Chapter 1.pdf

CHAPTER 1

INTRODUCTION TO PHOTOVOLTAIC

AND PHOTOELECTROCHEMISTRY

Photovoltaic (or PV) systems convert light energy into electricity. The term

”photo” is a stem from the Greek ”phos,” which means ”light.” ”Volt” is named

for Alessandro Volta (1745-1827), a pioneer in the study of electricity. ”Photo-

voltaics,” then, could literally mean ”light-electricity.” Photovoltaics (PV) is a

method of generating electrical power by converting solar radiation into direct

current electricity (converts sunlight into electricity).

IUPAC definition of Photoelectrochemistry: A term applied to a hybrid field

of chemistry employing techniques which combine photochemical and electrochem-

ical methods for the study of the oxidation-reduction chemistry of the ground or

excited states of molecules or ions. In general, it is the chemistry resulting from the

interaction of light with electrochemical systems. (See also photoelectrochemical,

photogalvanic, photovoltaic cell)

1

2 Introduction to Photovoltaic and Photoelectrochemistry

1.1 Photovoltaic History

The first photovoltaic observation: A. E. Becquerel discovered the first pho-

tovoltaic effect, the operating principle of the solar cell, in 1839.1

Figure 1.1: The first PV observa-tion

Figure 1.2: The first sesitizationsolar cell

1.1.1 Some milestones

The origin of photovoltaic effect started from the observation at the solid-liquid

interface of a photoelectrochemical cell called the ”Becquerel effect”[1] as a

coincidence, just like many other discoveries in the science history. Forty years

later, photovoltaic effect was observed by Adams and Day[2]. In parallel with

the technology progress of photography, photoelectric effect remained a focus

of scientific interest, though this phenomenon was not well understood till the1A. E. Becquerel, (1839). ”Memoire sur les effets electriques produits sous l’influence des

rayons solaires”. Comptes Rendus 9: 561

1.1. Photovoltaic History 3

modern era of photochemistry in 1954 when Brattain and Garrett presented the

electrochemistry on Ge electrode by controlling the semiconductor properties

and light illumination[3]. The pioneering work of Ge was rapidly followed up

with other semiconductor electrodes such as Si, CdS, ZnS, CdSe, ZnO, TiO2,

SrTiO3 Ta2O5 ... etc[4]. The fundamental was established by Gerischer on the

work of kinetics and energetics of electron transfer across the semiconductor-

electrolyte junction and the characteristics of the space charge layer at the semi-

conductor surface in contiguous to the semiconductor-electrolyte interface. In

brief, the junction is formed between a semiconductor and another phase ini-

tially with difference of Fermi level between them. The majority carrier, upon

contact, will transfer through the interface to equalize the Fermi level and es-

tablish a thermodynamic equilibrium which generate a potential barrier at the

interface. Based on the new equilibrium, phenomenon of current rectifying

diode behavior and photovoltaic response upon illumination will prevail at the

junction. Some valuable reviews and books describe well the history and fun-

damentals of this type of photoelectrochemical photovoltaic cells [4, 5, 6, 7, 8].

The first solid state photovoltaic effect with Se was discovered by Willoughby

Smith in 1873[9]. In 1876, R. E. Day, W. G. Adams found that illuminating a

junction between selenium and platinum also has a photovoltaic effect, and an

EMF (Electromotive force) is produced. These two discoveries were a founda-

tion for the first selenium solar cell construction, which was built in 1877[2].

Charles Fritts first described them in detail in 1883. This effect is the basis for

the modern solar cell. The silicon solar cell has remained essentially dominat-

ing the market since it was invented in 1954 by three American researchers,

Gerald Pearson, Calvin Fuller and Daryl Chapin, who designed a silicon solar

cell capable of a six percent energy conversion efficiency with direct sunlight

at Bell Labs[10]. A comprehensive account on solar cell history can be found


elsewhere [11, 12, 13].

1.2 Energy and CO2

emission

1.2.1 Fossil energy:How much is the reserve of fossil energy?

To estimate the maximum reserves of fossil energy, we assume that neither

free carbon or free oxygen was present on the earth before the beginning of

organic life. During photosynthesis, water and carbon dioxide combine to form

carbohydrate with the reaction below :

n⇥ (H2O + CO2) ! n⇥ CH2O + n⇥O2 (1.1)

1.2. Energy and CO2 emission 5

(Glucose is a typical product of photosynthesis, C6H12O6 = 6⇥ CH2O).

This means 1 mole O2 ! 1 mole C or 32g O2 ! 12g C stored in carbohydrate.

(MC =

12

32

MO2)

Then, the amount of oxygen :

So we get the amount of carbon :

The reserves which have accumulated over millions of years will go up in the air

in a time of only hundred years!!! However, the elimination of energy is not yet

a major problem. A much worse issue will be the alternation of the atmosphere

and climate change as a result of the product of combustion. These effects last

long and have profound impact on our life and environment.

Figure 1.3: Annual consumption of oil (From P. Wurfel)


Figure 1.4: CO2 content raising

1.3 Energy Demand and Supply

Figure 1.5: Forecast energy demand

1.3. Energy Demand and Supply 7

Some Facts

1. Current global energy consumption rate : 13⇥ 10

12W (13TW )

2. About 3TW is needed to maintain the current quality of life in USA.

3. The weather is getting more and more extreme.

4. Other choices of energy sources: geothermal, solar, wind, bio, ocean, etc.

Figure 1.6: Energy supplies from 1971 to 2008


Figure 1.7: Scale of different energy supplies

And ....

1.3. Energy Demand and Supply 9

Figure 1.8: Energy supplies from different sources

Figure 1.9: Area required for various energy supplies to satisfy the US demand


1.4 Solar Energy Resource

1.4.1 Scale of solar energy and earth temperature

Figure 1.10: Absorption of sunlight from species in the atmosphere

The pros and cons of solar energy:

1.4. Solar Energy Resource 11

Figure 1.11: Cost of PV as function of mass production

Figure 1.12: Cost of PV as function of different technologies


From http://pv.energytrend.com.tw/

1.4. Solar Energy Resource 13

Figure 1.13: Capacity of Si solar cell

How the energy is managed.

The indicator of happiness : energy/capital.

But, how effective this value is created : GDP/energy.


Figure 1.14: The cost of GDP per Watt

1.5. Radiation 15

1.5 Radiation

1.5.1 Blackbody Radiation

An object that absorbs all radiation incident upon it, regardless of the frequency,

is consider as a blackbody. An blackbody can be approximated by a hollow

object with a very small hole leading to its interior (Fig. 1.15). Any radiation

strikes the hole enters the cavity where it is trapped until being absorbed. At

equilibrium, radiation emitted must equal radiation absorbed. Therefore, the

body that emits the maximum amount also absorbs the maximum amountwhich

should look black. Practically, a blackbody is constructed by opening a small

hole on a large cavity.

Figure 1.15: Blackbody Radiation

Rayleigh-Jeans Law and Planck’s Formula

⇢(⌫, T ) = (from standing wave in cavity) (1.2)


Equation 1.2 is the energy density of radiation per unit frequency interval in a

cavity of temperature T . It is not directly observable. The directly observable

quantity is the spectral radiance µ(⌫, T ), that is, the energy radiating from a

unit area of the hole per unit frequency range.

µ(⌫, T ) =1

4

c⇢(⌫, T ) =2⇡⌫2

c2kBT (1.3)

The Rayleigh-Jeans distribution fits the low-frequency behavior of the ex-

perimental energy density very well. However, as the frequency increases, the

spectral irradiance increases, the total irradiation energy is infinite (so called

ultraviolet catastrophe). This contradicts the experimental fact that the total

blackbody radiation is finite, and the spectral density has a maximum. Max

Planck found an empirical formula that fits accurately the experimental data

using the energy quantum and Maxwell-Boltzmann statistics. He assumed

that the energy of radiation with frequency ⌫ can only take integer multi-

ples of a basic value h⌫. The probability of finding a state with energy nh⌫

is exp(�nh⌫/kBT ). Then, the average value of energy of a given component of

radiation with frequency ⌫ is

✏ =

P1n=0 nh⌫ · e�nh⌫/k

B

T

P1n=0 e

�nh⌫/kB

T= (1.4)

Replacing the kBT in Equation 1.2, with Equation 1.4, Max Planck found

an empirical formula that fits accurately the experimental data:

µ(⌫, T ) =2⇡⌫2

c2· h⌫

eh⌫/kbT � 1

(1.5)

1.5. Radiation 17

Initially, Max Planck believed that the quantization of energy is only a math-

ematical trick to reconcile his empirically obtained formula with the knowledge

of physics known at that time. The profound significance of the concept of

quantization of radiation and the meaning of Plancks constant were discovered

by Albert Einstein in his interpretation of the photoelectric effect, which is the

conceptual foundation of solar cells.

Figure 1.16: The discrepancy be-tween the Rayleigh-Jeans formulaand the observed blackbody radia-tion.

Figure 1.17: Comparison betweenRayleigh-Jeans Law and Planck’sLaw

Plank’s Radiation Formula

⇢(⌫) d⌫ =

8⇡h

c3⌫3d⌫

eh⌫/kT � 1

(1.6)

where h is the Planck’s constant given by h=6.626 ⇥ 10�34 (J· s).


The power of radiation-Stefan’s Law

G. Kirchhoff proved that any object in thermal equilibrium with radiation the

emitted power is proportional to the power absorbed:

e⌫ = µ(⌫, T )A⌫ (1.7)

where e⌫ is the power emitted per unit area per unit frequency by a heated

object. A⌫ is the fraction of the incident power absorbed per unit area per

unit frequency by the heated object, and µ(⌫, T ) is a universal function for all

materials that depends only on frequency and temperature. A blackbody that

absorbs all the electromagnetic radiation falling on it would have A⌫ = 1 for all

frequency and so Kirchhoff’s theorem for a blackbody becomes

e⌫ = µ(⌫, T ) (1.8)

Stefan found experimentally the that the total power per unit area emitted

at all frequencies by a hot object, U(T ), was proportional to the fourth power

of its absolute temperature.

U(T ) =

Z 1

0

e⌫ d⌫ = �T 4 (1.9)

U(T ) =

Z 1

0

2⇡⌫2

c2· h⌫

eh⌫/kbT � 1

· d⌫ = (1.10)

where U is the power per unit area emitted at the surface of the blackbody, T is

the absolute temperature of the body, and � is the Stefan-Boltzmann constant

given by �= 5.67 ⇥ 10�8 (W · m�2 · K�4).

Example 1 - Stefan’s Law Applied to the Sun

Estimate the surface temperature of the sun from the following information. The

1.5. Radiation 19

sun’s radius with Rs=7.0 ⇥ 108 m. The average Sun-Earth distance R= 1.5 ⇥

1011 m. The power per unit area from the Sun (for all frequencies) is measured at

the Earth to be 1400 W/m2. Assume that the Sun is a blackbody.

Example 2 Derive Stefan’s Law from Planck Distribution

Show that the Planck spectral distribution formula leads to the experimentally ob-

served Stefan Law forthe total radiation emitted by a blackbody at all wavelength

is U(T ) = 5.67⇥ 10

�8T 4 (W·m�2 ·K�4)

Ans-


Example 3 Wien’s displacement Law

Consider the black body radiation, we note that as T increases, the wavelength

maximum �max shifts toward shorter wavelengths.

(a) Show that there is a general relationship between T and �max stating that

T�max = const

(b)Obtain the numerical value for this constant.

Ans-

1.5. Radiation 21

1.5.2 Solar Radiation

The Solar Radiation can be considered as a blackbody around 5800c.


Figure 1.18: The earth’s orbital around the sun and the position of the earth’saxis at various seasons over a year

Figure 1.19: Solar radiation at different seasons

1.5. Radiation 23

(a) Solar declination angle over a year (b) Description of the sun’s position

(c) Solar path for different months (d) Relationships between various irradi-ances

(e) Variation of extraterrestrial global dailyirradiation at different geographic latitudes

(f) Variation of extraterrestrial global daily ir-radiation versus latitude of locations

Figure 1.20: Solar Radiation in different conditions-1


(a) Daily variation of global irradiation G (b) Monthly mean values of global daily irra-diation H

(c) Tilting an angle relative to the horizontalplane increases the solar irradiation from thesun

(d) Effect of tilting angle HG on a clear winterday in Burgdolf (CH)

(e) Effect of tilting angle on a overcast winterday in Burgdolf (CH)

(f) Variation of global radiation at differentlocations

Figure 1.21: Solar Radiation in different conditions-2

1.5. Radiation 25

Figure 1.22: Mean monthly global radiation varies over years

Figure 1.23: Components of radia-tion G and H

Figure 1.24: Formation of reflectedradiations HR and GR


Figure 1.25: Irradiance GG and irradiation HG arriving at a tilted PV receivedfrom direct beam, diffuse solar and diffuse reflected radiation

Figure 1.26: Shadowing diagram for location at 47o N

1.5. Radiation 27

Air Mass

The Air Mass is the ratio of path length which light travels through the atmo-

sphere normalized to the shortest possible path length (that is, when the sun is

directly overhead). The Air Mass quantifies the reduction in the power of light

as it passes through the atmosphere. The Air Mass is defined as:

AMfactor =optical length to the sun

sun directly overhead optical length=

1

cos ✓(1.11)

Figure 1.27: The angle to define Air Mass

Figure 1.28: The solar intensity mapping


Figure 1.29: The distribution of solar energy received per day

Example 4 Installation of Solar Panel

A house consumes energy 500W per day (in average). If the solar radiation in-

tensity per day (insolation) is 6 kWhm�2. What area of solar panel (10 % PV

module) do we have to install to fulfil the energy demand for a house?

Ans-

1.5. Radiation 29

Estimating solar land area

Example 5 Solar land Requirement for Taiwan

Find out the energy consumption of Taiwan per year. Assume we have an average

insolation value of 4 kWhm�2/day. Suppose you have a PV system with 10%,

what is the size the solar land if we like to cover the energy demand.

Ans-


Example 6 Green House Effect

The energy current density outside the earth’s atmosphere : jE,sun = 1.3kW/m2.

The energy current on earth : IE,abs = ⇡Re2jE,sun (Re = 6370km)

From Stefan-Boltzman Law : jE,earth = �T 4(� = 5.67⇥ 10

�8W/m2K2)

The energy current emitted by the entire earth : IE,emi = 4⇡Re2�Te

4

For steady-state condition, IE,abs = IE,emi, we have the mean temperature of the

earth Te = 275K.

Green House effect : A great part of the energy emitted from the earth surface is

absorbed by the atmosphere and re-emitted back to the earth. This warm up the

atmosphere and rise the temperature. From Fig1.30, we have the relation of

IE,earth = IE,sun +1

2

IE,atm

Figure 1.30: Balance of the absorbed and emitted energy current on the surfaceof the earth.(From P. Wurfel)

What is the temperature ( T ghe =?) with such greenhouse effect

0.5IE,atm = IE,sun this leads to IghE,earth = 2IE,sun = 2IE,abs = 2IE,emi = 8⇡Re2�Te

4

Since IghE,earth = 4⇡Re2�T gh

e4 we have T gh

e4= 2Te

4

T ghe = 54

�C

1.5. Radiation 31

Figure 1.31: The Solar Spectral and its current density

Figure 1.32: Theoretical currentdensity vs absorber bandgap

Figure 1.33: Theoretical efficiencyvs absorber bandgap


1.5.3 Quantifying Solar Radiation

Solar Spectrum

Figure 1.34: The Solar Spectral Irradiance

Photon Flux-the number of photons per second per unit area:

� =

Number of photon

second⇥ area

Homework 1 Transfer Solars Irradiance spectrum (Power) into Flux spectrum

(No. of photons).

Ans-

1.6. Basics in Photovoltaics 33

1.6 Basics in Photovoltaics

What is a Photovoltaic device or a solar cell?

Figure 1.35: A simple model of PV converter

Figure 1.36: Comparison of photoelectric effect (left) and a photovoltaic de-vice (right). The photovoltaic device needs certain spatial asymmetry such asselective contact to drive excited charges to external circuit.


Figure 1.37: Various mechanisms of photovoltaics devices

Figure 1.38: I-V characteristics of a diode


How do their I-V characteristics look like?

Figure 1.39: I-V characteristics of a solar cell under dark and illumination

Figure 1.40: A typical IV cure of asolar cell

Figure 1.41: Characteristics of thecurrent-voltage and power-voltagean ideal solar cell

FF = (1.12)

n = (1.13)


Figure 1.42: Equivalent circuit of a solar cell

Figure 1.43: Equivalent circuit of a solar cell with series and shunt resistance

Iph = Is[exp(qv

kt)� 1]� Il photocurrent equation (1.14)


High Rs and low Rsh will kill the performance of a solar cells.

Figure 1.44: Effects of series (a) and shunt resistance (b) in a solar cell.

Figure 1.45: Effects of light inten-sity on the IV cure of a solar cell

Figure 1.46: The efficiency of anideal Si solar cell as function of lightintensities

Crystalline Si solar cell performs much better at high illumination intensity.

However, not all the solar cells behaves the same. For example, dye-solar cells

work better under low light and higher temperature.

A STUDY OF DYE SENSITIZED SOLAR CELLS UNDER INDOOR AND LOW LEVEL OUTDOOR LIGHTING: COMPARISON TO ORGANIC AND INORGANIC THIN FILM SOLAR CELLS AND METHODS

TO ADDRESS MAXIMUM POWER POINT TRACKING

Nagarajan Sridhar1 and Dave Freeman1 1 Texas Instruments, 13560 N. Central Expressway, MS 3747, Dallas, TX 75243, USA.

ABSTRACT: With increasing applications in consumer electronics such as smart phones, laptops and tablet PCs, the need for pervasive computing with a requirement of lower power consumption is increasing every day, This opens the door for energy harvesting that could charge the batteries in these devices to keep them continually functioning in some useful state. There has been a lot of attention on flexible thin film solar cells, such as dye sensitized (DSSC), organic and inorganic, given their low cost and improving efficiency. Such cells are suitable for these applications both under outdoor and indoor conditions due to their larger spectral response. Understanding the behavior of solar cells such as DSSC under indoor light conditions along with power management algorithms to extract maximize the collected energy is vital for consumer electronics applications. This analysis is compared to organic and inorganic thin film solar cells. Keywords: Dye-sensitized, Spectral response, Shading, Power Conditioning.

1 WHY ENERGY HARVESTING? With the advent of ubiquitous computing and information exchange through consumer applications using smart phones and laptops to name a few, power consumption requirements are expected to become stringent. Furthermore, the usage of conventional batteries is becoming a concern as it requires constant replacement or maintenance. Energy harvesting has gained a lot of attention to address this challenge. Energy harvesting is defined as the process of utilizing ambient energy to perform functionalities of mobile/small electronic devices. Typical such sources are light, mechanical (vibration), and thermal. Table I summarizes these sources with respect to performance and harvesting techniques to extract each of these energy sources [1]. In addition to performance, other factors such as integration of energy harvesting methods to a self-rechargeable battery, size, shape, weight, mechanical flexibility, water resistance and operating temperature ranges strongly dictate the choice of the energy harvesting methods. While it is clear that all these techniques have the potential and hurdles to climb, solar cells appear to be a preferred choice in many of these applications. Mobile applications are used in locations where there is always some availability of light, thereby making solar cells as a convenient solution. It is important to note that there is a lot of ongoing work on embedding multiple energy harvesting methods into the same system [2, 3], which would ideally be the best solution. The challenge of such systems however is enormous as they involve optimization of power electronics topologies and intelligence to ensure maximum energy extraction and power conditioning. The discussion in this paper is limited to light energy harvesting using solar cells. 2 SOLAR CELLS FOR INDOOR APPLICATIONS Since majority of the mobile devices are primarily used in indoor applications, it is important to understand the behavior of solar cells in indoor lighting. Currently, crystalline silicon solar cells dominate the solar cell market. However, this technology is targeted for high power outdoor applications due to their coverage of the solar spectrum. On the other hand, the light

spectrum is quite different when it comes to indoor applications. Therefore it is vital to look at other cell types that may have a stronger spectral response which in turn has an impact on cell efficiency. The cell efficiency which is defined as the ratio of the solar cell peak output power divided by the incident power on the solar cell is dependent on the lighting source. A significant portion of the spectrum under outdoor light conditions falls in the red region of visible light. It turns out that crystalline silicon has a much stronger spectral response in this region in comparison to lower wavelengths. Whereas, indoor conditions which is primarily fluorescent lighting have a significant portion of the spectrum in the 600 nm range and below. The other indoor lighting source is incandescent. Solar cell technologies based on amorphous silicon (a-Si), organic solar materials (OPV), and dye sensitized materials (DSSC) [4, 5] fit this regime very well. They are therefore thought to be more suitable for indoor applications. DSSC technologies in particular, even though are lagging in conversion efficiencies compared to inorganic cell technologies such as crystalline Si and a-Si, they have the advantages of low cost processing, flexibility, conformabilities to different shapes – a key enabler for consumer applications, light weight and display of different colors. The goal of this paper is to discuss a quantitative feasibility analysis of DSSC in response to indoor and low light outdoor conditions, while also addressing power management algorithms that help maximize the collected energy. This analysis is compared to organic and inorganic thin film solar cells. Finally, the paper will discuss single and multi-cell topologies and the effect of shading on the robustness of these algorithms. 3 MEASUREMENT SETUP

There are three categories of indoor lighting: fluorescent, incandescent, and daylight. Even though a brief comparison will be made across all the three these lighting conditions, predominant focus will on fluorescent lighting, due to its popular use in home, business, and warehouses.

The cell performance measurements are made inside a Pantone color viewing light box that has three sources: fluorescent, incandescent and daylight. This box allows

experiments to be conducted in a controlled manner. Care is taken to ensure that there is no stray light affecting the measurements. The solar cells are placed in a horizontal position (parallel to the light source).

Illuminance values are measured used a lux meter. Combinations of neutral density filters are used to obtain different illuminance values. Lux is a typical unit for measuring indoor lighting. For outdoors, irradiance is measured in Watts/m2.

Fundamental difference between illuminance and irradiance is the weighting of the spectral response. Irradiance includes the power from all wavelengths weighted equally, whereas illuminance weights the power from each wavelength in proportion to the sensitivity of the human eye, which in turn is most sensitive to green light.

Majority of the solar cell characterization studies are reported under outdoor or sunlight conditions. The peak output is reported at standard test conditions (STC) conditions with intensities of 1000W/m2. On the other hand, indoor lighting conditions are significantly different. Table II shows these values that were measured in this study in various zones of an office environment.

The lower limit of the office environment could be extended down that may include a conference room where lighting was turned down during projected presentations, that is 50 – 100 lux. On the other hand, the higher limit was extended in a bright indoor lighting such as a studio, assembly areas and warehouse lighting to 3000 lux. Based on the empirical relationship between lux and W/m2, it turns out that the average indoor lighting is approximately 1 to 2 W/m2, less than 500 times lower than outdoor conditions. As a result, all of our measurements and analysis are done using lux values.

IV measurements are done using a Keithley 2400 source meter to measure the solar cell open and short circuit values, as well as the maximum power parameters of the solar cell. Normalization is done for current and power to one square centimeter to perform comparative studies among the various solar cells.

Cell and maximum power point parameter correlations are done as part of maximum power point (MPP) algorithm determination. 4 SOLAR CELL BEHAVIOUR UNDER VARIOUS INDOOR LIGHTING CONDITIONS Fig. 1 illustrates the various solar cell maximum power density (mW/cm2) values that were determined from the IV scan for different illuminance conditions at room temperature for fluorescent lighting. Further demarcation is highlighted in Fig. 2 using the light level measurements done for realistic indoor office lighting environment as shown in Table II. This demarcation method helps the understanding of the applicability for a given solar cell for a given light condition. This analysis indicates that among the various cells measured, DSSC shows higher power density across indoor conditions relative to a-Si and (OPV) solar cells. On the other hand, similar drill down analysis done under incandescent lighting conditions indicates that poly Si solar cells show a superior performance over others as shown in Fig. 3. These results, in general is consistent with the spectral response curves that therefore results in poly Si showing the best performance relative to other cells considered in our study for incandescent conditions.

While it is known that the wide band-gap for DSSC [5] would explain the higher performance for fluorescent conditions, it is relatively better than other wide band-gap materials in incandescent conditions as well as indicated in Fig. 3. A slightly lower performance for DSSC for a given lux value under fluorescent condition compared to daylight condition (Fig. 4) can be explained by the additional infrared component in the daylight spectral response. For indoor conditions, since fluorescent light is more popular, it is vital to understand the amount of power that can be generated for realistic lighting conditions of around 250 – 500 lux is in the order of 25 uW/cm2. This number is key for the end product manufacturers to wrap an energy harvesting solution to their application. The other challenge is to ensure that the system extracts MPP from the solar cell and the method of extraction.

5 MPP ALGORITHMS

It is well known that cell parameters and the maximum power parameters strongly predict the efficiency of the solar cell. Most studies focused on STC conditions or primarily outdoor conditions. However, a quantitative understanding the behavior under low-light (indoor) conditions is vital. Reason is that this enables the system to maintain or work towards staying at the MPP location under varying conditions such as lighting change due to source distance or light source, angle of incidence, or temperature change. This is done through the implementation of an appropriate MPP algorithm. Several MPP algorithms have been developed [6 - 8], some of which are used very commonly in high power applications such as the P&O, incremental conductance etc. However, the implementation of such algorithms requires high performance controllers which could be costly as well as high in power consumption. It turns out that for energy harvesting systems, the MPP algorithm developed is the one that needs the least amount of resources or circuits as these systems are embedded in consumer applications which need to be low cost and ones that need to consume very low power.

Detailed investigation for all three cells reveals that the short circuit and maximum current are strongly linear with illuminance. Similarly, the open-circuit voltage and the maximum voltage show linearity on a logarithmic scale of illuminance, all with extremely high correlations. Figure 5 and 6 shows the correlation plots between Vmax versus Voc and Imax versus Isc respectively for the DSSC cell.

These strong correlations open the door to design simple cost-effective MPP algorithms for controllers associated with these solar cells for indoor applications where cost is a priority for commercial feasibility. Equation 1 is called the fractional voltage method that can be used to estimate the maximum power point voltage (Vmax) after finding the open circuit voltage (Voc). Similarly, Equation 2 is called the fractional current method that estimates the maximum power point current (Imax) based on the short circuit current (Isc). Figure 2 shows a simple algorithm that corrects the maximum power point voltage for changes in illumination. The following relationships are simple MPP algorithms for indoor applications of a DSSC cell.

Fractional voltage method Vmax = 0.74*Voc (1)

Fractional current method Imax = 0.93*Isc (2)

Equations 1 and 2 are simple algorithms that are adapted as most suitable choices considering their spectral response and their ability to conform to the application. It turns out that the values of the constants in the fractional methods are very close for the various cells considered in this study. Besides, the same is true across fluorescent and incandescent light sources for a given cell type, suggesting these fractional methods could be used with little loss of MP for a system independent of the type of solar cell used. However, these techniques are presented with some challenges due to non-optimal conditions such as shading that will be discussed in the next section. 6 SINGLE VERSUS MULTI CELLS AND THE EFFECT OF SHADING

The effect of implementing multi- versus single-cell solar panels in low-power energy harvesting systems is considered to understand the impact on non-optimal conditions such as shading. Multi-cell systems produce higher output voltages, whereas single-cell systems produce a low voltage. However, this is well below what is the usability by the majority of electronics produced today. Ten single-cells connected in series generate approximately 10 x Voc of a single cell which is usually around 5.0 V. This can be used directly by today’s electronics or regulated down to a lower voltage to support the current micro-controllers that run at 3.3V or 1.8 V. The easy use of this voltage has contributed to multi-cell popularity. However, multi-cell topologies are more expensive relative to the system cost for these applications. Moreover, multi-cell topologies suffer from the shading problem that could hamper the effectiveness of the simple fractional MPP algorithms.

In more recent history, single-cell panels are receiving a stronger focus. This is most likely the result of the convergence of several factors. The cost of a single-cell panel is lower than a multi-cell. Construction of single-cell panels is simpler and maximizes the cell area since there is less wiring for inner-connecting the cells. Also, the area available on today’s electronic products is smaller and the overhead of the inner-connection of a multi-cell takes up precious area that could be used to generate current. Lastly, the single-cell does not have the same weakest cell problem like the multi-cell when shadowing occurs. However, a single-cell generates approximately 0.5V, which is a relatively low voltage and difficult to use to directly power existing electronics.

Shading is a known non-optimal condition that could occur often in indoor applications. For example, consumer applications like cell phones or remote controls could have some unavoidable temporary one or more cell shading issue by a person or an object while the device is in use. Shading can severely distort the IV and PV curve. End result is that the system would no longer be able to extract maximum power under such conditions. Fig. 7 shows a set of PV curves for a 4 cell DSSC connected in series with and without shading (simulated using opaque

strips of various thicknesses) under realistic indoor office condition. The multiple MPP peaks and the shift of the single peak cases due to shading changes the value of the constant in the fractional MPP algorithms.

This explains why the MPP algorithm might need to be sophisticated besides simply using fractional methods that are applicable under optimal conditions. However as explained before, sophisticated methods involve the use of a more complex power conditioning electronics that might make the solution more expensive for a given application while consuming a lot more power than the ones using simple fractional methods.

An alternative solution is to use a single-cell topology that can avoid multiple peaks in the PV curve. The tradeoff is low-cell voltage and lower efficiency compared to a multi-cell with a same area and cell type. On the other hand, implementation of MPP method in a single-cell is much simpler since there is only one peak on the PV curve and also the power required to implement the MPPT function would be significantly smaller than in the multi-cell system.

7 CONCLUSIONS Our study shows that DSSC under indoor conditions for energy harvesting applications is a better candidate than a-Si and OPV in terms of maximum power density. Derivation of the constants in the fractional MPP algorithms indicate that they could be implemented with little loss of maximum power across various solar cell types as well as across fluorescent and incandescent conditions. However, shading could render the method ineffective for the popular multi cell solutions. One solution to this problem is to implement a single cell topology. 8 ADDITIONAL COMPONENTS 8.1 Illustrations

Figure 1: Cell comparison under fluorescent lighting condition.

Figure 2: Windowing cell comparison for realistic indoor conditions.

Figure 3: Cell comparison under incandescent lighting condition.

Figure 4: DSSC cells under different lighting conditions

Figure 5: Vmax plotted as a function of Voc for a fractional voltage algorithm for DSSC.

Figure 6: Imax plotted as a function of Isc for a fractional current method algorithm for DSSC.

Figure 7: Effect of shading in a multi-cell topology. 8.2 Tables Table I: Performance and harvesting techniques by energy source [1]

Table II: Measurement of lighting in indoor conditions

8.3 References [1] B. Atwood, B. Warneke, K. S. J. Pister, Proceedings

of 14th Annual International Conference on Microelectromechanical Sytsems, (2001) 357.

[2] S. Chalasani, J. M. Conrad, Southeastcon IEEE, Vol. III (2008) 442.

[3] J. Colomer-Farrarons, P. Miribel-Catala, A. Saiz-Vela, J. Samitier, IEEE Transactions on Industrial Electronics, Vol. 58, (2011) 4250.

[4] B. O’Regan, M. Grätzel, Nature 335 (1991) 737 [5] M. Grätzel, Journal of Photochemistry and

Photobiology C: Photochemistry Reviews, Vol. 4 (2003) 145.

[6] N.Femia, D.Granozio, G.Petrone, G.Spaguuolo, M.Vitelli, IEEE Trans. Aerosp. Electron. Syst., Vol. 2 (2006).

[7] D.P.Hohm, M.E.Ropp, Proc. Photovoltaic Specialist Conference (2000) 1699.

[8] R. Faranda, S. Leva, V. Maugeri, Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, IEEE, (2008) 1.


Figure 1.47: Effects of temperatureon the IV cure of a solar cell

Figure 1.48: Temperature depen-dency on the photovoltaic charac-teristics of a Si solar cell

Figure 1.49: Photon flux under various environment



Figure 1.50: Progress roadmap of different type of Solar Cells

Figure 1.51: 1st generation solar cell Figure 1.52: 2nd generation solarcell


Figure 1.53: Various type of Solar Cells based on Solution Process


Figure 1.54: Quantum effect tuning the absorption for PV


1.7 Basics of Photoelectrochemistry

Figure 1.55: The utilization of solar energy

1.7. Basics of Photoelectrochemistry 49

Figure 1.56: Energy diagrams of photocatalytic water splitting based on (a)one-step excitation and (b) two-step excitation (Z-scheme); and PEC watersplitting using (c) a photoanode, (d) photocathode, and (e) photoanode andphotocathode in tandem configuration. The band gaps are depicted smaller in(b) and (e) to emphasize that semiconductors with a narrow band gap can beemployed. (From Chem. Soc. Rev., 2014, 43, 7520–7535)


Figure 1.57: Different types of phooelectrochemical systems

1.7. Basics of Photoelectrochemistry 51

Figure 1.58: Photocleavage of H2O at n-type electrode

Figure 1.59: Photocleavage of H2O at p-type electrode

Figure 1.60: Photocleavage of H2O at n-type Ru2S under external bias.


Homework 2 Can you draw a p-n H2O photoelectrolysis cell?

Ans-

Figure 1.61: Band edge position of various metal oxide

1.8. Structure of real Solar Cells 53

1.8 Structure of real Solar Cells

1.8.1 Crystal Si Solar Cells

Figure 1.62: Crystal structureof Si

Figure 1.63: Intrinsic Si crystal and andbandgap

Figure 1.64: n type Si crystal andand bandgap

Figure 1.65: p type Si crystal andand bandgap


Figure 1.66: Characteristics of a pn junction


Figure 1.67: Basic structure of a crystalline Si solar cell (150 - 300 um)

Figure 1.68: Appearances of mono crystalline Si (right) and polycrystalline (ormulticrystalline) Si (left) solar cells.


Figure 1.69: Structure of a back sur-face field solar cell

Figure 1.70: Charge separationrate profile in a solar cell

Figure 1.71: Buried-contact, surface texture and back surface field design


Figure 1.72: Special design of a sc-Si PERL solar cell with inverted pyramidsand bilateral area parallel point contacts back surface field architecture

1.8.2 Thin film Solar Cells

Thin film a-Si solar cells

Semiconductor compound thin film solar cells


Figure 1.73: Stucture of p-i-n Si thin film solar cell

Figure 1.74: Structure of amor-phous Si

Figure 1.75: Process of Staebler-Wronski effect


Figure 1.76: Degradation behavior of a-Si solar cell

Figure 1.77: CdTe thin film solarcell

Figure 1.78: CIS solar cell

Figure 1.79: CIGS solar cell


Figure 1.80: Cross section of CIGS solar cell

1.8.3 Tandem Solar Cells

To improve the efficiency of a sold cell. we can combine a complementary

absorber into one solar cell.


Figure 1.81: CIS and a-Si tandem solar cell

Figure 1.82: Semiconductor compound tandem solar cell


Figure 1.83: mc-Si and a-Si tandem solar cell

Figure 1.84: Triple tandem Si solar cell

1.9. Further Readings: 63

1.9 Further Readings:

Please see the contents on the Moodle.


BIBLIOGRAPHY

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65

66 BIBLIOGRAPHY

[11] R. H. Bube, Photovoltaic Materials; Vol. 1 of Properties of Semiconductor

Materials; Imperial College Press, 1998.

[12] J. Nelson, The Physics of Solar cells; Imperial College Press, 2003.

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[14] W. Schockley and H. J. Queisser, J. App. Phys., 1961, 32, 510.

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