations solar energy may also be utilized to save fuel costs.
However, to achieve long life and optimal performance from hybrid system of
diesel, wind and solar energy particularly those supplemented with battery
storage required that their operation be carefully controlled. For the consumer
contemplating the installation of such a system the major concerns may be the
suitable sizes for each component to satisfy a given load profile and the
costing of alternate systems in a consistent framework.
Solar energy is one of the inexhaustible, free, abundant, site-dependent, non-
polluting and potential sources of renewable energy options, which is being
pursued by a number of countries with monthly average daily solar radiation
level in the range of 3-6 kWh/m2, in an effort to reduce their dependence on
fossil-based non-renewable fuels (Post and Thomas, 1999). Solar collectors
can be classified as either solar to thermal energy converters or solar to electric
energy converters. Devices that directly convert solar/light into electricity are
called photovoltaic. Moreover, concept of PV is well conceived and presently
(in spite of market barriers/impediments) thousands of PV systems are being
installed worldwide, for providing power to small, remote, grid-independent or
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stand-alone applications (Post and Thomas, 1999). Annual worldwide
shipments of PV panels have grown from 2 MWp in 1975 to 135 MWp in
1998. World PV market grew by 26% in 2002 when compared with 2001 in
the year 2003. During last coupled of years, substantial progress has been
made in key area such as quality, reliability (20+ year warranties) and
efficiency of solar panels. The price of PV modules has dropped by a factor of
1/30 during the past 20 years. Germany, Japan and the Netherlands are leading
the solar race notably (Greenpeace briefing). Typical ratings of PV modules
vary from 30 to 300 Wp.
A hybrid technology is a combination of multiple sources of energy; such
renewable energy and diesel generator and may also include energy storage
such as battery. Mini-grid power generation is meant to supply remote areas
where grid connection is almost impossible in terms of cost and geography,
such as islands, aborigine’s villages and areas where conservation of nature is
a concern. For these, we intend to design a solar-diesel hybrid system based on
the local needs.
The design and operation control is not a linear problem due to non-linear
component characteristics with a large number of variables. The optimal
design of problems like this cannot be achieved easily using classical
optimization methods. This study presents a method of optimization for PV-
Diesel systems using a Genetic Algorithm (GA) (Dufo-Lopez, R et al., 2005).
Genetic Algorithms are an adequate search technique for solving complex
problems when other techniques are not able to obtain an acceptable solution.
The PV-hybrid system studied is an AC-only system.
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There are some programs that simulate hybrid systems, such as HYBRID2
developed by the NREL (National Renewable Energy Laboratory, USA) and
TRNSYS developed by the university of Wisconsin (USA). HYBRID2
simulates hybrid systems with very high precision calculations, but it does not
optimize the system. TRNSYS was initially developed to simulate thermal
systems but it has incorporated PV systems to simulate hybrid systems such as
those proposed here, however it cannot optimize them.
HOGA, the program used in this study optimizes the hybrid PV-Diesel system
using Genetic Algorithms (Dufo-Lopez, R et al., 2005). The program
calculates the optimal configuration of the system. This optimal configuration
is described very precisely: the number of PV panels and the type of battery,
the inverter power, the diesel generator power, the optimal control strategy of
the system with its parameters, the Total Net Present Value of the system and
the different relative costs such as the fuel cost, and finally, the number of
running hours for the diesel generator per year. The program also optimizes
the dispatch strategy, as does HOMER, but it also optimizes the SOC set point,
that is an important variable.
1.2 Positive impact of solar-diesel hybrid system
A robust power supply and downtime minimization during power outages
could be achieved by virtue of varying the power sources, which is vital indeed
due to its ability to provide backup power. System failure or disruption of
diesel supply to the community is the factor leading to utilize an alternate
generating system encompassing renewable energy/diesel hybrid system as to
encourage continuous and in power supply. Photovoltaic and wind energy
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systems are attributive to fewer moving parts, repairs or routine maintenance.
In fact, renewable energy sources being indigenous and free, is more securing
than diesel thus, beneficial to facilities.
Improved energy services
The ability of renewable energy working in tandem with diesel, contributes to
high quality and dynamic electricity services for 24 hours/day whilst in a
conventional system, the high diesel operating costs limits the power supply
only to 12 hours/day. The cost of photovoltaic or wind power generation lies in
the form of upfront capital expenditures whereby the operation and
maintenance expenses are low. Therefore, the generating cost via photovoltaic
or wind is marginally more than a conventional system with respect to the
additional generating capacity promising a satisfaction of a continuous supply
of electricity supply to the customer.
Reduced emission and noise pollution
Diesel generation emits air and water pollution agents as well as loud noise,
proving the essentiality of renewable energy or diesel retrofits application in
generating power, which adopts an environment-friendly technology. In fact,
renewable energy system is also substantially better than diesel generators.
Continuous power
By incorporating diesel generator with renewable energy system, diesel
generator is able to boost up the electricity supply during sudden increase in
energy demand or when the batteries capacity decreases and thus, facilities
face no interruption of supply.
Increased operational life
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The alternate operation at regular intervals and specific occurrences of
renewable energy and diesel hybrids could prolong the life of the overall
system on account of the discontinuous usage of the diesel set. Furthermore,
the discharging level of the batteries is optimum, contributing to its increased
operational life.
Reduced cost
Hybrid system promotes efficient use of power since renewable energy system
could be configured to cope with base load whilst the peak load could be met
through diesel generator.
1.3 Objectives
The main objective of this study is optimal designing of mini-grid using
Genetic Algorithms (GA).The specific objectives are:
i) Optimization of PV-diesel hybrid systems for mini-grid using
Genetic Algorithm (GA)
ii) Optimization of operational control strategies
XIX
REVIEW OF LITERATURE
Chapter 2
Yang et al. (2007) developed the hybrid Solar-Wind System Optimization
Sizing (HSWSO) model, to optimize the capacity sizes of different
components of hybrid solar-wind power generation systems employing a
battery bank. With the incorporated HSWSO model, the sizing optimization
of hybrid solar-wind power generation systems can be achieved technically
and economically according to the system reliability requirements. A case
study is reported to show the importance of the HSWSO model for sizing the
capacities of wind turbines, PV panel and battery banks of a hybrid solar-wind
renewable energy system.
Ashok (2006) reported that Hybrid energy system is an excellent solution for
electrification of remote rural areas where the grid extension is difficult and
not economical. Such system incorporates a combination of one or several
renewable energy sources such as solar photovoltaic, wind energy, micro-
hydro and may be conventional generators for backup. This paper discusses
different system components of hybrid energy system and develops a general
model to find an optimal combination of energy components for a typical rural
community minimizing the life cycle cost. The optimal operation shows a unit
cost of Rs. 6.5/kWh with the selected hybrid energy system with 100%
renewable energy contribution eliminating the need for conventional diesel
generator.
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Dufo-Lopez, R et al. (2006) reported a strategy, optimized by genetic
algorithm, to control stand-alone hybrid renewable electrical systems with
hydrogen storage. The strategy optimizes the control of the hybrid system
minimizing the total cost throughout its lifetime. The optimized hybrid system
can be composed of renewable sources (wind, PV and hydro) batteries, fuel
cell, AC generator, and electrolyzer. If the renewable sources produce more
energy that the one required by load, the spare energy can be used either to
charge the batteries or to produce H² in the electrolyzer. The control strategy
optimizes how the spare energy is used. If the amount of energy demanded by
the loads is higher than the one produced by the renewable energy sources, the
control strategy determines the economical way to meet the energy deficit. The
optimization of the various system control parameters is done using genetic
algorithms.
Koutroulis et al. (2006) presented a methodology for optimal sizing of stand-
alone PV/WG systems. The purpose of the proposed methodology is to
suggest, among a list of commercially available system devices, the optimal
number and type of units ensuring that the 20-year round total system cost is
minimized subject to the constraint that the load energy requirements are
completely covered, resulting in zero load rejection. The 20-year round total
system cost is equal to the sum of the respective component’s capital and
maintenance costs. The cost (objective) function minimization is implemented
using genetic algorithm, which, compared to conventional optimization
methods such as dynamic programming and gradient technology have the
ability to attain the global optimum with relative computational simplicity. The
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proposed method has been applied for the design of a power generation
system, which supplied a residential household.
Agustin et al. (2005) reported the Strength Pareto Evolutionary Algorithm to
the multi-Objective design of isolated hybrid systems. The design is posed as
an optimization problem whose solution allows obtaining the configuration of
the system as well as control strategy that simultaneously minimizes both the
total cost through the useful life of the installation and the pollutant emissions.
Dufo-Lopez, R et al. (2005) reported Hybrid photovoltaic systems (PV-hybrid)
use photovoltaic energy combined with other sources of energy, like wind or
diesel. If these hybrid systems are optimally designed, they can be more cost
effective and reliable than PV-only systems. However the design of hybrid
systems is complex because of the uncertain renewable energy supplies, load
demands and the non-linear characteristics of some components, so the design
problem cannot be solved easily by classical optimization methods. When
these methods are not capable of solving the problem satisfactorily, the use of
heuristic techniques, such as the genetic algorithm, can give better results.
Bala et al. (2007) reported that renewable energy can offer an ideal source of
electricity for the communities far from a grid on an island, or other isolated
situation and also presented design and economics of a solar-diesel hybrid
mini-grid system for 132 families in an isolated island-sandwip. The electrical
load based on the local needs and electrical load demand is 20 kWh. The
sizing of the hybrid system consists of 31 solar modules, 10 number of 24 V
batteries, 2 Inverters having a total capacity of 24 kW, 24 V DC/220 V AC. A
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diesel generator set of 12 kW capacities is selected for backup during shortfall.
The life cycle cost (LCC) is estimated and LCC is found to be Tk.15.51 per
kWh compared to electricity price of Tk.4.00 per kWh.
Hontoria et al. (2004) developed several methods for sizing stand- alone
photovoltaic (PV) systems. The more simplistic were called intuitive methods.
Those were useful tool for a first approach in sizing stand-alone photovoltaic
systems. Nevertheless they were very inaccurate. Analytical methods also used
to describe the PV systems size as a function of reliability. These ones are
more accurate than the previous ones but they are also not accurate enough for
sizing of high reliability. In a third group there are methods, which use system
simulations. These ones are called numerical methods. Many of the analytical
methods employ the concept of reliability of the system or the complementary
term: loss of load probability (LOLP). An improvement for obtaining LOLP
curves based on the neural network called Multiplayer perception (MLP) is
also described.
Ismail et al. (2003) reported a pilot project on solar hybrid power system. The
objective of this project was to design and install the solar power station at
remote location and to develop a standard design of stand-alone solar power
station suitable for Malaysia. The main domestic energy is for residential
purpose (e.g. small lighting unit, radio, b/w television, small fans, charger
etc.). The load demand was calculated based on the diesel generator set. The
generator capacity is 18.6 kW. The design of solar panel was done both for the
home and small application at ‘Kampung Denai’. The maximum demand was
4195.35 kW. The plot centralized solar power station consists of 10 kW
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inverter, 150 kWh batteries and other balance of systems. The status of the
system, operational and maintenance issues, load profile of the solar power
station and economics and system designs of the whole system are also
presented.
Sopian et al. (2003) studied a photovoltaic (PV) system to provide the
electricity for a single residential household. It was found that providing
electricity for a household by using PV system would be beneficial and
competitive for long-term investment, especially in case of reduction of the
price of the system as well as efficiency.
Malayappan et al. (2003) design a decentralized hybrid energy system using
solar, wind and biomass gasifier coupled with diesel generator for a particular
village. The energy requirement of electrical lamps, fans, TV, radio and home
appliance equipments in database to calculate the demand for the village. The
solar radiation data, wind energy data, availability of carbonaceous waste
materials of that particular village and the demand are given as input data for
the program .The proposed program will calculate the amount of energy that
can be extracted from the sources, number and size of solar panels, size and
capacity of wind generator and size of gasifier. The wind sweep and solar
panel area are optimized based on the cost of energy generation (wind @ Rs.
2.75 per unit and solar @ Rs 2.60 per unit) by using LPP graphical method.
From the obtained wind sweep area (17.4085 m²), the diameter of the windmill
is calculated to be 4.71 m
Rahman (2003) reported the renewable energy technology for off-grid power
generation using solar-hybrid system. The hybrid technology offered a solution
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to off-grid power generation in terms of reducing operation, cost, maintenance
and logistic problems providing 24-hrs reliable supply at an effective cost as
well as preserving the nature.
Colles et al. (2003) reported an analytical approach to evaluate and to optimize
the life cycle saving of hybrid diesel-photovoltaic plants. The life cycle
savings is evaluated, considering one or more diesel generator sets, operating
in different fixed power levels, with special attention to the case of high
specific fuel cost. The condition under which optimum photovoltaic module
area exists is analyzed. In the particular region of the northern part of Brazil, it
is shown that there were several favorable conditions to implement
photovoltaic generation, in the range of current electricity tariffs and diesel oil
costs practiced in market.
Sarma and Mahapatra (2002) reported that the choice between off-grid solar
photovoltaic power generation and conventional option of extending the
national grid of remote village electrification. The initial capital investment of
photovoltaic (PV) system is very large but this study shows that for the
villages having low-load demand and being remote decentralized power
supply by PV system can be cost competitive in terms of life cycle cost of unit
energy (LCC). Costs and distances from the load centre to the exiting grid line
are high though the per unit electricity generation cost of conventional
centralized power generation is low. The LCC of grid extension is dominated
by the grid extension cost and the generation cost has little impact on it,
whereas LCC of PV system is dominated by the cost of arrays and battery
replacement cost. The life cycle cost of PV (Rs./kWh) varies from Rs.17.62 to
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Rs. 17.45 for the load variation of 7.5 kW to 50 kW. The optimal economic
distance (OED) for having a PV system for different loads; for low load profile
the PV is more cost effective than the conventional extension of grid and vice
versa.
Bhuiyan and Asgar (2002) studied the performance of the SAPV system and
found to be satisfactory and the experimental results coincide well with the
theoretical estimates. The charging time of the battery is found to decrease by
about 2% when the azimuth angle of the array is changed from 00 to ± 450
facing south, thus increasing the output power for the PV modules.
Vandenbergh et al. (2003) presented the operational experience of mini-grids
of two pilot plants based on the AC coupled PV technology. The first plant
was the ‘Starkenburger Hùtte’ a single user system in the Austrian Alps and
the performance ratio of the PV field could be improved by adding new battery
capacity. The second system was a multi-user micro-grid located in a remote
area on the Greek Island of Kythnos. Both systems were operated with
satisfactory performance.
Sasithranuwat and Rakvichian (2003) added the concept of single-user mini-
grid applied to design the electrical power system for isolated offices located
in remote areas that have no access to electricity. This electric power system
was referred as Photovoltaic for Isolated Office System (PIOS). The operating
principle of the system is to use the solar radiation during daytime to supply to
the load directly via the photovoltaic panels and at the same time, storing the
excess energy in the battery bank for continued electricity supply at night. The
system made use of two different inverters to perform the above tasks. The
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first inverter, known as a grid inverter, converts DC from the PV array to AC
and supplies directly to the load during the daytime, while the second inverter,
also known as a battery inverter, is used to convert DC from the battery to
supply to the AC at night. In case of insufficient solar radiation, a built-in load
management control module will be used to ensure the stability and reliability
of the system. From the simulated results by using the Res. 2.0 simulation
software, it was found that the yearly average solar fraction was 87.3%; the
performance ratio was 33.3% hr/day.
Zahedi (2003) reported an accurate sizing of components in the PV hybrid
power systems. Accurate sizing, which avoids wasting electricity and money,
is an important part of the design of PV hybrid systems. Another issue was to
show the results of this sizing method for a system which supplies electricity
for facilitated lighting at the site located at the latitude of 370-32' south and
longitude 1430-49' east. Normally the generator will charge the batteries from
about 20% to about 70%. Charging the batteries from 20% to 70% in five
hours requires a charging rate of c/10. Size of components required to supply
electricity for a load which needs 18 kWh per day, efficiency of the
components:-inverter: 95%; battery efficiency: 85% with depth of discharge of
80%, wiring efficiency: 98%, size of the inverter: 3.5 kW, 48 DC to 240 AC, 2
diesel generators, 4 kW each to support the battery bank. Volume of fuel
required in one year: 3200 litre, total electricity required by load in one year:
6570 kWh, % of load covered by solar PV by solar PV array: 65% considering
number of consecutive no sun. 5 days and finally presented the performance
predication of the PV hybrid system was presented.
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Sarkar and Obaidullah (1999) conducted a performance study on the prospects
of solar photovoltaic for vast application in the far-flang rural areas of
Bangladesh. The performance and economic analysis of a stand-alone solar PV
system in Bangladesh was analyzed. The system was designed and installed to
supply electricity to 14 fluorescent lamps in a laboratory of BUET. The system
consists of PV modules, charge control unit, batteries, switching circuits, and
connecting wires. The total load is estimated for four hours of operational day.
The performance data presented the proper choice of system components and
proper installation, the PV system operated properly. The life cycle cost (LCC)
of the PV system had been compared to the diesel generator. From the
economic analysis of the system it was found that the life cycle cost of the PV
system lesser than that of diesel generator.
Jurge and Huacuz (2001) attempted to test new schemes that could help to
solve the problem of electrifying remote rural communities. The Mexican
Government launched an initiative in 1989 to use locally available renewable
energy sources to do the job. Since then, small hydroelectric power plants,
wind generators, photovoltaics and hybrids thereof have been installed in
growing numbers. Over 1700 small rural communities had been electrified
using solar home systems, amounting to almost 50,000 photovoltaic
installations consisting of one or two PV modules, one lead-acid battery and
one electronic charge controller. Users can also use three or four compact
fluorescent lamps, radio and small B & W television for entertainment where
TV signals are available. Communal services such as medical dispensaries,
schools, meeting halls and churches have also been PV powered by the
XXVIII
hundreds, while over 12,000 rural telephones powered by photovoltaics had
been installed in Mexico.
Sarkar and Obaidullah (1999) designed a stand-alone PV home lighting system
for 4 hours daily operation. The PV array was selected to operate on average
weather conditions of solar radiation. The array sizes were designed to operate
even in the month of minimum insolation considering different types of power
losses. Three panels (3 × 60 Wp) charge five lead-acid batteries and it supplies
electricity to twelve fluorescent tube lights of each 8-watt capacity.
Post and Thomas (1999) examined photovoltaic power system applications,
including remote SPV, dispersed grid-connection and large generation centers.
Photovoltaic system options for both current and future application are
described and costs for each of these options are determined. The computed
results show that the future applications will utilize the renewable energy
technology available today.
Huq (1999) reported the implementation of Narsingdi SPV project, which was
under supervision, and partial finance of the Govt. of Bangladesh with a major
finance of French Govt. The main objective of the project was to test the
financial and economic viability, technical acceptability, social demand,
acceptance and popularity of SPV systems by the rural population. Selection of
project area was on the basis of non-electrified, remote rural communities, far-
flung islands.
Bhuiya et al. (1999) designed a stand-alone photovoltaic power system to
operate residential appliances such as fluorescent lamp incandescent light
XXIX
using standard methods. The total load is estimated for four hours of operation
per day. The battery is sized considering different factors that affect battery
efficiency to reliably operate the estimated loads during a sequence of below
average isolation. The minimum battery size is obtained to be 128 Ah @ 100
hr, 24 V. The PV array is sized to operate the load on a daily basis based on
average weather conditions using monthly average daily values of solar
radiation data for eleven years. The array is sized to proper sizes in order to
operate the estimated loads reliably in the month of minimum insolation taking
into account different types of power losses. The minimum array size was
obtained as 6 × 47 Wp.
Vosseler et al. (1999) applied the integrated approach for high standard energy
services by Multi-user PV Hybrid Grids. An integrated socio-technical
methodology was developed which reduces costs due to optimized sizing and
reduction of failures due to inadequate user behaviour. The methodology
“Energy Dispenser/Meter” (patented) gave a solution to the energy distribution
in the rural community of Spain.
Murali et al. (1999) designed a solar-wind hybrid system. In choosing the
contribution of renewable energy sources, the storage demand and its
associated decrease in energy price were taken into consideration. The costs of
battery bank account for the critical components in the overall project cost.
The hybrid power plants can work with small battery banks. Moreover solar
and wind energy complement each other in that wind is available whole the
day-night in areas with good regime. For reliability and efficiency, a
Permanent Magnetized Generator (PMG) of 1.5 kW nominal output is chosen.
XXX
The rotor of the generator is directly coupled to the rotor of wind turbine.
Mono-crystalline modules with high efficiency (nearly 30%) will be used for
hybrid system. The total module area will be 12 m2. It is almost the same
swept area of the wind generator. The lead acid battery compensates the
difference between the current energy production and current consumption. It
stores energy in the battery when electricity is produced by solar or wind.
Cardona et al., (1999) reported on a general model for sizing a stand-alone
photovoltaic system, using as energy input data, the information available in
any irradiation atlas. The parameters of the model were estimated by
multivariate linear regression. The results obtained from the numerical loss of
load probability size method (LOLP) were used as initial input data of fit the
mode. For this fit there were used daily global irradiation data from 222 US
meteorological stations for the period 1961-1990. The expression proposed
allowed to determine the photovoltaics array size, with a co-efficient is
independent of the used LOLP value. System parameters and mean monthly
values for daily global irradiation on the modules surface were taken as
independent variables in the models. It also shows that the proposed model can
be used with the same accuracy for other locations not considered in the
estimation of the model. They also proposed model, which would allow
calculating the optimum tilts for the array surface taking the latitude into
account as well as the variability of the incident irradiation.
El-Rafey and El-Sherbiny (1988) simplified the technique for predicting the
photovoltaic (PV) array and system performance. A load/solar/weather database for
seven different locations in Egypt was also provided to aid in the necessary
XXXI
calculations. The insolation data had been collected using of home made, resistance
loaded standard so that their responses were linear with insolation level. Mean
temperature and wind speed have been collected or measured on an hourly basis and
averaged to give daily values. Typical values were 30 Ah at 10 h discharge rate, 2.17
V per battery cell, 0.7 as the battery depth of discharge, and the number of batteries
equal to the total numbers of battery cells used. The fraction of the load that is met
by the solar photovoltaic system is calculated for each of the seven locations.
XXXII
Chapter 3
SOLAR PHOTOVOLTAIC SYSTEM
3.1 Introduction
A photovoltaic system consists of photovoltaic module, energy storage,
converter, charge controller and Balance-Of-System (BOS) components. The
solar cells are the heart of a PV system. A typical PV cell produces less than 2
watts at approximately 0.5 volt DC. So, for high power applications,
photovoltaic cells must be connected in series parallel configurations to
produce enough power. A single solar cell or a suitable interconnected matrix
of solar cells when hermitically sealed with a transparent front cover and
durable back cover constitutes a solar PV module. The cells are configured
into modules and modules are connected as array. Modules may have peak
output powers ranging from a few watts to more than 300 watts. Typical array
output power may be of hundred watts to kilowatt range, although megawatt
arrays exist. Fig 3.1 shows the schematic diagram of a photovoltaic system.
The PV cells produce electricity only when it is illuminated i.e. at daytime only as
shown in Fig.3.2 (a). That means PV systems need energy storage so that the captured
electrical energy may be available at nighttime as shown in Fig.3.2 (b). Generally the
storage mechanism consists of rechargeable batteries, but more exotic storage
mechanisms are also available. In addition to storage, batteries also provide transient
suppression, system voltage regulation and a source of current that can exceed PV array
capabilities. Fig. 3.2 (c) shows a system that operates during shortfall.
A charge controller accompanies the battery storage in order to prevent the batteries
from reaching either an overcharged or over discharged condition. Sometimes an
XXXIII
inverter is also needed to convert the DC from battery or directly from PV array to AC
when connected loads are AC load. In case the PV system does not produce adequate
energy, a back up system is incorporated to it and in that case the system will need a
controller to operate the back up system.
Figure 3.1 Schematic diagram of a photovoltaic system
3.2 Systems
PV systems fall into two basic categories: stand-alone and grid linked. The
grid is the low AC voltage electricity supply network, also known as the
‘utility’ or the ‘mains’. Each of these categories is described below:
Solar
Battery
DC Load AC
Controller
Bi-directional
XXXIV
XXXV
Fig. 3.2 Schematic of daily operation of a typical solar-diesel hybrid system
3.2.1 Stand-alone systems A stand-alone PV system is any system incorporating PV modules and not
having a connection to the grid. The simplest stand-alone system consists of a
module supplying a load directly. Such a system is shown in
Fig. 4.3, which can be used to power a pump or to charge a battery.
Fig. 3.3 PV system directly connected to load
Beyond a certain size of system a charge regulator is necessary to protect the
battery from over-charging with subsequent reduction in life. This forms the
basic DC PV system and is illustrated in Fig.3.4. As loads are added the charge
regulator would also serve the function of protecting the battery from over-
discharging.
PV Array DC Load
XXXVI
Fig. 3.4 Basic stand–alone PV system
Further energy generator can be added to contribute charge to the battery resulting in a
‘hybrid’ system, as shown in Fig.3.5. These generators can include diesel generators,
wind turbines or fuel cells.
The diesel generator is usually limited by automatic control to run for short periods at or
near its most efficient operating point to supply large loads, such as washing machines,
and also to charge the battery. Other generators each have their own method of
regulation with the battery PV charge regulator protecting the battery from over-charge
by the PV system and over-discharge by the load.
Fig. 3.5 Hybrid stand-alone PV system
3.2.2 Grid linked systems Grid linked systems are sub-divided into those in which the grid acts only as an auxiliary
supply (grid back-up) and those in which the grid acts as a form of storage or two-way
supply (grid-connected). In these systems surplus energy flows into the grid and energy
deficit is met from the grid. Alternatively, the grid connected PV system energy supply to
the grid can be considered totally separately from building energy demand which is met
from the grid.
XXXVII
In grid back-up systems the grid could be unavailable at meeting the demand so a stand-
alone AC system consisting of PV array, batteries and stand-alone inverter is used, with
changeover to inverter output when the grid supply goes. Fig. 3.6 illustrates the basic grid
back-up PV system.
Fig. 3.6 Grid back-up PV system
In grid connected systems the grid is assumed to be available most of the time and a grid
connected inverter converts the DC output of the PV array to 230V or 400V 50Hz AC for
direct connection to the grid supply without the need for a battery. Fig.3.7 illustrates a
typical grid connected PV system. The disadvantage of the system is the need for the
presence of the grid for the inverter to function; if the grid fails then no energy is generated
even at times of high irradiance.
XXXVIII
Fig 3.7 Grid connected PV system
Four configurations of metering are possible for grid-connected systems:
(i) C-B, A-D, E-F Parallel metering, no demand offset
(ii) C-B, A-D, E-F, C-F parallel metering with demand offset
(iii C-E, E-F Reversible or no metering with demand
offset
(iv) C-B, C-E, E-A Series metering with demand offset.
3.3 The Solar Cell
A typical solar cell is a specially designed p-n junction diode or Schottky
barrier diode. The performance of a solar cell is governed by different
properties of semiconductor materials like band gap, carrier concentration,
mobility etc. The semiconductor materials are characterized as being perfect
insulators at absolute zero temperature; with charge carriers being made
available for conduction as the temperature of the semiconductor material is
increased. The semiconductor materials have an energy band gap (Eg) between
the valence band and the conduction band. The valence band is the highest
occupied energy band, which is completely filled by the outer valence
electrons of the solid at zero Kelvin. The band immediately above the valence
band is called the conduction band, and is empty at zero Kelvin.
Semiconductors can be divided as intrinsic and extrinsic. In intrinsic
XXXIX
semiconductor the density of electron (n0) in conduction band is exactly equal
to the density of hole (p0) in valence band & is equal to the intrinsic carrier
concentration (ni).
i.e. p0 = n0 = ni (3.1)
Extrinsic semiconductors are those, which have been doped with trivalent or pentavalent
material and depending on type of impurity material it is divided into p-type & n-type
semiconductor.
3.3.1 Doping
Doping means introducing a very small amount of impurity, of
the order of one in a million atoms. One can either introduce
donors, i.e., atoms with excess electrons, or acceptors, i.e., an
atom with a lack of electrons. Silicon generally prefers to share
its four valence electrons with four other partners as shown in
the intrinsic case in Fig3.8. The arrangement is a stable
tetrahedral structure. Each Silicon atom has exactly four
neighboring atoms in a very ordered structure.
Fig. 3.8 Illustration of the effects of doping
Boron is a material with three valence elections. Doping with it results, as
shown in Fig. 3.8 in an acceptor, i.e. it can trap free electrons. It leaves so-
missing electron surplus electron
p-type n-type intrinsic
XL
called ‘holes’ in the lattice. These holes are unsatisfied valence electrons.
They act like positive charges. These charges can move through the material
in exactly the same way as an electron. This ‘p-type’ silicon has holes as
majority carriers, i.e. a current are normally carried though hole transport.
Phosphorus, on the other hand, has five valence electrons. It is a donor because it donates the unsatisfied electron easily. Silicon so doped is called ‘n- type’. The majority carriers are electrons.
Holes, like electrons, will move under the influence of an applied voltage but, as the mechanism of their movement is valence electron substitution from atom to atom, they are less mobile than the free conduction electrons.
3.3.2 The p-n junction If a junction is formed between a p-type semiconductor and an n-type semiconductor, the junction is called a p-n junction. Such a system is illustrated in Fig. 3.9. When a junction is formed between n-type and p-type semiconductor, the first thing to happen is that the conduction electrons on the n-side of the junction notice the scarcity of the same on the p-side, and valence holes on the p-side notice the scarcity of valence holes on the n-side. Since both types of charge carrier are undergoing random thermal motion, they begin to diffuse to the opposite side of the junction in search of wide-open space. This diffusion of charge particles constitute a current known as diffusion current given by
Jp = -qDp dp/dx, for holes and (3.2)
Jn= qDn dn/dx, for electron (3.3)
Where Dp and Dn are known as diffusion constant for hole and electron
respectively.
XLI
Fig. 3.9 Schematic diagram of p-n junction including the charge density and
electric intensity
When an electron leaves the n-side for the p-side, however, it leaves behind a
positive donor ion on the n-side, right at the junction. Similarly, when a hole
leaves the p- side for n-side, it leaves a negative acceptor ion on the p-side. If
large number of holes and electrons travel across the junction, large number of
fixed positive and negative ions is left at the junction boundaries. These fixed
ions, as a result of Gauss’s law, create an electric field that originates on the
positive ions and terminates on the negative ions, thus the number of positive
and negative ions is same across the junction.
The electric field across the junction gives rise to a drift current in the direction
of the field. The holes move in the direction of the electric field and electrons
move opposite to holes. The drift current is given by-
J=σE (3.4) Where J represents the current density in A/cm2, σ the conductivity in Ω-1 cm-1 and E the field strength in V/cm.
For both electrons and holes, the drift current component is opposite to the diffusion current component. The drift and diffusion components for each charge carrier must be equal and opposite, since there is not current flow through the junction region. This phenomenon is known as the law of detailed balance. The built in potential across the junction can be shown as,
Vi = kT/qln (pn0/np0) (3.5)
Considering pn0 ≈ ND and np0 ≅ ni2/NA , we get
Vi = kT/qln (NA ND/ ni2) (3.6)
Where ND is the donor doping density, NA is the acceptor doping density. The
concentration of holes (p = pn0 in equilibrium) which are the minority carriers
XLII
in n-type semi-conductor is many order of magnitude smaller than the electron
(majority carrier) concentration and is given by
pn0 = ni2/ND (3.7)
Again n=np0 is the minority carrier (electron) concentration in equilibrium in p-
type semiconductor and given by
np0 = ni2/ND (3.8)
The magnitude of intrinsic carrier concentration is given by
ni2 = pono
i = NcNve-Eg/kT (3.9)
Where, Nv and Nc are constants known as valence band effective density of
states and conduction band effective density of states respectively.
NV = 2(2πmh * kT/h2)3/2 (3.10)
Nc = 2(2πmc * kT/h2)3/2 (3.11)
Where mh,, mc are effective mass of hole and electron respectively. Another important parameter is Fermi level, which in intrinsic semiconductor is situated near middle of the band gap, with a small offset caused by the difference between the effective densities of states in the valence and conductions. Basically Fermi level is the highest occupied energy level in a semiconductor & is given by
EF = (EV+EC) / 2 + kT / 2 ln (NV/NC) (3.12)
Where,
EC = Conduction band edge energy,
EV = Valence band edge energy.
The Fermi level in doped semiconductor (n-type) is very close to the
conduction band edge energy and is given by
XLIII
EF = EC - kT ln (NC/ND) (3.13)
The respective parameter for a p-type semiconductor is
EF = EV + kT ln (NV/NA) (3.14)
Here the Fermi level lies very close tot he valence hand band edge.
3.3.3 The volt-ampere characteristics
The current, ID in a p-n junction is related to the applied voltage V by the following
equation.
ID = I0 [e (v/η) V
T -1] (3.15)
Where, I0 is the reverse saturation current and VT is the volt equivalent of temperature and it
is given by:
VT = KT / q = T/11600 (3.16)
Where,
T is the temperature in degree Kelvin.
Positive value current, ID implies that current flows p-side to n-side, if V is positive, the
diode is forward biased signifying that the p-side is positive with respect to the n-side. The
symbol η is 1 for germanium diode and is approximately 2 for silicon diode at rated
currents.
Fig. 3.10 shows the volt- ampere charlatanistic of a typical diode and follows the equation
(3.15). With applied voltage V positive and several times VT, the unity within the bracket in
equation (3.15) may be neglected. Hence the current, ID, increases exponentially with
voltage V except for very small values of V, with the diode reverse biased and |V| several
times VT, current ID approximately equals I0. The reverse current is thus constant,
XLIV
independent of the applied reverse bias. This is the reason why IO is called the reverse
saturation.
Fig. 3.10 Volt- ampere characteristics of an ideal p-n diode
To display the forward and reverse characteristics more clearly, we may use two different
scales for forward and reverse currents. Fig.3.11 shows the forward current in milli-amperes
and the reverse current in microamperes. The dotted portion of the reverse current curve
shown that at reverse voltage VZ, the reverse current no longer follows equation (3.15) but
increases abruptly. At this critical voltage, a large reverse current flows and breakdown is to
take place.
Fig. 3.11 Volt- ampere characteristic of an ideal p-n diode with expanded scale for reverse current
I0
I
V
VZ
V,V
μA
I,mA
5 4 3 2 1
6
1.0 0.5
XLV
3.3.4 Generation of electron-hole pair by photon absorption
According to Einstein quantum theory, light is composed of quanta of energy known as
photon. The energy of each photon is given by
E = hc/λ in joules (3.17)
Where h is the Plank’s constant, h = 6.63 x 10-34 joules-sec, c is the velocity of
light equal to 2.998 x 108 m/sec, λ is the wave length of light expressed in
meters.
E=1.24/λ in eV (3.18)
In order to be absorbed a photon must have energy greater than the band gap energy (Eg).
Photons with energy in access of the band gap can be converted into electricity. If a photon
has energy greater than the band gap it still can produce only a single electron hole pair
(EHP), the remaining energy being lost to the cell as heat.
3.4 Technical Aspects of Solar Cells
3.4.1 I-V characteristic of solar cells
When a load is connected to an illuminated solar cell, the current that shows is
the net result of two counteracting component of internal current:
(a) The photo generated current or simply photocurrent, IL, due to
generation of carriers by the light.
(b) The diode or dark current, ID, due to the recombination of carriers
driven by extended Voltage, This voltage is needed to deliver power
to the load.
Let us assume these two carriers can be superimposed linearly and this
is true in for many practical cases. Then the current in the external
XLVI
circuit can be calculated as the difference between these two
components. Thus, we can write,
I = IL-ID ( V) (3.19)
If we assume that in the diode can be expressed by a single exponential
equation, then the characteristic equation for the device is
I = IL- I0 [ exp [( eV / mkT )-1] (3.20)
The I-V curve represented by this equation is shown in Fig.3.12
Fig.3.12 Illuminated I-V characteristics
5
-0.2 0 0.2 0.4 0.6 0.8 1.0 1.2
15
10
30
25
20
0
-5
Voltage, (V)
Cur
rent
den
sity
(
mA
/cm
2 )
XLVII
3.4.2 Short- circuit current and open-circuit voltage
As seen from Fig 4.12, the greatest value of current with the cell as a
generation (in the first quadrant) is obtained under short- circuit conditions,
when V=0. According to equation (3.19) the short circuit current Isc, is given
by
Isc ≡ I (V=0) IL (3.21)
If the device is kept in open-circuit, so that I = 0, it biases itself with a voltage that is the
greatest that can arise in the first quadrant. This is called the open circuit voltage Voc. Its
value is such that the photocurrent is completely cancelled by the bias current, that is to say
IL = ID(VOC) under open-circuit conditions. Then from equation (3.23), we find:
VOC = mkT / q ln [IL / I0 + 1] (3.22)
The definition of the above two operating parameters allows as the alternative
form of characteristics curve:
I=ISC [1-exp (-e (VOC-V / mkT) (3.23)
This form is sometimes useful. The formula is accurate about the open-circuit point, but its
viability for the whole working range its questionable since the parameters m and I0 of
equation (2) depend, to some extent, on the position on the curve.
XLVIII
3.5 Effect of irradiation
The cell current is directly proportional to the cell irradiance. Thus if the cell
current is known under standard test conditions G0=1 kw/m2 at 1.5 AM, then
the cell current at any other irradiance, G, is given by
IL (G) = (G/Go) IL (Go) (3.24)
Where IL = the component of cell current due to photons.
The I-V characterististics of solar cell under different illumination levels is
shown in Fig. 3.13
Fig. 3.13 I-V characteristics of solar cell under different illumination levels
3.6 Effect of Fill Factor
The fill factor is a measure of the quality of the cell. Cell with large internal resistance will
have smaller fill factors, while the ideal cell will have a fill factor of unity. Typical FFs for
real solar cell may vary from 0.5 to 0.82. The secret to maximizing the fill factor is to
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0
3-
2-
1-
1 kw/m2
750 w/m2
500 w/m2
250 w/m2
Cel
l Cur
rent
, A
Cell Voltage, V
Real Cell
XLIX
maximize the ratio of photocurrent to reverse saturation current while minimizing series
resistance and maximizing shunts resistance within the cell.
3.7 Effect of Temperature
The PV cell I-V & P-V curve is temperature sensitive. The open circuit voltage is
directly proportional to the absolute temperature of the cell. The reverse saturation
current is also highly temperature dependent. The net result is that the open circuit
voltage of a silicon PV cell decreases by 2.3 mV for each degree Celsius (°C) increase
in temperature. The temperature dependence of PV curve of solar cell is shown in Fig.
3.14.
Fig. 3.14 Temperature dependence of PV curve of a solar cell
3.8 Radiation Absorption and Material Selection
The interaction of radiation with different materials is characterized by the
absorption coefficient ∞. The variation of ∞ with photon energy is as shown in
Cell Voltage, V
Cel
l Pow
er, W
0 0.2 0.4 0.6
0
0.5
1.0
1.5
2.0
50ºC 25º C
0ºCX C
-25º C
L
the Fig.3.15. The absorption edge is determined by the energy band gap of the
material. The nature of the band gap also affects the efficiency of absorption in
the material. Depending upon the nature of the band gap the semiconductor
materials are divided into direct and indirect band gap materials. The direct
band gap materials e.g. GaAs, CdTe and amorphous silicon absorb photons
much more rapidly than the indirect materials, such as crystalline silicon. As a
result, the direct band gap material does not need to be nearly as thick to
absorb a significant part of the incident radiation.
Fig. 3.15 Variation of á with photon energy for different semiconductors
0 0.5 1.0 1.5 2.0 2.5 3.0
103
104
105
106
CuInSe2
GaAs
CdTe
C-Si
a-Si
hv [eV]
α [V
cm]
LI
According to Beer’s law describing the penetration of radiation through
matter, we have
I(z) = IJ (I-r)e - dz (3.25)
Where Ij is the irradiance, r is the reflection coefficient at the surface, and the
direction of penetration. The absorption coefficient ∞ determines the r equired
thickness of photovoltaic layers. Let ∞ be in the order of 5, which express that
within the thickness more than 99% of the radiation is absorbed, then this
absorption depth describes the thickness of the absorber material.
Zs=Lá=5/∞ (3.26)
LII
It can be reduced by the use of reflecting back surfaces. Whereas crystalline Si solar
cells require thickness ranging between 50 and 100 µm, cells consisting of direct band
gap semiconductor or amorphous Si can be realize with thickness below 1µm. The use
of direct semiconductor implies a substantial reduction of material and leads to the
concept of thin film solar cell.
3.9 Maximizing of Solar Cell Performance
The following steps can maximize the performance of a PV cell:
• Minimizing the reverse saturation current
• Optimizing photocurrent
• Minimizing reflection of incident photons
• Maximizing minority carrier diffusion length
• Maximizing junction width
• Minimizing surface recombination velocity
• Minimizing cell resistance losses.
3.10 The PV Module and Array
In a module, a number of cells are connected together in series that shown in Fig. 3.16.
The electrons flow from one cell into conductors that carry them to next cell. In that cell
they are once again struck by photons, being lifted to a higher potential energy and
swept out of the cell. Finally electrons leave the last cell in the module and flow to the
load. As soon as an electron leaves the last cell in the module and enters the wire, an
electron at the other end of the wires moves into the first cell in the module. So, a PV
cannot run down like a battery nor produces electricity in response to light. A PV cell
cannot store electrical energy; it can only convert light energy into electrical energy.
Since PV systems are commonly operated at multiples of 12 volts, the modules are typically
designed for optimal operation in these systems. The design goal is to connect a sufficient
LIII
number of cells in series to keep Vm within a comfortable range of the battery and system
voltage under conditions of average irradiance.
When the module is not illuminated then it can be considered as a series connection of
diodes that may be forward biased by the system shortage batteries. To prevent the
current from flowing in the reverse direction a diode is connected in series with the
module. This blocking diode has forward voltage drop and associated with power loss of
more than 1 Watt when the module is providing photocurrent. Again shading of
individual cells may result heating of the cell and this phenomenon can cause premature
cell failure. To protect the system against such failure, arrays are generally protected
with bypass diodes.
Fig 3.16 Photovoltaic Module
The cells in a module are covered with anti-reflective coating, then with a
special laminate to prevent degradation of the cell contacts. The module
housing is generally metal, which acts as a heat sink for the heat generated in
the modules from the fraction of absorbed sunlight not converted to electricity.
+ – Silicon photovoltaic ll
Silicon photovoltaic cells
DC electricity
Frame (plastic or metal)
Sun
LIV
When the PV cells are mounted in a module, they can be characterized as
having a Nominal Operating Cell Temperature (NOCT). The NOCT is the
temperature that the cells will reach when they are operated at open circuit in
an ambient temperature of 20°C with G=0.8 kW/m2 and a wind speed of less
than 1 m/s. For variations in ambient temperature and irradiance the cell
temperature (in °C) can be estimated quite accurately with the help of
following relation:
TC = TA +G (NOCT-20) / 0.8 (3.27)
The cells in a module being series connected must be matched as closely as possible. If this
is not the case, while some cells are operating at peak efficiency, others may not be
optimized. As a result, the power output from the module will be less than the product of the
number of the cells and the maximum power of one single cell.
To obtain higher voltages or currents than are available from a single module are
required, modules must be connected into arrays. Series connections result in higher
voltages, while parallel connections result in higher currents.
3.11 Energy Storage
PV cell can generate electricity under illumination only. So for nighttime application energy
storage is a must. The most common energy storage is Lead Acid Storage Battery. This is
relatively economical storage of relatively large quantities of electrical energy. Basic
properties of lead acid storage batteries shown in Table 3.1.
Table 3.1 Properties of lead acid storage battery
State of Charge Specific Gravity Cell Voltage Voltage of 12 Freezing
LV
(SOC) (%) (V) V Battery Point 0F
100% 1.265 2.12 12.70 -71
75% 1.225 2.10 12.60 -35
50% 1.90 2.08 12.45 -10
25% 1.155 2.03 12.20 +3
0% 1.120 1.95 11.70 +17
Source: (Messenger, 2000)
3.12 Associate Electronic Components
3.12.1 Charge controller
In nearly all system with battery storage, a charge controller is an essential component. The
charge controller controls the charging and discharging process of the battery. The charge
controller cuts off the load when the battery reaches a prescribed state of discharge.
3.12.2 Inverter
An inverter converts DC current into AC current. Depending upon the requirements of the load, a number of different types of inverters are available. Selection of the proper inverter for a particular application depends on the waveform requirements of the load and on the efficiency of the inverter. Inverter selection will also depend on whether the inverter will be a grid connected system or stand-alone system. Inverter failure is one of the PV system failures. Different inverters with their specification are given in the following Table 3.2.
Table 3.2 Summary of inverter performance
Parameter Square wave Modified sine
wave
Pulse Width
Modulated
Pure Sine
wave
Output Power
Range (watts)
Up to
10,00,000
300-2.500 Up to
20,000
Up to 2,000
Efficiency 70-98% 70-85% > 90% Up to 80%
Source: (Messenger, 2000)
3.13 Balance of System Components
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The Balance-Of-System (BOS) components include mounting materials for the
modules, wire and all wiring components, lighting protectors, grounding
connections and battery containers. BOS can be divided into two categories-
power related BOS and area related BOS. The area related BOS includes
module support structure, foundations, electrical wiring and control elements
for the system. The power related BOS are power conditioning unit (inverter,
controller, maximum power taker etc), metering and safety provisions.
LVII
Methodology CHAPTER 4
4.1 Optimal Design Using Genetic Algorithm
A PV-diesel hybrid system has a greater reliability for electricity production
and least costly than the systems that use a single source of energy. When
designing a hybrid system both the sizing of the elements and the most
adequate control strategy must be obtained. Obtaining a good control strategy
is essential, since the performance of a PV hybrid system can be significantly
affected by relatively small changes made in the control strategy. The optimal
design of such a system can not be achieved easily using classical optimization
methods. Genetic Algorithms (GA) appears to be the adequate search
technique for such complex problems.
The Genetic Algorithm (GA) is a global optimization method based on the
principle of survival of the fittest Darwin's hypothesis of evolution. The basic
principles of the GA are attributed to Holland (1975) and further developed for
engineering applications by Goldberg (1989) and Michalewicz (1996).
Genetic algorithms simulate the phenomena of reproduction, selection,
crossing and mutation that are observed in nature using a computer program.
GA may manipulate a population of candidate solutions to a problem. The
candidate solutions are typically binary strings, but any representation may be
used. At every generation some of the candidate solutions are paired and parts
of each individual are mixed to form two new solutions; this is crossover,
uniform crossover exchanges individual bits whereas multi-point crossover
exchanges whole substrings. Additionally every individual is subjected to
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random change - mutation. The next generation is produced by selecting
individuals from the current one on the basis of their fitness, which is a
measure of how good each candidate solution is. Eventually the population
should become saturated with individuals of very high fitness.
The PV-diesel system is studied using an hourly time step for one hour. Every
hour the following inputs are estimated: the current from PV which depends on
solar insolation, the ac load current which depends on predicted load and the
battery state of charge (SOC).
The GA used here is divided into two parts: main algorithm and secondary
algorithm. The main one searches for possible component configurations of
the hybrid system whereas the secondary one searches for the best strategy for
each of the configurations found in the main algorithm. The main algorithm
works with an integer vector with the number of PV in parallel, the solar
generator type code (PV panel), the battery type code, the number of batteries
in parallel and the diesel generator type code. The secondary algorithm works
with a Boolean vector with the dispatch strategy.
The main requirements for system design are:
(i) The site information (environmental data), such as solar intensity,
ambient temperature, relative humidity and cloudiness should be collected.
(ii) The electrical load information such as the load type and time of use of
electrical appliances should be identified.
LIX
(iii) The specifications and cost information of solar panel, battery, inverter,
charge regulator and diesel ac generator set.
There is a "Frugal" option that can be applied in all the strategies, The critical
discharge load (Ld) is the net load above which the marginal cost of generating
energy with the Diesel generator is less than the cost of drawing' energy out of
the batteries. If the Frugal option is applied, then the Diesel generator meets
the net load whenever the net load is above the critical discharge load,
regardless of whether or not the battery bank is capable of meeting the net
load.
The cost of generating energy with the Diesel generator and the cost of
drawing energy out of the batteries are equal when the net load is Ld:
B. PNgen Prfuel + Co &Mgen + Crep-gen-h + A. Prfuel Ld
=Ccycling-bat Ld
ηinv (4.1)
then, Ld can be calculated as follows:
Ld =
Pgen is the Diesel generator acquisition cost plus O & M cost throughout Diesel
generator lifetime (Є) and Lifegen is the Diesel generator lifetime (h). Ccycling_bat
(Є/kWh) is the cost of cycling energy through the batteries.
B. PNgen Prruel + Co &Mgen + Crep-gen-hCcycling_bat-ηinv. A. Prfuel
(4.2)
where CO&Men is the Diesel generator's hourly operation and maintenance cost
(Є/h), Prfuel is the fuel price (Є/I).
A = 0.2461/kWh and B = 0.84151/kWh are the fuel curve coefficients. The fuel
cost of 1 h Diesel running, Cfuel (Є) is,
Cfuel = Prfuel . (B. PNgen + A. Pgen) (4.3)
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Ccycling_bat = Cbat
CN . Nbat_p . UDC . Ncycles_eq ?1000
The Critical Charge Load is the net load where the cost of generating, this load
with the Diesel generator (exactly this load and no more) for 1 h is the same as
(4.4)
Cbat is the batteries bank acquisition cost plus O&M cost throughout batteries
lifetime (Є), CN is the nominal capacity of one battery (Ah), Nbat_p is the
number of batteries in parallel, and Ncycles_eq is the number of full cycles of
battery life. We have assumed that the batteries can cycle a certain amount of
energy, which divided by its nominal capacity, gives the equivalent cycles (full
cycles). It is true that the energy that a battery can cycle depends on the depth
of discharge, but is almost constant if the discharge is never allowed to fall
below SOCmin, this being greater than 20%.
4.1.1 Cycle charging strategy
If the batteries cannot meet the net load, the Diesel. generator runs at full
power (or at a rate not exceeding the maximum energy that batteries are
capable of absorbing) and charges the batteries with any surplus power. If a
SOC set point is applied, the Diesel generator will continue running until the
batteries reach this SOC set point.
The Frugal option also can be applied in this strategy.
4.1.2Combined strategy
This strategy combines both strategies. If the net load is lower than the
Critical Charge Load, Lc, (kW), the Cycle Charging strategy is applied. If the
net load is higher than L, the load Following strategy is applied.
/
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the cost of supplying this load, for 1 h, with the batteries that have been
previously charged by the Diesel generator. Mathematically this is:
B. PNgen . Prfuel + Crep_gen_h + A . Prfuel . Lc
=A.Prfuel . Lc
ηch . ηinv +
Ccycling-bat Lc
ηinv (4.5)
Where Lc is
Lc = ηch . ηbat PNgen Prruel + Co &Mgen + Crep-gen-h
ηch . ηbat Ccycling_bat-ηinv. A. Prfuel
(4.6)
Where ηbat is the battery efficiency in the charging process.
The Frugal option also can be applied in this strategy.
4.2 Genetic algorithm
The problem to be solved has a great number of possible solutions
(combinations of solar generator, batteries, Diesel generator and strategy
variables), and it is difficult to solve this problem with classical mathematical
techniques.
The Genetic Algorithms technique works with individuals (possible solutions).
A vector whose components represent the parameters of the system using an
integer code can represent an individual.
The GA developed in HOGA by Dufo-Lopez et al. (2005) is divided in two
parts: main and secondary algorithm.
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4.2.1 Main algorithm
The main algorithm works with an integer vector with the number of PV
panels in parallel (4 the solar, generator type code (PV panel) (b), the battery
type code (c), the number batteries in parallel,(d) and the Diesel generator type
code (e): |a|b|c|d|e|.
Each solar generator is from a different manufacturer and their characteristics
are: power, voltage and acquisition cost.
Each battery is from a different manufacturer and their characteristics are:
rated capacity, voltage, acquisition cost, DODmax number of equivalent cycles
and efficiency.
Each Diesel generator is from a different manufacturer and their characteristics
are: power, voltage, acquisition cost, lifespan, minimum output power, and
O&M hourly cost.
The algorithm simultaneously uses Nm vectors such as the one described
beforehand.
The main algorithm obtains the optimal configuration of PV panels, batteries
and Diesel generator, minimizing the Total Net Present Cost of the system
(CTOT), which includes all the costs throughout the useful lifetime of the
system, which are translated to the initial moment of the investment using the
effective interest rate, according to' standard economical procedures.
CTOT = CSEC + CACQ-PB + CACQ-B + CACQ-BCH
+ CACQ-GEN + CREP-BCH + CO&M_PV + CO&M_B (4.7)
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Where CSEC includes the costs that depend on the optimal strategy. It is
evaluated in the secondary algorithm, explained CACQ-PV, CACQ-B, CACQ-BCH
CACQ-GEN are the costs of the acquisition of the PV panels, the batteries, the
battery charger and the Diesel generator. CREP-BCH is the cost of replacing the
battery charger throughout the life of the system (it does-not depend on the
strategy because we assume it has fixed initial cost and life). CO&M_PV, CO&M_B
are, respectively, the costs of maintenance of the PV panels and the batteries
(they do not depend on the strategy). CTOT must be calculated for each
combination, represented by one of the Nm vectors which constitute the
population.
The fitness function of the combination i of the main algorithm is assigned
according to its rank in the population (rank 1 for the best individual
considering the objective function, and rank Nm for the worst solution),
FitnessMAIN = (Nm + 1) - i
Σj [(Nm + 1) - j] , j = 1 ………. Nm (4.8)
4.2.2 Secondary algorithm
The secondary algorithm works with a Boolean vector with the dispatch
strategies (Cycle Charging or Combined), the "Frugal" option, and 5 bits that
represent the SOC set point in Gray code (better than binary code for GA):
|Stmtegy|Frugal|go|gl|g2|g3|g4|.
The Load following strategy is evaluated at the end, as this strategy has no
SOC set point.
The algorithm use Nsec vectors such as the one previously described.
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For each vector of the main algorithm, the optimal strategy is obtained
(minimizing the non-initial costs, including operation and maintenance costs,
CSEC) by means of the secondary algorithm.
CSEC = CACQ-INV + CACQ-REG + CREP-B
+ CREP-INV + CREP-REG + CREP-GEN
+ COKM-GEN + CFUEL (4.9)
where CACQ_NV, CACQ_REG are the acquisition costs of the inverter and the
charge regulator respectively (the inverter maximum power and the charge
regulator current depend on the strategy, so their cost must be here). CREP-B,
CRWJNV, CREP-REG, CREP-GEN are the Costs of replacing the batteries, the inverter,
the charge replator and the Diesel generator throughout the life of the system.
CO&M_GEN is the cost of operation and maintenance of the Diesel generator
throughout the life of the system. CFUEL is the cost of the fuel consumed
throughout the life of the system.
We assume that the system life is the life of the PV panels which are the
elements that have a greater lifetime.
The fitness function of the combination i of the secondary algorithm is
fitnessSECi, = (Nsec + 1) - i
Σj [(Nsec + 1) - j] , j = 1 …………. Nsec (4.10)
4.2.3 Implementation of the GA
HOGA has been implemented in the following way:
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1. Initially, Nm vectors are obtained randomly from the main algorithm. These
vectors have been described in 4. 1, each one representing a possible
configuration of PV panels, batteries and Diesel generator.
2. For each vector N. of the main algorithm, the secondary algorithm is
executed, obtaining the optimal dispatch strategy for each Nm vector:
2.1. Nsec vectors are obtained randomly from the secondary algorithm. These
vectors have been described in 4.2, each one representing a possible dispatch
strategy.
2.2. The Nsec vectors are evaluated by means of their aptitude .
2.3. The best vectors (fittest) have a greater probability of reproducing
themselves, crossing with other vectors. In each cross of two vectors, two new
vectors are obtained (descendents). The descendents are evaluated and the best
of them replace the worst individuals of the previous generation (iteration).
2.4. To find the optimal solution and not to stay in local minimal, some
solutions randomly change some of their components (mutation). The
mutations can effect the change of the control strategy or the change of a bit of
SOC set point.
2.5. The individuals (vectors) obtained from reproduction and mutation are
evaluated, making the next generation.
2.6. The process continues (from 2.2 to 2.5) until a determined number of
generations (Ngen_max) have been evaluated.
3. Nm solutions will have been obtained (vectors of the main algorithm
with their optimal dispatch strategies). The Nm solutions am evaluated by
means of their aptitude.
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4. Reproduction, crossing and mutation are carried out on the obtained
solutions, making the next generation.
5. The process continues until a determined number of generations have been
evaluated. The best solution obtained is that which has the lowest value of
CTOT.
The flow diagram of the algorithm is represented in Fig. 4.1.
Fig. 4.1 Flowchart of HOGA
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4.3 Case study: Sandwip-an Isolated Island
Sandwip is a small island in Bangladesh, which is totally disconnected from the main land.
The island is situated in a very remote area and lacks both an electricity supply and modern
facilities. But the island has great potential for developing electricity from solar energy.
This case study examines how decentralized, small–scale, community-based power can be
generated.
4.3.1 Characteristics of the island-Sandwip
(a) Location and population: Sandwip is a deltaic island in the Bay of Bengal region of
Bangladesh, adjacent to Chittagong and a mere 15 km from, the main land. The population
is around 330,000 on an area of 240 km2. The entire Island is a mudflat created from the
Ganges (Fig.4.3).
(b) Natural hazards: The island is situated in the middle of the Meghna estuary where
strong storm surges propagate deep into mainland through the river channel. The storm
surges height is higher than at any other part of coastal Bangladesh. Thus, the coast of
Sandwip is very vulnerable to cyclones and flooding.
(c) Infrastructure: Sandwip is one of the oldest islands in the Chittagong region. Most of
its natural and historical heritage has been drowned in the Meghna River through the
erosion process. Most of there people are very poor. Therefore, apart from some private
building, most of the concrete
LXVIII
Fig. 4.2 The location map of Sandwip, Bangladesh
buildings on the island belong to the government including the multipurpose cyclone
shelter.
LXIX
(d) Electricity characteristics: Most of the energy in Sandwip is derived from fossil fuel
(mainly from biomass, coal and oil) which is not a good option for sustainable development.
However, in common with the predominantly agro-based population of the rest of
Bangladesh, bio-fuel is mostly used for cooking in Sandwip. On the other hand, lighting
needs are met by using kerosene. Expenditure of lighting is minimized by short evening
hours and limited nighttime activities. A short electricity grid is available linking the main
commercial areas on the island. (Shahjahan, 2004).
Therefore, the opportunities for expansion of electricity-based industry are limited. In this
context, the need for developing energy, in particular solar-diesel hybrid power generation
system carries a greater sense of urgency on this island.
(e) Lifestyle characteristics: The humane-resource relationships that operate at present
Sandwip are characterized by:
(i) Widespread poverty, limited livelihood opportunities;
(ii) Poor levels of service provision and very poorly developed institutional structure;
(iii) Highly unequal social structure, high levels of conflict and poor law and order;
(iv) A few powerful people dominate the mass of the coastal population;
(v) Rapid decline in common resources;
(vi) The constant threat of cyclones and storm surges;
(vii) The long-term effects of climatic change;
(viii) Widespread pollution and resources degradation; and
(ix) Very poor access to infrastructure and technology.
(f) Communication characteristics: The Island has a very poor transportation system in
most of the areas. Lack of good transport blocks the growth of trade and commerce.
Traditional bullock cart is used as the main vehicle. Besides road communication, there is
no electro-communication system, except walkie-talkie and a very poorly managed
telephone system
4.3.2 Energy requirements in the community
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The household in rural Bangladesh is simple and does not require a large quantity of
electrical energy for lighting and electrical appliances. Salequzzaman (2003) estimated 20
kWh for about 132 families in the island Sandwip. Monthly average daily load profile
illustrated in 4.3. The average daily solar radiation varies from 6 kWh/m2 to 4 kWh/m2 with
average value of 4.3 kWh/m2. The standard deviation is 0.67 shown in Fig. 4.4.
Fig. 4.3 Monthly average daily load profile
Figure 4.4 Average daily solar radiations.
LXXI
4.4 Design Layout
The design layout of the distribution system of a solar diesel hybrid system in
Sandwip is shown in Fig. 4.4 (Bala, 2004).
Fig. 4.5 Schematic diagram of batteries in series and parallel connection
+
Solar panel
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CHAPTER 5
RESULTS AND DISCUSSION
The system has been designed and optimized using the software HOGA
developed by Dufo-Lopez et al. (2005)
The parameters considered are: cross over rate, 0.9 and mutation rate 0.01. Maximum number of different PV panel in parallel is 35. Maximum number of different batteries in parallel is 20. The daily load profiles are represented by a sequence of powers, each considered as constant over a time-step of one hour.
The effective interest rate considers is 10 %. The O&M cost of the system is 7 %. The system life time 25 years and Installation cost is 1500 € according to the present local market cost.
5.1 The optimized system configurations
4 PV panels serials × 25 panels parallel 125 Wp, 4 Batteries serials× 9 batteries parallel Cn= 96 Ah, 230 V 1.9 kVA AC generator and 3300 VA inverter. The system supplies 48 V DC and 230 V AC.
The Additional data for PV panel fixed operational and maintenance costs
is 40 €/year. Loss factor is 1.2, Annual inflation rate for PV panels Cost is
4% and maximum variation of PV panels cost is 20 %.
The fixed operating cost are independent of the number and the type of PV panel use for the photovoltaic generator. Fixed operator overheads and cost for maintenance materials are included regardless of the size of the generator.
HOGA calculates cycled energy throughout battery life for each
discharge. Depth-life cycles pair; since batteries will not operate for high
depth.
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Capacity batteries varies from 43Ah to 462 Ah and the cost of the
batteries varies 20%. Fixed operational and maintenance cost (O&M) 50
€/year. Annual inflation rate is 4 %.
The inverter selected has a rated output power is 3300 VA. The hourly average output power is 6.9 % of the rated output power of the selected inverter. The average efficiency is 85.4 %.
Auxiliary equipment includes battery charge regulator and AC/DC
converters (rectifiers) are used in the system. Both equipment lifetimes
are 10 year and efficiency is 90 %. State of charge (SOC) of the batteries
and minimum power of the ac generator are 40% and 1,368 W
respectively.
5.2 The control strategies
If the power produced by the renewables is higher than the load, the
batteries are charged with spare power from the renewables. If the power
produced by the renewables is lower, the power not supplied to meet the
load is supplied by batteries (if they can not supply the whole, the rest will
be supplied by the ac generator).
The results were obtained with the following values: main algorithm generations 50;
population 20 and secondary generations 25 and population 10. Figure 4 shows the
evolution of the best total net present cost as a function of the main algorithm
generations in an optimization where the number of generations in the main
algorithm is 50 and the net present cost is 135,638 Euro. Also the emission is 548 kg
CO2
LXXIV
Figure 5.1. Total net present cost and emissions a function of the main Algorithm generations. Figure 5.2. Shows the power generation capacity of the PV generator, ac generator and inverter. The component capacity of the PV generator, ac generator and inverter are 12.5 M, 1.9 kVA and 3.30 kVA respectively. The PV generator is the single largest unit responsible to supply the electricity.
Fig. 5.2. Power generation capacity of the PV generator and inverter
Figure 5.3. Shows the annual energy balance of the hybrid PV-diesel
system. Total load and energy charged by the battery are supplied by
solar PV system and ac generator. The major share of the energy (10,890
kWh) comes from solar PV while the contribution of diesel generation is
very small (664 kWh).
12.5
1.9
3.3
0
2
4
6
8
10
12
PV Gen. INV
Pow
er (k
W)
LXXV
Fig. 5.3. Annual energy balances the hybrid system
Figure 5.4. Shows the costs of the different elements of the hybrid PV-diesel system as a percentage of the total net present cost. Batteries cost 15% of the total net present cost. The item others have the least share of 2% and the PV panels are the most expensive elements at 67%. Thus, the cost of PV panels is the most important leverage point where action program is essential for reduction of the production cost of solar panels.
Fig. 5.4. Cost of the different elements of the hybrid system as percentage of
the total net present cost
PV67%
Inverted + AUX8%
Batterys 15%
AC gen. fuel8%
Others 2%
PV
Inverted + AUX
Batterys
AC gen. fuel
Others
7475
1072
10890
664
4563 4579
0
2000
4000
6000
8000
10000
12000
Total load Exc. PV AC Gen. C. Battery D. Battery
Ene
rgy
(kW
h)
LXXVI
CHAPTER 6 CONCLUSIONS
An optimization of PV-diesel hybrid systems for mini-grid for an isolated
island - Sandwip in Bangladesh using genetic algorithm is presented.
It gives the best solution of all possible combinations, finding the best solution
with the help of GA. It gives the number of PV panels and its type, and the
number of batteries in parallel and their type, the number of generator and their
type and number of Inverter and its type.
This study reveals that the major share of the cost is for PV panel-Inverter and technological development in solar photovoltaic technology would make rural electrification in the isolated islands more promising and demanding.
It is clear that human life can be sustained on earth in future only if, within a
short period, we are able to replace conventional energy sources with an
alternative source of energy. Solar energy in general and solar PV in particular,
constitutes such a potential alternative. Solar energy utilization currently has
many deficiencies. However, such deficiencies cannot remain permanent in
front of human ingenuity and intelligence.
The goal for the century ahead must be the complete substitution of
conventional sources of energy by constantly available solar energy - in other
words, a complete solar energy supply for humankind.
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GLOSSARY
AC
An acronym for alternating current; the electric current that reverses its direction 50
times and the frequency in Bangladesh is 50 Hz.
Array
An appropriately interconnected combination of PV panels installed outdoors in the
field or rooftop.
Azimuth
Horizontal angle measured clockwise from true north; 180° is due south.
Balance-of-system (BOS)
All mechanical, electrical and electronic components and subsystems in a PV system
taken together, other than the PV array and the storage battery.
Cycle battery The cycle consisting of charging and discharging of a storage battery. [[
DC
Direct current; electric current that always flows in the same direction, positive to
negative. Batteries and solar cells are both dc devices.
Grid
LXXVIII
A term generally used to designate the electrical utility distribution network. However,
with reference to a solar cell, the grid describes the metallic contact pattern on its top
surface.
Insolation
An acronym for Input solar radiation; generally expressed in the energy units of kWh
per m2.
Inverter
An electronic equipment that converts direct current (dc) electricity to alternating
current (ac).
Irradiance
The power of instantaneous solar radiation incident on a surface; generally expressed
in units of kW per m.
Module
An appropriately interconnected combination of solar cells with two output terminals
when hermetically sealed with a front transparent cover. A module is the smallest
energy building block available to the user of solar photovoltaic.
Panel
An appropriately interconnected combination of a number of PV modules on a solid
frame structure. Even one single module on a frame may constitute a panel.
PV
An acronym for photovoltaic (adjective) or photovoltaic (noun).
LXXIX
Rectifier
A two-terminal device, made of an appropriate semi-conductor material like silicon,
which has a unidirectional current carrying characteristic. Also known by the name of
diode.
Solar battery
The term was coined during the 1950’s to designate silicon solar cell. It appears to be a
misnomer because the word battery normally refers to a device that can store energy.
Although solar cells convert sunlight into electricity they cannot store the generated
energy. However, referring a solar cell as solar battery is in general practice even now.
Wp
An acronym to designate the peak watt or watt peak in solar photovoltaic. It represents
the unit to denote the capacity of a solar cell, module, panel or an array at STC. while
kWp designates kilo watt peak, MWp designates mega or million watt peak.
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NOMENCLATURE
A = Cost for a group of batteries
Ac = Auxiliary cost
B1 = First group of batteries
BC = Cost of first group of batteries
BCC = Battery charge controller
d = Discount rate (%)
DOD = Allowable depth of discharge for battery
EL = Electrical load, kWh/day
GA = Genetic Algorithms
H = Average solar energy input/day (kwh/m2/day)
HOGA = Hybrid optimization by genetic algorithms
i = Annual inflation rate (%)
Ic = Inverter cost
Ic = Initial cost
ICPV = Initial cost of PV system
INc = Installation cost
LCC = Life cycle cost
MToe = Mega ton of oil equivalent
N = Life time (years)
n = Day of the years
Nc = Number of continuous cloudy days
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NOMENCLATURE(Contd.)
NREL = National renewable energy laboratory, USA
OMc = Operation and maintenance cost
Pe = Electrical power (Watt)
PSI = Peak solar intensity at the earth surface (1000W/m2)
PVac = PV array cost
PW = Present worth
SOC = State of charge
TCF = Temperature correction factor (º C)
φ = Latitude angle
ηb = Battery efficiency (%)
ηINV = Inverter efficiency (%)
ηpv = Efficiency of PV module (%)
ηout = Battery efficiency × inverter efficiency (%)
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Appendices
Appendix - A Table A.1. PV modules specifications and costs Type Nominal
voltage (V)
Shortcut current
(A)
Nominal power (Wp)
Acquisition cost (€)
Maintenance cost per year
(€/year)
Life span
(years) Panel 0 12 0 0 0 0 25
Panel 1 12 3.17 50 387 0 25
Panel 2 12 4.80 80 564 0 25
Panel 3 12 7.54 125 892 0 25
Appendix - B Table B.1. Batteries specifications and costs Type Nominal
capacity (Ah)
Voltage (V)
Acquisition cost (€)
Maintenance cost per year
(€/year)
Float life
(years) Battery 0 0 12 0 0 50
Battery 1 43 12 155 0 12
Battery 2 96 12 258 0 12
Battery 3 200 12 555 0 12
Battery 4 462 12 1017 0 12
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Appendix - C
Table C.1. Inverter specifications and costs
Type Power (VA) Lifetime (year) Acquisition cost (€)
Inverter 0 0 50 0
Inverter 1 3300 10 3608
Inverter 2 4500 10 4138
Inverter 3 10000 10 16048
Inverter 4 13500 10 17786
Appendix – D
Table D.1. Generator specifications and costs
Name Rated power (kVA)
Acquisition cost (€)
Maintenance cost per year
(€/year)
Life span
(hour)
P. min (% of Pn)
Fuel type
Gen 0 0 0 0 100000 30 Diesel
Gen 1 1.9 1269 0.2 7000 30 Diesel
Gen 2 3.0 1514 0.2 7000 30 Diesel
Gen 3 5.5 2314 0.2 7000 30 Diesel
Gen 4 13.5 7200 0.2 7000 30 Diesel
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Appendix – E
Table E. 1. Monthly Average daily solar insolation
Horizontal Surface
Month Irradiation
(kWh/m2) Month
Irradiation
(kWh/m2)
January 4.5 July 4
February 5 August 4.2
March 5.8 September 4.1
April 6.0 October 4.6
May 5.2 November 4.5
June 4.2 December 4.2
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REFERENCES
Adhikary, R. B. (1999). Status of Solar PV and Thermal Technology in Nepal,
Practice and Prospects of Income Generating Activities Through Solar
Photovoltaics. 2nd International Seminar on Renewable Energy for
Poverty Alleviation, at IEB, Dhaka, Bangladesh, Nov., 266-27, 1999.
Agustin et. al. (2005). Design of isolated hybrid systems minimizing energy
costs and pollutant emissions. Renewable Energy, 31(14): 2227-2244.
Ashok S. 21. (2006). Optimised model for community-based hybrid energy
system. Renewable Energy 32 (2007) 1155-1164.
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