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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