PV System Component

Contents
Introduction 3
Solar Cell energy: History & Technology 6
PV Cell, how it works, types and characteristic 8
Shading Effect: 10
Temperature & Insolation effect on IV Curve: 12
Shading Effect 13
Scale-up PV Cells to Module, string and Arrays 17
PV System Component 18
PV Module 18
First Generation of Solar Cell 18
Second Generation: Thin film Silicon solar modules 18
Third Generation: Polymer Solar cells: 19
Inverters 21
Inverter Efficiency: 21
DC and AC Wiring: 23
Loss Rate 23
Mitigation methods: 23
Energy Storage ; Batteries 23
What is a solar battery? 23
Type of Batteries 24
Power, capacity ratings and energy storage 25
Depth of discharge (DoD) 26
Round-trip efficiency 26
Protections and cables 27
Automation Control, monitoring and Battery management system: 27
Chapter II 28
2.1 Introduction: 28
2.2. Meteorological: 28
2.3. Literature review 29
2.3.1. Review of PV System Size Optimization Techniques 29
2.3.2. Evaluation Criteria for sizing Standalone system 34
2.4. Challenges for PV System Size Optimization 37
Chapter III : MODELING OF A STANDALONE PV SYSTEM 38
3.1. Introduction 38
3.2. MODELING OF A PV ARRAY 39
3.3. OPTIMAL SIZING OF INVERTER IN STANDALONE PV SYSTEMS 43
Chapter IV 45
Chapter V- Conclusion 46
References 46

Chapter I: Introduction

The world we are living in constantly is looking to use different sources of energy for their needs. It is a fact that there is a direct relationship between the growth rate of population and consumption rate. Thus, as the concern of increasing demand is concurrent with predictions that, nonrenewable energy will be ending by end of 21st century. We can mention over dependency, continuous increase in nonrenewable energy price and shipping charges are other factors that affects our view toward this source of energy. Hence, although electricity is a crucial to society’s development and indispensable to our world. , yet around 1.3 billion people in remote areas still do not have access to it.[1]
To overcome the mentioned issues and for sake of humanitarian side to aid people in remote areas /islands, people started to endeavor and invest in this field to find alternate source of energy to resolve current needs and their future generations. Thus, beside increase in electricity production, new technology and sources of energy for production studied and introduced to grid systems.[2, 3]
Renewable energy sources are Wind, Solar, Biomass & Waste, geothermal and ocean energy. Below we will give a brief explanation for each energy source and technology used with it.
A. Biomass is sustainable organic matter, which comes from energy crops, agro industrial waste, agricultural waste, municipal waste, animal waste and forest waste, which considered potential sources of fuels. Utilization of biomass as a source of energy is done either directly or indirectly, which converts biomass into liquid, gaseous or solid fuel. Different process used for conversion, such as Thermo-chemical and bio-chemical/biological figure.1 shows different sources, process and application for biomass. Countries in recent year focused in this source of energy For Instance In country like Malaysia this source of energy provides 16% of energy generation of country.[4, 5]

Figure 1.Power and heat generation process from residues, by-products and wastes[2]

B. Geothermal are accumulation of the earth’s natural heat, or geothermal energy, which can be extricated and utilized monetarily now or in the sensible future. Noted that a significant part of the world’s potential for geothermal vitality is related with regions of volcanism .Thus all commercial geothermal production is limited and restricted to presence of heat water in permeable rocks. The economic use and extraction of it depends on sufficient water, temperature and productivity ratio. These systems of hot water are known as hydrothermal geothermal systems [2].
The principle use of natural thermal fluid is for electrical production. The thermal water comes through system of pipelines to a turbine-generator unit and harnessed to produce electricity. Water will be injected back to reservoir well. Figure 2. Illustrates two Different method for extraction of geothermal energy and generating electricity[2, 6]. Philippine owing to its location along ring of fire considered as one of top electricity producer using geothermal energy. Out global capacity of 12.7GW in 2017 It has installed 1.9 GW ,which account 17% of total country electricity production.[7]

Figure 2.Schematic of a flashed-steam power plant (Left) ,Schematic of a typical binary power plant using an air-cooled condenser(right)
C. Ocean and wave energy: numerous type of waves exist in the ocean surface and below, the waves were always prominent energy observed by humankind through history. Swell waves and local sea waves are two type of wave which we are interested for generation of electricity. Wave energy converter (WEC) is center of focus for a lot of researcher as a new source for renewable technology, however it is still considered to be in its early stage comparing to other renewable energy technologies. Hence, WEC comparing to solar and wind energy they can operates 90% of time which works 30-50% of time. WEC can be categorized based on distance (onshore, nearshore and offshore) or It can be categorized based on device size and directional wave characteristic. There are different stage in conversion and different ways to extract power in waves (pneumatically, hydraulically and mechanically). Studies in wave energy in UK as a one of potential location for this energy indicates around 20% of total electricity demand can be resourced by wave energy. Image below depicts wave energy extraction methods and conversion stages [8, 9].

D. Wind Energy is a movement of air due to difference in temperature of air masses, which is a byproduct of solar energy. Wind Energy with history of thousands years considered first renewable energy which, has been introduced to our society, by converting wind energy to mechanical energy, and later on it developed by converting it to electrical energy. Utilizing wind energy starts after profound appraisal and evaluation of location (offshore/onshore), height and size of turbine, wind speed and characteristic, and terrain uniformity. Wind farms can be offshore or inshore which each one has its own advantage and disadvantage. Offshore wind farm came to picture lately to solve environmental effect such as large lands occupation, noise and visual pollution. [2, 10, 11]

Different aspect in wind turbine technology studied while developing or selecting for wind farm these factors are summarized as design, load, blade, gearbox, generator, and transformer. While wind energy conversion can be generally divided into two category of aerodynamic draft / lift ,which aerodynamic lift can be divided further based on orientation of axis (horizontal and vertical)[12, 13]. One of the main technological hindrance in wind energy is power fluctuation and surge in power. It can be resolved through energy storage system or variable speed operated wind turbine. Hence, output with smoother and controllable power achieved. Over all wind, energy has a bright and long future with many areas for innovation, marketwise by end of 2015, the installed world capacity reached 425GW, while in 2018 reached 591GW, furthermore the price/kWh will reduce 25% by end of 2030 comparing to 2018.[14]

Figure 4.Main components of a horizontal axis wind turbine[15]
E. Solar energy
Solar Energy is enormous source of energy with potential to secure at least dozen times of our energy needs. It is the cleanest source of energy available with prominent role in our future of renewable energy sources.
It can be harnessed and captured using different methods and technologies such as solar thermal capture and Photovoltaic (PV).This technologies convert solar energy into thermal and electrical energy. The energy utilized for generating electricity, light, or heating water
In next section more details regarding solar energy, its components, and how it works has been explained.
1.2. Solar Cell energy: History & Technology

History of solar system energy all begins with alexander Becquerel who discovered the photovoltaic effect and created first PV cell in 1839. Unfortunately due to low efficiency which didn’t reach 5% in next 100 years and also high cost and low power production it was abandoned note that cost of 1 Watt cell in 1956 was 256$ meaning a homeowner needed to pay 1,430,000 $ for sufficient power system.
After that, the pace for development of solar cell for commercial purpose stopped but another source caused development in solar cell and that is space exploration, which used solar cell as a source of power. From 1957 to 1960, efficiency increased from 4.5% to 14%. One of several satellite, which launched during that period, was Explorer 6, in 1959. It had large winged-shaped solar arrays, nowadays it became feature in satellite

Figure 5. From the left Vanguard 1, Explorer 6, Telstar, and Skylab.
This development progressed with progress of Diode and PN junction. The crystal-silicon PVs were major material used in Photovoltaic, however thin film PVs is challenging that dominance.
Figure6. Shows the cost change of both modules over time. It can be seen,as the production of c-Si production doubled the c-Si decreased by 24.3% (named learning rate), however for thin film technologies it has decreased 13.7%/doubling/

Figure 6.Photovoltaic module costs and cumulative production for both crystal silicon
Also in figure below, you can see major development Different PV material conversion efficiencies versus time of development are shown.

Figure 7.Historic PV cell efficiencies in laboratory testing

1.2.1. PV Cell, how it works, types and characteristic

Photovoltaic are material/devices, which can convert energy of photos into electrical voltage and current. To break free the electron of atoms in PVs photons with high energy and short enough wavelength is needed. Moreover, if there is field nearby these electrons can flow toward metallic part where it produce an electric current.
Hence, that amount of illumination on cell directly affect the amount of produced current by source. First, let understand what happens when it is exposed to direct sunlight in the region of a p – n junction. When photons strike, pairs of hole – electrons will shape. If these mobile charging carriers reach the junction’s area, the depletion region’s electric field will push the holes into the p-side, and the electrons into the n-side. The p-side piles up holes and the n-side builds up electrons, making a voltage, which is used to supply a charge with current.
Equivalent Circuit: PV devices act similarly despite using different material. They all can be modelled as a current source parallel with diode with two different resistance.
The equivalent circuit diagram consist of a current source arising from the absorbed light, a diode from the directional properties of the solar cell stack.
To comprehend the electronic behavior of a PV cells, constructing an electrically equivalent model may be useful. In Figure.8 equivalent circuit for ideal Photovoltaic Cell is shown. PV Cells has a parasitic resistance, which reduces power delivered and its value determines quality of electrical connection. While Rsh , for the resistance in the closed diode state should be high to restrict current flows to any path/elements except diode in other, hand Rs for the resistance in the open diode state,should be very small so it won’t waste the power which it should be supplied to load.

Figure 8. Equivalent circuit diagram for Ideal Photovoltaic Cell
The governing equation of the equivalent circuit is

IPh is the photo current and ID is the current running through the diode. IS is the reverse bias saturation current, VD is the voltage across the diode, VT is the thermal voltage (kT/q, Boltzmann constant times temperature divided by electron charge), and η is the diode ideality factor.
Current and Voltage Behavior
The quality of PV electrical circuit is explained using IV Curve, which describes the current, and voltage behavior. Moreover, drawing IV curve using formula in above we get same characteristic of solar cell curve.
Knowledge about a PV unit can be obtained from the general shape of the IV curve and four significant points along the curve: Voc , Isc, and Vmp, Imp ( current and voltage at maximum power). This curve shows the operation of solar cell/module and connection between voltage and current in specific preset irradiance and temperature.

Figure 9. Characteristic IV curve of a solar cell. The short circuit current (ISC), and open circuit voltage (VOC) is marked along with the maximum power point current (IMPP) and voltage (VMPP).
Following figure illustrates the IV Curve relation to irradiance

Figure 10. Characteristic curve (IV curve) with increasing incident radiation

1.2.1.1. Shading Effect:
Of most common incident, which happens in solar system, has a significant effect on production and conversion of energy. To study this pattern let’s consider what happens in both cases of parallel and series connection.
Parallel: if cell is shaded 50%, the Isc will drop to half while Voc has a slight change, which means a parallel relation behaves well in partial shading missing just the shaded field.

Figure 11. A module consisting of three cells connected in parallel is shown. When all cells are illuminated the blue IV curve is obtained. When one module is in shadow the module IV curve corresponds to the green curve. You can see the IV curve of a single unshaded (dotted orange) and a single shaded cell (dotted grey).
Series: In series, in case one cell is shaded the other cells shall push to make current goes through. Hence, this cause the solar cell to have a negative voltage thus the output power will reduce. The shaded cell will heat up enormously.

Figure 12. A module consisting of three cells connected in series is shown. When all cells are illuminated the blue IV curve is obtained. When one module is in shadow the module IV curve corresponds to the green curve. You can see the IV curve of a single unshaded cell (dotted orange) and a single shaded cell (dotted grey).
Using bypass diode we can overcome this issues to to allow current to pass through and prevent hotspots as well in shaded cell ,figure 13.

Figure 13. Bypass diode mounted over shaded cell. The bypass diode drastically reduces the losses caused by the partial shading.
Until now, we spoke about each one separately but in real modules, this solution is not implemented on each cell, since it is not practical instead, it is used on string of cells. Therefore, it is critical the place and position where partial shading is expected, modules shall not be installed.[16]

Figure 14.A module consisting of three cells connected in series is shown. When all cells are illuminated the blue IV curve is obtained. When one module is in shadow the module IV curve corresponds to the green curve without any bypass diode and to the red curve with a bypass diode. You can see the IV curve of a single unshaded cell (dotted orange) and a single shaded cell (dotted grey).

1.2.1.2. Temperature & Insolation effect on IV Curve:
Usually manufacturers will give I – V curves in datasheet, demonstrating how curves move as the insolation and cell temperatures alter. Hence as the insolation falls, the current in the short-circuit decreases in directly proportional.
As shown in Figure 15, the open-circuit voltage declines by a significant amount while the short-circuit current only increases very slightly as PVs get hotter. So PVs, likely interestingly, work better on cool, clear days than on hot days.

Figure 15. Current–voltage characteristic curves under various cell temperatures and irradiance levels for a Kyocera KC120-1 PV module.
The temperature impacts for the five different Photovoltaic technology are listed in Table 1. For instance, for each degree Celsius, the Yingli multicrystalline silicon module shows a fall in PMPP of about 0.45 percent, which raises cell temperature, while the FirstSolar CdTe module only decreases by 0.25 percent / K. That in hot climates gives CdTe a performance advantage on a watt-by-watt basis.
Given this major output shift as cell temperature varies, it should be obvious that temperature must be included in any calculation of the performance of PV systems. Due to variation of insolation on cells, as well as ambient temperatures change, cells temperatures varies. Because only a tiny portion of the insolation that reaches a panel is converted into electrical energy and extracted, great deal of the radiation emitted is absorbed and transformed into heat.

Table 1. Some parameters related to temperature Effect on different solar pv modules

In following section we will talk about how to scale up solar cells to a PV array

1.2.1.3. Shading Effect
The output of a PV system is directly related to how much sunlight can be converted into electric power. We always think of trees and cloud when we think of objects that obstruct the sun, however we need to be concerned about smaller scale object that can block sun. Dust and dirt accumulate over time on solar panels and all other surfaces, we call this soiling. So soiling over all refers to power loss arising from snow, gravel, dust and other contaminants that cover the PV module’s surface. This will result to the absorption of sunlight by these small-scale objects.
Shading attributable to soiling can be further divided into two groups, which include soft shading such as air pollution, and hard shading that happens when a substance like debris blocks the sunlight. The effect reveals that soft shading changes the PV module’s current but the voltage stays the same. The efficiency of the PV module in hard shading depends on if some of the cells are shaded or all of the PV module cells are shaded. If some cells are shaded so as long as the unshaded cells absorb solar irradiance, current flows while the voltage output of the PV module will decrease.
a) Causes of soiling
A lot different factors causes soiling or changes its intensity or variation, these factors are usually interdependent. Some of these factors are Type of module and coating, tilt of module, rains strength and frequency, wind, location that system is located, number of birds in area and also atmospheric properties which is depicted with details in figure 16.[17].
Soiling will be higher and changes significantly as Tilt angle becomes more horizontal. Slow breeze accumulates dust on panels while strong winds can clear surface of panels. In addition, as particle concentration in environment is higher daily deposition will increase as well. Dust property can be size, shape, weight, components. Furthermore, previous research, have shown a clear positive association between dry deposition velocity and wind speed. Humidity also affects the velocity of dry deposition, which may greatly increase the rate of particle deposition[18]. Usually, bird droppings are counted under soiling losses as they reflect a transient environmental blockage in the panel but have some distinct behavior. First off, rain is sometimes not enough to wash away the mess, and manual cleaning is required. Unlike soil particulate matter, it only affects one or two cells at a time but it greatly blocks the cells that it effects. Because of the series connection of cells inside a row, if bypass diodes are not present, the one cell affected may knock out the cell string or the whole array. The best suggestion is to quickly clean off the panel.[17, 19]

b) Extent of loss:
As we have explained before the partial shading of panels in soft shading does not change voltage but changes current. In hard shading, it will reduce voltage but in case of single string, the inverter will sense this decrease and control it automatically. However, where the hard dust on various strings is inconsistent in parallel, a voltage mismatch occurs [17].

Figure 17. Characteristics of the PV array under partial shading condition.

Figure 18.Characteristics of the PV array under partial shading condition.
NREL ‘s Efficiency Criteria Report, the industry standard for PV losses indicates a typical soiling loss of 5 per cent. It should be noted that dust accumulates over time and does not build up all at once, in highly dusty environments ranging from 0.01 percent / day to 0.5 percent / day. That means panels should be regularly washed to avoid undue accumulation [20].

c) Mitigation methods:
Mitigation and solution to soiling are different the first comes into mind is rainfalls. Rainfalls are free but seasonally can not be predicted and might not be enough to wash away soil. The next option that comes into mind is manual cleaning which adapts it procedure of cleaning building windows. The other option would be mobile cleaning which uses machinery to clean the surface of the PV.[17, 18]
The next step in cleaning is how frequent should be these cleaning either by precipitation or washing. That will depend on the local soiling rate but also the cleaning costs of the modules.[18]
The are some other preventive approach such as passive method which uses anti soiling coating (ASC) or active method which implements novel approach of repelling by charge

Figure 20. effect and approach on soiling[21]

Based on NREL model that uses a steady increase in soiling it has been found, for a system that would create soil that covers 1.9 percent of sunlight over the span of a year, doing one annual cleaning would keep the loss of around 1.5 percent, two cleanings annually could lower the average loss to 1.3 percent, and three cleanings each year would lower the average annual loss to 1.2 percent [22].

1.2.1.4. Scale-up PV Cells to Module, string and Arrays

Since the solar cell produces a small amount of electricity (about 0.5V) by themselves, it is uncommon that single cell will have potential for any application. Fundamental unit of PV system is solar cells and using this basic block, Modules are made.
PV Modules or PV panels in another word are consisting of prewired number of solar cells connected in series to produce larger amount of voltage and power.
Few years ago, 36 cells were used for 12-V battery charging, although they are capable of producing even higher voltage than that. Because the demand has moved towards larger and larger units, there are now 72-, 96-, and 128-cell packages. More cells per panel means less modules and less interconnection between them, which for larger PV systems is a significant benefit, and these panels all encased in tough, weather-resistant packages.
The modules, which is connected in series, will make string. String intended to increase total voltage to desired one and gives higher power output. Connection of several string together are referred to as an array. The parallel connection in array will give higher current with increase in power.figure15.

Figure 21. shows this distinction between cells, modules, and arrays.
The string/arrays design are very important , since it is a major factor in deciding number of modules to be connected in series/parallel. Typically speaking, modules are initially arrayed in a series string to build up voltage to as high as safety considerations to enable power to be increased before paralleling such strings. This technique aims to reduce the loss of I2R power when linking cables while delivering required voltage and power. The components of a PV array can be observed in figure 16

1.3. PV System Component
A standard PV system comprises mainly of a PV arrays, Batteries (energy storage), an inverter, cables and protection devices (i.e. OCPD, SPD, and GFPD and …) , monitoring and controlling system ,genset solution and Loads.
1.3.1. PV Module
And as we clarified in previous section the building blocks for PV system are solar cell, PV module, string and arrays. Which can be connected in different way to produce different voltage, current and different power in result. The solar cell represents the basic unit of a PV system. Several PV modules connected together to form An array, the arrays can be small with few modules or it can be big as a utility power plant. It is essential to remember that the basic materials, designs, performance quality and cost of the PV cells and modules themselves differ. There are three generation for solar cell technologies:
1.3.1.1. First Generation of Solar Cell
The solar cells of the first generation are mainly based on silicon wafers and usually display a output of around 15-25%. These types of solar cells lead the market, and are those seen on the rooftops in particular. The advantages of this solar cell technology reside in both their strong efficiency and their high stability. They are however rigid and need a lot of energy in manufacturing [23].
Mono-crystalline Silicon solar modules.
In monocrystalline is made of single crystal of silicon. It has higher efficiency comparing to other technology and generally their efficiency rate are 20%-25%. Higher power rates in this technology makes it space efficient but it has higher price due to process in taking out its impurities.
Also, the process causes a lot of silicon to be wasted in the process, also it is highly affected under the shadow and reduces its performance, but it has a long life expectancy.
Poly-crystalline Silicon solar modules.
As name implies it is made of multiple crystals of silicon. These are considered most popular solar modules with lower efficiency comparing to monocrystalline but lower cost in production and fewer silicon wasted in process of making it with longevity that would be at least 25 years [24].
1.3.1.2. Second Generation: Thin film Silicon solar modules
The solar cells of the second generation are based on materials such as amorphous silicon, CIGS and CdTe, where the average output is 10-15%. Solar cells of the 2nd generation are known as thin film solar cells because they use direct bandgap materials, which can be rendered much thinner than solar cells of the first generation.
Even the solar cells of the second generation can be manufactured so that they are versatile to some extent. However, since the production of second-generation solar cells also requires vacuum operations and high-temperature treatments, the manufacture of these solar cells also involves a significant energy consumption. Furthermore, solar cells of the second generation are dependent on scarce elements and this is both a limiting factor in price as well as in their subsequent success.
The thin-film silicon solar cell is considered to be second generation of solar cell. Thin film solar cells are made by placing on a substratum one or more thin layers, or thin films of photovoltaic material, such as glass , plastic or metal. It is more shade tolerant and less sensitive to temperature. It is cheaper comparing to others but less efficient (16%-22%) and market share of it never exceeded 20% , and in recent years it is declining to be less than 8% of worldwide PV system installation [24].
1.3.1.3. Third Generation: Polymer Solar cells:
Polymer solar cell can be integrated in modules during assembling and production process. It is considered as a latest generation of solar cells which are currently is being researched intensively.
It has some disadvantages, such as stability and performance, but have other obvious advantages like speed of production, abundance of materials and low power processing. Performance and reliability issues have seen major development over the years, and today record efficiencies are on the order of 12 percent, whereas multi-year lives are being achieved.
The third generation also includes costly experimental multi-junction solar cells with high performance, which hold the world record in solar cell performance, plus generally novel products. A new category of thin film solar cells under research are perovskite solar cells which display tremendous potential in very small areas with record efficiencies above 20 per cent [25].

Figure 24. Comparison of various solar cell technologies

1.3.2. Inverters
After PV-Modules , inverters are second part in any PV system. The Inverter job is simply to convert DC output of PV to AC while doing wave shaving and considering standards regarding voltage and current quality. A solar inverter primarily plays the following roles in a solar power system: converting DC to AC, optimizing electricity output, ensure device operation are stable, increased grid aid capability, effective power output tracking. In the designing PV system and initial investment, estimation the overall energy yield that can be generated using the inverter must be weighed. However, the exact sense of the word “inverter quality” tends to be ambiguous[26].
I. Inverter Efficiency:
The definition of the efficiency of PV inverters is very complicated. It’s not just the ratio of maximum output to a black box’s energy input, as in the case of normal power converters. Conversely, it consist of two parts: conversion and MPPT efficiencies. The conversion efficiency is the proportion of the AC power output to the DC input energy within a specified time frame, whereas the MPPT efficiency is the ratio of the energy drawn by the test device within a defined time window to the electricity generated from PV simulator in the MPP theoretically. The total efficiency depicted in figure 25, is multiplication of MPPT and conversion efficiency [27].

Figure 25. Illustration of Total Efficiency concept
The conversion efficiency may also be of two forms, Peak performance and Weighted or Average efficiency. On the other hand, weighted efficiency can prove to be European efficiency, or even CEC efficiency. The same goes for the performance of the MPPT which could also be of two types; static and dynamic. The breakdown of these classification is illustrated in figure 26 [26].

Figure 26.Classification of Inverter Efficiency.
Conversion
Peak efficiency:
Possibly the most overrated word used to characterize the performance of PV inverters is peak quality or valued production quality, although it is seldom or not at all achievable. It refers to percentage of output to input power.
Weighted/average:
PV inverters do not necessarily work in ideal conditions. Weighted or averaged performance thus gives a more practical example of how an inverter will behave during the day. This quality also tests the output of an inverter across its power spectrum
MPPT Efficiency:
MPPT is a fundamental component for setting the operating point of the PV system as optimal as possible and regardless of changing environmental situations in terms of both solar insolation and temperature. The effect of MPPT on efficiency of the PV system relies on both its static nature and its dynamic output[28].
Static
The static performance of MPPT explains the MPPT’s capability to locate and maintain MPPT given steady environmental conditions such as cell temperature and solar irradiance.
Dynamic
The dynamic efficiency of MPPT explains the MPPT’s ability to monitor the MPP in case of varying conditions[26].
Total Efficiency

Absolute efficiency or total efficiency blends the weighted performance measurement with the MPPT performance. It is used to mean a more reliable output of a single inverter, because all of the inverter sold on the market is paired with an integrated MPPT[26].
Nowadays inverter with transformer has declined in market in comparison to transformer-less inverter due to fact that transformer-less inverter has higher efficiency and low cost. If more than two-level output voltage can be produced, inverter costs can be reduced further. Any multilevel inverter optimization techniques can be useful for this purpose[26, 29, 30].
Nowadays part of protection is integrated inside inverter; these protections are including SPD, fuses and etcetera figure.27[31].

Figure 27. typical protection components in inverter-Schneider CL60E
1.3.3. DC and AC Wiring:
Cables as an essential part of solar system always considered in design and design and optimization of solar system size. The loss of the DC and AC wiring involves the resistive losses of the cables and wires used in the entire PV plant, from photovoltaic plant to national electricity grid. In DC wiring losses. DC wire losses are mostly caused by the cabling’s ohmic resistance that interconnects strings and PV devices, but the contacts and fuses can also result in losses. [32].
1.4. Loss Rate
In developing a solar PV array, a voltage decrease of 3 per cent or less is appropriate according to international best practices. An array that has huge voltage drop will not feed the inverters sufficiently, leading to a deteriorating performance of system.
 Mitigation methods:
a) Decreasing the length of cable: Reducing the wire length would give electron lesser surface and operating area which will aid us in designing the device according to the requirements and standards.
b) Placement of Inverter in loop should be optimized: The wires from the inverter to the control panel are vulnerable to higher voltage drops than those linking the inverter and solar panels with high dc voltage cables. The cable which provides a higher voltage will force out more current and will lead to a decrease in voltage afterwards. An inverter can be configured based on the circumstances but the thumb rule suggests the location of the inverter near the low voltage-end.
For e.g., off-grid systems typically generate low voltages, thus the device is operated by high voltage charge controllers.
c) Increase the Diameter (Wire size): In case cable with bigger size is acceptable in system, it can help to reduce the voltage drop. It is due to fact electron in cable with higher diameter, will face less friction and more room to travel. The drawback is that larger-sized cables seem to cost quite a lot.[33]
Cable losses, mismatch losses etc. have not been considered at all.
1.5. Energy Storage ; Batteries
Although batteries are not compulsory in PV-Systems , they are essential in system with not reliable power supply. There are different technologies and different means to store electricity, in solar system batteries are used which are divided into three main category of lead-acid , Lithium-ion and nickel-cadmium. There are factors in selecting solar batteries such as budget, location, weather condition. Other The important factor in batteries are state of charge, DoD is considered and life cycle for battery under DoD. And State of charge (SOC) of a storage battery indicates the amount of energy that can be stored in a system for the purpose of selecting a suitable battery capacity for a given system.
1.5.1. What is a solar battery?
There will be a lot of solar production during day, which may not be needed at that time. Batteries are the alternative solution for this excess power production and can be used during nighttime or emergency time.
Depending on the size of the solar array, battery banks will be of 12V, 24V or 48V and in total several hundreds of amperes. It is considered as a important element in standalone solar system
There are couple of benefits using batteries such as: Less Grid Dependency ,Enable renewable energy ,Access to power backups, And Important value on your electric bill
In addition, other benefits, which can mentioned are: ability of intrinsic and automatic property of regulating the array’s output voltage, in order to contain it within their own acceptability range of load voltage , as well as the ability to have considerably higher current spikes than the instantaneous current available from the array.
1.5.2. Type of Batteries
There are two types of batteries used for solar energy storage: Lead-Acid batteries and Lithium Ion batteries.
Battery costs depend on the design, power, climate conditions under which they will work, service frequency and chemicals required for storing and releasing electricity.
For safety reasons, batteries should be store in room with good ventilation and separated from living places and appliances. Since they contain harmful chemicals, which release hydrogen and oxygen gas when being powered. Moreover, the room can provide protection from high temperatures and allows servicing, repair, replacement and recycling.

A. Lead acid solar batteries
Lead acid batteries has been used for long time. Two main type of lead acid batteries are: Flooded and sealed lead acid batteries. They are considered to be one of cheapest as well as heaviest and largest comparing to others. The DoD of lead acid batteries are lower, thus it needs to be charged more frequently. With life span of 5-10 years which need to be considered while designing system to calculate return of investment[34].
B. Lithium-ion solar batteries
Lithium-ion batteries are smaller and lightweight than lead-acid batteries, but with the same amount of energy they take up far less volume. They have a longer lifetime, with approximately at least 10 years. Which is due to fact they have DoD’s of 90% or higher. It also allows user to expand it system easier and without being recharged it can handle extended period.
One drawback to lithium-ion solar batteries is that if they are not used properly or overcharged, they may catch fire; this is called “thermal runaway” But thermal runaway is extremely unlikely, particularly because lithium ion solar batteries have advanced software to prevent this from happening.
Other drawback is its price but in recent years due to excessive investment in R&D also high demand, the price reduced significantly[35].
Table 2.Strengths and weaknesses of lead-acid, Li-ion[36]

It should be noted as well if there is a charge controller in the PV-battery system; it can automatically avoid overcharging of the batteries.
There are couple of features needs to be observed while designing PV system and choosing Energy storage.
1.5.3. Power, capacity ratings and energy storage

The capacity tells you the total amount of electricity in Watt-hours that can be stored in battery. It indicates the real supply of energy from that contained in battery. The power output of a battery also needs to be weighed. The power level shows you how much energy a battery can supply to your appliances at one time.
Thus A low-capacity battery with a high power rating would be able to power the entire home, but only for a few hours, since the battery contains less kWhs.
Usually, energy storage in a battery is provided at some nominal voltage and at a certain specified discharge rate in units of ampere-hours (Ah). For eg, a lead-acid battery has a nominal voltage of 2V per cell (for example 24 cells for a 48-V battery) and factories usually define the Ah potential at a discharge rate that will discharge the battery down at a temperature of 25 ° C ,over a given period of time.
A C-rate is a measure of the rate at which a battery is discharged relative to its maximum capacity. A 1C rate means that the discharge current will discharge the entire battery in 1 hour. For eg, a fully charged 12-V battery that is stated to have a storage of 20-h, 200-Ah could produce 10 A for 20 h. This value of the ampere-hour is referred to as a cost of C/20 or 0.05C, where the C corresponds to power ampere-hours and it will take 20 hours to deplete. For a battery with a capacity of 100 Ah, this equates to a discharge current of 100 Amps. A 5C rate for this battery would be 500 Amps, and a C/2 rate would be 50 Amps [37].
1.5.4. Depth of discharge (DoD)
The depth of discharge (DoD) indicates percentage that a battery’s capacity that has been used. Most manufacturers will have a specific DoD for optimal performance and longer battery lifetime.
The bigger the DoD ratings, you can get from more energy stored in your solar battery before recharging it. For starters, let us assume you have a 20 kWh solar battery with a 50 percent recommended DoD capacity, you shouldn’t use more than 10 kWh of power until you recharge it. It could damage the battery by using much more than 10 kWh. Usually, the highest DoD value of batteries is defined to shield batteries from over-discharge, but the minimum DoD value at which the DG is switched OFF has to be modified to obtain the best results in terms of cost / pollution [38].
1.5.5. Round-trip efficiency
It shows the amount of electricity needed to store the energy, compared to the amount of electricity you will draw from your solar battery. Thus, if your solar panels were to deliver 5 kWh of energy into your battery, you have 4 kWh of electricity to use. This makes the round-trip productivity of the battery 80 percent. Battery device used the remaining 1 kWh to actually preserve and discharge the energy figure 28.

Figure 28. Round trip efficiency

Battery lifetime: Depends on several factors the main factors are Cycling, Temperature and SoC.
a) Cycling: As the battery charges and then discharges (cycle) to the specified DoD,the strength of the battery capacity or ability to carry a charge, reduces every time after battery cycles.
b) Temperature: Batteries in high-temperature environments experience from shorter lifetimes, where high temperatures are a major factor in electrode corrosion, impacting battery life
c) SoC: Furthermore, leaving batteries at low charge for extended periods of time or using high voltage batteries decreases their service life[34, 39].

1.6. Protections and cables

The protection however does not stop in inverter level, and all system needs to be protected part by part. Following are some of the protection needs to be considered:

1. Grounding: “a conducting connection, whether intentional or accidental between an electrical circuit or equipment and the ground or some conducting body that serves in place of the ground”[40]. The main benefits we harness from grounding ais Overvoltage protection: the ground wires reduce damages by giving alternative paths when there are incidents such as lighting, unintentional contact with higher voltage line, or line surge. Thus, it gives an additional level of safety for human in case of equipment failure/system disruption
   Through this study, simulation models and fault analysis methods can also be applicable to an ungrounded system in European countries and Japan. Two ground faults in an ungrounded system, for example, have the same fault characteristics as those found in a grounded system.
2. Overcurrent Protection Devices: An overload, ground fault, or short circuit may cause the current exceeds the ampacity of a conductor or rating of the equipment. The overcurrent protection device rating should be higher than 156% of the rate Isc of the module.

1.7. Automation Control, monitoring and Battery management system:
In order to have this steady power flow in complex power system, energy management system is required. It ensures efficiency and reliability of power in entire network. Energy management system which combination of all component of system including BMS, manages reliable and balanced power flow based on supply and demand
Different software and tools has been developed for these purpose that can smooth out power fluctuation, gives us real system data and inform us with any error/fault occurring in any part of system. Battery management system (BMS) for instance works alongside other parts of system as STATCOM, so it can respond fast to improve and stabilize power quality. In remote network, the instantaneous integration of renewable energy into system is around 30 percent of total system load before DER starts destabilizing system.
Monitoring & Control: takes data from all unit in network, including diesel/gas generator sets, Energy storage systems and inverter generation. It provides data acquisition system, event report , and remote controlling , which based on supply and demand it can turn on/off generators to optimize system performance and fuel consumption. It monitors feeders load and set system on most economical configuration. So in general it maximize saving, penetration of renewable energy in same time.
Chapter II
2.1 Introduction:

The common method to generate electricity in remote areas is by using generators that burns fossil fuels, like oil or gas. However, islands and remote areas might have an excess amount of renewable energy sources, like wind, water and sun, which can be utilized. The increase in independence of secluded communities inspires advance in technologies to study, develop, integrate, control and manage renewable energy in remote networks. Fortunately, technology exists that can ensure access to reliable sources of quality electricity, even in isolated areas far away from regular power networks. This technology helped in increase of production and integration of renewable energy into remote power networks, which once depended in nonrenewable energy sources alone.
The main challenge is developing distributed renewable generation that can optimize investment return and provides reliable, stable power supply while considering availability and accessibility of resources. The availability of resources and stable power depends mostly on meteorological factors such as solar radiation, ambient temperature and wind speed [41]. So in order to optimize number of solar panels and battery size, there are a lot of research is going on optimization techniques.
Furthermore, autonomous PV system requires the understanding of certain criteria in order to get an optimal configuration such as the data source natural, the system’s elements model, the current sizing methodologies, government energy policy, and the consumer specifications. Designers can use this data to increase the performance of system and total system system efficiency to satisfy the requirement of end user at optimal level of performance and reasonable cost range[42].
Based on statements above, in these different techniques for optimum size of PV array and batteries in standalone system has been addressed and advantage and disadvantage of each one is highlighted.
In addition, current sizing methodologies are being studied for the implementation of a standalone PV device. Finally, the complexities of designing a standalone PV device are illustrated.
2.2. Meteorological:

The first things come to mind for developing and calculating the scale of solar system that needs to be developed is climate condition. The climate condition like ambient temperature, solar irradiation, wind speed and humidity in certain area are important factor in availability and evaluation of meteorological data. The meteorological data can be collected and studied in two forms: statistical data or time series.
In time series, usually the weather information is in form of hourly meteorological data of ambient temperature and solar irradiance. The time series data has advantage of reflecting variety in parameters, which makes system performance more reliable. However getting accurate data for each location is difficult especially in remote areas[43, 44]. The benefit of predictive meteorological data is that if data is not accessible or if incomplete data is available, it can be used to decrease computational efforts in applications modeling. However, the drawback is that the performance of PV systems is less reliable with respect to parameter variance[45, 46].

2.3. Literature review

2.3.1. Review of PV System Size Optimization Techniques
There a lot of research is going on for optimization of PV system either as a whole scale of system or part of system. In subsystem, they focus on optimization of battery storage, diesel generator capacity and operation mode, PV array and inverters. In this section the optimization techniques for sizing system has been presented, with concentration on standalone PV system.

2.3.1.1. Standalone PV system Size Optimization
In general , in order to decide the optimum size of a standalone PV system, a particular location for a standalone PV system is first specified and then meteorological data such as solar radiation and atmospheric temperature are collected. The power of the components of the stand-alone PV system, such as the PV array, the storage battery and the size of the inverter, are then determined. In standalone PV system sizing, it is noted that several factors have to be considered, such as the kWh / yr required to meet the load requirements, the kWh / yr supplied by the PV system, the Ah of battery systems, the area of the system to be occupied and the cost of the system [37].
Different method being used in PV system sizing techniques. Each techniques has its own strength and weakness in some point, in here major methods has been briefly explained and discussed.
a. Intuitive Methods
Intuitive method is a simple calculation for sizing system without taking into account the variation in irradiation or creating a linear relation between subsystems. It takes either average of month with lowest irradiation or annual average. However, this method results in both oversizing system and cost or reduce in system reliability[47, 48].
b. Numerical Method
In numerical method, the time frame will be set in order to conduct simulation for total energy and battery load calculation, and usually it’s a daily or hour basis. This technique has greater precision, and higher reliability in comparison to intuitive method. There are two types of numerical method for calculation of energy balance, namely deterministic and stochastic. In deterministic due to difficulty in obtaining data set for a specific system (hourly solar energy / load demand), there will be uncertainty associated with results. Meanwhile, the stochastic approach considers the solar radiation variability and variation in load demand, thus it gives results that are more accurate. Over all in numerical method, In the case of linear shifts in the decision parameters computations, non – optimal results are obtained.[49].
The numerical approach is the basis of the most of the research in optimized sizing of standalone PV systems. Arab et al.[50] conducted a study in Algeria for optimization of PV system using loss of load probability (LPP) by dividing the area under study to four region based on clearness index. At a given LLP and load demand, the simulation software measured the feasible PV system size, and the optimal design of the PV system was decided based on the cost of the system resources. Based on a clear-sky model for solar energy prediction in Sudan, an optimized PV system architecture was developed in Ibrahim’s work[51]. The issue with work was that the efficiency of battery in charging and discharging was ignored, which let us to have an unreal model without any loss in storage of battery.
Shrestha and Goel [52] presented an optimization method for defined load demand and considering stochastic nature of solar energy on hourly basis. The developed model considered minimum total system cost and LLP to generate different PV array and battery sizes, taking into account the volatility of solar energy and the heterogeneity of the load demand for energy. The only drawback of this research was that optimum sizing method limited to assumed load demand.
Kaushika in [53] aimed to minimize system capital cost and to find out optimum PV/battery configuration. In order to calculate LLP, daily averages metrological data and load demand was used, and sizing curve were calculated based on LLP results. Arun in[54] used chance constrained program, which means Taking into account the resource variability in the sizing method. Sizing curves using the energy status in the battery and based on solar radiation and load demand data, present the collection of all feasible PV / battery configuration sizes. Based on the required LLP, the design of an acceptable PV / battery sizing curve is carried out and the optimal configuration is selected based on the minimal cost of the energy unit produced. However there few drawback regarding this method, first of all basic mathematic model for PV array did not consider temperature effect on PV array generated power. Second, sizing curve developed was for various LLPs where the lowest LLP was 0.021, considered to be large and lastly the daily or annual solar radiation were used.
An optimal sizing of standalone system in Oman was presented [48], using hourly meteorological and load demand data. For the system position, the tilt angle is designed to maximize system reliability and minimize the size of the PV array and the storage battery power. First, the sizing technique determines device element efficiencies and load requirement and then acquires the actual solar radiation for the location chosen. Then, by plotting LLP vs PV array sizes, and storage battery sizes vs. PV array sizes, appropriate measurements of the PV array and the storage space are found at the desired LLP. based on the lowest system’s capital cost, the optimal configuration is chosen.
An optimization model for standalone system in Algeria was developed using Matlab-Simulink platform. The key purpose of this work is to incorporate load control in the optimization of technological and economic parameters and their effect on the life cycle of the system. The main three steps in developing system was; designing elements of system, forming model for load management, and creating the optimization requirements based on LPSP and energy cost [55].
In another study in Malaysia, an optimization sizing of standalone PV system based on metrological data and load demand is presented [56]. The study uses hourly base data to perform search space for sizing methodology of the batteries numbers and PV Array . All combination in search space is studied to find loss of power supply probability(LPSP) to determine best combination of desired LPSP , then depending on the LCE, the right design is selected. In this study, linear model used to describe PV array, while dynamic model is used for battery requirements. However linear model of PV array may leads to under/over sizing results.
c. Analytical Method
In order to determine the feasibility of the PV system as a function of reliability, computational mathematical models for components are built using analytical methods. It is possible to predict system output of system component for various sets of possible sizes. Through evaluating single or multiple output indexes of various configurations, the best configuration of a standalone PV system is determined. The key benefit of the analytical method is that it is easy to determine and calculate PV system size, while drawback of the method is that in difficulty in determining coefficient of these equation which depends on location.
To optimize the size of the PV array size and battery power for a site in Italy, a method was implemented for estimating load fraction covered by a PV system based on PV system components such as PV array size and battery power, meteorological parameters and load profile. part sizes PV array size and battery power. The developed method used to estimate portion of the demand for load fulfilled by the PV array area, based on the PV location, storage capacity, monthly weather variables and the load profile. Analytical characteristics are employed to estimate a portion of the demand for load fulfilled by the PV array, depending on the PV location, storage capacity, monthly weather variables and the load profile. The optimal size is thus determined by the PV array area and the storage capacity of a standalone PV system [57].
Markvart et al.[58] study is done over a particular period on the basis of the time series of long measured solar radiation results. In this study, solar radiation is calculated and then separated into two climatic periods. The first cycle belonged to solar radiation of days which was equal or higher than total average solar ration, while other cycle belonged to days with lower radiation average. After this, it was determined on the basis of all climate cycles to establish the total size curve the required and to calculate PV generator and storage battery sizes. Finally, an exponential function has been used to obtain the resulting scaling curve for specifically measuring the size of the PV system. In this study, however, the author believed that all demands for load occurred at night, which is hardly accurate. In comparison, the author used the day-to-day need for loads and solar radiation data without taking account of solar radiation uncertainty and shifts in energy demand.
An economic and technological model is proposed in Italy to build a hybrid PV / battery system with a grid-connected configuration. The sizing technique for suggested scheme is planned and controlled according to the analytical method. In this research work, hourly weather and demand info is used. SOC as an optimization tool and leveled energy costs as an objective function where applied with hourly meteorological and demand data [59].
In Ipoh Malaysia, for measuring the regular output energy of the device, the authors have used basic PV and battery models. The sizing curve is plotted for a needed LPSP. In this analysis, system capital expense and LCC are the key features. The optimal configuration based on the practical usable PV panel size and storage battery capacity is selected using the design space strategy, since it work better than deterministic approach for this purpose. Hence, since simplistic models for the elements of the systems is used ,thus it made the performance of the system uncertain since the variance of the meteorological data could not be represented by these models[60].

d. Software Tools

There are several commercial software tools available for simulation of PV system or even standalone system. Some of these software’s which can be named are Hybrid Optimization Model for Electric
Renewables (HOMER), Improved Hybrid Optimization by Genetic Algorithms (IHOGA), Transient Systems Simulation Program (TRNSYS), RETScreen , PV F-Chart, and PVSYST. [61].
HOMER considered one of best commercial for sizing of standalone and grid connected PV system. It can work as an optimization tool for all forms of PV systems while providing economic and emission analyses. The information can be inserter in software through either embedded function or user choses available hourly meteorological data of desired location. The drawback and disadvantage of the HOMER app, however, is that it is not capable of forecasting the efficiency of the built PV system. Researchers in their study of standalone system to calculate size of system also have used it widely[62].
RETScreen in other hand is a software tool, which used to determine and analyze the capability and feasibility of renewable energy system. It assess energy efficiency, economic risk, environmental effect, and cogeneration projects of renewable system. The optimization strategies used in the app are therefore comparatively limited. IHOGA is a software for configuration of PV system based on hourly simulation to solve optimization problem in single or multi objective problem based on genetic algorithm. Few disadvantage related to this software can be named such as excluding net metering and ignoring probability analysis. The program PV F-Chart analyzes the production of individual PV systems but does not include hybrid PV systems like PV-wind and PV-diesel system. The app also offers a monthly forecast of results that cannot be as reliable as a daily forecast. TRNSYS software has the capability of unit sizing varies between 0.01sec to 1hour. The platform is used for modeling solar and traditional energy applications. An example of how the modeling method functions is to model a solar hot water system continuously over a normal meteorological year so that a device can calculate long-term cost savings[63].
The app PVSYST is able to determine how standalone and grid connected PV systems are to be configured but not for hybrid PV systems. The identified location uses monthly weather statistics and synthetically creates hourly details in the software. To evaluate the optimum PV array and battery capacity a loss of charge likelihood is used. It is also possible to choose the PV module, the system autonomy period, the storage battery types, the type of PV module and the inverter type. It is a user-friendly software; however, it converts monthly meteorological data to hourly using statistical method [64].
Based on the literature, all pervious methods are backed by technological and economic research, but HOMER and RETScreen are commonly used technological tools for the sizing of stand-alone PV devices. Although all of mentioned software’s, are considered powerful tools in solving and helping to optimize system but component can’t be enhanced and we are unable to change its specification.

e. Artificial Intelligence
To solve issue of unavailability of metrological data of remote area, Artificial Intelligence (AI) is used. AI can accommodate non-linear variations in the solar energy supply and can be defined as a prediction algorithm such as an artificial neural network (ANN) and a genetic algorithm (GA) to determine the size ratios of the standalone PV system and to search algorithms such as fuzzy logic (FL) and tab search (TS). The major drawback of AI system is it’s hard to design and train system components.[65-67].
For instance in[68, 69] ANN used to predict optimum PV array size and status battery capacity using latitude and longitude in Algeria. In another research [70] for sizing lighting system with storage battery, Optimum setup of the PV / battery to achieve LPSP of 2% with minimal system expense annualized. PV array scale, angle of slope of the PV module and storage space are the GA outputs.
The algorithm for maximizing the PV / battery combination used in the Tabu Search (TS) method by [71]. In two sections, an integrated device schedulation focused on minimum operating costs, and an expanded preparation aspect centered on reduced overall system costs is considered for the optimization of the issue. The technological aspect can not be adequately addressed by the economic aspect which is integrated in the optimization feature on both parts.
Salah et al.[72] in the Sfax region of Tunisia , to improve battery storage and the surface area of the PV panel of the stand-alone PV system, applied fuzzy logic. The PV / battery configuration is measured using a sizing approach in a single PV system. The Size approach consists of a MATLAB / Simulink FL algorithm. As inputs, photovoltaic surface and battery power, the energy requirement of the load, and the average monthly solar radiation have been used.
f. Hybrid Methods
All techniques and method mentioned till now has its own disadvantage, but in order to overcome this a hybrid approach can be execute. The hybrid method is used to produce the maximum outcome for a given system, which is a functional mixture of two or more different techniques. Furthermore, most of problems studied in PV system optimization has a several targets, so the hybrid approach is considered suitable for solving multi-objective problems, however due to use of sophisticated algorithm functions the design of system will be complex [73-75].
For sizing a standalone PV system in Vellore, India, a hybrid method has been developed. The size algorithm uses MATLAB simulation and is based on LLP to integrate analytical and iterotic approaches. This research studies the relationship between the hourly fluctuations in load demand and weather data for 100% reliability of the system. The optimal PV / battery setup based on minimal device capital cost is achieved by an Adaptive Input Iteration Process. Furthermore, a parameter study is done to analyze the effects on size outcomes of load time and the low voltage charging controller detach. The findings are confirmed by laboratory tests and contrasted with other methods of sizing[76].
In Iran, an optimization method [77] was carried out on off grid system, which operates without battery. Based on the minimum ACS, the best PV size is chosen. The sizing algorithm was applied using the GA-based numerical procedure.

2.3.2. Evaluation Criteria for sizing Standalone system
One of the essential tasks in achieving the optimal PV configuration is the selection of evaluation requirements for the configuration of a standalone PV device for the appropriate venue [78]. On the basis of environmental, economic, physical, political and other non-controllable aspects, the Topcu et al. interpreted the potential energy scenarios [79]. In [80] rating method for various situations, called multi-criteria decision analysis, was also developed. This rating tool is based on parameters that include facets of the technological, fiscal, cultural, quality of life and the labor market.
Therefore, different requirements for the construction of standalone PV systems as seen in the figure.29 are taken into account for the assessment criteria.

Figure 29. Evaluation criteria for a standalone PV system size optimization
In order to assess and predict the availability and viability of a standalone PV system, these performance metrics are used to help programmers build an acceptable system for a specific application. Several of the following parameters are described:
A. Technical Parameters
It is well known that solar energy are variable and fluctuate through year, therefore the designers shall ensure that standalone PV system will satisfy load demand.

1. Loss of Power Supply Probability (LPSP)
   The LPSP is the percentage of power supply that it cannot meet the demand, and it shows the reliability of the power supplied to load. It’s ratio of all loss of power supply (LPS) in period (t) over loss of demand on same period (t). Total energy produced by system known as Esys [81].

(2.1) In which, (2.2)

If LPSP is equal to 0, that indicates at time period (t) the load demand has been satisfied. Otherwise, it shows load demand is not satisfied and power produced is not enough due to inadequate solar irradiation and available storage battery is at minimum allowable state of charge (SOC) or maximum permissible depth of discharge (DOD).

2. Loss of Load Probability (LLP)
   The LLP represent how much a system cannot meet the demand or the total load percentage not met by a system. The ratio of total energy deficit to overall demand for load during a given period is specified as follow[48]:

(2.3)

Deficit energy (DE) is disability of power supply at specific time to load, while Pload(t) shall be the load demand at that time, and ∆? is period for both terms.

3. Loss of Load Expected (LOLE)

The estimated load loss happens when the load demand exceeds the system’s energy generation, which is due to a lack of ability to produce, a shortage of energy supplies and/or a sudden rise in load demand. It is characterized as the quantity of energy not supplied to the load demand

4. Equivalent Loss Factor (ELF)
   The word equal loss factor (ELF) provides the required details for the amount and severity of the load outages, which is defined as the ratio of the effective time duration in load loss hours to the total running time in hours. The accepted value of ELF in standalone system is <0.1 , however in developed countries the supplier aiming for the ELF=0.0001 [82].
5. Total Energy Loss (TEL)
   Total loss of energy means the loss of energy due to the additional power produced by a standalone energy source. By implementing a rule in which power generation at an evaluated time span does not surpass the desired threshold, TEL should be reduced.
6. State of Charge
   The SOC of the storage battery represents the amount of energy that can be stored in the device for the sake of choosing the necessary storage capacity for the system.

B. Economic Parameters
The overall concept of the optimal design is to design a standalone PV system that can meet the load requirement at a certain level of safety, with minimum costs for capital and operations. Therefore, in developing a standalone PV scheme, economic aspects are of interest and the economic criteria are listed as follows:

1. Net Present Value (NPV)
   By applying the existing amount of revenue and subtracting the present amount of outcomes over the life of a standalone PV system, NPV can be measured.
2. Annualized Cost of a System (ACS)

The amount of annualized capital system costs, Cacsc, annualized operating and maintenance costs, Cao&am, and the annualized replacement cost, Carcsc, are ACS for a standalone PV system. and it is given by [70],

(2.4) ??? = ? ???? + ???&?? + ????

3. Total Life Cycle Cost (TLCC)

TLCC shall be specified as the sum of all device expense net current values such as capital costs, operating and maintenance costs, replacement costs, etc.

4. Levelized Cost of Energy (LCE)
   The ratio of total cost of component of system to generated power by standalone system, annually .

(2.5)
Where ??? is the total annualized system cost, and ???? is the total annual energy generated by the system.

C. Social and Political Parameters
When designing renewable sources of energy, there are many social and political considerations that need to be taken, because they may influence the device configuration at the deployed site. The following social and political criteria are described: Social approval and Portfolio Risk.

2.4. Challenges for PV System Size Optimization

From the previous research works, it is noted that The challenges facing optimization and modelling of PV systems are numerous, such as the following:

I. Availability of weather data
One of the difficulties in the optimization process is the lack of meteorological data such as ambient temperature, solar energy in a small time stage , such as hourly or daily records. is difficult to obtain this data it affects device efficiency and results accuracy.
II. Load forecasting
A full-load demand profile for a whole year must be collected to achieve an optimal PV device size. It is difficult, however, to achieve this profile; thus, the designers used hourly or daily averages for one day.
III. Model accuracy
For correct PV sizing, detailed modeling of components is needed, taking into account both internal and external variables that may influence model operation.
IV. generalizing the conclusions
PV device optimization is location-dependent. Therefore, generalizing the outcome achieved based on the particular location is necessary for use in neighboring areas.
V. Loss level
The power losses of a standalone PV system must be minimized to an appropriate amount.
VI. Energy Management
To monitor and regulate the power flow depending on the variance in the demand for load, an effective energy management strategy is needed to enhance the operation of an independent PV system.
VII. Life cycle
Innovation technology would increase the life cycle of storage batteries in order to increase system sustainability while reducing system costs.

This chapter discussed the methods for obtaining meteorological data, including forms of meteorological data, meteorological time series and meteorological statistics, data formulas, advantages and drawbacks of these methods. A review of the different techniques used for the modeling of a PV array is made and the limitations of the techniques are discussed. An optimization of a standalone PV system involves assessment requirements for standalones scale of the photovoltaic system, methodologies for sizing and sizing problems.

Chapter III : MODELING OF A STANDALONE PV SYSTEM
3.1. Introduction
A standalone PV system is not connected to grid and compromises of PV Arrays , inverter, charger controller, battery , AC and/or DC load, as shown by Figure 30.
If electricity generated by the PV generator surpasses the load demand, the battery stores surplus power and releases this energy when the output of the PV array is deficient. There might be several types of loads, DC and/or AC loads for a standalone PV system. Nowadays the inverter contains other devices such as DC/DC converter and charger controller.

Figure 30.Basic components of a standalone PV system
The approaches provided in this project aim to address mentioned difficulties and challenges in optimization of PV system. The ambient temperature and solar radiation level, strongly affects performance and sizing of standalone system. The current aim is to perform and implement optimization technique that storage battery capacity, PV arrays are optimized well. Therefore, standalone system needs the knowledge of meteorological data, model of system components, and load demand variance.
The optimization of standalone systems considers two main aspects; System element optimization and operational strategy optimization. Both aspects are optimized on the basis of the energy costs produced, typically expressed as system capital costs, system running costs, system repair costs and substitution costs.
By using meteorological data which has been collected in past years , the limited supply of meteorological data is solved. Also load demand of existing system based on hour acquired instead of prediction method. Meanwhile, for the optimum size of energy sources in photovoltaic systems a numerical based optimization approach was used.
The sum of energy produced for each time stage is estimated and compared with the load demand during the simulation. The task of charging / discharging of the battery is specified on the basis of this method. At the end of this simulation, possible system configurations are created at particular level / levels of reliability. Then the cost of each design is determined, and based on the minimal cost, the optimal device size is chosen. The cost of the device usually requires costs of capital, repair , and replacement

The advanced numerical-based optimization approach using the iterative methodology to evaluate the optimum size of the standalone PV device is deemed reliable since the advanced PV array and battery storage models have been deemed.
In sizing standalone system several criteria and factors should be considered before hand below
3.2. MODELING OF A PV ARRAY
The PV array output power/current needs to be estimated, before deciding on size, installing, and controlling standalone PV system. The fluctuation in output current of the PV array influence the efficiency of the electrical components of the system, the capital and maintenance costs of the system as well as the reliability of the system. The biggest disadvantage of these systems is the variability of the output current for the PV array. The foundation for optimum size of a standalone PV system is to correctly estimate the current generated by the photovoltaic array. The current output of the standalone PV system is determined by meteorological variables, such as solar energy (G) and ambient temperature ( T). Thus, a PV model is normally built with these meteorological variables[43, 83].

This dataset contains hourly solar radiation, ambient temperature, and actual system output current. The proposed method used hourly time series meteorological and load demand data
which considered the variation of the metrological and load demand data. The proposed method used hourly time series meteorological and load demand data which considered the variation of the metrological and load demand data.

By using a linear model, the PV array is modeled in terms of energy. The hourly energy output from a PV array is given by equation below,where ??? is the area of a PV module in m2 , ??(?) is the hourly solar radiation W/m2 and ???, ????, and ????? are efficiencies of a PV module, an inverter, and wires, respectively.

(2.6)
The temperature effect on the conversion effectiveness of a PV module is seen as (2.7) Where ???,??? is the reference PV module efficiency, ? is temperature coefficient for the efficiency, ??,??? is reference cell temperature, and ??(?) is the cell temperature.

(2.7)

The PV array is sometimes first modeled in terms of power and then the energy output is calculated. The hourly output power of PV array, ???(?) is given by eq. (2.8) where ? is the PV conversion efficiency, ??(?) is the total hourly solar radiation fallen on a PV module surface in kWh⁄m2, and ? is the surface area of the PV array.

(2.8)

The annual energy output of a PV array can be estimated as: (2.9)

This algorithm is divided into two phases ; the first phase has three stages . The first stage starts by specifying the parameters of the components that are used in the system such as the PV module efficiency, the wire efficiency, the battery voltage, the battery charging efficiency, the hourly load demand, the level of availability, and the meteorological variables such as solar energy, and ambient temperature. The range of the search space in the sizing process is approximated according to the average daily load demand using the intuitive method.

The initial value of the search space for the number of PV modules can be calculated using

However, the initial value of the search space for the battery capacity can be obtained by using

Kazem et al. 2013
In the second stage, an hourly current flow model is implemented to calculate the LLP for each configuration utilizing the implied models.
The proposed hourly current flow model is operated based on three cases which depends on the value of net current, ????(?) in order to calculate the LLP value of each configuration. The first scenario, (????(?) = 0) show the case that ???(?) is equal to the ??(?). In this case, the load demand current is totally met by the PV array’s current and there is no current supplied by the battery. Consequently, the deficit and excess energy values are equal zero.
The second scenario, (????(?) > 0) show the case when ???(?) is greater than the ??(?). In this case, the load demand current is totally covered by the PV array’s current thus resulting in excess energy. The amount of excess energy depends on the instantaneous SOC of the battery. If the battery is fully charged, all of excess energy will be damped. Otherwise, excess energy amount may be used to charge the battery.
The third scenario, (????(?) < 0) shows the case when ???(?) is less than the ??(?). In this case, the PV array’s current is insufficient to meet the load demand.
Therefore, the battery must supply the load based on the following subcases:
i. In case the battery is not fully discharged, the battery will supply power to the load with/without the PV array to meet the load demand as much as possible.
ii. In case the SOC of the battery is less than the minimum SOC of the battery, the PV array and the battery will not be able to meet the load demand. Therefore, the energy deficit is equal to the load demand.
In the third stage, all of the configurations that are obtained from the previous stages are nominated based on the desired LLP.
After defining the optional design space that meets the desired LLP, the best configuration in the design space is chosen.

3.3. OPTIMAL SIZING OF INVERTER IN STANDALONE PV SYSTEMS

To achieve optimum PV array output power, the rated power of a PV array must be matched to the rated power of the inverter. The optimum PV inverter size depends on local solar radiation , ambient temperature and inverter efficiency. For eg, a PV array produces power at just one part of its rated power under low solar radiation levels, and thus the inverter works in lower device efficiency under part load conditions. On the other hand, surplus pv output power that is higher than the inverter rating capability is wasted during high solar radiation levels and overloading conditions. The performance of the PV array is also negatively impacted where the rated output of the inverter is much smaller than the rated PV capacity. This means that optimum PV inverter sizing plays an important role in optimizing the reliability and viability of the PV system [84, 85].

Figure 31.The proposed optimization algorithm for determining the design space at the desired LLP

Chapter IV

Chapter V- Conclusion
In rural areas where no access to the power grid occurs, isolated PV systems are commonly used. At a given level of protection, the PV device must be optimized to satisfy the required load demand. The over-sizing of a standalone PV system increases the system’s initial cost and does not enable a customer to use the system’s full energy produced. Additionally, a stable process is not guaranteed by the under-sizing of a standalone PV device. In order to provide a stable and economically viable PV system, optimum system sizing is therefore a necessary design step. PV system configuration is a location-dependent approach in which its reliability relies on metrological variables such as solar energy and atmospheric temperature.

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