Exploring opportunity for energy supply through solar pv technology for stone processing industry of riico industrial area, deoli (rajasthan)

Exploring Opportunity for Energy Supply through Solar PV
Technology for Stone Processing Industry of RIICO Industrial
Area, Deoli (Rajasthan)
Submitted in partial fulfillment of the requirements for the award of degree of
MASTER OF TECHNOLOGY
In
Renewable Energy
Guided By: Submitted By:
Dr. G. D. Agrawal Gaurav Gupta
Associate Professor M. Tech. (Renewable Energy)
Department of Mechanical Engineering 2013 PCV 5068
Centre for Energy & Environment,
Malaviya National Institute of Technology Jaipur
June 2015

ii
MALAVIYA NATIONAL INSTITUTE OF TECHNOLOGY
JAIPUR
Centre for Energy & Environment
Jawaharlal Nehru Marg, Jaipur-302017 (Rajasthan)
CANDIDATE’S DECLARATION
I hereby certify that the work which is proposed for the thesis “Exploring Opportunity for
Energy Supply through Solar PV Technology for Stone Processing Industry of RIICO
Industrial Area, Deoli (Rajasthan)” in partial fulfillment of the requirements for the award of
the Degree of M. Tech and submitted to the Centre for Energy & Environment of the
Malaviya National Institute of Technology Jaipur is an authentic record of my own work under
the supervision of Dr.G. D. Agrawal, Associate Professor, Department of Mechanical
Engineering, Malaviya National Institute of Technology, Jaipur.
Gaurav Gupta
2013PCV5068
M.Tech, IVth
Semester
. Renewable Energy
MNIT Jaipur

iii
MALAVIYA NATIONAL INSTITUTE OF TECHNOLOGY
JAIPUR
Jawaharlal Nehru Marg, Jaipur-302017 (Rajasthan)
Certificate
This is to certify that the dissertation entitled “Exploring Opportunity for Energy Supply
through Solar PV Technology for Stone Processing Industry of RIICO Industrial Area,
Deoli (Rajasthan)” that is being submitted by Mr. Gaurav Gupta M. Tech. – IV Sem
(2013PMV5068) required for partial fulfillment of award of the degree of Master of
Technology, Renewable Energy, Center for Energy & Environment, Malaviya National
Institute of Technology, Jaipur is found to be satisfactory and is hereby approved for submission.
Dr. G. D. Agrawal
Associate Professor
Department of Mechanical Engineering
Malaviya National Institute of Technology
Date Jaipur, Rajasthan

iv
Acknowledgement
It gives me great pleasure in conveying my heartfelt thanks and profound gratitude to my
supervisor, Dr. G. D. Agrawal, Associate Professor Department of Mechanical Engineering,
Malaviya National Institute of Technology Jaipur for providing me with the guidance,
encouragement and support at every step in formulating this work. His valuable feedback, advice
and moral support have been a great source of inspiration for broadening my horizons in this area
of research.
I feel great full to express my heartiest thanks to Dr. Sanjay Mathur (HOD, Center for Energy
and Environment) for trusting and supporting me, I also thankful to Dr. Jyotirmay Mathur
(Professor, Center for Energy and Environment) for supporting me and encouragement to the
great completion.
I am also indebted to my father Dr. Rajendra Prasad Gupta for his support and enhancing my
skills in all aspects, also Dr. Sanjay Vashishtha, CEO Firstgreen Consulting Private Limited,
Gurgaon for his support. I would also like to thank Chandra Prakash (J. En, DISCOM Deoli) to
support and refine my survey of RIICO industrial area, Deoli.
I also thank all the faculty of the department, colleagues, research scholars and friends for their
helpful suggestions and encouragement.
Place: MNIT Jaipur
Gaurav Gupta
M.Tech, Renewable Energy,
(ID 2013PCV5068)

v
Abstract
India is a developing country, the industries play a significant role in the development of the
country. Industrial sector the is second largest electricity consumer, making electricity as
backbone for the development of the country. Presently, most of the Indian industries are using
conventional sources of energy (fossil fuels) for meeting their energy demand. Fossil fuels are
limited, polluting and day by day getting costlier. As the fossil fuel reserve is also dieing, there
is a great requirement of abundant, reliable and affordable energy source to meet the future
energy demands. The solar energy is the most promising, easily available source of energy
among all other renewable energy options. Solar energy is the largest available carbon-neutral
energy source. The incident energy from the sun to earth in one day is larger than what is
consumed on the planet in an entire year.
Here RIICO industrial area Deoli, Rajasthan has been studied, to explore the possibility of
substitution of conventional energy sources by solar energy. In the RIICO industrial area, Deoli,
most of the industries are dealing in stone processing field. The stone industry requires electrical
energy to run its machinery and office equipments, but does not require heat or hot water. The
existing source of electricity is DISCOM grid and DG generators in the absence of grid
availability. To substitute the existing energy source (grid source) by solar energy there are two
technologies that are available, first is solar thermal and second is solar PV technology. As the
solar thermal technology has very high cost in comparison to solar PV technology, solar PV
technology has been considered here.
After the detailed survey execution on the RIICO industrial area, Deoli, the study has been
carried out for two options of solar PV plant. One is offsite solar PV plant of 2.4 MWp and other
is combined onsite offsite solar PV plant of 148 kWp and 2.25 MWp respectively. To calculate
and assess the technical parameters and performance of the plant, PVSyst simulation software
has been used. The parameters studied by simulation are capacity utilization factor (C.U.F.),
performance ratio (P.R.) and annual energy yield for the offsite, onsite and remaining offsite
SPV plants.

vi
In the study economic feasibility also has been calculated, with four different locations for the
offsite plants. The results of detailed feasibility study are
 The project IRR for offsite PV plant ranges from 11.55% to 15.22%,
 For onsite fraction of combined onsite offsite SPV plant, project IRR calculated as 8.39
% and for offsite fraction project IRR ranges from 11.43% to 15.08%.
To reduce the cost of generation or levelised cost of energy (LCoE) of the solar PV plant, policy
support options from government side also has been considered in the study. The study has
suggested some recommendations to get a cut in capital cost of the solar PV plant. The variation
in the LCoE with variation of these parameters has shown, and then some feasible options have
been selected. For the selected options for both offsite solar PV plant and combined onsite offsite
solar PV plant, grid parity analysis has been done between LCoE and existing cost of energy
from grid source.
The analysis gives a result that the substitution of conventional energy source for RIICO
industrial area Deoli by solar PV plant is technically & economically feasible. In comparison of
offsite and combined onsite offsite solar PV plants, offsite solar PV plant has more economic
feasibility. By adding some of policy support options suggested in the study, solar PV
technology can competes with the existing low cost electricity from the grid source.

vii
Contents
Abstract………………………………………………………………………………………………… v
Contents ……………………………………………………………………………………………….. vii
List of Figures…………………………………………………………………………………………..x
List of Tables…………………………………………………………………………………………… xii
Abbreviations…………………………………………………………………………………………. xiv
Nomenclature………………………………………………………………………………………… xvi
Chapter 01: Introduction……………………………………………….......................... 1 - 10
1.1 Background ………………………………………………………………………… 1
1.2 Solar Energy ………………………………………………………………………. 1
1.2.1 Solar thermal technology....…………………………………………………….2
1.2.2 Solar PV technology...….………………………………………………………3
1.2.3 Why Solar PV only……….…………………………………………………… 5
1.3 Stone Processing Industry...…….…………………………………………………... 5
1.3.1 Types of Stones & applications....……………………………………………...5
1.3.2 Processing method and machines used……………………………………… .. 6
1.3.3 Energy consumption……………………………………………………………7
1.3.4 Number of industries and turnover…………………………………………… 8
1.3.5 Geography of RIICO Industrial area, Deoli…………………………………. . 8
1.4 Objective of Study…………………………………………………………………. 10
Chapter 02: Literature Review………………………………………………………….. 11-21
2.1 Indian Scenario……………………………………………..……………………. 11
2.2 Case study analysis for industrial application of SPV plants…………..…...…… 13
2.3 Analysis of solar irradiation ……………………………………………………… 14
2.4 Technical aspects of SPV plant (Onsite offsite)....…………………………….. 15
2.5 Financial aspects & LCoE of SPV plants ………………………………………… 17
2.6 Techno- economic study of SPV plant……………………………………………. 19
2.7 Grid Parity……..…………………………………………………………………. 19
2.8 Recommendation based on literature review……………………..……………….. 21

viii
Chapter 03:Case Study: RIICO Industrial Area – Deoli ………………………………22-32
3.1 Stone industrial area……………………………………………………………. 22
3.2 Survey and data collection……………………………………………………… 23
3.3 Pilot survey………………………………………………………………………24
3.4 Electrical energy demand & future forecast……………………………………. 24
3.4.1 Future forecast……………………………………………………………… 28
3.5 Area of Industries ……………………………………………………………… 30
3.6 Proposed SPV plant……………………………………………………………. 30
3.6.1 Offsite option……………………………………………………………… 31
3.6.2 Combined onsite offsite option………………………………………..….. 32
Chapter 04: Design of Solar PV Plant …………………………………………………...33-65
4.1 Offsite SPV plant……………………………………………………………….. 33
4.1.1 Solar resource assessment………………………………………………….. 33
4.1.2 Site assessment………………………………………………………………36
4.1.3 Panel generating factor………………………………………………………37
4.1.4 Required SPVplant capacity (MWp)……………………………………….. 37
4.1.5 PV modules…………………………………………………………………. 38
4.1.6 Inverter sizing………………………………………………………………..39
4.1.7 PV module string arrangement…………………………………………… 40
4.1.8 Land required……………………………………………………………….. 41
4.1.9 PVsyst simulation and modeling…………………………………………. . 44
4.1.10 Project cost………………………………………………….…………… 48
4.1.11 Financial parameters & LCoE of the plant…………………………………49
4.2 SPV plant with Combination Onsite Offsite location.….....…………….……… 51
4.2.1 Onsite SPV plant requirement………………………………...……………..51
4.2.2 Combined onsite offsite SPV plant sizing…………………....……………. 52
4.2.3 PVSyst simulation and modeling...…………………………....……………. 56
4.2.4 Project cost and financial parameters…...…………………………………...61

ix
Chapter 05: Financial Analysis of SPV plant …………………………..…………… 66-93
5.1 Land and Transmission line……………………………………………………… 66
5.1.1 Site Location 1…………………………………………………………….. 67
5.1.2 Site Location 2…………………………………………………………….. 69
5.1.3 Site Location 3…………………………………………………………….. 71
5.1.4 Site Location 4…………………………………………………………….. 73
5.2 Capital cost subsidy…………………………………………………………….. 78
5.3 Variation in Interest Rate……………………………………………………….. 79
5.4 Variation in Debt & Equity Ratio………………………………………………. 80
5.5 Result and Discussion…………………………………………………………... 82
5.5.1 Variation in LCoE of SPV plant with available options…………………... 82
5.5.3 Estimation of Grid Parity for SPV plant …………………………………... 86
Chapter 06: Conclusion and Future Recommendation………………...……………. 94-97
Publications…………………………………………………………………………… 98
References...…………………………………………………………………………... 99
Appendix …………………………………………………………………................. 103

x
List of Figures
S.No. Figure No. Title Page
No.
1 1.1 Concentrating solar thermal technologies 3
2 1.2 Solar PV modules of three different technology 4
3 1.3 Process flow chart of stone 6
4 1.4 Geographical Location of RIICO industrial area Deoli, Rajasthan 9
5 2.1 Solar Irradiation level of various countries 11
6 2.2 annual average global horizontal irradiance (GHI) of India by
NREL
12
7 3.1 Geographical location of RIICO industrial area Deoli, Rajasthan 22
8 3.2 Monthly electricity consumption data of 25 industries and total 59
industries
27
9 3.3 Extrapolation of average energy consumption of 25 industries
exponentially
39
10 3.4 Schematic diagram of offsite ongrid solar PV plant 31
11 3.5 Schematic diagram of offsite solar PV plant with battery bank 32
12 4.1 Monthly average DNI comparison of two sources 34
13 4.2 Solar PV panel physical dimensions 41
14 4.3 Mounting strategy and inclination angle of solar PV module 42
15 4.4 Placement of solar PV module in an array 42
16 4.5 complete SPV plant layout 43
17 4.6 PVSyst simulation plant layout 45
18 4.7 Monthly energy production simulated by PVSyst 47
19 4.8 Onsite SPV plant layout 54
20 4.9 Remaining offsite SPV plant layout of capacity 2.25 MWp 56
21 4.10 Onsite solar PV plant PVSyst simulation layout of capacity 2.5
kWp
57
22 4.11 Onsite SPV plant monthly electricity generation 58
23 4.12 Remaining offsite SPV plant PVSyst layout 59
24 4.13 Monthly electricity generation of remaining offsite SPV plant 60
25 5.1 Distance between location 1 and RIICO industrial area Deoli 68
29 5.5 Comparative graph of IRR and LCoE for all locations of offsite
SPV plant
76
30 5.6 Comparative graph of IRR and LCoE for all locations of offsite
fraction of combined SPV plant
77
31 5.7 Variation of LCoE of offsite SPV plant with different clubbed
option
82
32 5.8 Variation of LCoE of offsite fraction and combined onsite offsite 84

xi
SPV plant with different clubbed option
33 5.9 Grid parity estimation by extrapolation of LCoE of offsite SPV
plant and grid cost
88
34 5.10 Grid parity estimation by extrapolation of LCoE of combined
SPV plant and grid
89
35 5.11 Grid parity estimation by extrapolation of LCoE of offsite SPV
plant and grid
91
36 5.12 Grid parity estimation by extrapolation of LCoE of combined
SPV plant and grid cost.
92

xii
List of Tables
S.
No.
Table no. Title Page
no.
1 1.1 Details of electrical loads of industries (Both machine load and
office eqp. loads)
7
2 3.1 Surveyed data of RIICO industrial area of 25 industries for
electricity requirement
25
3 3.2 Data surveyed of electricity consumption of total RIICO
industrial area Rajasthan Deoli, Rajasthan
26
4 3.3 power requirement estimation of RIICO industrial area Deoli,
Rajasthan
26
5 3.4 past three year data of surveyed 25 industries 28
6 3.5 forecasting energy consumption data for RIICO industrial area,
Deoli
30
7 4.1 Monthly average DNI data for location RIICO Industrial area
Deoli, Rajasthan
34
8 4.2 Average mean sunshine hours (Daily basis) 35
9 4.3 Equivalent no. sun days for RIICO industrial area Deoli 35
10 4.4 Technical specification of solar PV module 38
11 4.5 Bonfiglioli Vectron Technical specification of inverter 39
12 4.6 Financial parameters of offsite SPV plant 50
13 4.7 Combined onsite offsite SPV plant parameters 51
14 4.8 Inverter technical parameters of SMA sunny boy 52
15 4.9 Technical parameters of inverter for remaining offsite SPV plant 55
16 4.10 Financial parameters of remaining offsite SPV plant 64
17 5.1 Capital cost estimation of offsite SPV pant 68
18 5.2 Financial parameters for offsite SPV plant at location 1 68
19 5.3 Cost estimation of combined SPV plant at location 1 68
20 5.4 Financial parameter for combined SPV plant at location 1 69
21 5.5 Capital Cost estimation of offsite SPV plant at location 2 70
22 5.6 Financial parameters of offsite SPV plant at location 2 70
24 5.8 Financial parameters of combined SPV plant at location 2 71
25 5.9 Cost estimation of offsite SPV plant at location 3 72
29 5.13 Cost estimation of offsite SPV plant at location 4 74
33 5.17 Capital cost estimation of offsite SPV plant for all location 76

xiii
34 5.18 Financial parameters & LCoE of offsite SPV plant for all location 76
35 5.19 Capital cost estimation of combined SPV plant for all locations 77
36 5.20 Financial parameters of combined SPV plant for all locations 77
37 5.21 LCoE of offsite SPV plant with capital subsidy 78
38 5.22 LCoE of combined SPV plant with capital subsidy 79
39 5.23 LCoE of offsite SPV plant with different rate of interest 79
40 5.24 LCoE of SPV plant with capital subsidy of offsite SPV plant 80
41 5.25 LCoE of offsite SPV plant with variation in debt & equity ratio 80
42 5.26 LCoE of combined SPV plant with variation in debt & equity
ratio
81
43 5.27 Variation of LCoE of offsite SPV plant by clubbing different
policy support options for offsite SPV plant
83
44 5.28 Variation of LCoE of combined SPV plant by clubbing different
policy support options for combined offsite SPV plant
85
45 5.29 Extrapolation of LCoE of offsite SPV plant and grid cost for grid
parity estimation
87
46 5.30 Year of grid parity estimation for offsite solar PV plant (Base
case)
88
47 5.31 Extrapolation of LCoE of combined SPV plant and grid cost to
grid parity estimation
89
48 5.32 Grid parity year estimation for combined onsite offsite solar PV
plant (Base case)
90
49 5.33 Extrapolation of LCoE of offsite SPV plant and grid cost for grid
parity estimation
90
50 5.34 Year of grid parity estimation for offsite solar PV plant
(Accelerated case)
91
51 5.35 Extrapolation of LCoE of combined SPV plant and grid cost to
grid parity estimation
92
52 5.36 Grid parity year estimation for combined onsite offsite solar PV
plant (Accelerated case)
93

xiv
Abbreviations
ADB Asian Development Bank
Ah Ampere Hour
CdTe Cadmium Telluride
CERC Central Electricity Regulatory Commission
CSP Concentrating Solar Power
CUF Capacity Utilization Factor
DISCOM Distribution Companies
DNI Direct Normal Irradiation
DSCR Debt – Service Coverage Ratio
EAI Energy Alternatives India
FIT Feed in Tariff
FY Financial Year
GHG Green House Gas
GHI Global horizontal Irradiance
GSS Grid Sub Station
HP Horse Power
IRR Internal Rate of Return
JNNSM Jawahar lal Nehru National Solar Mission
kJ Kilo Joule
kWh Kilo Watt Hour
LCoE Levelised Cost of Energy
MAT Minimum Applicable Tax
MCS Monte Carlo Simulation

xv
MPPT Maximum Power Point Tracker
NASA National Aeronautics and Space Administration
NH National Highway
NPV Net Present Value
NREL National Renewable Energy Laboratory
PGF Panel Generating Factor
PPA Power Purchase Agreement
PR Performance Ratio
RE Renewable Energy
RIICO Rajasthan state Industrial and Investment Corporation
RoE Return on Equity
Sec Section
SPV Solar Photovoltaic Plant
STC Standard Test Conditions
VGF Viable Gap Funding

xvi
Nomenclature
α Inclination Angle (Degree)
ƞPV module Efficiency of PV module (%)
Imp Maximum current (A)
Isc Short Circuit Current (A)
Vmp Maximum Voltage (V)
Voc Open Circuit Voltage (V)
kWhAC Units of electricity at output of inverter
kWhDC Units of electricity at input side of inverter
Wp Watt peak (Watt)
° Degree
‘ Minute
“ Second
€ United States currency (Cents)
δ Temperature coefficient of Pmax
β Temperature coefficient Voc
θ Temperature coefficient Isc

1 | P a g e
Chapter: 01
Introduction
1.1 Background
India is a leading country in stone processing sector, deals in various types of stones, like marble
stone, sand stone, slate stone, flaggy limestone and granite [1]. The bulk of the Indian stones are
produced in the Indian states of Rajasthan, Tamilnadu, Karnataka and Andhra Pradesh.
Rajasthan itself accounts for nearly 90% of total marble production of the country [2]. Stone
industries weather raw stone production industry or stone processing industry both have
requirement of electricity, to operate the machines (cranes, cutters, calibrators etc.). This
required electricity has a huge share in the total industrial energy demand in Rajasthan. Presently
the electricity demand of the stone industries is fulfilled by the grid electricity option. But
because of this the industry faces a number of problems like power cuts, discontinuous power
supply and high cost of power. This is due to being dependent on fossil fuels for power
generation. The reserves of conventional energy sources are declining steeply around the world,
also they are cause of various impacts on environment. That is why there is high requirement of
alternative energy sources for sustainable development by switching from conventional energy
sources to nonconventional energy sources. Solar energy is one of the most promising renewable
energy sources. It is reliable and promotes decentralized energy production which again reduces
the loss of energy in transmission and distribution.
1.2 Solar Energy
India is a country which is blessed with abundant solar energy, because of its geographical
location. India has a potential of producing 5000 trillion units (kWh) [3] of solar energy. India
has on an average of 300 days of good sunshine over a year with an insolation of about 4 to 7
kWh/ m2
per day. According to the ‘Desert Power India 2050’ report, India has a potential of 315
GW of renewable energy, in which electricity production from solar energy sources accounts for
about 285 GW [4], only from the desert areas (waste lands) of the country which are defined as
Rann of Katch, Thar, Laddakh and Lahul & spiti. Except this India has a potential of about 25
GW capacities of solar rooftop systems.

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Solar Energy can be harnessed by two main technologies of solar devices utilized for the
purpose of power generation, which are solar photovoltaic and solar thermal.
1.2.1 Solar thermal technology
Under solar thermal systems, solar devices use the heat energy of the incident solar rays, by
collecting or concentrating them through various ways and design of solar collector devices. The
collected heat may be used to produce hot water or steam requirement to produce electricity by
running turbine or generators. There are various types of solar collectors available, they can be
divided in mainly in two types,
a) Stationary solar collectors – these are non-concentrating collectors, which use the
common area for both interception and absorption of incident radiation. (Ex. Solar flat
plate collector)
b) concentrating solar collectors – they are sun tracking solar collectors, which use optical
elements to focus large amounts of radiation onto a small receiving area and follow
the sun throughout its daily course to maintain the maximum solar flux at their focus.
Concentrating solar power technologies use system of concentrating mirrors to focus
solar beam to receiver that convert the solar energy to high temperatures for power
generation [5]. (Ex. Solar trough, solar tower)
At the industrial application level second type of solar collectors (Concentrating type) are
employed, as they are more efficient to produce heat. Mainly four type of solar thermal collector
configuration are used, which are
i) Solar tower
ii) Parabolic trough
iii) Parabolic dish
iv) Linear Fresnel reflector

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Figure 1.1: Concentrating solar thermal technologies
1.2.2 Solar photovoltaic (PV) technology
Solar photovoltaic technology converts solar energy into electric energy by directly absorbing
solar photon particles of sun light which are individual units of energy. Solar cells are devices
that convert sunlight directly into electricity. Solar cell is the smallest unit in a photovoltaic
technology which is made of semiconductor material like silicon (Si) and Germanium (Ge).
Solar cell absorbs the incident solar irradiation and allows electrons to loose from their atoms to
flow in the circuit, which ultimately produces electric current and electric energy.

4 | P a g e
Series and parallel connection of solar cells make a panel, which called solar PV module. The
power rating of a solar PV module is defined in Wp (watt peak) which is DC in nature, and the
voltage – current output depends on the solar cell arrangement inside the PV module.
There are three types of solar PV modules are used for the solar PV plants, which are –
 Monocrystalline SPV module
 Polycrystalline SPV module
 Thinfilm (CdTe) SPV module
Figure 1.2: Solar PV modules of three different technologies
The construction difference between these PV module is that monocrystalline cell is sliced from
a single crystal of silicon, and polycrystalline cell is sliced from a block of silicon which consist
a large number of crystals, while amorphous or thinfilm cell is manufactured by placing a thin
film of amorphous (non crystalline) silicon onto a wide choice of surfaces.
The efficiency of generating electricity is higher for monocrystalline cell than polycrystalline and
thinfilm cells. Similarly the aperture area required of a PV module for a certain electrical power
is, higher for thinfilm cell than polycrystalline and monocrystalline cells. Monocrystalline cells
are the most expensive and thinfilm is least expensive [6]. On the basis of this specification of

5 | P a g e
the different technology of solar PV cell, polycrystalline is the most used SPV module for MW
scale power plants because of low cost, moderate efficiency and lower space requirement. That is
why in this study for the SPV plant, polycrystalline PV module has been considered.
1.2.3 Why solar PV only ?
In the stone industry (RIICO Industrial Area Deoli, Raj.), the main machineries are cranes,
cutters, polishing machine, calibrators etc, which require electricity to operate, not the heat, hot
water, steam, or hot furnaces. So for this purpose the solar thermal technology is not suitable.
Secondly electricity generation through solar thermal technology is costly in comparison with
solar PV technology. According to the latest CERC capital cost benchmarking order (FY 2015 –
2016), the capital cost of 1 MW solar thermal plant (all four types) is Rs. 1200 Lacs/ MW [7]
while that is for solar PV plant is only Rs. 605 Lacs/ MW.
Solar thermal plant requires essentially tracking system, with moving parts and moving structure.
A moving structure is complex and the maintenance cost is higher than a fixed structure.
Generally solar PV plants do not employ tracking system (Fixed tilt south facing) which allows
reducing the monthly maintenance charges in compare to a solar thermal plant.
Other than maintenance charges, for a solar thermal power plant requires skilled labor and
supervisors to deal with equipments like turbine, boiling tubes etc. which is not in the case of
solar PV plant. For this reason in this study we have selected the solar PV plant instead of solar
thermal plant.
1.3 Stone Processing Industry
1.3.1 Types of stone and application
The stone processing industries in RIICO Industrial area at Deoli Rajasthan is mainly of three
types which are Slate stone, Sand stone and Lime stone. Among the three, slate and sand stones
are the most processed stones. In this industrial area the stones used as raw material from the
mines which are situated at various locations of Rajasthan. The main application of the processed
stones is for decorative purpose in the building interior. The processed slate stone is used for
decorative and for the elevation purposes and the sand stone is used for decorative and flooring

6 | P a g e
purposes. Both types of stones are mostly exported to the European countries. The lime stone is
used in the chemical industries for production of chemicals.
1.3.2 Processing method and Machine used
In the processing of stone, the raw stone are cut, polished and calibrated according to the
requirement. The methodology of processing stone in most of the industries of the RIICO
industrial area Deoli is (with machine used in each process) shown in Fig. 1.3.
Figure 1.3: Flow chart of stone processing
Some small industries do not employ whole steps of processing, employing a part of it, like only
cutting and calibration. As shown in the above diagram there are specific machines for every
step, which can be divided on the basis of their automation or human requirement. For cutting of
stone, edge cutting and surface cutting machines are used, for calibration process the KL2 and
KL3 are the most popular machines, for polishing, line polish machine (Automatic), table polish
(human operated) and brush polish (auxiliary polishing machine non automatic) machines are
used. The rating of the machines is described in the sec. 1.3.3 of energy requirement.
1
• Stone blocks procured (Raw Material)
2
• Cutting (Edge cutting, Surface cutting)
3
• Caliberation of one surface (Auto Caliberation KL2, KL3)
4 • Polishing of another surface (Line polish, Brush polish, Table polish)
5 • Decorative/ elevation stone (ready to transport)

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1.3.3 Energy consumption
The energy required for a stone industry is mainly electricity (Sec. 1.2.3). Stone industry requires
electrical energy basically for two purposes, first is for the operation of machines and secondly
for the office electricity equipments. The rating of the machines and office equipments is shown
in the Table 1.1.
Table 1.1 Details of electrical loads of industries (Both machine load and office eqp. loads)
Description of Electrical Load
Machine Loads Office Loads
Machine Name Rating
(HP)
Rating (W) Office Equipment Rating
(W)
Calibration KL1 10 7460 Tube Light 40
Calibration KL2 20 14920 CFL 18
Calibration KL3 25 18650 Bulb 30
Edge Cutting 7.5 5595 Fan 75
Surface Cutting 5 3730 Air Conditioner 1500
Table Polish 5 / 10 3730/ 7460 Personal Computer 50
Line Polish 50 37300
Tumble Machine 15 11190
Honed Machine 15 11190
Water Motor 0.5/ 1/ 2 373/ 746/ 1492
Most of the industries run for about 8 hours. For the financial year April 2014 – March 2015, the
total electricity requirement (Office Eqp. and machines) of the RIICO industrial area Deoli of
total 59 industries was 3567292 kWh/ year with average electricity requirement of 9909 kWh/
day.

8 | P a g e
1.3.4 Number of industries and turnover
There are total 59 industries in RIICO Industrial area Deoli. All but one are the stone processing
industries. The history of the stone industry in Deoli starts in the year of 1980 with marble
procession, at that time it was not declared as RIICO by the government. During the year 1992,
marble processing industry faced a high competition with the other areas of the Rajasthan, which
created a downfall in business. Due to this downfall and significant demand of decorative stone
by the European countries, one by one industry owners switched to slate and sand stone business.
Till the year 2000, the total number of stone industries was 27 when this area was declared as
RIICO (Rajasthan state Industrial Development and Investment Corporation). After RIICO
declaration of the area number of stone industries increased significantly. At present there are 59
industries in RIICO, out of which only one industry (Metro Chem.) is not dealing to stone
business.
According to the electricity connection allotment the industries can be divided in three types
namely small scale industry (connection upto 25 HP), medium scale industry (connection upto
70 HP) and large scale industry (connection above 70 HP). 29 number of industries fall under
small scale, 26 under medium scale and 4 under large scale type. Average turnover of the small
scale industry is Rs. 50 Lacs/ year, the minimum being of Khatuwala Stone pvt. Ltd with
turnover of Rs. 20 Lacs/ year and the maximum of Metro Chem. with turnover of Rs. 25 Crores/
year.
1.3.5 Geography of RIICO industrial area Deoli
RIICO industrial area Deoli, is situated near town Deoli (about 4 k.m.) district Tonk Rajasthan,
and about 150 k. m. far from Jaipur, capital of Rajasthan. The geographical details of the site are
latitude 25° 45’ 22” and longitude 75° 23’ 3” and altitude of 340 meters [8]. The industrial
area has one 33/11 grid substation nearby, and has a total area of about 1.6 lacs m2
comprising 59
industries. The location of RIICO Deoli is shown in Fig. 1.4.

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Figure 1.4 Geographical Location of RIICO industrial area Deoli, Rajasthan.
Also the site is only 22 k.m. far from marble mining area Sawar, (Ajmer, Rajasthan), and having
a number of slate and sand stone mines near its vicinity. The site is situated on the national
highway 12 (NH 12), with good road connectivity to Jaipur and Kota. The nearest railway station
is Bundi about 58 k.m. away from site.

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1.4 Objective of Study
The purpose of the study is to explore opportunity of electricity supply through solar PV plant
for the stone processing industry at RIICO industrial area Deoli, Rajasthan. The main objectives
of this study are,
a) To Explore and study the literature on the topic and to state recommendations.
b) To study existing energy supply source and to estimate energy demand of RIICO
industrial area.
c) To design and propose solar PV plant to substitute or support the existing energy source.
d) To define the techno-economic feasibility of the proposed solar PV plant.
e) To propose policy support options to reduce the cost of generation of electricity by solar
PV plant.
f) To select the feasible policy support options and estimating the grid parity for solar PV
plant.

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Chapter: 02
LITERATURE REVIEW
Solar PV technology is the most popular renewable energy source after wind energy in the
world. Presently there are large installations of solar PV plants occurring in India. To figure out
the feasibility of energy supply option through solar PV plant to the stone industry, a detailed
literature survey has been carried out, which will give base to the topic in all aspect technical and
financial with the data of potential of solar energy in the country.
2.1 Indian scenario
India is solar rich country as of its geographical location. India has a potential of producing 5000
trillion units (kWh) [3] of clean energy. India has on an average of 300 days of good sunshine
over a year with an insolation of about 4 to 7 kWh for every square meter each day. Indian
government trying to harness this unlimited natural power of solar, which if synthesizes
efficiently it accounts to counter our energy deficit scenario with less effect on environment and
to encourage for clean energy production or low carbon emission.
According to the Desert Power India 2050 report, India has a potential of 315 GW of renewable
energy, in which electricity production from solar energy sources accounts for about 285 GW
[4], only in the desert area (wastelands) of the country which are defined as Rann of Katch, Thar,
Laddakh and Lahul ansd spiti. On the other hand India has a potential of about 25 GW capacities
of solar rooftop systems. The solar radiation level of India is higher than Spain, USA, Germany,
and China, which are still have more installations of solar PV than India, this all makes India as a
solar Hub [9] can be seen in Fig. 2.1.
Figure 2.1: Solar Irradiation level of various countries
0
2
4
6
India Spain USA Australia Italy Japan China Germany
Irradiation(kWh/Sq.mt)

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The map of India for estimating of annual average global horizontal irradiance (GHI) by NREL
is shown in Fig 2.2.
Figure 2.2: Annual average global horizontal irradiance (GHI) of India by NREL
It can be stated that more than the half of the India is coming in the excellent zone for producing
electricity from the solar energy sources. It gives the indication that India has a huge potential of
generating electricity through solar energy sources.
Industrial sector is the largest consumer of energy in India, consuming about half of the total
energy consumption. The transport sector is the next biggest consumer at 22% of total
commercial energy consumption. It consumes nearly half of the oil products, mainly in the
form of diesel oil and gasoline. Agriculture sector mainly consumes electric power and diesel
oil, major portions of which go into pump sets used for irrigation purposes. Residential sector is
another significant consumer in India [10].

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2.2 Case study analysis for industrial application of SPV plants
Case study for industrial application of solar PV technology for garment zone of Jaipur has been
studied by Chandel et al. [11] and in this they stated that, India has the excellent solar profile
specially in the state of Rajasthan and can meet the industrial load of textile industry of RIICO
industrial area Sitapura at Jaipur (India). This study shows that it can make the solar PV
technology more financially feasible. The area calculated for a solar PV plant is 13.14 Acres
calculated under designing section of the study. For Land requirement two options has been
taken on the basis of location as, Onsite and Offsite. The results of the study are, for the onsite
solar PV power plant The results of financial study of the SPV plant are internal rate of return
(IRR) is 11.88%, NPV is 119.52 million INR with 10% discount rate for onsite option of SPV
plant with simple payback period and discounted payback period of 7.73 years and 15.53 years
with 10% discount rate, while IRR is 15.10%, NPV is 249.78 million INR, simple payback
period is 6.29 years and discounted payback period is 10.14 years for off-site power plant.
Mohammad et al. [12] have carried out a case study to use solar energy (thermal) for the textile
industry, Author has stated that high energy requirement is there in the industries at lesser
temperature, and for this solar energy can be suitable source than any other conventional one,
which save energy and will give good effect to the environment. The energy demand of textile
industry has been categorized in two parts, preheat solar system that can feed the boiler with hot
water. As this system can work in various flow rates, different conditions and output
temperature, it can be employed efficiently. The second category to meet low temperatures
requirement is to feed the textile dyeing process with hot water supply. For this, the collector
area is depends on available area of the factory. In the study economic and technical comparison
between these two categories has done to determine the optimal system. Also the environmental
constrains was studied for air and water pollutants of different region. The study of environment
has given that the solar system is the most promising and friendly to the environment than the
conventional fuels.
shrimali et al. [13] has explained about the policy aspects for renewable energy (solar energy) in
India. Author has described that cost of renewable energy in India may raise by 24 to 32 % as
compared to other countries like USA as the high cost of debt, which is the most difficult
problem. It shows that if part of borrowed amount decreased, then also loan terms with short

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tenors and different interest rates will arises as significant parameters. Study shows for
sustainable development of renewable energy sources in India there is a need of an interest-rate
subsidy by the government, which will reduce the cost of debt and hence the LCoE of the SPV
plant. Again it will help to reduce the subsidy burden by 13–16%. Author also gave suggestion to
the policy makers for low-cost, long-term debt like other developing countries China and Brazil.
The analysis which was calculated here stated that an interest rate subsidy of 5 percentage points
reduces the subsidy burden by 13–16%.
2.3 Analysis of solar irradiation
With reference of case study carried out for Sagardeep Island in West Bengal state (India)
Moharil et al. [14] has applied Monte Carlo simulation (MCS) technique and MATLAB
program for reliability study of decentralized power systems through solar PV plants. It also
presents the hourly mean solar radiation and standard deviation inputs to simulate the yearly
solar radiation. The analysis divided in two parts, first is by comparing various solar radiation
data with hourly mean solar radiation method, it compare predicted electricity power generation
with the nominal power generation by the plant which also carried in monsoon days. In the
second part various indices are obtained by HMSR method using Monte Carlo simulation which
also analyzed the fuel saving calculations. The final result of the analysis predicted by HMSR
technique gives a deviation of ±10% from real values for non monsoon months and some higher
for monsoon months.
To analyze solar irradiation in Iran region Besarati et al. [15] has divided solar radiation map in
five cases, total radiation on a south facing fixed surface tilted at the latitude angle, total
radiation on a surface tilted at the latitude angle with East West tracking, total radiation on a
surface tilted at the latitude angle with azimuth tracking, direct beam radiation on a horizontal
surface with East West tracking, and direct beam radiation on a surface with two axis tracking. In
which first 3 are for SPV plants and remaining for CSP. After that as a case study 50 cities of
Iran carried out with a SPV plant of 5 MW and then annual generation, greenhouse gases
emission reductions are compared, which shows a great opportunity of electricity generation
through solar energy.

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Regarding the grasping of solar incident rays on the PV panel the tilt angle is the important
parameter, for optimizing this Mehleri et al. [16] explained how to maximize yield with tilt
angle through well established models and collected data from the particular area. This model
divided in four steps. First, by predicting diffuse solar irradiance recorded, most accurate
anisotropic models chosen. Second, both the data and model are used to design a database which
contains the averages and the variances of the hourly global solar irradiance on tilted surfaces
with a number of tilt angles. Third, the database of the previous step is used to produce
metamodels that adjoin the tilt angle and orientation with mean global irradiance and the
variation on tilted surfaces. Finally, an optimization problem deduced, projecting to find the
optimum values of tilt angle and orientation.
2.4 Technical aspects of SPV plant (Onsite and Offsite)
Optimizing the resource is one of the major factors for financial feasibility of project, Hielndro
et al. [17] has analyzed the optimum sizing of inverters and string sizing for a solar PV grid
integrated plant. To size the optimum SPV plant the constraint which considered are unmet load,
excess electricity, contribution of renewable electricity, net present cost and carbon dioxide
(CO2) emissions with software HOMER. In Makkah, Saudi Arabia the PV inverter size ratio of
R¼1 achieved with minimized CO2 emissions and inverter size also can be reduced to 68% of the
PV rated power rating which minimizes the cost of inverter which ultimately reduces the cost of
the total plant.
Solar power is available in extreme electricity demand days like summer sunny days, but for a
particular day the peak demand hours (evening) and peak generating hours of solar plant
(afternoon) don’t concides, to address this problem Richardson et al. [18] has done study on the
basis of case study of Province of Ontario, Canada. To resolve this problem author has stated
three different strategies: optimally orienting PV modules, combining geographically dispersed
arrays, and using a simple energy storage system. Based on their bridging ability to supply-
demand correlation, levelised cost of energy, and the capacity credit, it has compared the
strategies. The cost of energy increases between 30 and 40%. Geographically dispersed PV
arrays and energy storage offer a better approach to improving the correlation between PV
production and electricity demand.

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To fulfill the increasing energy demand of developing country India, Bhoye et al. [19] has given
solar PV plant as a option to the industrial loads. In this study it has taken 1 MW solar PV plant
with its all detail designing of inverter, string sizing, battery sizing and land requirement. Author
also has defined the levelised cost of energy and other financial parameters like IRR and payback
time period of the plant. The study gave the available options to optimize the generation of the
plant through plant designing and cost reduction policy recommendations. Author has justified
the sustainable energy production option through solar PV technology and shown the gross
energy generation after deducting various types of the losses.
In a solar PV plant the high cost is a major concern which always tried to be reduce, in the same
concern the inverter sizing also matters, Demoulias et al. [20] has given the calculation for
optimization of the inverter sizing for a grid connected solar PV plant. Four parameters are used
in analytical methodology in which three are related to the inverter and the other one is with to
the location and rated power rating of the plant. Also analytical expressions for the calculation of
the annual energy injected into the ac grid for a given PV plant with given inverter, are also
provided. Moreover, an expression for the effective annual efficiency of an inverter is given.
Author has stated that this analytical tool is very useful to design engineers for comparing
different inverters without having to perform multiple simulations, as is the present situation.
The validity of the proposed analytical model was tested through comparison with results
obtained by detailed simulations and with measured data.
Decentralized solar PV plant are the most efficient source of energy as it reduces the
transmission losses of the energy, but the energy generated through solar PV plant is of variable
in nature which can be constant by adding battery bank. Weller et al. [21] suggested battery
bank addition and sizing to a solar PV plant to make maintain system voltages within the limits.
In this study it is proposed an optimization based algorithm for the sizing of residential battery
storage co-located with solar PV, with taking PV incentives such as feed-in tariffs. The main aim
of this study is to optimize daily savings and reducing large voltage variation. For this a
quadratic program (QP) based algorithm used. For this load and generation data is collected from
145 residential customers in Australia. The results of this analysis is QP-based scheduling
algorithm significantly penalizes reverse power flow.

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For the solar PV rooftop and hybrid systems, Ayad [22] has stated that solar PV and wind energy
both are the most energetic source of energy in renewable sector. Author has performed a
methodology for optimizing of size and design, strategy control based on differential flatness
approach is applied to the hybrid stand-alone PV-WG systems with applying Matlab/ Simulink
software. The aim is to find the optimal number of units ensuring that the 20 years round total
system cost is minimized subject to the constraint that the load energy requirements are covered.
The optimization methodology, using the genetic algorithm and the formulation of the problem
are detailed.
2.5 Financial aspects & LCoE of SPV plants
High capital cost of solar PV plant is a major concern which ultimately increases the LCoE of
significantly. Ouyang [23], analyzed that for large-scale development of RE it is necessary of
reduction in cost of generation and accurate capital cost estimation. For this study author has
taken SPV plant in country China. The results shows that feed-in-tariff (FIT) for SPV plant
should be improved and adjusted variably based on the LCOE to provide a better support of the
development of RE. The current FIT in China can only cover the LCOE of wind (onshore) and
solar photovoltaic energy (PV) at a discount rate of 5%. Subsidies to renewable based electricity
generation, except biomass energy, still need to be increased at higher discount rates. The
conclusions are, first Government policy should be focus capital cost problem which directly
affect the LCoE of the plant, and second is capital subsidy arrangement problem can be solve in
by reforming electricity price in the mid-and long term which make the RE competitive.
In the gulf country scenario, excess availability of fossil fuel is there which fulfill the energy
requirement of the country but it is also responsible for the negative effect on the environment,
Ramashan et al. [24] has studied the solar PV plant financial viability in Kuwait, which is a
solar rich country which gives positive indication to the feasibility of a solar PV plant. In his
analysis author found that for a 1 MW solar PV plant the LCoE is estimated to be around
$0.20/kWh, with the price of 5$/W for solar PV panel and 15% efficiency. This LCOE can be
feasible only when the cost of oil is around 100$/barrel. The Cost Benefit Analysis showed that
when the value of saved energy resources used in producing traditional electricity, and the cost
of lowering CO2 emissions are accounted for, the true economic cost of LCOE of a PV system

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will decline significantly. The recommendation given by the study is that the solar PV
technology implementation is economically feasible in Kuwait.
Similarly for the Egypt, Shimy et al. [25], has carried out analysis for a 10 MW solar PV plant
with feasible site assessment. For the study long-term meteorological data for every 29
considered sites in Egypt from NASA collected and studied to find out the pattern of solar
irradiation, sunshine hours, air flow, temperature and humidity over Egypt, and also to determine
the compatibility of the meteorological data in Egypt with the safety operating conditions (SOC)
of PV-modules. The financial feasibility and GHG emission reduction of the project has been
calculated by RETScreen software. The result of the study shows that among the all 29 sites
Wahat Kharga site offers the highest profitability, energy production, and GHG emission
reduction by proposed 10 MW PV plant. The lowest profitability and energy production values
are offered at Safaga site. Therefore, it is recommended to start building large-scale PV power
plants projects at Wahat Kharga site.
Fuentealba et al. [26], has performed a comparative study at costal land of Atacama desert,
Chile, for two technologies of solar PV, which is thinfilm and multicrystalline silicon solar cells
for 21 years of monitoring. However the performance of photovoltaic technology can be
influenced by the climate at costal desert area. the study show that solar irradiation reached mean
values of 8.6 kW h/m2 day in summer and 6 kW h/m2 day in winter which shows the irradiation
level is high enough. Due to the dust accumulation thin film performance ratio has been
decreased at a rate from 4.2 to 3.7%/month for decreasing temperature and from 4.8 to
4.4%/month for increasing temperature. Similarly for Polycrystalline PV modules the
degradation rates were 2.4 to 1.8%/ month for decreasing temperature, and 6.2 to 3.7%/ month
for increasing temperature. It was concluded that the electricity costs were 14.48 cents€/kW h
and 15.65 cents€/kW h for thin film and mc-Si, respectively. The study recommended that thin
films had additional advantages (after cleaning) than multicrystalline modules.

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2.6 Techno- economic study of SPV plant
The residential grid connected solar PV plants are the most advantageous as has load center very
near to it, but as high capital cost of SPV plant, there is a necessity of financial support by the
government side. Lui et al. [27], has done study at Queensland, Australia, which stated that till
now there is lack of clear information that how much benefits, energy generation are there with a
grid connected solar PV plant at residential level. The solar irradiation data of the 4 typical
climate zones of Queensland investigated. Using HOMER software taking input parameters of
the system is simulated and optimized. The optimized system not only satisfies the typical
residential load of 23 kWh per day but also meets the requirement of minimizing the total costs
of system investment and electricity consumption during the system’s lifetime. Also, a 6 kW PV
system in Townsville is able to deal with 61% of the total electricity load and saves more than
90% of electricity payments and reduce approximately 95% of CO2 emission.
Miranda et al. [28], has stated rooftop solar PV is a promising energy source but still high
capital cost makes it unviable. Author has analyzed the techno-economic study of solar PV
rooftop system in context of end users, who will compare the price of electricity of solar PV with
grid electricity. This study evaluates installing SPV for residential sector in Brazil which focuse
to socio-economic characteristics, the electric power consumption, capital cost and financing,
availability of rooftops, load curve, were studied and considered here. To allow a spatial analysis
a tool related to Technical-economic simulation tools incorporated with geographical
information system (‘GIS’). As the result, it has been stated that in 2014, about 1500 sites would
be ready to install photovoltaic panels, and in 2016 this number would reach 68,000 homes. For
the year 2026, about 29 million residential units would be prepared to have photovoltaic panels
installed. Of these, 3% would be high-income residences, and 52% would be situated in the
country's Southeast region.
2.7 Grid Parity Analysis
Yang [29], explained the grid parity for solar PV technology, reducing the cost of generation
from solar PV plant in with competition of conventional grid supplied electricity. Grid parity
stated that the cost-effectiveness of distributed photovoltaic (PV) systems may be further away
than many are hoping for. Furthermore, cost-effectiveness may not guarantee commercial

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competitiveness. Like solar thermal technology (Hot water) is presently far more cost-effective
than photovoltaic technology and has already reached grid parity in many places. But the market
domination of solar water heaters remains limited because of unfamiliarity with the technologies
and high costs. Similarly in solar PV field, the rapid growth in PV deployment in recent years is
largely policy-driven and such rapid growth would not be viable until financial support from
governments continues, simultaneously address regulatory and market barriers.
To support the grid parity for SPV technology Lund [30], has explained the contribution of
economic and policy aspect for speeding up the market of these technologies to reach cost parity.
The combined global market share of renewable electricity in 2050 could reach 62% of all
electricity (now 19%) of which wind and solar power alone could account for almost two-thirds
corresponding to a carbon saving in the range of 8e16 GtCO2. The estimates for financial
support to achieve cost parity were very sensitive to the assumptions of the input parameters in
the base case the extra costs or learning investments for solar power wereV1432 billion and for
wind power V327 billion, but with more conservative input data these values could grow
manifold. On the other hand, considering the potentially cheaper electricity from new
technologies above the cost parity point and putting a price on carbon could result in a positive
yield from public support. This will lead the industry to reach cost grid parity in the SPV sector.
Spertino et al. [31], explained that various constraints to SPV grid parity in EU market. Real
cases are described for residential/tertiary sector loads the PV penetration results, achieved
without investments in the distribution upgrading, are presented through the ratio of the
admissible PV energy ratio which can be close to 30% of the total consumption for residential
users and 45% for tertiary-users. The grid-parity problem is analyzed by the net present value
which provides the cost effectiveness or not of the PV installation. The results are obtained by
the interest rates of 3–6% in Germany and 4–10% in Italy. As the result grid parity is analyzed
for three typical cases, by including the distribution-network limits.

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2.8 Recommendations based on literature review
India is a developing country and it requires large units of electricity to run the industries and
day to day needs, which can be alternatively fulfilled through solar PV plants, the most
promising and easily available energy among other renewable energy options. After studying the
detailed literature survey and research reports in the solar energy sector, the following
recommendations have been figured out here.
a) India has an excellent solar radiation profile and has a potential of producing 285 GW of
solar energy from the desert areas and 25 GW from rooftop systems. Promotion of MW
scale solar power plants and rooftop SPV systems can solve the energy crisis of the
country.
b) The technically perfect design of a solar PV plant (like tilt angle, inverter sizing, battery
sizing) can optimize the generation of electricity which ultimately can improve the
financial condition of SPV plant.
c) Capital cost of solar PV plant is very high, which can be reduced in two ways, first is
proper technical design (by proper sizing of equipments) and second is through financial
support by the government policies.
d) One of the main advantages of solar PV plant (rooftop or onsite plants) is reduction of
transmission losses of electricity because of decentralized energy production.
Encouraging solar PV rooftop plants will benefit both government and consumers.
e) Industrial sector is the largest sector using the electricity in India, empowering industries
with solar energy will help them to maintain better energy security, reduction in losses
etc. Also the existing cheap conventional energy can be used to enlighten small villages
of the country.
f) The government subsidies on the solar PV plants (Onsite) will result in the reduction of
LCoE of the plant, which allow the consumers to compare this with the existing grid
electricity cost. With this we can achieve grid parity for solar PV plants in India by year
2018 to 2020.

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Chapter: 03
CASE STUDY: RIICO INDUSTRIAL AREA, DEOLI
3.1 Stone industrial area
The RIICO industrial area of Deoli, Rajasthan, India has been considered to perform the analysis of
electricity generation through solar PV technology. There are total 59 industries in the RIICO
industrial area Deoli. Most of the industries deal in the stone processing sector and only one industry
is dealing with copper wire production that is why this industrial area is known as a stone industrial
area. The geographical details of the site has discussed in chapter 1, which are latitude 25° 45’ 22”
and longitude 75° 23’ 3” and altitude of 340 meters. The actual map of RIICO Deoli is shown in
Fig. 3.1.
Figure 3.1: Geographical location of RIICO industrial area Deoli, Rajasthan
There is a 33/11 grid substation near the Industrial area, to supply the electricity from the grid the
existing energy source for the industries. The working hours of most of the industries are from 9 a.m.
to 6 p.m. including lunch hour, with about 8 working hours. Production and working days of the
stone industry are not dependent on season or month of year, but purely depend on the raw material
and labor availability.

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3.2 Survey and data collected
To estimate the total energy consumption of the RIICO industrial area Deoli, a detailed survey has
been carried out from March 2015 to April 2015 for the selected 25 industries among total of 59
industries. This survey is a combination of two data sheet from different source of information. The
first part of the survey is designed for the owner of industry, which is a questionnaire based survey
form to collect the following data
a) Total number, type and rating of the machines used
b) Number of the working hours
c) Office electrical equipment and hours of use
d) Daily/ monthly/ annual production of the industry
e) Turnover of the industry
f) Total area acquired and roof & machine shade area occupied.
In the second part of the survey, the electricity bill data for each industry have been collected from
DISCOM Rajasthan. It comprises the following data
a) Sanction and connected load (in kW)
b) Supply voltage (in kV or V)
c) Monthly electricity bill details of one year
d) Average electricity bill of past 3 years
The total energy consumption and billed data of the industrial area, for one year also collected from
the source of DISCOM Rajasthan. For this survey, first a pilot survey has been conducted on five
industries and thereafter main survey has been conducted on 25 industries with revised survey form
adopting some improvements and inputs after pilot survey. The revised survey form and pilot survey
forms are shown in Appendix 1.

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3.3 Pilot survey
To conduct the data collection, pilot survey was done on five industries, to know additional inputs
from the industries and to collect more perfect data. The adaption and changes done in revised form
are as follows –
a) As the working days are dependent on raw material supply and labor availability only, not on
the month or season, the ‘working days option on the monthly basis’ was removed, also day/
night shift option added.
b) In the pilot form the rating of the machines was in only kW, but all industry owners, and
DISCOM bills mention the machine rating in horse power (H.P.), so H.P. column added.
c) The name of machines and general office equipments were mentioned in the revised survey
form, which made the survey easier for both owner and surveyor.
d) Land area measurement unit which was taken as m2
in the pilot survey has been changed in
ft2
, which reduced the complexity of changing units.
e) In the DISCOM part, month and year added in the DISCOM format.
f) The past three year average ‘electricity consumption data option’ was added in the revised
survey form for forecasting the energy requirement for coming years.
3.4 Electricity energy demand and future forecast
The monthly electricity consumption data of 25 industries surveyed out of total 59 industries in the
RIICO industrial area for one year from April 2014 to March 2015 is shown in table 3.1.

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Table 3.1: Monthly electricity consumption data of 25 industries in RIICO industrial area (all values in kWh)
S.No. Industry Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15 Average
1 Saurabh Stone Industries 3820 2280 3760 5010 5010 3320 3320 4160 4160 3540 2720 2720 3652
2 G.K. Stone Industries 1440 1360 1960 4050 4050 2970 2970 2630 2630 2980 2230 2230 2625
3 Marbol Minerals Industries 780 1360 1540 1730 1730 1920 1920 2890 2890 1400 1510 1510 1765
4 Jai Ambay Stone 418 275 738 791 790 810 810 486 487 490 446 447 582
5 Dinesh Industries 2780 2260 2580 3040 3040 2900 3000 2040 2040 1980 5200 5200 3005
6 Navdeep Minerals 780 600 780 750 750 500 500 940 940 840 750 750 740
7 Stone Legend 530 520 440 440 560 590 590 550 550 880 880 880 618
8 K.R. Industries 6440 4860 7080 7140 7140 7930 7930 5340 5340 5400 8760 8760 6843
9 Nice Stone Industries 1300 1408 1398 1350 1350 1115 1116 1373 1372 1845 1102 1102 1319
10 Shri Mahaveer Industries 2880 2300 2940 2320 2320 2270 2270 2620 2620 2900 2200 2200 2487
11 Goyal Minerals and Chem. 586 525 552 1451 1450 3326 3327 3305 3304 2025 2025 3446 2110
12 Rahul Stone Industries 7508 7944 9742 8864 8864 6236 6236 5841 5841 8510 6001 6001 7299
13 Metro Chem. 68118 46440 46788 61662 63654 57072 70710 58260 56058 89328 64760 74748 63133
14 Khatuwala Stone 1321 1166 892 1040 1039 983 984 799 800 881 1095 1095 1008
15 V. M. International 1632 1754 2040 2485 2485 2174 2174 1663 1662 2226 2001 2001 2025
16 Tripathi Slate and Stone 3172 2246 2637 2900 2900 2650 2650 2678 2678 3008 2390 2390 2692
17 Beauty with Stone 1520 1580 2160 1290 1290 1360 1360 1220 1220 1210 1120 1120 1371
18 Om Industries 284 11 88 103 103 129 129 495 496 163 423 424 237
19 B. N. Industries 600 380 440 480 480 280 280 390 390 380 300 300 392
20 Roop Stone Impex 6602 6934 6746 7941 7941 6312 6312 7569 7569 7420 7287 7287 7160
21 Deepak Stone Industry 1040 1400 1240 1600 1600 1210 1210 1690 1690 1720 930 930 1355
22 Bhagwati stone 1176 1192 1218 1312 1313 1592 1592 1580 1580 1070 1070 1070 1314
23 Shri Jaldevi Stone 1240 1360 1380 2030 2030 1410 1410 1520 1520 1620 1860 1860 1603
24 Balaji Stone Export 2820 3720 3480 3510 3510 3130 3130 3370 3370 3060 2480 2480 3172
25 Umesh Industries 880 589 718 919 919 683 683 539 540 856 813 813 746
Total electricity consumption
of 25 Industries
119667 94464 103337 124208 126318 112872 126613 113948 111747 145732 120353 131764 119252

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The data sheet shown above is based on the survey of 25 industries, but the total electricity
consumption of RIICO industrial area of 59 industries is also collected from DISCOM Rajasthan. The
incorporated data of 25 industries with that of remaining 31 industries are shown below in Table 3.2.
Table 3.2: Electricity consumption data of RIICO industrial area Deoli, (All values in kWh)
Industry Apr-
14
May-14 Jun-14 Jul-14 Aug-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15 Average
25 industry data 119667 94464 103337 124208 126318 112872 126613 113948 111747 145732 120353 131764 119252
Other 34
industry data
166233 172416 203563 208612 148082 162516 165707 170712 181688 166812 213823 176105 178022
Total energy
consumption of
59 industries
285900 266880 306900 332820 274400 275388 292320 284660 293435 312544 334176 307869 297274
Here the ‘Average’ column represents the average monthly value of electricity consumption over a
year from April 2014 to March 2015, for a particular industry or industries option. Here in the table
3.2, right most entry in bottom line (valued 297274 kWh) shows the monthly value of electricity
consumption over a year from April 2014 to March 2015 of all 59 industries. This is the energy
requirement which should be fulfilled by the proposed solar PV plant. The average daily electricity
consumption of RIICO industrial area has been calculated as 9909 kWh/ Day (assuming 365 working
days in a year). This energy requirement will be the base for required capacity of solar PV plant.
Maximum demand estimation of RIICO industrial area is shown in Table 3.3.
Table 3.3: Maximum demand estimation of RIICO industrial area Deoli, Rajasthan
Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15 Average
Energy
consumption
of RIICO
(kWh)
285900 266880 306900 332820 274400 275388 292320 284660 293435 312544 334176 307869 297274.3
Operating
Hours (h)
30*8 31*8 30*8 31*8 31*8 30*8 31*8 30*8 31*8 31*8 28*8 31*8 30*8
Power
requirement
in (MW)
1.2 1.1 1.3 1.3 1.1 1.1 1.2 1.2 1.2 1.3 1.5 1.2 1.2
Here we can see that by dividing the energy consumption of a month with the operating hours of the
same month (with taking weekend operating and 8 hours daily working), we get the power
requirement of the RIICO industrial area varies from 1.1 MW to 1.5 MW, which make an average
value of 1.2 MW over a year. The maximum demand found to be 1.5 MW in the month of February.

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If we plot the electricity consumption data of 25 industries surveyed with total 59 industries, we get
the graph as shown below in Fig. 3.2.
Figure 3.2: Monthly electricity consumption data of 25 industries surveyed and total 59 industries
In the bar chart shown above, the first 12 bars show the monthly energy consumption and the last bar
shows the average monthly consumption of 12 months. The energy consumption of 25 industries
which are surveyed is slightly less than half of the energy consumption of total 59 industries’ in all
individual months. The energy consumption of RIICO industrial area is higher in months of January,
February and July, with highest in the month of Feb 2015. In the months of May, August and
September the energy consumption is lower with Aug 2014 having the lowest energy consumption.
0
50000
100000
150000
200000
250000
300000
350000
400000
EnergyconsumptioninkWh
Month
Monthly variation of electricity energy consumption
Energy
consumption of
59 industries
Energy
consumption of
25 industries

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3.4.1 Future forecast
To get the future growth and demand of electricity of RIICO industrial area, a trend graph has been
plotted (Fig. 3.3) on the basis of past three years data. The average monthly energy consumption data
for past three years of 25 industries has been collected as shown in Table 3.4.
Table 3.4: Past three year average monthly data collected of 25 industries (all values in kWh)
S. No. Industry 2012 2013 2014
1 Saurabh Stone Industries 3748 3575 3916
2 G.K. Stone Industries 2341 2905 2515
3 Marbol Minerals Industries 2630 1138 1643
4 Jai Ambay Stone 971 628 601
5 Dinesh Industries 3798 3125 2585
6 Navdeep Minerals 912 935 731
7 Stone Legend 1563 1870 885
8 K.R. Industries 4998 5951 6286
9 Nice Stone Industries 463 697 1203
10 Shri Mahaveer Industries 2131 2023 2251
11 Goyal Minerals and Chem. 5546 1952 1983
12 Rahul Stone Industries 6927 7213 7336
13 Metro Chem. 52274 61217 59224
14 Khatuwala Stone 543 551 931
15 V. M. International 1995 1997 2020
16 Tripathi Slate and Stone 2335 2347 2554
17 Beauty with Stone 984 978 1357
18 Om Industries 365 731 282
19 B. N. Industries 415 450 420
20 Roop Stone Impex 7080 7102 7056
21 Deepak Stone Industry 1059 1198 1581
22 Bhagwati stone 1121 1183 1328
23 Shri Jaldevi Stone 1425 1395 1473
24 Balaji Stone Export 2134 2056 3203
25 Umesh Industries 350 366 691
Total energy consumption/ month 108108 113583 114055
Difference 5475 472
Growth % 5.06 0.42
Here we can see that the difference of energy consumption of year 2012 and 2013 that is 5475 kWh
which accounts for 5.06% of year 2012 consumption and for the year 2013 and 2014, difference is
472 kWh which is 0.42% of year 2013 consumption.

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After that an exponential trend line up to year 2025 has been plotted based on past three year energy
consumption data shown in Fig. 3.3.
Figure3.3: Extrapolation of average monthly energy consumption of 25 industries
The graph shows the exponential extrapolation of energy consumption of 25 industries. To evaluate
the extrapolated data of all 59 industries, multiplication factor (Comparing data of 25 industries with
that of 59 industries) has been calculated on the basis of 2014 energy consumption.
For the year 2014 the average monthly energy consumption of surveyed 25 industries is 114055 kWh/
month and for total 59 industries it is 284205 kWh/ month. Comparison of these two data gives a
multiplication factor of ‘2.49’ to calculate forecasted energy consumption of total 59 industries. This
can be shown as-
Average monthly energy consumption = 2.59 x average monthly energy consumption
of 59 industries of 25 industries
The extrapolated forecasted energy consumption in kWh and SPV plant requirement for 59 industries
(capacity formulation in chapter 4) are shown in Table 3.5.
R² = 0.8077
100000
110000
120000
130000
140000
150000
160000
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
EnergyconsumptioninkWh
Year
Energy
consumption
(25 industries
in kWh)
Expon.
(Energy
consumption
(25 industries
in kWh))

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Table 3.5: Forecasted energy consumption data & SPV plant requirement for RIICO industrial area, Deoli
Forecasted Year 2014 2015 2017 2019 2021 2023 2025
Energy consumption for 25
Industries (kWh)
114055 118000 125000 132000 138500 146000 154000
Energy consumption for 59
Industries (kWh)
284205 294036 311478 328921 345118 363807 383741
SPV plant requirement (MW) for
total 59 industries
2.21 2.29 2.42 2.56 2.68 2.83 2.99
Here forecast of the energy requirement of RIICO industrial area has been shown for 2 years interval
till year 2025. From the data it can be stated that in the end of year 2025 the required SPV plant rating
will be 3 MWp which is 600 kWp higher than the proposed SPV plant (Calculated in chapter 4). The
site to be chosen for the SPV plant should have enough extra space to accommodate more SPV plant
capacity required in upcoming years.
3.5 Area of Industries
The motive of collecting details of area acquired by the industry is to check area availability for the
onsite rooftop solar PV plant, which requires open space or roof in the industry premises. In the stone
processing industries there are two types of open area available for placing solar modules, one
machine shades and other office roof area. The data of available area for SPV modules are collected
through survey, (Appendix 2). The average onsite SPV plant for an industry calculated as 2.5 kWp
(Calculated in chapter 4) requires area of 25 m2
. According to the surveyed data the available area of
both machine shade and office roof ranges from 27 m2
to 292 m2
. This means that for installation of
onsite SPV plant of 2.5 kWp there is enough space in each industry.
3.6 Proposed SPV plant
The existing energy source to the RIICO industrial area Deoli, is the grid electricity provided by the
DISCOM Rajasthan. This study is to explore opportunity to replace/ support the existing energy
source with solar PV plant, which will benefit both industries and government. Decentralized energy
generation promotes power system with low transmission loss. Better energy security & quality (less
power cut) will promote more reliable operation of industries.
Here study proposal of the solar PV plant (discussed in chapter 1) is categorized in two types, first is
complete Offsite solar PV plant, in which complete rated capacity of solar PV plant is installed far

31 | P a g e
from the RIICO industrial area, and second is combined Onsite and Offsite solar PV plant, in which
some of the capacity is installed inside the industry premises as onsite and the remaining is installed
as offsite.
3.6.1 Offsite option
The proposed offsite solar PV plant, should be located far from the industrial area, to reduce the land
cost as SPV plant requires a large area (8.25 Acre for 2.4 MWp SPV shown in Sec. 4.1.8). In the
offsite solar PV plant all the electricity generation will feed the 33/11 substation (grid) located near to
the RIICO industrial area. Offsite SPV plant does not have any energy storage system like battery
bank (As this much capacity can’t stored in the battery bank), so industries have to take additional
electricity from the grid in night hours.
The major components of a MW size grid integrated SPV plant are solar PV modules (arrays),
mounting structure, cables, junction box & inverters, transformer, transmission line and control room.
The schematic diagram of a grid integrated offsite solar PV plant is shown in Fig. 3.4.
Figure 3.4 : Schematic diagram of offsite ongrid solar PV plant

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3.6.2 Combined onsite and offsite option
In this type of solar PV plant, the load of an industry is divided in two types, office load and machine
load. The office equipment loads are lighter and can be energized by the smaller onsite solar PV plant
mounted on the roof of the industry and the remaining capacity of SPV plant is installed as offsite
location (to feed energy to the machines).
Figure 3.5: Schematic diagram of onsite solar PV plant with battery bank
In the onsite SPV plant there is an additional advantage of battery storage facility, by which
electricity can be used in the non sunshine hours also. For this the major components are solar PV
modules (strings), mounting structure, cables, junction box, charge controller, battery bank, and
inverter. The schematic layout of a onsite SPV plant is shown in Fig. no. 3.5.

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Chapter: 04
DESIGN OF SOLAR PV PLANT
Generation of electricity by Solar PV technology is based on conversion of photon energy of the
incident sunrays into electricity. The designing parameters of a solar PV plant play a significant
role in the working efficiency. Solar PV plant has high capital investment, so any faulty
installation would result a setback to the project. To prevent this, simulation and modeling
techniques are used in which SPV plant can be checked with various type of designing
methodologies. To assess the energy production from a solar PV plant, the assessment of
availability of sun is required at the site.
4.1 Offsite SPV plant
As mentioned earlier two types of the SPV plants are considered in the study, first is offsite SPV
plant and second is combined onsite offsite SPV plant. The offsite SPV plant has been analyzed
at offsite locations without battery backup facility and the combined onsite offsite SPV plant has
been analyzed for their respective capacity on onsite location with battery backup and offsite
location without battery backup. Solar resource assessment has been done to estimate capacity of
offsite SPV plant.
4.1.1 Solar resource assessment
The assessment of solar resources has been done for the site RIICO industrial area Deoli,
Rajasthan. This assessment comprises three steps –
 Collection of monthly average Direct Normal Irradiance (DNI) data
 Mean sunshine hours
 Annual mean irradiation
i) Collection of monthly average DNI data
The definition of Direct Normal Irradiance (DNI) is the amount of solar energy incident on a unit
surface area in one day. DNI is expressed in kWh/m2
/day or kJ/m2
/day. DNI is on the prime
consideration while designing solar power plant. The output of solar power plant is mainly
dependent on average DNI of the site. Here irradiation data has been collected from different

34 | P a g e
sources for the specific site (latitude and longitude). In this study data are taken from two sources
namely NASA solar data [32] and data from Energy Alternatives India (EAI) [33], which is
shown in Table 4.1.
Table 4.1: Monthly average DNI data for location RIICO Industrial area Deoli, Rajasthan
DNI
source
Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Average
NASA 6.33 5.93 5.24 3.42 3.08 5.07 6.07 6.35 6.23 6.27 6.54 6.38 5.58
EAI 6.04 6.79 5.22 3.51 3.39 5.84 6.59 5.84 5.44 5.67 6.45 6.69 5.61
(Units in kWh/m2
/day)
Figure 4.1: Monthly average DNI comparison from two sources
Here it can be stated that
a) Both the sources of data of DNI, are showing approximately same results and giving very
less variation between them. The DNI value of the site depends on the month of the year.
b) The months July and August showing the lowest DNI, this may be because of monsoon
season in Rajasthan. It may affect the generation of electricity.
c) We have taken NASA data for further analysis, as it is more reliable source of data and
used in the PVSyst software also (Used here for simulation and modeling in study).
0
1
2
3
4
5
6
7
8
Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar
DNIinkWh/m2/day
Month
Comparative graph of average monthly DNI
NASA
EAI

35 | P a g e
ii) Mean sunshine hours
Mean sunshine hour is the average number of hours of bright sunshine of one day in a calendar
month of year. Sunshine hours include only the bright sunshine, which is less than the amount of
visible sunshine. The sum of bright sunshine hours i.e. the mean sunshine hours for RIICO
industrial area Deoli is shown in the Table 4.2.
Table 4.2: Average of mean sunshine hours for one year
Months Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Average
Mean sunshine
hours
9.5 9.3 10.1 8.3 8.4 9.4 9 10.1 9.5 9.4 9.8 9.1 9.32
Similarly the equivalent ‘no sun days’ or ‘black days’ for the site RIICO industrial area Deoli, is
shown in Table 4.3.
Table 4.3: Equivalent ‘no sun days’ for RIICO industrial area Deoli
Months Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar
Period
1 day 0.52 0.85 0.88 0.95 0.94 0.95 0.88 0.65 0.95 0.83 0.84 0.83
3 day 0.95 1.3 1.47 2.59 2.15 1.89 1.93 1.3 1.88 1.04 1.35 1.12
7 day 1.38 2.39 2.09 4.7 3.71 2.86 2.96 1.81 1.79 2.01 2.71 1.76
14 day 1.59 2.16 2.61 6.75 5.02 3.49 2.95 3.17 2.31 2.89 3.29 2.48
21 day 1.97 2.36 2.26 7.48 6.41 3.53 3.09 3.56 2.84 2.7 2.8 2.93
Month 2.74 2.54 3.42 6.32 6.36 3.69 2.56 3.6 2.37 3.22 3.42 3.14
iii) Annual mean irradiation
Annual mean irradiation for a site can be calculated by the two data, DNI and average sunshine
hours which are discussed above. For the site RIICO industrial area the monthly average DNI is
5.58 kWh/m2
/ day (source NASA), and the average mean sunshine hours calculated is 9.325
hours/ day. So –
(4.1)
= ((5.58 x 1000)/ 9.325) W/ m2
Annual mean irradiation = 598.39 W/m2

36 | P a g e
4.1.2 Site Assessment
For a solar PV plant land requirement is a big concern as it requires a large land area, which
should not be obstructed by any other surrounding objects like buildings, mountains, or trees
which may cause loss of generation. Selection of the site depends on the solar irradiation profile,
which is good for Deoli region as it in the state of Rajasthan with average sunshine hours of
about 9 hours a day and minimum 300 sunny days in a year.
Location of the site of SPV plant (Distance from load center) is a major factor. Secondly the
property of the land weather it is cultivable or barren, as barren land is comparatively lower in
cost and easier to transfer for SPV plant. We have taken four different type of cases based on the
classification mentioned above.
a) Barren land near to load center (Location 1)
b) Barren land far to the load center (Location 2)
c) Cultivable land near to load center (Location 3)
d) Cultivable land far to load center (Location 4)
The geographical location of all these sites have been discussed in detail in chapter 5, and the
tradeoff analysis between the transmission line cost and land cost is also shown there. As the site
far from load center is also far from the city area (Deoli), the cost of land will reduce cost of
plant but simultaneously far location will increase the cost of transmission line to the GSS of
33/11 at RIICO industrial area Deoli.
The capital cost varies with the different locations of plant and so the cost of generation (LCoE),
which is described as financial analysis through site selection in chapter 5.
For the basic financial assessment in the chapter 4, the location 1, is taken as base location to
calculate the IRR and other financial parameters.

37 | P a g e
4.1.3 Panel Generating Factor
Calculation of the solar PV plant capacity requires relation between the electrical energy
required and incident solar irradiation at the location of the plant. The panel generating factor
(PGF) is the connecting element, used in calculation of ‘Total Watt-Peak Rating (Wp)’ while
designing the size of solar photovoltaic plant. The formula of PGF is -
(4.2)
= 5.57
4.1.4 Required SPV plant capacity (MWp)
The total SPV plant rating depends on the PGF and average daily energy requirement. The
required SPV plant capacity can be calculated by dividing the average daily energy consumption
by PGF, as shown –
Energy requirement of RIICO industrial area (annual average/ day) = 9909 kWh/ day
Assuming energy loss of the SPV system [11] = 30%
Net energy required from the SPV modules = 1.3 x 9909 = 12882 kWh/ day (4.3)
(4.4)
= 2313 kWp
Rounded kWp rating of solar PV plant = 2400 kWp
= 2.4 MWp
The required SPV plant capacity or total capacity of module to be installed will be 2.4 MWp.

38 | P a g e
4.1.5 PV modules
The SPV module manufactured by Renesola JC250M-24/ Bb has been selected for the SPV
plant. The PV module taken here is manufactured by the company by polycrystalline silicon
wafer technology, the DC Wp capacity of the module is 250 Wp with 26 volts. The detailed
specification of the SPV module is shown in Table 4.4.
Table 4.4: Technical specification of solar PV module
S. No Specifications Measuring unit Values
1 Maximum DC power output Pmax W 250
2 Max. power voltage Vmp V 30.1
3 Max. power current Imp A 8.31
4 Open circuit voltage Voc V 37.4
5 Short circuit current Isc A 8.83
6 Output power tolerance % 0%/ +2%
7 Maximum circuit voltage V 1000
8 Efficiency (Module area) % 15.46
9 Temperature coefficient of Pmax (δ) %/ °C -0.4
10 Temperature coefficient Voc (β) V/ °C -0.112
11 Temperature coefficient Isc (θ) mA/ °C -0.04
12 Series fuse rating A 20
All the values are based on STC
Standard test conditions (STC) : Cell temp. 25 °C, Irradiance 1000 W/ m2
, Air mass 1.5
The performance of solar PV module is dependent on variation of temperature and solar
irradiance which is shown in the Appendix 3. By taking the STC power rating of the PV module
into consideration, the total no. of PV module required can be calculated.
(4.5)
= 9600
Total number of Renesola 250 Wp PV modules required for the plant is 9600.

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4.1.6 Inverter sizing
The sizing of inverter depends on two parameters, first is the demand of electricity of the site and
second is the rated Watt peak capacity of the solar PV plant. Here the required rated solar PV
plant capacity is 2.4 MWp (DC). The rated capacity of inverter is taken slightly less than DC
watt peak rating of SPV plant installed. The simulation of the SPV plant has been carried out in
the software PVSyst (sec. 4.1.9) which suggests the best suited and optimized size of the inverter
based on Watt peak rated capacity of the solar PV plant. According to the PVSyst simulation
results, the specification of selected inverter is as follows.
Manufacturer - Bonfiglioli Vectron, Model no. RPS 1220 multi MPPT
Table 4.5: Bonfiglioli Vectron Technical specification of inverter
S. No Specifications Measuring unit Values
1 Nominal AC power rating kW 1100
2 Minimum input voltage Vmin V 500
3 Maximum input voltage Vmax V 875
3 Maximum AC current Imax A 1920
4 Maximum efficiency % 98.6
5 Power threshold W 5500
6 No. of MPPT used 6
Number of inverter = 2
Total electrical rating of inverter (kW) = 2 x 1100 = 2200 kW
So the calculated capacity of inverter i. e. 2200 kW or 2.2 MW (AC), is to be connected to the
AC grid. Each inverter is employed with total 6 Maximum Power Point Tracking (MPPT)
devices, which means that the total MPPT used in the plant are 12 employing two inverters. The
MPPT is an electronic system that operates the PV modules in a manner that allows the modules
to produce the energy at the point of maximum power by varying the voltage. It is not a
mechanical tracking system, but it is a fully electronic system that varies the electrical operating
point of the modules so that the modules are able to deliver maximum available power [34].

40 | P a g e
4.1.7 PV module string sizing
Connection of the solar PV module is categorized in two types, first is electric connection and
second is physical connection. The physical arrangement of the solar PV module is described in
the plant design section in detail.
In the electric connection process, series connection of solar PV module is done to reach the
desired voltage level, that’s why it depends on the maximum voltage of individual PV module
(Vmp) and the maximum and minimum input voltage of inverter. This series connection of solar
PV module is known as ‘string’. The maximum voltage of string of solar PV module should
remain below to maximum rated input voltage of inverter. Similarly the paralleling of a no. of
string creates a certain current level, which depends on the maximum current of an individual
solar PV module (Imp) and input current rating of the inverter. The electrical arrangement of
solar PV module is as follows-
For each inverter -
No. of PV modules in a string = 24
Total maximum voltage of string = 24 x individual Vmp of PV module
= 24 x 30.30 = 727 Volts
Total no. of strings = 200
The maximum current of the total strings = 200 x individual Imp of PV module
= 200 x 8.250 = 1650 A
This maximum string voltage ranges within acceptable input voltage range of inverter which is
500 – 875 Volts and the maximum current of the circuit lies below the limit of maximum current
rating of inverter which is 1920 A.
The circuit specifications mentioned above are for first inverter, and same is applied for the
second inverter. So the total no. strings in the whole plant will be 400, each string having 24
series connected SPV module. Which make a total no. of SPV module of 9600 in the plant.

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4.1.8 Land required
A solar PV plant requires a huge land area, to accommodate the solar PV modules with inter row
spacing (to reduce the inter row shading losses). Series placement of solar PV modules is known
as array, and a solar PV plant may have a no. of arrays in parallel. Different strategies of
placement of solar PV panel array give different land use patterns. The optimization of land use
for a solar PV plant is necessary to reduce the cost of plant.
To calculate the land requirement, first the physical dimension of solar PV module and mounting
strategies are shown in Fig. 4.2.
Renesola 250 Wp solar PV module
Figure 4.2: Solar PV module physical dimensions
Here the PV modules are mounted on the structure in the fashion of double stacked with portrait.
Means the two modules are stacked one above the other on the structure joining their length as
the height of array (portrait). In this the total height of structure becomes 3.28 meter. The
inclinational angle (α) is taken as 27 °, which is the optimum angle for the generation of
electricity, this angle has been calculated by the software PVSyst for this particular site. The
physical dimension of this arrangement is shown in Fig. 4.3 for only two solar PV modules –

42 | P a g e
Figure 4.3: Mounting strategy and inclination angle of solar PV module
With the mounting arrangement of modules shown above (unit), the array is designed with the
series connection of 96 units. This makes an array of length of 95.2 meters (96 x 0.99 m = 95.2
m) and height of 3.28 m, which is shown in Fig. 4.4.
Figure 4.4: Placement of solar PV module in an array
In a single array there are total 96 units accommodated in which each of unit have two SPV
modules, which make a number of 192 solar PV modules in an array.

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Plant layout – There are total 50 number of arrays in whole plant, The inter row spacing (pitch)
between the two arrays is calculated by the formula -
Inter row spacing (center to center) (m) = 2 x cos (α) x Length
(Here α represents the inclination angle which is 27 ° and the length will be taken as 3.28 m for
double stacked portrait solar PV array.)
So, Inter row spacing (center to center) (m) = 2 x cos (27) x 3.28
= 5.84 m ≈ 6 m.
With taking inter row spacing/ pitch as 6 meter, the layout of the plant can be designed and
shown in Fig. 4.5.
The layout shown in Fig. 4.5 is the complete layout of offsite solar PV plant for RIICO industrial
area. In this there are 50 numbers of arrays containing 9600 SPV modules. The arrays have been
arranged in two sets each having 25 arrays, with 3 meter spacing between sets for the way of
tractors (water tankers) and maintenance. One control room and inverter space is an additional
space requirement. There is a one meter gap between control room and last array. Extra 3 meter
space has been left around the array field area. Control room and inverter space has same
Figure 4.5: Complete SPV plant layout of offsite SPV plant

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dimension of 20 x 10 meter2
. So the final length and width of the whole solar PV plant can be
calculated as –
Width (m) = 3 + 95.2 + 3 + 95.2 +3 = 200 m
Length (m) = 3+ 150 + 1 + 10 + 3 = 167 m
So the total area of offsite SPV plant = Length x Width = 167 x 200 m2
= 33400 m2
The total land required for the offsite SPV plant has been calculated as 33400 m2
, which can be
written as 8.25 Acres (1 Acre = 4047 m2
), and as 13.2 Bigha (1 Acre = 1.6 Bigha) for cost
calculation as local measurement of land is in Bighas.
4.1.9 PVSyst simulation and modeling
Solar PV plant has a high capital cost, any installation or designing defect can result in increased
cost of project. To rectify the problems and to make a defect free design it is required to simulate
the plant on software.
Here PVSyst simulation software (Version 5.55, Feb. 2012) is used to design the solar PV plant.
By the simulation and modeling with these software technical parameters of the plant has been
calculated with consideration of the effect of the temperature variation on the electricity
generation. Software is taking the meteo profile and temperature profile of the particular site
through input of latitude and longitude information of the location. The technical parameters like
annual electricity generation, detailed losses, capacity utilization factor (CUF) and performance
ratio (P.R.) can be calculated by the simulation.
The inputs to the software are, latitude and longitude of the site, total capacity of the plant in
kWp, SPV module rating, inverter rating (suggested also), physical placement of array of solar
PV (to estimate the shading losses), and addition of some losses (optional like soiling losses).
Here PV plant rating, PV module rating, inverter rating and physical plant placement are taken as
described in previous chapters, the soiling loss is taken as 3% (As of Rajasthan). The SPV plant
layout in PVSyst is shown in Fig. 4.6 for estimation of shading losses.

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The simulation of offsite SPV plant, gives following results –
a) Electricity Generation/ year = 3466 MWh/ year = 3466000 kWh/ year
Specific Production = 1444 kWh/ kWp/ year
(Specific production calculated by dividing total electricity generation per year (kWh)
with total rated capacity of PV plant in kWp.)
b) Capacity utilization factor (C.U.F.) - The C.U.F. is significant term for a solar PV plant‘s
economics. With the majority of the expense of a PV power plant being fixed capital
cost, LCOE is strongly correlated to the power plant‘s utilization. The PV power plant
capacity factor can be calculated as under –
(4.6)
= 16.48 %
Figure 4.6: PVSyst simulation plant layout of offsite SPV plant

46 | P a g e
c) Performance ratio (P.R.) - Performance ratio is an another factor to evaluate the SPV
plant performance like C.U.F., but it is related more with the quality of plant, that’s why it is
sometimes referred as quality factor also. It is expressed in the form of %, and shows the
relationship between actual and theoretical output of energy of the PV plant, as follows [35] –
(4.7)
Here, Energy modeled = Irradiance (kWh/ m2
) x Area of PV panel m2
x ƞPV module (4.8)
ƞPV module = Efficiency of PV module.
The P.R. of the plant is calculated by the PVSyst, from the simulation the value of average
annual P.R. For the plant it is 71.7%.
Increasing value of P.R. represents the increasing efficiency of the plant. P. R. cannot be 100%
of any SPV plant because unavoidable losses are always there practically (like temperature
losses due to heating of PV module). Normally P.R. value ranges from 70 to 75% for a solar PV
plant.
One more type of P.R. is also calculated by the NREL, which is temperature corrected. The
efficiency of the solar PV module varies according to the temperature of solar PV cell, over a
year (average cell temperature is taken for a year time period) [36]. But this method is much
complex and currently not used in the industries.
The P.R. factor is more informative and accurate than C.U.F, as P.R. takes in account of effect of
the temperature of cell and irradiation for annual generation, also P.R. can be used to compare
two SPV plants at different location, as it is taking in account the environmental factors of
locations individually.
But for the further energy generation calculation and financial analysis the C.U.F. is used here
instead of P.R. because C.U.F. is more popular in the industries than P.R.

47 | P a g e
d) Losses details – PVSyst simulation results give a sanky diagram of energy generation of the
solar PV plant, with various types of losses mentioned in terms of percentage. The energy
production based on incident solar irradiation is 4561 MWh/ year, which is reduced to 3466
MWh/ year after deduction of all the losses. There are various type of losses incorporated in
a SPV plant, some of them are shading losses, IAM losses, temperature losses, soling losses,
module array mismatch loss. But among all these the temperature loss has the largest portion
of 13% (of 4561 MWh/ year) as geographical location of RIICO industrial area is in
Rajasthan which is a hot and dry climate region, and the inverter loss during operation is
1.6%, which shows that the inverter has high efficiency.
The simulation results of monthly energy production of the SPV plant can be plotted as in
Fig 4.7.
Figure 4.7: Monthly energy production simulated by PVSyst
Here the base portion of the bar, represents the total electricity energy available out of inverter to
feed the grid (kWh generated AC), and the top portion of bar represents the collection loss of PV
module which include all losses related to the PV modules like shading loss, temperature loss.
The middle one represents the system losses like inverter losses, cable losses. The detailed
PVSyst simulation report for offsite SPV plant can be found at Appendix 9.

Exploring opportunity for energy supply through solar pv technology for stone processing industry of riico industrial area, deoli (rajasthan)

Exploring opportunity for energy supply through solar pv technology for stone processing industry of riico industrial area, deoli (rajasthan)

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