Summer Internship Report
On
“PREPARATION OF MODEL FOR DAY AHEAD SCHEDULING OF
GRID CONNECTED SOLAR PV POWER PLANT
AND
FINANCIAL MODELING OF 10 MW SOLAR PV POWER PLANT” Under the guidance of
Mr.S.K.Chaudhary, Principal Director, CAMPS, NPTI
&
Mr. Neeraj Agarwal, V.P., Welspun Energy Ltd.
At
Submitted by
Abhishek Dixit
MBA (Power Management)
Sector-33, Faridabad – 121003, Haryana
(Under the Ministry of Power, Govt. of India)
Affiliated to
MAHARSHI DAYANAND UNIVERSITY, ROHTAK
i
CERTIFICATE
ii
DECLARATION
I, ABHISHEK DIXIT, student of MBA-Power Management (2012-14) at National Power
Training Institute (NPTI), Faridabad hereby declares that the Summer Training Report entitled
“PREPARATION OF MODEL FOR DAY AHEAD SCHEDULING OF GRID
CONNECTED SOLAR PV POWER PLANT AND FINANCIAL MODELING OF 10 MW
SOLAR PV POWER PLANT” is an original work and the same has not been submitted to any
other institute for the award of any other degree.
Signature of the Candidate
iii
ACKNOWLEDGEMENT
Apart from efforts of the person doing the project, the success of any project depends largely on
the encouragements and guidelines of many others. I take this opportunity to express my gratitude
to the people who have been instrumental in the successful completion of the project.
I feel deep sense of gratitude towards Mr.S.K.Chaudhary, Principal Director, CAMPS, Mrs.
Manju Mam, Director, CAMPS, NPTI for arranging my internship at Welspun Energy and being a
constant source of motivation and guidance throughout the course of my internship.
I thank to Mr. Neeraj Agarwal, Senior V.P. ,Welspun Energy for giving me the opportunity to
work on such an insightful project .I would like to extend my thanks to my guide Mr. Arun Kumar
Biswal, Senior Manager, Welspun Energy for showing me the right path and approach towards the
project.
I also extend my thanks to all the faculties and my batch mates in CAMPS (NPTI), for their
support and guidance throughout the course of internship.
Thank you all for being there for me always.
Abhishek Dixit
iv
EXECUTIVE SUMMARY
The share of the global energy production coming from solar power is increasing, and forecast of
the solar power is the key for a successful integration of solar power into the existing electricity
grid. The Indian economy faces significant challenges in terms of meeting its energy needs in the
coming decade. The increasing energy requirements coupled with a slower than expected increase
in domestic fuel production has meant that the extent of imports in energy mix is growing rapidly.
India has set a voluntary target to cut the emissions intensity of GDP by 20-25 percent by 2020
compared to the 2005 level. In this backdrop, the thrust on renewable sources of energy is a step
in the right direction. The Prime Minister‟s National Action Plan on Climate Change (NAPCC)
released in June, 2008 envisages meeting 15 percent of our power requirements from renewable
energy sources by 2020.
Recently CERC approved the Procedure for the implementation of the mechanism of renewable
regulatory fund. It is valid for all the grid connected solar generating plants with a capacity more
than 5MW. Under this “The schedule of solar generation shall be given by the generator based on
availability of the generator, weather forecasting, solar insolation, season and normal solar
generation curve and shall be vetted by the RLDC in which the generator is located and
incorporated in the inter-state schedule.”
So, here a model is created by taking the weather forecast data and past data of actual generation.
In the starting phase data analysis is done. Based on this analysis weather parameters are selected
which has high correlation with solar irradiance. Model is based on the linear least squares
regression. By using this method radiation is forecasted. Based on this forecasted radiation and
past actual generation with past radiation data is used to forecast day ahead schedule in 15 minutes
time block for the whole day.
Finally forecasted generation is matched with the actual generation and with the help of this
variation, model‟s reliability is checked. Variation of all the 96 blocks is taken and it‟s variation
per block is also taken and overall efficiency of the model is checked. When it is verified with
actual generation for the day then variation comes as 6%.
v
Second part of the project which is Financial Modeling of a 10 MW solar PV power plant. Aim is
to determine the tariff in accordance with the AERC regulations and further calculate the project
economics. The project economics tools, such as IRR and NPV help investor to make a learned
decision. Also, sensitivity analysis is done on the basis o variation in a few parameters.
Calculations are done with utmost care and to give the perfect picture to the investor.
vi
Table of Contents CERTIFICATE ..................................................................................................................................................... i
DECLARATION ................................................................................................................................................. ii
ACKNOWLEDGEMENT .................................................................................................................................... iii
EXECUTIVE SUMMARY ................................................................................................................................... iv
1 CHAPTER 1 ............................................................................................................................................... 1
1.1 Introduction:- .................................................................................................................................. 1
1.2 Problem Statement ......................................................................................................................... 2
1.3 Scope of Project .............................................................................................................................. 2
1.4 Objective of project ......................................................................................................................... 3
2 Chapter -2 ................................................................................................................................................ 4
2.1 ORGANIZATION PROFILE ................................................................................................................. 4
3 CHAPTER 3 ............................................................................................................................................... 9
3.1 Literature Review:- .......................................................................................................................... 9
3.2 Methodology:- ............................................................................................................................... 10
4 Past Data Analysis:- ............................................................................................................................... 15
4.1 Block Average Variation for The Day:- .......................................................................................... 16
4.2 Block Average Variation for the Month:- ...................................................................................... 19
4.3 Relation between actual generation and weather parameters :- ................................................ 20
4.4 Monthly correlation analysis on different parameters:- .............................................................. 24
5 Chapter -5 .............................................................................................................................................. 26
5.1 Flow chart ...................................................................................................................................... 27
5.2 Steps for Model Generation .......................................................................................................... 28
STEP-1 Input weather parameters and past data:- .............................................................................. 28
STEP-2 Find the coefficient of equation used for forecasting the solar radiation using least square
regression .............................................................................................................................................. 32
STEP-3 Determine the equation for forecasting solar radiation ........................................................... 33
STEP 5 :- Find out the schedule generation with the help of forecasted radiation and past actual
generation data ..................................................................................................................................... 34
STEP 6:- Find out the final schedule of 15 minutes time block ............................................................. 37
6 CHAPTER 6: FINANCIAL MODELING ..................................................................................................... 40
6.1 Introduction .................................................................................................................................. 40
6.2 Tariff determination for solar PV power plant in Assam .............................................................. 41
vii
Technological aspects:- ......................................................................................................................... 41
6.3 General Principles ........................................................................................................................ 41
Project Specific tariff:- ....................................................................................................................... 42
Tariff Structure................................................................................................................................... 43
6.3.1 Tariff Design .......................................................................................................................... 43
Dispatch principles for electricity generated from Renewable Energy Sources:- ..................... 44
6.4 Financial Principles ...................................................................................................................... 45
6.4.1 Capital cost .......................................................................................................................... 45
6.4.2 Debt Equity Ratio ................................................................................................................ 45
6.4.3 Loan and Finance Charges ................................................................................................ 45
6.4.4 Interest Rate ....................................................................................................................... 46
6.4.5 Depreciation ........................................................................................................................ 47
6.4.6 Return on Equity ................................................................................................................ 47
6.4.7 Interest on Working Capital ............................................................................................. 47
6.4.8 Operation and Maintenance Expenses ........................................................................... 48
6.4.9 Rebate .................................................................................................................................. 48
6.4.10 Late payment surcharge ................................................................................................... 49
6.4.11 Sharing of CDM Benefits .................................................................................................... 49
6.4.12 Subsidy or incentive by the Central/State Government ............................................... 49
6.4.13 Taxes and Duties .................................................................................................................. 50
6.4.14 Capital costs .......................................................................................................................... 50
6.4.15 Capacity Utilization Factor ................................................................................................ 50
6.4.16 Operation and Maintenance Expenses ........................................................................... 51
6.4.17 Useful life .............................................................................................................................. 51
6.4.18 Power Generation ......................................................... Ошибка! Закладка не определена.
6.4.19 Financial Assumptions ........................................................................................................... 51
6.5 Project Economics and Financial Indicators .................................................................................. 54
6.6 Sensitivity Analysis ........................................................................................................................ 55
6.6.1 Sensitivity Analysis of Capital mix: ........................................................................................ 55
7 Conclusion and Recommendations ....................................................................................................... 56
1
1 CHAPTER 1
1.1 Introduction:-
India is a rapidly growing economy which needs energy to meet its growth objectives in a
sustainable manner. The increasing energy requirements have meant that the extent of imports in
the energy mix is growing rapidly. Oil imports already constitute nearly 75 percent of our total oil
consumption. Coal imports which were negligible a few years back are likely to rise to around 30
percent of the total coal requirement by 2017. Globally, there is intense competition for access to
energy resources. This is a serious cause for concern as the Indian economy gets exposed to the
global fuel supply market which is volatile and rising. Moreover, being amongst the top five
greenhouse gas (GHG) emitters globally, India has a responsibility to achieve the growth
trajectory in an environmentally sensitive and responsible manner.
India is a tropical country with abundant sunshine. From time immemorial, Indians have idolized
the Sun as the Visible God that provides vital energy for sustenance of life. It is time we utilize
this immense potential of solar power which addresses the twin objectives of Energy Security and
Carbon Mitigation for India. Moreover, being modular in nature, solar power can meet demand
for wide ranging market applications where the size of installations can vary from as low as KWp
to MWp scale projects. Further, solar power can meet requirements in areas where conventional
power was unable to reach economically due to infrastructure bottlenecks.
Solar power is expensive when compared to conventional sources of power and hence, the solar
market development is currently dependent on Government support.
During the last few years, there has been significant cost reduction in solar power and the cost
curves of solar power are declining. On the other hand, costs of power from conventional sources
are increasing due to higher fixed costs and rising fuel prices. Moreover, there is considerable
research that is underway to further explore cost reduction possibilities for solar power.
Solar power forecasting involves knowledge of the Sun´s path, the atmosphere's condition, the
scattering processes and the characteristics of a solar energy plant which utilizes the Sun's energy
to create solar power. Solar photovoltaic systems transform solar energy into electric power. The
2
power output depends on the incoming radiation and on the solar panel characteristics.
Photovoltaic power production is increasing nowadays. Forecast information is essential for an
efficient use, the management of the electricity grid and for solar energy trading.
The energy generation forecasting problem is closely linked to the problem of weather variables
forecasting. Indeed, this problem is usually split into two parts, on one hand focusing on the
forecasting of solar PV or any other meteorological variable and on the other hand estimating the
amount of energy that a concrete power plant will produce with the estimated meteorological
resource. It is useful to classify these techniques depending on the forecasting horizon, so it is
possible to distinguish between now-casting (forecasting 3–4 hours ahead), short-term forecasting
(up to 7 days ahead) and long-term forecasting (months, year) Solar radiation is a most important
power follower of the physical and biological development in our earth.
1.2 Problem Statement
Despite the immense potential of solar power in India the industry faces a serious impediment in
the face of day ahead forecasting and scheduling of energy generation. If this industry is
facilitated with a model that can help them forecast the energy generation, the power producers
can strategies the short term trade of their power of the generated power through various
instruments such as power exchanges, short term open access, etc.
1.3 Scope of Project
This project is useful for grid connected solar PV power plant because it will give day ahead schedule for
these plant. As mentioned in the RRF (Renewable Regulatory Fund) mechanism :-
The schedule of solar generation shall be given by the generator based on availability of the
generator, weather forecasting, solar insolation, season and normal solar generation curve and
shall be vetted by the RLDC in which the generator is located and incorporated in the inter-state
schedule. If RLDC is of the opinion that the schedule is not realistic, it may ask the solar generator
to modify the schedule.
3
1.4 Objective of project
The main objective of the project is to develop a model based on which power plant can forecast
the generation with the help of weather forecasted parameters and past data. In the second part of
the project financial model is made for 10 MW solar PV power plant in Assam.
4
2 Chapter -2
2.1 ORGANIZATION PROFILE
Welspun Group (Welspun) (BSE: 514162) is a business conglomerate, manufactures, markets, and
exports terry towels, bed sheets, cotton yarn, polyester filament yarn, texturized and Line Pipes based
in Mumbai, Maharashtra. Its other operations includes Steel, Steel pipes, Infrastructure, Energy and
Oil & Gas. Terry Towel which is Asia's largest & world‟s 2nd largest home textile company Welspun
India Ltd. Welspun Corp Ltd. It also produces It is the second largest manufacturer of large diameter
pipes in the world through its subsidiary
A US$ 3.5 billion Welspun Group is an amalgamation of expertise, resources, opportunities and
engineering excellence. With global leadership position in Line Pipe and Home Textiles, its marquee
client list includes most of the Fortune 100 Companies operating in Oil & Gas and retail sector like
Chevron, TCPL, Exxon Mobil, Wal-Mart, Target amongst others. With strong foothold in over 50
Countries, over 24,000 employees & 100,000+ shareholders, Welspun is one of India's fastest growing
conglomerates. Besides being a global leader in most of the businesses, Welspun acts as a responsible
corporate citizen, sincerely practicing empowerment of the underprivileged and sustenance of the
environment. With a participative approach towards social development, the company is guided by the
three „E's - Education, Empowerment and Health. At Welspun each and every Welspunite contributes
to the community at large.
Table 1- AREAS OF BUSINESS COMPANY
COMPANY AREAS OF BUSINESS
HOME TEXTILES
Welspun India Ltd. Home Textiles - Towels, Bed Linen, Bath
Rugs and Decorative Bedding
Welspun Zucchi Textiles Ltd. Manufacture Bathrobes
Welspun Syntex Ltd. Manufacture Specialty Polyester Filament
Yarn,Texturised & Dyed Yarns
5
Welspun Global Brands Ltd. Marketing, sales & distribution of Home
Welspun Retail Ltd. Textiles.
Retail in India under brands „Spaces
Welspun UK Ltd. Home & Beyond' and „Welhome
Retail under brands 'Christy' And
Welspun USA Inc. 'Kingsley Home
Marketing, sales, distribution &
design of Home Textiles
LINE PIPES AND PLATES & COILS
Welspun Corp Ltd Manufacture LSAW, Spiral and HFIW Pipe -
with coatings and bending of Pipes, Steel
Plates and Coils
Welspun Tubular LLC, SAW pipe coating.
Little Rock (US)
Welspun Middle East Pipe LLC SAW Pipe
Welspun Middle East Pipe Coating LLC Pipe Coating
(Dammam, KSA)
STEEL
Welspun Steel Ltd. Ingots / Billets and TMT bars
Welspun Maxsteel Ltd. Sponge Iron (DRI & HBI)
Remi Metals Gujarat Ltd. Alloy Steel, Ingots and Seamless Tubes
OTHER BUSINESSES
Welspun Natural Resources Pvt. Ltd. Oil and Gas exploration & Production
6
Welspun Investments and Investments / Trading
Commercials Ltd.
Welspun Infratech Ltd. Infrastructure - Road & Water
Welspun Energy Ltd. Solar Energy, Thermal and Energy Parks
Welspun Projects Ltd. Infrastructure
Welspun Logistics Ltd. Air Charter Service
CORPORATE IDENTITY
For A Company to be seen and respected by its customer‟s vis-à-vis competitors, Corporate
Identity is a quintessential requirement. And Welspun has always made it a point to stand out
amongst the rest by clearly demarcating its identity be it in terms of the Logo, Vision or the
recently created Welspun Anthem for the 25th year celebration.
LOGO
The insignia is a creative visualization of The
visualization depicts the flight to greater heights at
ground realities.
a flying pair of sea gulls. the same
time remaining in touch with
7
MOTTO
“Dare To Commit” It is the vigour and commitment of all at Welspun that has brought it so far and helped to
reach the zenith of success in whichever business we are in. It is through this quality that
the motto of Welspun is „Dare to Commit‟. Welspun doesn‟t create products, it engineers
satisfaction. Within Welspun, quality of product and service is of paramount importance.
Welspun's state-of-art manufacturing facilities reaffirm world-class quality products and
nothing less. Each and every project is treated as an opportunity and every achievement as
a platform to set new goals. This strategy has enabled Welspun to have delighted
customers in 50 Countries.
“ We dare to commit and deliver on our promises.”
MISSION
Company endeavor to reach the leadership position in each Segment / Sector of our
Product / Service. Company committed to satisfy our customers by providing best quality
and service, which gives the highest value for money.
Company believe that employees are it‟s most important asset through which company can
reach the top in each category of our Product / Service. Therefore, company will emphasize
on their continuous improvement through upgrading relevant knowledge and training.
Company commits itself to continuous growth, so as to fulfill the aspirations of it‟s
Customers, Employees and Shareholders. WELSPUN ENERGY We are here to give Power to People and to Empower them. India is experiencing rapid
economic growth. However, for India to truly shine, she needs to bring light and
opportunities to her citizens living in rural areas. In a land of 1.2 billion people, there are
540 million people who even today light oil lamps and candles at night, 540 million people
8
for whom electricity is just a dream! Electrification goes beyond lighting a village at night. It signifies economic opportunities.
Opportunities that open doors to better standards of living; health care infrastructure
enabling timely medical care; education and career opportunities.
Concerns for environment sustainability are being raised. While economic advancement is
a right of every citizen, an organisation‟s impact on the environment also needs to be
addressed. The question that arises is - How can India collectively balance economic needs
with environmental concerns? How can wE generate power without destroying our delicate
ecosystem?
“ This is where we come in. We are Welspun Energy”
9
3 CHAPTER 3
3.1 Literature Review:-
Procedure for the Implementation of the Mechanism of Renewable Regulatory
Fund:-
1. Introduction:
1.1. This Procedure is issued in compliance with Regulation 6.1(d)read with Clause 9 of
Complimentary Commercial Mechanism (Annexure-1) of Central Electricity Regulatory
Commission (Indian Electricity Grid Code) Regulations, 2010 (hereinafter termed as „ the
IEGC 2010‟).
1.2. This procedure shall be implemented with effect from 1.1.2012.
Applicability:-
The Solar generating plants with capacity of 5 MW and above connected at connection
point of 33 KV level and above and who have not signed any PPA with states/UTs or
others [for which declaration has to be submitted to SLDC/Control Centre by the applicant,
which in turn would submit the same to RPC, RLDC and NLDC] as on the date of coming
into force of IEGC, 2010 with effect from 3.5.2010.
General Conditions:-
The scheduling jurisdiction and procedure, metering, energy accounting and
accounting of Unscheduled Interchange (UI) charges would be as per the relevant
Regulations of the Central Commission, as amended from time to time.
Wind Farm/Solar Energy Generators, which are intra-State entities, shall furnish the
details of Contracts along with contracted price to the concerned RPC and
RLDC through the respective SLDC. Wind Farm/Solar Energy Generator, which
are regional entities, shall furnish the details of Contracts along with contracted
10
price through the respective RLDC to the concerned RPC.
A Fund shall be opened by the National Load Despatch Centre (NLDC) on a
national level known by the “Renewable Regulatory Fund (RRF) on the lines of
UI Pool Account at the Regional level. All payments on account of Renewable
Regulatory charges, as described in Para 5.2, levied under the Regulations, and
interest, if any, received for late payment shall be credited to the RRF.
Scheduling and settlement of accounts in case of Solar Generators:-
The schedule of solar generation shall be given by the generator based on
availability of the generator, weather forecasting, solar insolation, season and
normal solar generation curve and shall be vetted by the RLDC in which the
generator is located and incorporated in the inter-state schedule. If RLDC is of
the opinion that the schedule is not realistic, it may ask the solar generator to
modify the schedule.
In case of solar generation no UI shall be payable/receivable by Generator.
In the case of intra-State sale of solar energy, the host State would pay the
solar generator at the contracted rate for actual generation.
In the case of inter-State sale of solar energy, the purchasing State would pay
the solar generator at the contracted rate for actual generation. The implication
of UI charges due to the deviation for purchasing State and host State would be
settled through the RRF.
3.2 Methodology:-
This project is started with the analysis of past data. Actually company has one fix schedule which
is used to send the 15 minute block scheduled. So the main task was to compare the actual with
schedule because as weather changes solar generation will change. So the project is divided into
two major parts:-
1. Past data analysis.
2. Model generation.
So there are different methodologies for different purpose. In the past data analysis part main
thing is to check the actual generation variation from the schedule. In this part variation is
11
checked block wise for the day and for the month. Following block diagram shows the overall
methodology of the project at macro level.
Here this block diagram shows that there are two major steps starting from data analysis
then it moves to model generation. So for data analysis we just used simple mathematics to
find out the variation. Past data analysis can be clearer with the help of figures.
PAST DATA ANALYSIS:-
In past data analysis first of all frames of all the inverters are added and then sum of all the
frames is divided by 10 which gives reading in terms of Kw.
Total generation for a particular time block (Kwh) = (Sum of frames generated by the all
the inverters for corresponding time block)/10*4
12
MODEL GENERATION:-
In the model generation part, Methodology used is based on the use of Linear Least
Squares Regression. Linear least square regression can be defined as follows:-
Linear Least Squares Regression:-
Linear least squares, is one of the mathematics/statistical problem solving methods, using
least squares algorithmic technique to increase solution approximation accuracy,
corresponding with a particular problem's complexity.
With the help of regression using excel, coefficients are find out which is put in the
equation to forecast solar radiation. When the radiation is forecasted then it becomes easy
to forecast generation. Past data of radiation and actual generation is available, with the
help of FORECAST () function it become easy to forecast day ahead schedule.
13
FORECAST ()
Calculates, or predicts, a future value by using existing values. The predicted value is a y-
value for a given x-value. The known values are existing x-values and y-values, and the
new value is predicted by using linear regression. You can use this function to predict
future sales, inventory requirements, or consumer trends.
Syntax
FORECAST(x,known_y's,known_x's)
X is the data point for which you want to predict a value.
Known_y's is the dependent array or range of data.
Known_x's is the independent array or range of data.
14
BLOCK DIAGRAM:-
Block diagram shows the understanding about the model; basically it shows the overall
structure of the model. It gives the information about what we have to give as input of the
project and what we get as output. It shows six weather forecast parameters and the past
data is used as input. When this data goes into the model it will give day ahead schedule as
output. All these weather parameters are taken from the weather forecast site and entered
into the model.
DAY AHEAD
SCHEDULE IN 15
MINUTE TIME
BLOCK
15
4 Past Data Analysis:-
First of all variation of actual generation from scheduled generation is checked with the
help of available past data of 4 months. To prepare a model first thing which is needed, i.e.
past data and its analysis. Past data always gives a trend and with the help of this one can
judge the need of a new model. Past data analysis gives that on what parameters we should
concentrate while preparing a day ahead schedule model or If any organization have some
fixed model then after how much time they should update their model. Whole data is
generated by the SCADA system. SCADA system gives generation reading at every 15
minute block. SCADA gives number of frames generated by the inverter at the particular
time instance and it gives such information after every 15 minutes. SCADA sheet contains
all this information generated to the next day which gives information regarding
generation.
Generation data can be calculated by adding all the frames corresponding to each time
block of all 13 inverters. This SCADA generated value is divided by 10 which give value
in Kw. This value is divided by 4 which give data in Kwh for every 15 minute block. This
value is now converted to Mwh. Scheduled generation is available in Mw, So it is
converted into Mwh and used for further comparison. It can also be done that Kw value is
directly converted into the Mw from actual SCADA generated sheet and can be directly
compared with the scheduled generation. All this can be formulized as follows:-
Total generation for a particular time block (Kwh) = (Sum of frames generated by the all
the inverters for corresponding time block)/10*4
This analysis is based on two things :-
1. Block average variation for the day
2. Block average variation for the month
16
4.1 Block Average Variation for The Day:-
When all the generation (actual and scheduled) data is ready block wise, then the next step
will be to calculate the variation of actual generation from the scheduled. Variation can be
easily calculated as follows:-
Variation for a particular block = (Scheduled – Actual)/Schedule generation for the
corresponding block
On calculating this variation for a particular block, It can be done easily be done for the
rest of the blocks for the whole day. After calculating the variation for each block average
is taken for all the blocks, which is called Block Average Variation for the Day.
Block average variation is very important term which gives the information regarding the
variation comes in actual generation from scheduled generation on an average in the whole
day.
Time slot
Sum of
all
frames
Blockwise
generation
(Kw)
Blockwise
Actual
generation
(KWh)
Actual
generation
(Mwh)
Scheduled
Generation
(Mw)
Scheduled
Generation
(Mwh)
Block
variation
from
schedule
Block %
variation
from
schedule
04-10-2013
06:19:52 0 0 0 0 0 0 - -
04-10-2013
06:35:28 0 0 0 0 0 0 - -
04-10-2013
06:51:04 244 24.4 6.1 0.0061 0 0 - -
04-10-2013
07:06:40 2799 279.9 69.975 0.069975 0.36 0.09 -0.6875 -68.75
04-10-2013
07:22:16 7328 732.8 183.2 0.1832 0.36 0.09 -1.94555 -194.5555
04-10-2013
07:37:52 12946 1294.6 323.65 0.32365 0.36 0.09 -3.50611 -350.611
04-10-2013
07:53:28 19757 1975.7 493.925 0.493925 0.36 0.09 -5.39805 -539.8055
04-10-2013 27790 2779 694.75 0.69475 3.8 0.95 0.218684 21.86842
17
08:09:04
04-10-2013
08:24:40 35718 3571.8 892.95 0.89295 3.8 0.95 0.010052 1.00526
04-10-2013
08:40:16 43777 4377.7 1094.425 1.094425 3.8 0.95 -0.20202 -20.20263
04-10-2013
08:55:52 52903 5290.3 1322.575 1.322575 3.8 0.95 -0.442184 -44.21842
04-10-2013
09:11:28 60717 6071.7 1517.925 1.517925 7.31 1.8275 0.996898
99.68980
8
04-10-2013
09:27:04 68869 6886.9 1721.725 1.721725 7.31 1.8275 0.885379
88.53796
17
04-10-2013
09:42:40 76107 7610.7 1902.675 1.902675 7.31 1.8275 0.78636457 78.63645
04-10-2013
09:58:16 83282 8328.2 2082.05 2.08205 7.31 1.8275 0.68821135 68.82113
04-10-2013
10:13:52 89513 8951.3 2237.825 2.237825 8.93 2.2325 1.23011478
123.0114
7
04-10-2013
10:29:28 94421 9442.1 2360.525 2.360525 8.93 2.2325 1.17515398
117.5153
9
04-10-2013
10:45:04 98688 9868.8 2467.2 2.4672 8.93 2.2325 1.12737122 112.7371
04-10-2013
11:00:40 103768 10376.8 2594.2 2.5942 8.93 2.2325 1.07048432 107.0484
04-10-2013
11:16:16 108222 10822.2 2705.55 2.70555 11.12 2.78 1.80678058 180.6780
04-10-2013
11:31:52 109315 10931.5 2732.875 2.732875 11.12 2.78 1.79695144
179.6951
9
04-10-2013
11:47:28 108014 10801.4 2700.35 2.70035 11.12 2.78 1.80865108
180.8651
0
04-10-2013
12:03:04 114821 11482.1 2870.525 2.870525 11.12 2.78 1.74743705
174.7437
05
04-10-2013
12:18:40 113049 11304.9 2826.225 2.826225 12.242 3.0605 2.13704795
213.7047
95
04-10-2013
12:34:16 120533 12053.3 3013.325 3.013325 12.242 3.0605 2.07591415
207.5914
1
04-10-2013
12:49:52 121188 12118.8 3029.7 3.0297 12.242 3.0605 2.07056372
207.0563
7
18
04-10-2013
13:05:28 119457 11945.7 2986.425 2.986425 12.242 3.0605 2.08470356
208.4703
56
04-10-2013
13:21:04 117084 11708.4 2927.1 2.9271 10.267 2.56675 1.42635845
142.6358
45
04-10-2013
13:36:40 113497 11349.7 2837.425 2.837425 10.267 2.56675 1.46129563
146.1295
63
04-10-2013
13:52:16 113282 11328.2 2832.05 2.83205 10.267 2.56675 1.46338972
146.3389
71
04-10-2013
14:07:52 107899 10789.9 2697.475 2.697475 10.267 2.56675 1.51581984
151.5819
83
04-10-2013
14:23:28 105857 10585.7 2646.425 2.646425 7.51 1.8775 0.46795273
46.79527
29
04-10-2013
14:39:04 97987 9798.7 2449.675 2.449675 7.51 1.8775 0.57274634
57.27463
38
04-10-2013
14:54:40 98819 9881.9 2470.475 2.470475 7.51 1.8775 0.56166778
56.16677
76
04-10-2013
15:10:16 91546 9154.6 2288.65 2.28865 7.51 1.8775 0.65851198
65.85119
84
04-10-2013
15:25:52 85796 8579.6 2144.9 2.1449 7.91 1.9775 0.89284766
89.28476
62
04-10-2013
15:41:28 81870 8187 2046.75 2.04675 7.91 1.9775 0.94248104
94.24810
36
04-10-2013
15:57:04 74426 7442.6 1860.65 1.86065 7.91 1.9775 1.03658976
103.6589
76
04-10-2013
16:12:40 67303 6730.3 1682.575 1.682575 7.91 1.9775 1.12664033
112.6640
32
04-10-2013
16:28:16 59322 5932.2 1483.05 1.48305 4.67 1.1675 -0.1027784
-
10.27783
6
04-10-2013
16:43:52 52268 5226.8 1306.7 1.3067 4.67 1.1675 0.04827088
4.827087
94
04-10-2013
16:59:28 43608 4360.8 1090.2 1.0902 4.67 1.1675 0.23370985
23.37098
50
04-10-2013
17:15:04 35017 3501.7 875.425 0.875425 4.67 1.1675 0.41767131
41.76713
06
04-10-2013
17:30:40 26156 2615.6 653.9 0.6539 0.831 0.20775 -2.9397831
-
293.9789
19
3
04-10-2013
17:46:16 18901 1890.1 472.525 0.472525 0.831 0.20775 -2.0667386
-
206.6735
6
04-10-2013
18:01:52 11602 1160.2 290.05 0.29005 0.831 0.20775 -1.1883992 -118.8398
04-10-2013
18:17:28 7134 713.4 178.35 0.17835 0.831 0.20775 -0.6507338
-
65.07334
5
04-10-2013
18:33:04 2985 298.5 74.625 0.074625 0 0 - -
04-10-2013
18:48:40 204 20.4 5.1 0.0051 0 0 - -
04-10-2013
19:04:16 127 12.7 3.175 0.003175 0 0 - -
4.2 Block Average Variation for the Month:-
After calculating block average variation for the day, now it becomes very easy to calculate
block average variation for the month. So for this purpose Average of all days of the month
is taken which is called Block Average Variation for the Month.
Date Average block variation for the day
Average block variation for the day (%)
01-04-2013 0.278418032 27.84180318
02-04-2013 0.082406018 8.240601784
03-04-2013 0.080165105 8.016510491
04-04-2013 0.343672197 34.36721972
05-04-2013 0.301722697 30.1722697
06-04-2013 0.010647144 1.064714421
07-04-2013 0.190496738 19.04967381
08-04-2013 0.548456118 54.84561178
09-04-2013 0.571633435 57.16334351
10-04-2013 0.395746632 39.57466323
11-04-2013 0.099025792 9.902579172
12-04-2013 0.057699114 5.769911367
13-04-2013 0.048919617 4.891961655
20
14-04-2013 0
15-04-2013 0.074461859 7.446185922
16-04-2013 0.039637831 3.963783115
17-04-2013 0.266707231 26.67072311
18-04-2013 0.490634251 49.06342506
19-04-2013 0.297558398 29.75583982
20-04-2013 0.458148222 45.81482224
21-04-2013 0.084035389 8.403538868
22-04-2013 -1.010334056 -101.0334056
23-04-2013 0.171556327 17.15563268
24-04-2013 0.26043148 26.04314805
25-04-2013 0.318400748 31.8400748
26-04-2013 0.458148222 45.81482224
27-04-2013 0.056392651 5.639265059
28-04-2013 0.181711452 18.17114522
29-04-2013 0.282695226 28.26952264
30-04-2013 0.289871097 28.98710972
Average block variation for the Month April = 19.7554%
4.3 Relation between actual generation and weather parameters :-
We collect weather forecast data and observational solar intensity data for 3 months
starting from March 2013. We obtain historical forecast data from the site. SCADA
generates data sheet from the site which gives the full information regarding
generation as well as weather. There are two sheets generated from the site:-
1. Day wise generation report.
2. Weather monitoring report.
We get forecasted data from www.accuweather.com, in this website you have to
give only location of the site and then it will give information of different
forecasted parameters. It gives forecasted data on hourly basis. We have taken the
following parameters to analyse the data as follows:-
21
1. Ambient temperature
2. Humidity
3. Rain
4. UV index
5. Cloud cover
6. Dew point
7. Wind speed
This forecasted data is available on hourly basis but we have our past data generated
from the site is available at every 5 minutes. So we take the average for every hour of
this past data so that it can be easily converted into comparable manner. Data which we
get from the site give the information of following parameters:-
1. Radiation
2. Ambient temperature
3. Wind speed
4. Module temperature
22
23
24
Solar intensity and wind speed shows little correlation. Solar intensity shows some
correlation with temperature and with dew point at high dew points. Solar intensity
generally decreases with increasing values of sky cover, relative humidity and precipitation
potential.
4.4 Monthly correlation analysis on different parameters:-
It can be shown that there is very less correlation between solar radiation with wind speed
and high correlation with ambient temperature. In the starting days some radiation values
were zero due to that error values correlation between radiation and wind speed also became
zero for the same.
Date
Correlation between solar radiation and wind speed
Correlation between solar radiation and Ambient temperature
01-Apr-13 0 0.03154323
02-Apr-13 0 -0.239505666
03-Apr-13 0 0.02783579
04-Apr-13 0 0.03478934
05-Apr-13 0 0.203485734
06-Apr-13 0 0.016985689
07-Apr-13 0 -0.017834576
08-Apr-13 0 -0.224598896
09-Apr-13 0 0.065043809
10-Apr-13 -0.493250494 0.635287104
11-Apr-13 0.061883258 0.65238355
12-Apr-13 0.048153935 0.654558631
13-Apr-13 0.167187627 0.627187956
14-Apr-13 0.315363928 0.668032835
15-Apr-13 0.154167305 0.639751286
16-Apr-13 0.154690588 0.725414179
17-Apr-13 -0.452854784 0.481399166
18-Apr-13 -0.028898084 0.441964194
19-Apr-13 -0.026286913 0.52621563
20-Apr-13 -0.026286913 0.622551649
21-Apr-13 0.361555595 0.558237932
22-Apr-13 -0.020838669 0.739632829
25
23-Apr-13 0.372817725 0.608312615
24-Apr-13 0.113819618 0.651788928
25-Apr-13 0.248095927 0.704169614
26-Apr-13 0.421049339 0.658433278
27-Apr-13 0.403449951 0.672372594
28-Apr-13 0.08168974 0.724015881
29-Apr-13 -0.170113875 0.703041819
30-Apr-13 -0.02654723 0.605293247
This monthly correlation analysis is based on the day wise analysis for the whole month April. First
of all correlation between radiation and wind speed, radiation and ambient temperature is
calculated for every day. Then average of all the values is taken which gives the data on monthly
basis.
26
5 Chapter -5
We apply multiple techniques to derive prediction models for solar intensity using multiple
forecast metrics, and then analyze the prediction accuracy of each model. We use
regression technique on a training data set of historical solar intensity observations and
forecasts to derive a function that computes future solar intensity for a given time horizon
from a set of forecasted weather metrics. We formulate models based on linear least
squares regression.
From data analysis part we get that actual generation is based on sun radiation. Sun
radiation is based on these following parameters:-
1. Ambient temperature
2. Humidity
3. Rain
4. UV index
5. Cloud cover
6. Dew point
So it means that radiation can be forecasted on the basis of above given parameters. But
each parameter has different correlation coefficient with radiation. As we know that every
variable has different value of coefficients. These coefficients can be defined with the help
of regression. Here we have six independent variables and one variable so it is the case of
multiple regressions.
Equation is the most important part of model generation, as equation is generated then to
generate a next day schedule we have just put the next day weather forecast value. Values
of all the parameters are put into the input sheet of the model which looks as follows:-
27
5.1 Flow chart
Input weather
parameters
and past data
Start
Find the coefficient of
equation used for forecasting
the solar radiation using least
square regression.
Determine the equation for
forecasting solar radiation
Forecast the solar radiation
Find out the schedule
generation with the help of
forecasted radiation and
past actual generation data
Find out the final
schedule of 15 minutes
time block
Stop
28
5.2 Steps for Model Generation
STEP-1 Input weather parameters and past data:-
Parameters are put into the table for forecasting the day schedule for the next day and past
data is also put into the past data sheet. Here a sample sheet is shown to input weather
parameters.
Date & Time
Ambient Temp Humidity Rain
UV index
Cloud cover
Dew point forecast date
6:00 AM
7:00 AM
8:00 AM
9:00 AM
10:00 AM
11:00 AM
12:00 PM
1:00 PM
2:00 PM
3:00 PM
4:00 PM
5:00 PM
6:00 PM
7:00 PM
These parameters can be taken with any forecasting site or through any other source. Here
one sample is shown through which these parameters can be entered with the help of
www.accuweather.com .
29
30
Now weather forecast values are put into the input sheet, which works as input data for the
model now these values are put into the equation. So we get the value of radiation
corresponding to the each forecasted value on hourly basis. As we have given the values on
hourly basis, so we also get radiation on hourly basis.
On putting these forecasted values in the input sheet, we‟ll get the following input sheet:-
Date & Time
Ambient Temp Humidity Rain UV index
Cloud cover
Dew point forecast date
06:00:00 23.81 96% 41% 0 96% 23
29-07-2013
7:00 AM 23.9525 95% 41% 0 96% 23
8:00 AM 23.95333333 95% 41% 0 96% 23
9:00 AM 24.255 94% 42% 0 96% 24
10:00 AM 24.34166667 94% 42% 0 96% 24
11:00 AM 24.5 93% 42% 0 96% 24
12:00 PM 25.08666667 91% 43% 0 95% 24
1:00 PM 28 79% 46% 3 93% 24
2:00 PM 27 83% 45% 2 94% 24
3:00 PM 26.5675 85% 44% 2 94% 24
4:00 PM 26.06 87% 44% 1 94% 24
5:00 PM 26.3725 86% 44% 1 94% 24
6:00 PM 26.05833333 87% 44% 1 94% 24
7:00 PM 25 91% 42% 0 95% 23
Now one more thing is needed as input parameters other than which is Past data. It can be
entered as following
31
Past Data Sheet
Date & Time Radiation
Ambient Temp Humidity Rain
UV index Cloud cover Dew point
25-07-2013
06:00 0 26 85% 55% 0 100% 24
7:00 AM 33.27272727 26 87% 61% 0 99% 24
8:00 AM 250.4166667 26 86% 61% 1 99% 24
9:00 AM 554.1538462 27 82% 49% 2 99% 24
10:00 AM 495.0909091 28 81% 61% 2 99% 24
11:00 AM 496.7692308 27 87% 61% 3 99% 25
12:00 PM 669.2307692 28 81% 49% 3 99% 25
1:00 PM 650.7272727 29 76% 49% 3 99% 24
2:00 PM 0.598958333 28 78% 54% 3 99% 24
3:00 PM 15.57204861 28 79% 54% 2 100% 24
4:00 PM 15.57204861 28 80% 49% 1 99% 24
5:00 PM 88.5 27 81% 47% 0 99% 24
6:00 PM 44.23076923 27 83% 51% 0 99% 24
7:00 PM 8.833333333 26 85% 25% 0 99% 24
8:00 PM 3 26 87% 20% 0 99% 24
26-07-2013 06:00 0.333333333 26 88% 25% 0 100% 24
7:00 AM 45.33333333 26 90% 49% 0 100% 24
8:00 AM 148.9166667 26 90% 54% 1 100% 24
9:00 AM 88.66666667 26 88% 54% 3 100% 24
10:00 AM 165.6481481 27 84% 49% 4 99% 24
11:00 AM 153.3333333 28 78% 56% 5 98% 24
12:00 PM 242.5 29 74% 56% 6 97% 24
1:00 PM 276.4166667 30 71% 25% 6 87% 24
2:00 PM 352.9166667 29 73% 20% 6 77% 24
3:00 PM 184.9166667 29 76% 20% 4 67% 24
4:00 PM 119.0833333 28 80% 20% 2 76% 24
5:00 PM 63.25 27 83% 20% 1 84% 24
6:00 PM 11.5 26 88% 16% 0 93% 24
7:00 PM 0 26 90% 7% 0 95% 24
8:00 PM 0 25 92% 7% 0 97% 24
32
Here data for only two days has taken just only to show the sheet but here mostly we take
more than three days data. As we have more past data than accuracy will be increased.
Here data for 25 July and 26 July 2013 has taken but it‟s very less to forecast the radiation.
STEP-2 Find the coefficient of equation used for forecasting the solar radiation
using least square regression
As past data has been taken and put into the table the next step is to calculate the
coefficient of the equation these coefficient are totally based on the past data of weather
sheet parameters. It‟s based on the following concept of linear least square regression.
Linear Least Squares Regression:-
We first apply a linear least squares regression method to predict solar intensity. Linear
least squares regression is a simple and commonly-used technique to estimate the
relationship between a dependent or response variable, e.g., solar intensity, and a set of
independent variables or predictors. The regression minimizes the sum of the squared
differences between the observed solar intensity and the solar intensity predicted by a
linear approximation of the forecast weather metrics with the help of excel these
coefficient can be defined as:-
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.745633723
R Square 0.555969649
Adjusted R Square 0.505702062
Standard Error 163.3060034
Observations 60
ANOVA
df SS
Regression 6 1769777.207
Residual 53 1413449.09
33
Total 59 3183226.297
Coefficients Standard Error
Intercept -7252.298915 2586.659247
X Variable 1 102.713152 65.94145801
X Variable 2 986.0306744 1463.71263
X Variable 3 199.2081878 159.550287
X Variable 4 29.3810006 20.74770473
X Variable 5 146.1231112 374.6563737
X Variable 6 149.0445255 66.29385442
STEP-3 Determine the equation for forecasting solar radiation
When the coefficients of equation are determined then it‟s very easy to find out the
equation for determining solar radiation. The coefficients are directly put into the equation
and multiplied with corresponding weather parameters. So finally value of solar radiation
comes. This equation can be given as follows:-
Radiation =-7252.29 + 102.71*variable1 + 986.03*variable2 + 199.21 *variable3 +
29.38*variable4 +146.12*variable5 + 149.04*variable6
Here
Variable1 = Ambient Temp
Variable2 =Humidity
Variable3 = Rain
Variable4 = UV index
Variable5 = Cloud cover
Variable6 = Dew point
Step -4 Forecast the solar radiation :-
As equation comes to forecast the solar radiation then it becomes very easy to calculate
solar radiation
Date & Time Forecasted Radiation(watt/m^2)
34
29-07-2013 06:00 0
7:00 AM 0
8:00 AM 0
9:00 AM 0
10:00 AM 0
11:00 AM 0
12:00 PM 18.93664411
1:00 PM 304.7495508
2:00 PM 204.605219
3:00 PM 161.2927955
4:00 PM 110.4695471
5:00 PM 141.7646508
6:00 PM 110.3026399
7:00 PM 0
8:00 PM 0
STEP 5 :- Find out the schedule generation with the help of forecasted radiation and
past actual generation data
On the basis of this forecasted radiation, It is easy to forecast the generation because we
know that generation depends on the solar radiation at the site. As we have taken the
forecasted data of 29/7/2013 so in the starting of the day radiation is zero due to rainy
season.
Now the next step is to generate hourly schedule, which become now very easy task. We
have the past data of actual generation and we also have the corresponding data of solar
radiation for the same. So with the help of FORECAST () function in excel we get
schedule, working of FORECAST () is already defined in the methodology section. Its
syntax is defined as follows:-
FORECAST(x,known_y's,known_x's)
Here
Known X = past values of radiation
Known y =past values of actual generation
X= forecasted radiation value
35
So here forecasted generation can be defined as follows:-
Scheduled generation = FORECAST (forecasted radiation value, values of actual
generation, past values of radiation)
With the help of this equation final day ahead schedule of generation is generated on
hourly basis. Which can be shown as follows:-
Date & Time Radiation Actual generation
25-07-2013 06:00 0 20.55
7:00 AM 33.27272727 453.65
8:00 AM 250.4166667 3907.15
9:00 AM 554.1538462 5189.725
10:00 AM 495.0909091 6700.525
11:00 AM 496.7692308 6232.775
12:00 PM 669.2307692 8345.425
1:00 PM 650.7272727 8014.1
2:00 PM 0.598958333 1492.275
3:00 PM 15.57204861 658.125
4:00 PM 15.57204861 1318.7
5:00 PM 88.5 652.8
6:00 PM 44.23076923 423.775
7:00 PM 8.833333333 0
8:00 PM 3 0
27-07-2013 06:00 0.916666667 56.05
7:00 AM 49.58333333 800.35
8:00 AM 104.75 1774.825
9:00 AM 238.5833333 3565.85
10:00 AM 447.1944444 8923.225
11:00 AM 755.5 9746.225
12:00 PM 915.1666667 9749.4875
1:00 PM 668.3333333 6650.1625
2:00 PM 507 6564.475
3:00 PM 343.8333333 3976.15
4:00 PM 222.0833333 2912.55
5:00 PM 44 484.45
6:00 PM 2.75 60
7:00 PM 0 0
36
8:00 PM 0 0
28-07-2013 06:00 0 1.875
7:00 AM 9.83 400.875
8:00 AM 78.67 1468.025
9:00 AM 183.5 2734.85
10:00 AM 305.25 3832.0375
11:00 AM 377.5 4581.2375
12:00 PM 532.91 6922.275
1:00 PM 347.75 4745.225
2:00 PM 369.45 4248.625
3:00 PM 214.83 2646.825
4:00 PM 33.5 544.8
5:00 PM 0 103.225
6:00 PM 0 0
7:00 PM 0 0
8:00 PM 0 0
Scheduled Radiation hourly Scheduled generation(KWh) hourly Scheduled generation(MWh)
0 282.2175853 0.282217585
0 282.2175853 0.282217585
0 282.2175853 0.282217585
0 282.2175853 0.282217585
0 282.2175853 0.282217585
0 282.2175853 0.282217585
18.93664411 504.3040494 0.504304049
304.7495508 3856.280114 3.856280114
204.605219 2681.800602 2.681800602
161.2927955 2173.838213 2.173838213
110.4695471 1577.78986 1.57778986
141.7646508 1944.814708 1.944814708
110.3026399 1575.832394 1.575832394
0 282.2175853 0.282217585
0 282.2175853 0.282217585
Total scheduled generation 16572.40062
37
STEP 6:- Find out the final schedule of 15 minutes time block
Blocks Period
(15 Min.) MW From To
1 0.00 0.15 0
2 0.15 0.30 0
3 0.30 0.45 0
4 0.45 1.00 0
5 1.00 1.15 0
6 1.15 1.30 0
7 1.30 1.45 0
8 1.45 2.00 0
9 2.00 2.15 0
10 2.15 2.30 0
11 2.30 2.45 0
12 2.45 3.00 0
13 3.00 3.15 0
14 3.15 3.30 0
15 3.30 3.45 0
16 3.45 4.00 0
17 4.00 4.15 0
18 4.15 4.30 0
19 4.30 4.45 0
20 4.45 5.00 0
21 5.00 5.15 0
22 5.15 5.30 0
23 5.30 5.45 0
24 5.45 6.00 0
25 6.00 6.15 0.282218
26 6.15 6.30 0.282218
27 6.30 6.45 0.282218
28 6.45 7.00 0.282218
29 7.00 7.15 0.282218
30 7.15 7.30 0.282218
31 7.30 7.45 0.282218
38
32 7.45 8.00 0.282218
33 8.00 8.15 0.282218
34 8.15 8.30 0.282218
35 8.30 8.45 0.282218
36 8.45 9.00 0.282218
37 9.00 9.15 0.282218
38 9.15 9.30 0.282218
39 9.30 9.45 0.282218
40 9.45 10.00 0.282218
41 10.00 10.15 0.282218
42 10.15 10.30 0.282218
43 10.30 10.45 0.282218
44 10.45 11.00 0.282218
45 11.00 11.15 0.282218
46 11.15 11.30 0.282218
47 11.30 11.45 0.282218
48 11.45 12.00 0.282218
49 12.00 12.15 0.504304
50 12.15 12.30 0.504304
51 12.30 12.45 0.504304
52 12.45 13.00 0.504304
53 13.00 13.15 3.85628
54 13.15 13.30 3.85628
55 13.30 13.45 3.85628
56 13.45 14.00 3.85628
57 14.00 14.15 2.681801
58 14.15 14.30 2.681801
59 14.30 14.45 2.681801
60 14.45 15.00 2.681801
61 15.00 15.15 2.173838
62 15.15 15.30 2.173838
63 15.30 15.45 2.173838
64 15.45 16.00 2.173838
65 16.00 16.15 1.57779
66 16.15 16.30 1.57779
67 16.30 16.45 1.57779
68 16.45 17.00 1.57779
69 17.00 17.15 1.944815
39
70 17.15 17.30 1.944815
71 17.30 17.45 1.944815
72 17.45 18.00 1.944815
73 18.00 18.15 1.575832
74 18.15 18.30 1.575832
75 18.30 18.45 1.575832
76 18.45 19.00 1.575832
77 19.00 19.15 0.282218
78 19.15 19.30 0
79 19.30 19.45 0
80 19.45 20.00 0
81 20.00 20.15 0
82 20.15 20.30 0
83 20.30 20.45 0
84 20.45 21.00 0
85 21.00 21.15 0
86 21.15 21.30 0
87 21.30 21.45 0
88 21.45 22.00 0
89 22.00 22.15 0
90 22.15 22.30 0
91 22.30 22.45 0
92 22.45 23.00 0
93 23.00 23.15 0
94 23.15 23.30 0
95 23.30 23.45 0
96 23.45 0.00 0
40
6 CHAPTER 6: FINANCIAL MODELING
6.1 Introduction
Financial modeling is the task of building an abstract representation (a model) of a real
world financial situation. This is a mathematical model designed to represent (a simplified
version of) the performance of a financial asset or portfolio of a business, project, or any
other investment. Financial modeling is a general term that means different things to
different users; the reference usually relates either to accounting and corporate finance
applications, or to quantitative finance applications. While there has been some debate in
the industry as to the nature of financial modeling - whether it is a tradecraft, such as
welding, or a science - the task of financial modeling has been gaining acceptance and
rigor over the years. Typically, financial modeling is understood to mean an exercise in
either asset pricing or corporate finance, of a quantitative nature. In other words, financial
modeling is about translating a set of hypotheses about the behavior of markets or agents
into numerical predictions; for example, a firm's decisions about investments (the firm will
invest 20% of assets), or investment returns (returns on "stock A" will, on average, be 10%
higher than the market's returns).
A financial model helps the developer to explore in detail the financial benefits and costs
associated with the investment. This facilitates the identification of key variables affecting
the project value and enables financing decisions. The following section describe the key
items and assumptions that are included in the financial modeling of a typical grid
connected 10 MW solar PV power plant, and discusses the conclusions based on the
calculation of various financial parameters:-
41
6.2 Tariff determination for solar PV power plant in Assam
The ASSAM ELECTRICITY REGULATORY COMMISSION issues a Suo-Motu order
every year, each year of the control period for determination of generic tariff for power
procured from various sources of energy. (TERMS AND CONDITIONS FOR TARIFF
DETERMINATION FROM RENEWABLE ENERGY SOURCES) REGULATIONS, 2012
Technological aspects:-
Norms for Solar Photovoltaic (PV) power under these Regulations shall be applicable for
grid connected PV systems that directly convert solar energy into electricity and are
based on the technologies such as crystalline silicon or thin film etc. as may be
approved by MNRE.
6.3 General Principles
Control Period or Review Period
The Control Period or Review Period under these Regulations shall be of three years, of
which the first year shall be the period from the date of notification of these
regulations to 31.3.2012.
Provided that the benchmark capital cost for Solar PV and Solar thermal projects may be
reviewed annually by the Commission.
Provided further that the tariff determined as per these Regulations for the RE projects
commissioned during the Control Period, shall continue to be applicable for the entire
duration of the Tariff Period as specified in Regulation 8 below.
Provided also that the revision in Regulations for next Control Period shall
be undertaken at least six months prior to the end of the first Control Period and in case
Regulations for the next Control Period are not notified until commencement of
next Control Period, the tariff norms as per these Regulations shall continue to
remain applicable until notification of the revised Regulations subject to
adjustments as per revised Regulations.
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Tariff Period:-
In case of Solar PV and Solar thermal power projects the Tariff Period shall be
twenty five years (25) years.
Tariff period under these Regulations shall be considered from the date of
commercial operation of the renewable energy generating stations.
Tariff determined as per these Regulations shall be applicable for Renewable
Energy power projects.
Project Specific tariff:-
Project specific tariff, on case to case basis, shall be determined by the Commission
for the following types of projects:
Municipal Solid Waste Projects
Poultry litter
Mixed feed
Any other new renewable energy technologies approved by MNRE
Solar PV and Solar Thermal Power projects, if a project developer opts for project
specific tariff: Provided that the Commission while determining the project specific tariff
for Solar PV and Solar Thermal shall be guided by the provisions of these Regulations.
Hybrid Solar Thermal Power plants
Biomass project other than that based on Rankine Cycle technology application
with water cooled/ air cooled condenser.
However, the Commission may consider any Renewable Energy projects for
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determination of project specific tariff as it may deem it appropriate.
Determination of Project specific Tariff for generation of electricity from such renewable
energy sources shall be in accordance with such terms and conditions as stipulated under
relevant Orders of the Commission.
Provided that the financial norms as specified under Chapter-II of these Regulations,
except for capital cost, shall be ceiling norms while determining the project specific
tariff.
Tariff Structure
The tariff for renewable energy technologies shall be single part tariff consisting of
the following fixed cost components:
a) Return on equity;
b) Interest on loan capital;
c) Depreciation;
d) Interest on working capital;
e) Operation and maintenance expenses;
Provided that for renewable energy technologies having fuel cost component, like
biomass power projects and non-fossil fuel based cogeneration, single part tariff with
two components, fixed cost component and fuel cost component, shall be determined.
The fuel cost component may be subjected to escalation factor.
6.3.1 Tariff Design
The generic tariff shall be determined on levellised basis for the Tariff Period.
Provided that for renewable energy technologies having single part tariff with two
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components, tariff shall be determined on levellised basis considering the year of
commissioning of the project for fixed cost component while the fuel cost component
shall be specified on year of operation basis.
For the purpose of levellised tariff computation, the discount factor equivalent to
weighted average cost of capital or by other appropriate discounted factor shall be
considered.
Levellised tariff shall be specified for the period equivalent to the tariff period.
Dispatch principles for electricity generated from Renewable Energy
Sources:-
13.1 All renewable energy power plants except for biomass power plants with installed
capacity of 10 MW and above, and non-fossil fuel based cogeneration plants shall be
treated as „MUST RUN‟ power plants and shall not be subjected to „merit order despatch‟
principles.
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6.4 Financial Principles
6.4.1 Capital cost
The norms for the Capital cost as specified in the subsequent technology specific chapters
shall be inclusive of all capital work including plant and machinery, initial spares, civil
work, erection and commissioning, financing and interest during construction, and
evacuation infrastructure up to inter-connection point.
Provided that for project specific tariff determination, the generating company shall
submit the break-up of capital cost items along with its petition in the manner specified
under Regulation 9.
6.4.2 Debt Equity Ratio
For generic tariff to be determined based on suo motu petition, the debt
equity ratio shall be 70 : 30.
For Project specific tariff, the following provisions shall apply:-
If the equity actually deployed is more than 30% of the capital
cost, equity in excess of 30% shall be treated as normative loan.
Provided that where equity actually deployed is less than 30% of the
capital cost, the actual equity shall be considered for determination of
tariff:
Provided further that the equity invested in foreign currency shall be
designated in Indian rupees on the date of each investment.
6.4.3 Loan and Finance Charges
Loan Tenure. For the purpose of determination of tariff, loan tenure of 10 years shall be
considered.
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6.4.4 Interest Rate
a) The loans arrived at in the manner indicated above shall be considered as gross
normative loan for calculation for interest on loan. The normative loan outstanding as on April
1st of every year shall be worked out by deducting the cumulative repayment up to March 31st
of previous year from the gross normative loan.
b) For the purpose of computation of tariff, the normative interest rate shall be considered as
average long term prime lending rate (LTPLR)/Base rate of State Bank of India (SBI)
prevalent during the previous year plus 150 basis point.
c) Notwithstanding any moratorium period availed by the generating company, the
repayment of loan shall be considered from the first year of commercial operation of the
project and shall be equal to the annual depreciation allowed.
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6.4.5 Depreciation
The value base for the purpose of depreciation shall be the Capital Cost of the asset
admitted by the Commission. The Salvage value of the asset shall be considered as 10%
and depreciation shall be allowed up to maximum of 90% of the Capital Cost of the
asset.
Depreciation per annum shall be based on „Differential Depreciation Approach „over
loan tenure and period beyond loan tenure over useful life computed on „Straight
Line Method‟. The depreciation rate for the first 10 years of the Tariff Period shall be
7% per annum and the remaining depreciation shall be spread over the remaining
useful life of the project from 11th year onwards.
Depreciation shall be chargeable from the first year of commercial operation.
Provided that in case of commercial operation of the asset for part of the year,
depreciation shall be charged on pro rata basis.
6.4.6 Return on Equity
The value base for the equity shall be 30% of the capital cost or actual equity (in case
of project specific tariff determination) as determined under Regulation 15.
The normative Return on Equity shall be:
a) Pre-tax 19% per annum for the first 10 years.
b) Pre-tax 24% per annum 11th years onwards.
6.4.7 Interest on Working Capital
The Working Capital requirement in respect of wind energy projects, small hydro
power, solar PV and Solar thermal power projects shall be computed
in accordance with the following :
Wind Energy / Small Hydro Power / Solar PV / Solar thermal
a) Operation & Maintenance expenses for one month;
b) Receivables equivalent to 2 (Two) months of energy charges for sale of
electricity calculated on the normative CUF;
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c) Maintenance spare @ 15% of operation and maintenance expenses
Interest on Working Capital shall be at interest rate equivalent to average
State Bank of India short term PLR/Base rate during the previous year plus 100
basis points.
6.4.8 Operation and Maintenance Expenses
„Operation and Maintenance or O&M expenses‟ shall comprise repair and
maintenance (R&M), establishment including employee expenses, and
administrative and general expenses.
Operation and maintenance expenses shall be determined for the Tariff Period
based on normative O&M expenses specified by the Commission subsequently in
these Regulations for the first Year of Control Period.
Normative O&M expenses allowed during first year of the Control Period (i.e.
FY 2011-12) under these Regulations shall be escalated at the rate of 5.72% per
annum over the Tariff Period.
6.4.9 Rebate
6.4.9.1 For payment of bills of the generating company through letter of credit, a
rebate of 2% shall be allowed.
6.4.9.2 Where payments are made other than through letter of credit within a period of
one month of presentation of bills by the generating company, a rebate of 1%
shall be allowed.
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6.4.10 Late payment surcharge
In case the payment of any bill for charges payable under these regulations is delayed
beyond a period of 60 days from the date of billing, a late payment surcharge at the rate
of 1.25% per month shall be levied by the generating company.
6.4.11 Sharing of CDM Benefits
The proceeds of carbon credit from approved CDM project shall be shared
between generating company and concerned beneficiaries in the following
manner, namely
a) 100% of the gross proceeds on account of CDM benefit to be retained by the
project developer in the first year after the date of commercial operation of the
generating station ;
b) In the second year, the share of the beneficiaries shall be 10% which shall be
progressively increased by 10% every year till it reaches 50%, where after the
proceeds shall be shared in equal proportion, by the generating company and the
beneficiaries.
6.4.12 Subsidy or incentive by the Central/State Government
The Commission shall take into consideration any incentive or subsidy offered by the
Central or State Government, including accelerated depreciation benefit if availed by the
generating company, for the renewable energy power plants while determining the tariff
under these Regulations.
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Provided that the following principles shall be considered for ascertaining income tax
benefit on account of accelerated depreciation, if availed, for the purpose of tariff
determination:
i. Assessment of benefit shall be based on normative capital cost, accelerated
depreciation rate as per relevant provisions under Income Tax Act and
corporate income tax rate.
ii. Capitalisation of RE projects during second half of the fiscal year.
Per unit benefit shall be derived on levellised basis at discount factor equivalent to
weighted average cost of capital or any other appropriate discounting factor considered by
the Commission.
6.4.13 Taxes and Duties
Tariff determined under these regulations shall be exclusive of taxes and duties as may be
levied by the appropriate Government:
Provided that the taxes and duties levied by the appropriate Government shall be allowed as
pass through on actual incurred basis subject to production of documentary evidence by
the generating company.
6.4.14 Capital costs
The normative capital cost for setting up Solar Photovoltaic Power Project shall be Rs.
1000Lakh/MW for FY 2012-13. Provided that the Commission may deviate from above
norm in case of project specific tariff determination in pursuance of Regulation 9 and
Regulation 10.
6.4.15 Capacity Utilization Factor
The Capacity utilisation factor for Solar PV project shall be 19%. Provided that the
Commission may deviate from above norm in case of project specific tariff
determination in pursuance of Regulation 9 and Regulation 10.
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6.4.16 Operation and Maintenance Expenses
The O&M Expenses shall be Rs. 11 Lakhs/MW for the 1st year of operation.
Normative O&M expenses allowed at the commencement of the Control
Period under these Regulations shall be escalated at the rate of 5.72% per
annum.
6.4.17 Useful life
Useful Life‟ in relation to a unit of a generating station including evacuation system shall
mean the following duration from the date of commercial operation (COD) of such
generation facility, Solar PV/Solar thermal power plants 25 years.
6.4.18 Financial Assumptions
Assumption
Head
Sub-
Head
Sub-Head(2) Unit Parameters
Values
Power
Generation
Capacity Installed power generation capacity
Auxiliary consumption factor
CUF
Commercial operation Date
Useful life
MW
%
%
MM/YYYY
Years
10
19
25
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Financial
Assumptions Tariff Period Years 25
Debt-Equity
Debt % 70%
Equity % 30%
Total debt component Rs. Lakh 700
Total Equity component Rs. Lakh 300
Debt
Loan Amount Rs. Lakh 700
Repayment period Years
10
Interest rate % 14
Equity
Total equity amount Rs. Lakh 300
Return on equity for first 10 years % 20
RoE period Years 10
Return on equity after 10 years % p.a 24
Discount rate % p.a
WACC Weighted Average Cost of Capital % p.a
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Financial
Assumptions
(Tax Related)
Fiscal Assumptions Income tax % 32.45
MAT rate(for first 10 years) % 20.08
Depreciation
Depreciation Rate (first 12 years) % p.a 7%
Depreciation rate 12th year
onwards % p.a 1.33%
Working capital
O&M charges
months 1
Maintenance spares (% of O&M expenses)
15%
Receivables for debtors
months 2
Interest on working capital
months 4
% 13.37%
Operation & Maintenance Expenses
11
Power Plant Rs. Lakh
Escalation % 5.72%
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6.5 Project Economics and Financial Indicators
Project financial model calculates a range of project value indicators in order to allow developers,
lenders, investors and relevant government bodies to assess the project economics from several
perspectives.
From an investor’s point of view, a project is generally considered to be a reasonable investment
only if the internal rate of return (IRR) is higher than the weighted average cost of capital (WACC).
Investors will have access to capital at a range of costs; the return arising from investment of that
capital must be sufficient to meet the costs of that capital. Moreover, the investment should
generate a premium associated with the perceived risk levels of the project.
As a result, the IRR for the equity component can be calculated separately from the IRR for the
project as a whole. The developer’s decision to implement the project or not, will be based on the
equity IRR.
As returns generated in the future are worth less than returns generated today, a discount can be
applied to future cash flows to present them at their present value. The sum of discounted future
cash flows is termed the net present value (NPV). Investors will seek a positive NPV, assessed using a
discount rate that reflects the WACC and perceived risk levels of the project.
Lenders will be primarily concerned with the ability of the project to meet debt service
requirements. This can be measured by means of the debt service coverage ratio (DSCR), which is
the cash flow available to service debt divided by the debt service requirements. The Average DSCR
represents the average debt serviceability of the project over the debt term. A higher DSCR results in
a higher capacity of the project to service the debt. Minimum DSCR represents the minimum
repayment ability of the project over the debt term. A Minimum DSCR value of less than one
indicates the project is unable to service the debt in at least one year.
Based on assumptions taken and calculations done in financial model following are values of various
financial indicators.
Project Economics
Project IRR
10.99%
Equity IRR
8.78%
Minimum DSCR
0.81
Average DSCR
1.13
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6.6 Sensitivity Analysis
Sensitivity analysis involves changing the inputs in the financial model (such as power tariff,
capital cost, interest rate etc.) to analyze how the value of the project changes (measured using Net
Present Value, Internal Rate of Return, or the Debt Service Cover Ratio). Sensitivity analysis gives
lenders and investors a greater understanding of the effects of changes in inputs on the project’s
profitability and bankability. It helps them understand the key risks associated with the project.
Lenders will conduct sensitivity analysis around the key variables in order to determine whether the
project will be able to service the debt in a bad year, for example if energy yield is lower than
expected, or operational expenditure is higher than expected.
Following sensitivity analysis was done:
6.6.1 Sensitivity Analysis of Capital mix:
The debt to equity ratio has been specified as 70:30 by the regulation issued by Maharashtra
Electricity Regulatory Commission for determination of renewable tariff however; an analysis would
be helpful in understanding the effect on levelised tariff with any modification in this ratio.
Ratio Tariff
(Rs/ kWh)
WACC Project IRR
Debt (%) Equity (%)
100 0 10.75 12.87% 10.95%
80 20 8.77 14.7% 10.97%
70 30 7.79 15.6% 10.99%
50 50 5.86 17.44% 10.89%
30 70 3.96 19.26% 10.66%
20 80 3.02 20.17% 10.54%
0 100 1.15 22.00% 10.19%
Table 1: Debt Equity ratio v/s Levelised Tariff v/s Project IRR
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7 Conclusions and Recommendations
7.1 Conclusions
When the forecasted generation is compared with actual generation for than we find the following
outcomings:-
When blocks with different values are taken than following conclusio comes:-
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The business of solar PV power plant is a profitable one and has project economics which are very
much favourable to the investor. The Project economics have been summarized as below:
Project Economics
Project IRR
10.99%
Equity IRR
8.78%
Minimum DSCR
0.81
Average DSCR
1.13
7.2 Recommendations
Following recommendations are suggested to the company:-
It is seen that forecast is more accurate when we have more past data, So it is recommended to
the company that more past data should be put in the model to get the accurate results.
Some times SCADA readings provide wrong reading. For example in the generation sheet some
time sheet provides high values of frames., sometimes weather monitoring sheet provide 0
values of all the parameters
In the sensitivity analysis, it is very well shown that major factors such as plant capital cost, plant
load factor, etc. all have a direct impact on the tariff. Thus it should be a constant effort from the
producer to cut down on such costs and maximize their profits because the tariff is set by the
regulator and if they can reduce the cost of generation by effective management the extra money
thus made counts for their profit.
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LIST OF REFERENCES
(n.d.). Retrieved july 27, 2012, from central electricity authority: http://cea.nic.in/ (n.d.). Retrieved july 15, 2012, from Indian Renewable Energy Development Agency:
http://ireda.in/
(n.d.). Retrieved july 2012, from Renewable Enenrgy certificate:
http://recindia.nic.in/ (n.d.). Retrieved july 2012, from Electricity Authority Of
India: http://www.eai.in/ Bais, M. P. (2012, june). Moserbaer PVT ltd. (A. Singh,
Interviewer)
central electricity regulatory commission. (n.d.). Retrieved August 2012, from
http://www.cercind.gov.in/
www.accuweather.com I.M.Pandey. (2010). Financial Management. Delhi: UBS. Ministry of new & renewable energy. (n.d.). Retrieved July 22, 2012, from
http://www.mnre.gov.in/
PWC. (2012). Utility Scale Solar Power Plant. IFC www.wikipedia.com www.google.com www.pvtech.com
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ANNEXURES
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