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Summer Internship Report
Regression Analysis of Operational Efficiency
Variables on Management Practices of Thermal Power Plants.
_______________________________________________________________________________
Under the Guidance of
Mr Rajkiran V Bilolikar, Assistant Professor, Energy Area , Administrative Staff College of
India , Hyderabad.
Dr. Manisha, Senior Fellow, National Power Training Institute, Faridabad
At
Administrative Staff College of India, Bella Vista, Hyderabad.
Submitted by
LAXMI SWARUPA
Roll No: 106
MBA (Power Management)
(Under the Ministry of Power, Govt. of India)
Affiliated to
MAHARSHI DAYANAND UNIVERSITY, ROTHAK
August 2013
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DECLARATION
I, Laxmi Swarupa, Roll No: 106 / Semester III/ Class of 2012-14 student of MBA (POWER
MANAGEMENT) at National Power Training Institute, Faridabad hereby declare that the
Summer Training Report entitled -
“Regression Analysis of Operational Efficiency Parameters on Management Practices of
Thermal Power Plants”
is an original work and the same has not been submitted to any other Institute for the
award of any other degree.
A Seminar presentation of the Training Report was made on _______________________
and the suggestions as approved by the faculty were duly incorporated.
Presentation In charge Signature of the Candidate
(Faculty)
Countersigned
Director/Principal of the Institute
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ACKNOWLEDGEMENT
Words can never be enough to express my true regards to all those who have helped me in
completing this project. I take this opportunity to thank all those who have been instrumental
in successful completion of my training.
I am highly obliged to Mr. J.S.S. Rao, Principal Director, Corporate Affairs, NPTI, Mr. S.K
Choudhary, Principal Director (CAMPS) NPTI, Mrs. Manju Mam, Director, NPTI, and
Mrs. Indu Maheshwari, Deputy Director, NPTI who gave me the opportunity to do summer
internship in a pioneer organization like Administrative Staff College of India. I would
again like to thank my internal Guide Dr. Manisha for helping me and guiding me
throughout my project.
I would like to thank Dr. Usha Ramachandran ( Chairperson , Energy Area) who always
took out time from her busy schedule and provided me with excellent insights and
suggestions in my project , which encouraged me for further excellence.
I wish to express my sincere gratitude to my mentor and guide, Mr Rajkiran V Bilolikar
(Assistant Professor , Energy Area), who not only extended his precious guidance and
suggestions but his incredible help coupled with relentless efforts, constructive criticism and
timely disapprobation‟s resulted in this project report. I also thank him for providing me such
a nice opportunity to work with an esteemed organisation like ASCI.
I also thank Dr. Sutanuka Dev Roy (Associate Professor , Centre for Economics and
Finance) for helping me out with my analysis.Without her I would have never gained
confidence in my quantitative analysis.
I place on record my deep sense of gratitude to the management of ASCI for giving me an
opportunity to pursue my summer training in their organization and for their valuable
support. I also thank the whole staff of ASCI for making my stay there pleasurable.
I am grateful to my friends who gave me the moral support in my times of difficulties. Last
but not the least I would like to express my special thanks to my family for their continuous
motivation and support.
Laxmi Swarupa
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TABLE OF CONTENTS
DECLARATION ....................................................................................................................... 1
ACKNOWLEDGEMENT ......................................................................................................... 2
TABLE OF CONTENTS ........................................................................................................... 3
LIST OF TABLES ..................................................................................................................... 5
LIST OF FIGURES ................................................................................................................... 6
LIST OF ABBREVIATIONS .................................................................................................... 7
EXECUTIVE SUMMARY ....................................................................................................... 8
OBJECTIVE .............................................................................................................................. 9
1. INTRODUCTION .............................................................................................................. 9
1.1.INDIAN ELECTRICITY SECTOR – BRIEF HISTORICAL REVIEW ........................ 9
1.2.TECHNOLOGICAL CHANGES .................................................................................. 16
1.3. MAN MW RATIOS DURING VARIOUS PLAN PERIODS ..................................... 21
1.4. CERC NORMS FOR VARIOUS SIZED THERMAL UNITS .................................... 22
1.5. MANAGERIAL EFFICIENCY AND CONCERNED PARAMETERS ..................... 26
1.6. OPERATIONAL PERFORMANCE PARAMETERS AND AWARDS SCHEME .... 27
1.7. ORGANISATIONAL PROFILE .............................................................................. 29
2. LITERATURE REVIEW .................................................................................................... 31
3. RESEARCH METHODOLOGY......................................................................................... 32
1.1. DATA COLLECTION .............................................................................................. 32
3.1.1. IDENTIFICATION AND FILTERATION OF MANAGERIAL AND
OPERATIONAL DATA SETS ....................................................................................... 33
3.1.2. SIGNIFICANCE OF PARAMETERS USED ................................................... 33
3.1.3 DATA SOURCING ................................................................................................. 36
4. DATA ANALYSIS AND INTERPRETATION .............................................................. 36
4.1. QUALITATIVE ANALYSIS ................................................................................... 37
4.2. QUANTITATIVE ANALYSIS USING REGRESSION TOOLS ........................... 39
5. CONCLUSIONS .............................................................................................................. 43
6. RECOMMENDATIONS.................................................................................................. 44
7. LIMITATIONS ................................................................................................................ 45
8. BIBLIOGRAPHY ............................................................................................................ 46
9. ANNEXURES : ................................................................................................................ 47
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Annexure 1 : PLF EXCEL ANALYSIS SHEET ............................................................... 47
Annexure 3 : SHR ANALYSIS EXCEL SHEET ............................................................... 49
Annexure 4 : QUALITATIVE MEASUREMENT DATA SHEET ................................... 50
Annexure 5 : MEASUREMENT SCALES ......................................................................... 50
Annexure 6 : RANK WISE SEGREGATION OF UNITS. ................................................ 53
10. REGRESSION ANALYSIS SHEETS……………………………………………….…..37
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LIST OF TABLES
Table 1 : MAN MW RATIO AT THE END OF VARIOUS PLAN PERIODS (PREDICTED FOR
2012 ) .................................................................................................................................................... 22
Table 2: CERC OPERATING NORMS FOR TPSs. ............................................................................ 23
Table 3 : CRITERIA FOR ASSIGNING AWARDS TO THERMAL POWER PLANTS ................ 29
Table 4 : WEIGHTAGE OF EACH PARAMETER ............................................................................ 29
Table 5 LIST OF UNITS TAKEN UNDER STUDY ........................................................................... 33
Table 6 : RANKING OF PLANTS AFTER QUALITATIVE AND QUANTITATIVE
PARAMETERS WERE TAKEN TOGETHER. .................................................................................. 41
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LIST OF FIGURES
Figure 1 : TRAVELLING THROUGH HISTORY ................................................................................ 9
Figure 2: UNIT SIZES AND THEIR GROSS DESIGN EFFICIENCY .............................................. 14
Figure 3 : TECHNOLOGIES IN THERMAL GENERATION ........................................................... 18
Figure 4 ................................................................................................................................................. 27
Figure 5 : FIVE STEP APPROACH ADOPTED FOR THE PREPARATION OF THE REPORT .... 32
Figure 6 : DATA ANALYSIS STEPS .................................................................................................. 37
Figure 7 : PLF TREND OF TOP PERFORMING UNIT FROM EACH SIZE. ................................... 39
Figure 8 : RANKING OF PLANTS AFTER QUALITATIVE AFTER QUANTITAIVE
PARAMETERS WERE TAKEN TOGETHER. .................................................................................. 41
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LIST OF ABBREVIATIONS
AEC Auxiliary Energy Consumption
ASCI Administrative Staff College of India
BU Billion Units
CERC Central Electricity Regulatory Commission
CEA Central Electricity Authority
SERC State Electricity Regulatory Commission
NTPC National Thermal Power Corporation
GHG Green House Gas
MW Mega Watt
PLF Plant Load Factor
SHR Station Heat Rate
Kwh Kilo Watt Hour
BHEL Bharat Heavy Electrical Ltd
M-BFP Motor driven Boiler Feed Pump
T-BFP Turbine driven Boiler Feed Pump
PAF Plant Availability Factor
NEP Net Electricity Produced
CV Calorific Value
CII Confederation of Indian Industry
TPS Thermal Power Station
QM Quality Management
SM Safety Management
ESP Environment Sensitive Practices
PLC Programmable Logic Controllers
SCADA Supervisory Control and Data Acquisition
System
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EXECUTIVE SUMMARY
Power sector is one of the fastest growing sectors in India , which essentially supports the
economic growth. The power sector needs to grow at the rate of 12% to maintain the GDP
growth of 8%.Presently the energy deficit is about 8.3% and the power shortage during peak
period is about 12.5%.In the present scenario apart from the capacity augmentation, there is
an immense need to improve the performance of generating units. But for improving and then
sustaining plant performance and at the same time meeting the regulatory requirements,
appropriate maintenance activities properly integrated with plant operation activities is
needed.
But this requires thinking in strategic and economic rather than purely technical terms. This
is not easy for power plant operators, which is heavily dominated by Engineers with a
“technical mind-set”. But even before we make these judgements we need to know what are
the factors that affect the plant performance apart from technical aspects of plant Operation
and Maintenance activities.
Plant management practises are not a fixed set of activities that is same at every power plant.
And this is one of the reasons why plant performance varies so much from one to other.The
purpose of this report is to identify those management activities and provide stakeholders and
utilities with information regarding the practices that will result in their plant performance
enhancement.
This project follows this sequence:
Taking sample plant size of each capacity starting from lowest capacity unit present to
highest capacity thermal generating unit present in India.
Collecting their last ten years data of AEC , SHR and PLF and analysing them to find
out is there any trend that is coming out with plant size and its efficiency.
Now some managerial practices were selected and then again comparison was made
to find out if the best performing plant on operational parameters are the ones having
good management practices.
To verify the qualitative analysis results two set of statistical tools were used : first
ranking method was used in which quantitative and qualitative parameters were
merged together by forming a scale and the plants were given ranks.
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Since these ranks and our qualitative analysis results pointed towards the same fact ,
Regression analysis tool was used to provide a quantitative proof for the finding.
At the end key recommendations has been made and limitations of the project has
also been provided with.
OBJECTIVE
Objective of this project is to identify the key practises that will enhance the thermal power
plant performance and the significance of these factors in overall plant performance.Also to
find out how age and plant size affects the operational parameters of the power plant.
1. INTRODUCTION
1.1.INDIAN ELECTRICITY SECTOR – BRIEF HISTORICAL REVIEW
Figure 1 : TRAVELLING THROUGH HISTORY
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When India became independent in 1947, the country had a power generating capacity
of 1,362 MW, growth has been tremendous with present installed capacity being 211766.22
MW1 as on 1
st August 2013. Similarly the size of the generating unit in the country in coal
based power stations has progressively increased from about 15 MW prior to the era of
planned development to 800 MW at present2.
With the introduction of new design of generating units, certain difficulties arose in their
efficient operation and maintenance. The availability of coal in the country is such that the
higher grades of coal, which have higher calorific value, have been exhausted and
progressively lower grades of coal are being made available for electricity generation in the
power stations. This had resulted into operational problems with the boilers designed for
higher grades of coal and also put more pressure on coal handling plants etc.
The all India Thermal PLF which was as low as 27% at the beginning of First Plan,
progressively increased to 47.% by the year 1963-64 and then declined to around 42% by
early seventies. During one year in the seventies i.e. during 1976-77, the PLF touched 55.4%
but this could not be sustained during subsequent years. Several factors such as inadequate
maintenance of generating units, the teething troubles faced in the operation of the newly
introduced 200/210 MW units and the deterioration in the quality of coal supplied to power
stations led to a gradual erosion in the PLF of the thermal power plants during 5th plan
period. During the 6th
Plan, Department of Power and Central Electricity Authority undertook
a comprehensive programme to renovate and modernize old units located in different States.
The performance of 200/210 MW units also begin to stabilize. Concerted efforts were made
by Ministry of Coal to monitor quality of coal supplies to power plants. As a result of all
these measures the PLF of thermal plants registered a gradual improvement during the
7th
plan period. The plant load factor of thermal power stations in the country, which was
only 44.2% in 1980-81, increased to 56.5% by the end of the 7th
Plan. The all India Average
PLF of the Thermal Power Plants has further increased to 64.4% by the end of eighth plan
and in year 2009-10 an avg of 77.5% was reached.
Power Sector is at a crucial juncture of its evolution from a controlled environment to a
competitive, market driven regime which endeavours to provide affordable, reliable and
quality power at reasonable prices to all sectors of the economy. For a developing country
1 Figures taken from Ministry of Power website.
2 Indianpowersector.com
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like India, development of the power sector is very important towards achieving sustainable
growth. The demand for electricity in India is enormous and is growing steadily. The vast
Indian Power Market, today offers highest growth opportunities for private developers and
investors. Since gaining independence from the British rule, the Indian Power Sector has come a
really long way. It has grown many folds in size and capacity. The installed capacity has
increased from a meagre 1362 MW in 1947 to almost 200 GW as on 31st May, 2012.
The demand for electricity in the country has been growing at a rapid rate and is expected to grow
further in the years to come. The energy availability in the country has increased by 6% in 2011-
12, while the peak demand met has increased by 6% in the same period. Despite the increase in
availability, India faced an energy deficit of 10.3% and a peak deficit of 12.9 % in 2011-12.
The average per capita consumption of electricity in India is a mere 478 kWh2 (2010),
compared to the world average of 2,300 kWh. The other comparable countries, like the other
BRIC nations, have significantly higher per capita consumption compared to India. The
average per-capita consumption has grown steadily at 1.3% CAGR annually over the last 10
years.
The power sector in India is basically divided into three categories :
Generation
Transmission
Distribution
GENERATION
The installed capacity of India is 206456 MW as on 31st July, 2011 as per Ministry of Power,
Government of India. It has the fifth largest generation capacity constituting to about 4 % of the
Global Power Generation behind USA, Japan, China & Russia (Former U.S.S.R.) which
contribute to about 49 % of the total Global generation. The generation constitutes of Thermal,
Hydel, Nuclear and Renewable sources of generation. The Generation sector can also be divided
on basis of generators. Generators are divided in three sectors as private , central and state
entities which can be further divided into categories based on type of fuel usage viz. Coal, Oil
and Gas.
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It is estimated that the total capacity demand in the next 20 years will cross 950000 MW
which is close to 5 times the current installed capacity. Hence the government will have to
draw up big capacity addition plans and ensure their implementation. The government had set
an ambitious target of 75 GW in the 12th Five Year plan (2012-17). But looking at the
present scenario of fuel shortage, erratic gas supply and low confidence on nuclear fuels due
to Japan calamity, the government will not be able to achieve the desired target. Therefore, it
is imperative for the government to step up their efforts considerably to get close to that
target.
The government has planned big capacity addition projects like Ultra Mega Power Plants
(UMPPs) and other supercritical projects to achieve the capacity addition targets. Already 4
UMPP‟s are in construction phase and 7 other UMPPs are planned to be commissioned in the
12th plan. The first supercritical unit commissioned in Mundra (Gujarat) in March, 2012.
Many supercritical projects are also expected to start operating by the end of 12th plan. So
targets have been set but it remains to be seen how much is implemented.
TRANSMISSION
The Transmission sector in India has also come a long way since independence. From 3705
ckm in 1950, it is now around 265000 ckm (2011). The country was divided into five
regional grids namely Northern, Southern, North Eastern, Eastern and Western grid. Now the
Northern, North Eastern, Eastern and Western Grid have been merged to form the NEW grid
and hence there are two grids now, NEW and Southern grids. Since the main focus will be on
increasing the installed capacity, investments in the transmission sector are expected to pick up in
the next decade. The government is planning to add inter regional transmission links of about
38,000 MW during 12th plan. Thus inter regional capacity at the end of the 12th plan is expected to
be of order of 63,000 MW. The government should invite private players in the transmission
sector to help achieve this target as the current work is only carried out by public utility PGCIL
and hence is slow. Private players would speed up the work considerably.
DISTRIBUTION
The biggest problem the distribution sector faces is the high amount of AT&C (Aggregate
Technical & Commercial Losses) which are roughly around 33 %. Many reforms have been
introduced in the distribution sector like unbundling of State Electricity Boards to introduction of
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private players and franchisee models to overcome the problem. Although some progress has
been made in reducing the losses, the loss levels are still high and pose a great challenge for the
distribution companies to move forward.
The different bodies concerning the power sector and their functions are as shown in the chart
below :
FUNCTIONS CENTRAL STATE PLANNING & ADVISORY PLANNING COMMISSION,
EGOM, NATIONAL
DEVELOPMENT COUNCIL,
PARLIAMENTARY
COMMITTEE ON ENERGY
STATE GOVERNMENT
CONCERNED MINISTRIES MINISTRY OF POWER, MNRE,
MINISTRY OF COAL,
MINISTRY OF ENVIRONMENT
& FOREST, MINISTRY OF
PETROLEUM & GAS.
STATE GOVERNMENT
AUTHORITY CEA REGIONAL POWER SURVEY
OFFICES (RPSOs)
REGULATION CERC SERCs
GENERATION NTPC, NHPC, NEEPCO, THDC,
NJPC, DVC, NPCIL
STATE UTILITIES, IPPs
SCHEDULING & DESPATCH NLDC, RLDCs SLDCs
TRANSMISSION PGCIL STUs
DISTRIBUTION ------- STATE DISCOMS, PRIVATE
DISCOMS, FRANCHISEES
FINANCE PFC, REC, NATIONALISED
BANKS,PRIVATE BANKS
STATE COOPERATIVE BANKS,
STATE LEVEL PRIVATE BANKS
TRADING PTC, POWER EXCHANGES
(PXIL, IEX)
------
APPLEAL APPELLATE TRIBUNAL,
SUPREME COURT
------
RESEARCH & TRAINING CPRI , NPTI
Table 1:INSTITUTIONAL STRUCTURE OF POWER SECTOR IN INDIA
The table above shows the Indian power sectors post Electricity Act 2003 and reforms.
Looking at it, the Indian Power sector is a well organized structure with different bodies for
different functions. But due to lack of proper coordination between different sectors and
organizations, the set targets are not met and hence the government is always short on the set
targets.
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Figure 2: UNIT SIZES AND THEIR GROSS DESIGN EFFICIENCY3
TYPE OF POWER PLANTS IN INDIA :
Power plants are facilities where power is generated on a large scale. In India the operating
power plants are mainly divided on basis of the method or fuel used. The main power plants
are Thermal Power Plants (Based on Coal, Oil or Gas), Hydro Power Plants & Nuclear Power
Plants.
1. THERMAL POWER PLANTS: - Thermal Power plants are power plants which use
Coal, Gas or Oil as fuel. Thermal Power constitute of about 67% of the total
generation capacity in India. Coal based Thermal Power plants constitute of about
56.8%, Gas based about 9.2% and Oil based about 0.6% out of the total installed
capacity in India. Most of the Coal based Thermal Power plants in operation are based
on Sub-critical Technology. The efficiency of these plants is in the range of 30-35%.
Sub-critical Technology also finds a place in many proposed and upcoming power
projects. Thermal Power projects in India are mainly under NTPC Ltd and State
Utilities. However there are many private thermal power projects which are in
operation and many are in proposition.
3 National Electricity Plan : Volum 1 Generation , January 2012 issue.
-5
5
15
25
35
45
30-50 60-100 210 LMZ 210 KWU 250 500 660 800
GR
OSS
DES
IGN
EFF
ICIE
NC
Y
UNIT SIZE
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2. HYDRO POWER PLANTS: - Hydro Power plants constitute of about 19.03 % of
the total installed capacity in India. In hydroelectric power plants the potential energy
of water due to its high location is converted into electrical energy. The total power
generation capacity of the hydroelectric power plants depends on the head of water
and volume of water flowing towards the water turbine. It is the most widely used
form of renewable energy. Once a hydroelectric complex is constructed, the project
produces no direct waste, and has a considerably lower output level of the greenhouse
gas carbon dioxide (CO2) than fossil fuel powered energy plants. Hydro Power
projects are mainly looked after by National Hydroelectric Power Commission
(NHPC) and state utilities and are mostly located in the northern parts of the country.
3. NUCLEAR POWER PLANTS: - Nuclear Power is the fourth-largest source of
electricity in India after thermal, hydro and renewable sources of electricity. As of
2011, India has 20 nuclear power plants in operation generating 4780 MW while 3
other are under construction and are expected to generate an additional 4800 MW.
India is also involved in the development of fusion reactors through its participation
in the ITER project. Nuclear power projects are looked after by Nuclear Power
Corporation of India Ltd. (NPCIL).
FUEL CONSUMPTION
More than 60 percent of the thermal power generation is coal based and in few cases oil is
used in conjunction as a secondary fuel to facilitate burning or as an alternative option in time
of constrained availability and in few plants due to use of low quality coal.
Limit has been specified in CERC tariff regulations for secondary fuel oil consumption as 1
ml/KWh in case of coal based generating stations. Similarly, for non CFBC based lignite
based generating stations, the limit has been specified as 2 ml/KWh and for CFBC based
lignite generating stations it is 1.25 ml/KWh. Moreover, for DVC generating stations, limits
have specified on particular station basis. Fuel consumption accounts for more than a quarter
of cost of power generated and with constraints in the availability of coal due to inability of
CIL to increase production and growing prices of oil and gases, globally are forcing
developers and government to take necessary steps to enhance plant efficiency and operating
life.
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It is evident from the operational parameters of the generating utilities that there is an urgent
need of further improvement in these parameters, particularly in case of older and inefficient
power plants. R&M of these plants can be considered as it offers significant improvements in
the overall performance, efficiency, expected life span and possess enough advantages as
compared to other alternatives like Greenfield projects or in setting up a new generation
plant.
It is therefore important for the developers to know in detail the overall improvement in
various performance parameters can be achieved by Renovation & Modernisation which
includes R&M, Life Extension (LE), Uprating and newly introduced Energy Efficient R&M.
CONSTRAINTS FACED BY POWER PROJECTS
Although many power projects are operating across the country, the efficiency of these plants
is considerably low (in the range of 28% - 33%). These power projects are also facing many
constraints affecting their performance and operation. Some of the major constraints can be
listed as
1. Operations and Maintenance
2. Renovation and Modernization
3. Issues related to securing of fuel
4. Re-financing of many plants
5. Consistency in supply to the grid
6. Carbon footprints
1.2.TECHNOLOGICAL CHANGES
During the last decade several developments have taken place , which are :
1. Considerable improvements have been made in turbine designs, due to improved
blade profiles thus leading to lower heat rates. For the same steam parameters , the
turbine cycle heat rate have been reduced by 30-40 kcal/kWh as compared to earlier
design due to use of more efficient blade designs.
2. While the turbine cycle heat rate of 210 MW turbine supplied by BHEL earlier were
about 1980 kcal/kWh, the turbine cycle heat rate of 250 MW machines being supplied
now is about 1950 kcal/kWh. Similarly for 500 MW machines, turbine heat rates have
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improved from 1980 kcal/kWh to 1945 kcal/kWh. It may be added here that heat rates
indicated above for 210/250 MW sets are with motor driven BFP whereas same for
500 MW machine corresponds to turbine driven BFP. Thus the heat rates for both
210 and 500 MW units have reduced appreciably.
3. Units from several inter-national manufacturers have been inducted in the Indian
power sector and the share of such units is gradually increasing.
4. Apart from 210/250 & 500 MW units mostly prevailing earlier, units of other sizes
like 300 MW, 600 MW and higher size supercritical units (660 & 800 MW) have
either been introduced or else are in the introduction stage. The turbine heat rate for
300 MW Units is about 1920 kcal/kWh which is much lower than prevailing heat
rates of comparable size units in the country of 250 MW capacity.
5. Even for the same unit size, different steam parameters are being adopted – for
example, instead of 150 kg/cm2 , 535/535˚C parameter normally is being adopted
for 250 MW units, NLC for their recent 250 MW units have adopted 170 kg/cm2
537/537 deg.C steam cycle. Higher reheat temperature of 565 deg.C is being adopted
in some new 500 MW units like Dadri thermal power project of NTPC. The 300 MW
units also have 170 kg/cm2 steam parameters.
6. In the bidding held for 800 MW supercritical units, there was a difference of over 25-
30 kcal/kWh in the guaranteed turbine cycle heat rates quoted by the two bidders.
7. Also , the role of imported coal has taken an important position in indian power sector
with more utilities using imported coal. Imported coal because of their superior
quality lead to a higher boiler efficiency by 2-3 percentage points, thus lowering the
unit heat rate of about 50-75 kcal/kWh.
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Figure 3 : TECHNOLOGIES IN THERMAL GENERATION
SUPERCRITICAL TECHNOLOGY : Super-critical units which provide better plant
efficiency have been operational in the world for the last three decades in various unit sizes
ranging from 500 to 1300 MW. Most of the super critical units are operating in USA, Russia,
Japan, Germany, Italy & South Korea. Design of these units vary from manufacturer to
manufacturer in regard to boiler configuration (two pass or tower type), type of water wall
tubing (vertical or spiral, bare or rifled) start up system etc. So far in India, we do not have
any operating plant with supercritical parameters.
Many units of 660 MW capacity are now planned with supercritical technology at Sipat, Barh
etc. However, with a view to decide on optimal size and supercritical parameters of the
thermal units to be inducted in the Indian power sector based on various technoeconomic
considerations, the “Committee to recommend the next higher unit size for Coal Fired
Thermal Power Stations” was constituted by the Central Electricity Authority with
representatives from utilities, manufacturers, Ministry of Power and Planning Commission.
The Committee in its report has recommended 800- 1000 MW as the next higher size of Unit
with Super Critical parameters in view of the advantages which are brought out in the report.
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CIRCULATING FLUIDISED BED COMBUSTION TECHNOLOGY : CFBC
technology used for power plant applications entails higher cost as compared to PC
technology. However, cost is comparable when PC technology necessitates installation of
FGD system. World over, maximum size of CFBC units is around 300 MW. First 250 MW
unit was installed in France (Gardanne) in 1995. Currently, quite a few 250 MW units are
operating using different fuels viz. coal, lignite, petcoke etc. CFBC technology has
selectively been applied in India for firing lignite.
Following power stations in India have adopted CFBC technology:
* Surat lignite (2x125 MW) - under
operation
* Akrimota (2x125 MW) - under
installation
* Neyveli (2x250 MW) - under installation
INTEGRATED GASIFICATION COMBINED CYCLE : Integrated Gasification
Combined Cycle (IGCC) System is one of the clean coal technologies in which coal is
converted into gaseous fuel which after cleaning is used in CCGT plants. The IGCC systems,
which are commercially available have shown higher efficiencies and exceptionally good
environmental performance in Sox removal, Nox reduction and particulate removal. IGCC, if
commercially proven, will be one of the most attractive power generation technologies for the
21st century. Integrated Gasification Combined Cycle technology is also being considered in
view of the development of advanced gas turbines with very high efficiencies. The Project
Advisory Group set up by MOP with Chairman CEA as its Chairman, submitted its Report
during Jan, 1999 for setting up a 100 MW IGCC demonstration Project either at NTPC Dadri
or at Kaperkheda TPS of MSEB. NTPC Dadri was ranked No. 1 in order of Preference. An
Inter-Ministerial Steering Group under the Chairmanship of Secretary (Power) was
constituted with the approval of Union Minister of State for Power (Independent charge) for
monitoring the IGCC Demonstration Project. The Group which included CEA also
wasintended to establish clearly whether the establishing the economics of the project and for
initiating measures for setting up a demonstration project. The group decided that a 100 MW
IGCC demonstration plant will be set up at NTPC, Auraiya jointly by BHEL and NTPC.
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COMBINED CYCLE TECHNOLOGY : With increased availability of natural gas/ LNG,
it is but natural to install combined cycle plants because of various inherent advantages such
as short gestation period, less space requirement, environment friendly native and easy to
operate & control. There has been a steady progress in combustion turbine technology with
availability of large capacity advanced class higher efficiency gas turbines in the range upto
330 MW ratings and single shaft machines. The performance of gas turbine technology has
improved dramatically over the past twenty years. The thermal efficiency of gas turbine
power systems has more than doubled at the same time reliability has improved and emission
levels have decreased significantly. The thermodynamic improvements that have allowed for
such a dramatic increase in gas turbine efficiency have been identified as rise in Gas Turbines
inlet temperature pressure, multi-pressure steam supply, and optimization of losses.
FUEL CELL TECHNOLOGY : Fuel cells are electro-chemical devices that convert energy
from fuel directly into electricity through electrochemical reactions. These cells normally use
hydrogen directly as fuel or as derived from natural gas or other hydro carbons. About 4-5
major technologies for fuel cells are in various stages of development worldwide. A fuel cell
development programme is under way in India under the ageis of Ministry of Non-
conventional Energy Sources and several organizations like BHEL, SPIC, Indian Institute of
Science, Bangalore, Central Glass and Ceramic Research Institute, Calcutta have undertook
research projects for development of various technologies of fuel cells indigenously. M/s
BHEL are in the process of developing 25 kW fuel cell stack with Phosphoric Acid Fuel Cell
(PAFC) technology. A study to observe performance of imported 200 kW fuel cells stack
under Indian conditions is also in progress at BHEL. M/s SPIC are in the process of
developing 5 KW solid polymer fuel cells stacks. M/s Electrochemical Institute, are engaged
in Molten Carbonate Fuel Cell (MCFC) technology. Project for development of direct
methanol fuel cell is in progress at IISc., Bangalore under a UNDP research programme. Fuel
cell applications include distributed generation in hospitals, airports, research institutes etc.
Apart from power generation, variants of fuel cell also find applications for transport in
electric driven vehicles.
21 | P a g e
1.3. MAN MW RATIOS DURING VARIOUS PLAN PERIODS
While large-scale investments have been planned and numerous projects are underway, the lack of
competent manpower to execute these projects and subsequently operate and maintain them is
already being felt. The scarcity is increasing by the day and unless the Government, industry and all
other stakeholders invest in attracting and training the available talent on an urgent basis, it has the
potential to become a major bottleneck and derail the rapid growth in the sector that has just
begun. This report addresses some of the key human resource challenges in the power sector today
and lays out strategies for attracting fresh talent, retaining existing manpower and creating the
necessary infrastructure for sustained training and development.
The total manpower in the power sector at the end of 10th plan was approximately 9.5 lakhs as per the
report of the Planning Commission‟s Working Group on Power for 11th Plan. Even in a scenario
where employee productivity is projected to increase leading to decreasing Man/MW ratio, it is
estimated that over five lakh technical manpower and 1.5 lakh non-technical manpower need to be
inducted into the sector in the 11th and 12th plan periods. In addition to the technical manpower, tens of
thousands of highly skilled managers will be required in areas such as project planning and
management, project monitoring, project finance, contracts and materials management, human
resources management etc. Further, with increasing focus on energy efficiency and renewable energy,
there is an opportunity to productively engage millions of people to participate in harnessing small
hydro, biomass & biofuels, solar and wind resources, provided they have the appropriate specialised
knowledge. Moreover, demand side management, power trading, carbon credits, smart grids etc. will
also require manpower with specialised training. One of the key hindrances to ensuring adequate
manpower for the sector is the lack of training infrastructure. While infrastructure for Thermal
induction is sufficient, it is grossly inadequate for Hydro and Power System induction. Further
infrastructure for Refresher Training required for updating skills and knowledge is just 3% of the
required capacity and is a key reason for inadequate availability of manpower with right skills and
competencies. Most importantly, there is huge deficit in infrastructure for managerial training, which
currently caters only to 4% of the requirements. This has a significant impact in decision making
capabilities, efficiency and effectiveness of organisations. At a time when the sector is undergoing
rapid growth amidst a changing environment, lack of managerial competencies would hamper the
ability of organisations to adapt and grow. In such a scenario, it is important that managerial talent is
oriented towards commercial, social and environmental aspects of the industry. Some of the strategies
outlined in this report for creating human capital for the power sector are :
• Attract talent by showcasing opportunities, improving brand image and changing the work
environment
22 | P a g e
• Expand training to cover beahavioral & attitudinal changes Strengthen ITIs and other vocational
skill development centres
• Standardise curriculum and develop certification standards
• Expand existing training facilities and create new infrastructure
• Ensure proper utilisation of training funds through direct reimbursements
END OF PLAN PERIOD OVERALL THERMAL
9th 9.42 1.78
10th 7.00 1.44
11th 5.82 1.16
12th 4.93 0.97
Table 2 : MAN MW RATIO AT THE END OF VARIOUS PLAN PERIODS
(PREDICTED FOR 2012 )4
The current norm for operation and maintenance of thermal generation projects is 1.1 Man
per MW.
1.4. CERC NORMS FOR VARIOUS SIZED THERMAL UNITS
Till the entry of central sector in the power generation in 1975, most of the generation was
with the State Electricity Boards (SEBs) which were vertically integrated entities having
generation, transmission and distribution under common fold and unified accounting. Thus,
the issue of transfer pricing from generation to transmission did not exist. With the entry of
Central Public Sector Undertakings (CPSUs) the Central Government had to determine the
tariff of generating stations set up in the central sector and need was felt for prescribing the
normative parameters for generation unit heat rate, secondary fuel consumption, auxiliary
energy consumption to work out the price of power to the beneficiary States. Thus, K.P Rao
committee was set up which prescribed operation norms for the stations under the CPSUs.
With the entry of private sector in power generation, the States started entering into Power
Purchase Agreements (PPAs) with the Independent Power Producers (IPPs). With a view to
maintain uniformity regarding operational parameters in the PPAs and also to guide the
4 Report of Working Group on Power for 12
th Plan.
23 | P a g e
States in this regard, comprehensive financial and operation norms were notified by the
Government of India (GOI) in March, 1992.
UNIT SIZE TARGET PLF/AVAILABILITY
(%)
OHR(Kcal/Kwh) AEC(%) SOC(ml/Kwh)
LESS THAN 200
80 2600 With cooling tower 9% Without cooling tower 8.5%
2.0
200/210/250 80 2500 With cooling tower 9% Without cooling tower 8.5%
2.0
500 MW UNITS WITH
TD-BFP
80 2450 With cooling tower 7.5% Without cooling tower 7%
2.0
500 MW UNITS WITH
MD-BFP
80 2410 With cooling tower 9% Without cooling tower 8.5%
2.0
Table 3: CERC OPERATING NORMS FOR TPSs.5
Later these norms were clarified to be ceiling norms and states could negotiate better norms
with the IPPs. Central Electricity Authority in 1997 prepared operation norms which
prescribed a framework to identify all the site specific and equipment specific factors and
incorporate them in the norms.
These norms specified:-
• Turbine heat rates corresponding to different PLF (100,
80, 60 & 50 percent)
• Working out boiler efficiency based on fuel quality, etc.
These norms were adopted by CERC as draft norms for central sector stations and were
circulated for public comments. Considering the diverse opinion expressed by the generating
utilities, CERC inter-alia directed the prevailing norms of 1992 be allowed for next 3 years
with effect from 1.4.2001.Subsequently in 2004 CERC revised the operating norms and then
again in 2009 , which is prevalent till now.
The Electricity Act 2003 provides that The Central Government shall, from time to time,
prepare the “National Electricity Policy” and “Tariff Policy”, in consultation with the State
5 Norms of Operation for tariff period 2009-14 by CEA.
24 | P a g e
Governments and the Central Electricity Authority (CEA) for development of the power
system based on optimal utilization of resources such as coal, natural gas etc. It also
provides that, the Central Commission, in discharge of its functions shall be guided by the
National Electricity Policy, National Electricity Plan and Tariff Policy. The Tariff Policy
notified by the Central Government provides that The Central Commission would, in
consultation with the Central Electricity Authority, notify operating norms from time to time
for generation and transmission.
The basic objective of operation norms is to lay down the benchmarks or standards of
operation efficiency to be followed by the generating companies (GENCO) for the purpose of
determination of tariff. It is thus an exercise towards balancing the interests of consumers and
the GENCOS allowing for reasonable constraints faced during plant operation.
The tariff policy provides that “Suitable performance norms of operations together with
incentives and dis-incentives would need be evolved along with appropriate arrangement for
sharing the gain of efficient operations with the consumers”. It also provides that “In cases
where operations have been much below the norms for many previous years the initial
starting point in determining the revenue requirements
and the improvement trajectories should be recognized at “relaxed” levels and not the
“desired” levels”.
Thus, in keeping with the objective of the tariff policy, the operation norms should
progressively provide for more efficient operation barring select cases of relaxations where
the desired norms cannot be applied.
The operation efficiency or heat rate and other performance parameters of a thermal power
station depend on a number of factors which can be broadly classified as follows:-
a) Technology and Equipment
b) Ambient conditions
c) Fuel quality
d) Plant operation and maintainance practices.
Thus any benchmarking exercise has to consider these factors fornormative operational
performance. As brought out above at Para 3.1, considerable variations exist in the unit sizes,
steam parameters for similar unit sizes and fuel quality amongst various operating units and
units likely to be inducted in future. Super-critical units of 660/800 MW are being introduced
25 | P a g e
where the heat rates are considerably better than the 500 and 600 MW units and thus, the
present norms of 500 MW units would not be applicable for these units. The benchmarking
exercise has to adequately provide for all these variations.
Possible approaches for specifying operation norms could be :
(i) Uniform single value norm for all stations
(ii) Norm in terms of % of design value
Uniform single value norm for all stations
The single value norms have presently been prescribed by CERC for station heat rate,
auxiliary energy consumption and secondary fuel oil consumption. The single value may be
expressed as either as absolute number as done in case of station heat rate and SFC or as a
percentage as done for AEC. Such norms are appropriate for
parameters like secondary fuel oil consumption (SFC) and auxiliary energy consumption
(AEC) which do not vary significantly with the unit size or other technological parameters.
However, the single value concept has limitations when applied to operating parameters like
the unit heat rate. As explained at Para 3 above, a large variations in heat rate exist due to
different equipment design, steam parameters, design fuel quality etc. Even for same unit size
& steam parameters, the heat rates vary due to improvements affected by the suppliers
progressively over time and therefore, considerable variations exist in heat rates offered by
different manufacturers for same unit size-steam parameters. Also even with the same
turbine generator, the unit heat rate could vary significantly at two different sites due to large
variations in coal quality, cooling water temperature, etc. Thus even with the same equipment
efficiency, a station could have considerably higher design unit heat rate due to site specific
factors beyond his control and the normative heat rate based on single value concept would
provide much lower operational margin to such a station.
Thus, while adopting a single value norm for heat rate covering such large variations,
considerations are invariably required to be made to accommodate the worst combinations of
turbine cycle heat rate, boiler efficiency. This leads to considerable variation in the margin
available to different utilities between the operating heat rate and design heat rate.
Thus, the single value concept provides very high cushion for operational variation (or leads
to high savings to units with lower design heat rates) and leads to undue penalty to those with
higher design heat rates which could be for reasons beyond the control of utility like coal
quality and cooling water temperature. Thus, instead of rewarding operational efficiency,
26 | P a g e
which should be the aim of any good benchmarking exercise, it rewards better designs or
better site inputs where the operator reaps the benefits of intrinsic advantages of the
equipment or site environment or coal quality without major operational efforts. However,
this approach provides incentive to the project developer to go for more efficient design and
technologies which may result in higher capital cost. In the
cost plus approach this will result in higher fixed charges for such units which will be passed
on to the beneficiaries of the project. However, the benefit of higher efficiency in operation
may not be passed on to the beneficiaries and may be retained by the project developer.
Norm in terms of % of design value The other approach could be to specify the normative parameter as a certain percentage above
the design parameters of the unit. The design heat rate indicates the intrinsic capability or the
best achievable efficiency of any generating unit. Such an approach automatically provides
for consideration of variations in design/technology, ambient
conditions and fuel quality in the norms and thus provides more rational basis for operation
norms specially in the developing scenario with large variations in design of the units. It also
provides for incentive to the project developers to achieve better operational efficiencies.
However, in this approach there is no incentive to the developer to adopt more efficient
designs/technologies as the entire benefit of having more
efficient designs/technologies is passed on to the beneficiaries. Thus, it is suggested that
while single value approach may be continued for specifying norms for AEC and SFC, the %
over design approach may be followed for specifying Unit Heat Rate with some benchmark
values for different unit sizes to ensure minimum efficiency standards in the future units by
the project developers.
1.5. MANAGERIAL EFFICIENCY AND CONCERNED PARAMETERS
Managerial Efficiency is the full measure of the combined effect of management, teaming,
and leadership skills on corporate productivity6. In the journal Assessment of Managerial
Efficiency , it is said that “The Managerial Efficiency is a comparative valuation of the
utilized resources versus the results achieved by the managerial activities that is by the
6 Managerial Efficiency : A product of leadership,management and teaming skills , by Simon R Mouer , PHD ,
PE.
27 | P a g e
manager”.It also says that Managerial Efficiency is a comparative or relative factor and in no
way is it an absolute value or a standard of any kind that could be applied in any
circumstances. While talking in context of power plants managerial efficiency can be talked
in terms of how efficient plant maintenance practises are and are they helping the plant
perform better ! It can also be defined as getting the job done using minimum resources to
produce maximum output.
Figure 4
1.6. OPERATIONAL PERFORMANCE PARAMETERS AND AWARDS
SCHEME
The electric power plant efficiency η is defined as the ratio between useful electricity output
from the generating unit, in a specific time unit, and the energy value of the energy source
supplied to the unit, within the same time.
Power station efficiency η = NEP/CV
28 | P a g e
The energy efficiency of thermal power plant is defined as the ratio of saleable energy to the
heating value of the fuel consumed. This overall efficiency of the plant is calculated as the
product of boiler, turbine, generator and the condensed efficiency.
Most of the thermal power plants are 30-40 years old and are operating at a level of 20-35
percent level due to the use of outdated technologies and depreciation of installed equipments
for the operating year‟s .In comparison to thermal plants, the efficiency of hydro power plants
is much higher, being around 85-90 percent.
The efficiency of the hydro power plant is limited due to losses by flow disturbances, friction
and design inefficiencies in turbine and the generator.
There are different technologies that can be pursued to improve the efficiency of thermal
power plants such as supercritical technology, Circulating Fluidized Bed Combustion
(CFBC), Integrated Gasification Combined Cycle (IGCC) but in context to improvement in
the old and efficient thermal and hydro power plants, the best suited alternative
is to increase the overall efficiency by pursuing renovation & modernization (R&M) that will
not only help introducing latest technology but will also uprate the overall generation
capacity of the plant depending upon the plant operating conditions and requirements
This technical efficiency is measured on a number of parameters , which are :
1. Turbine efficiency.
2. Boiler efficiency.
3. Auxiliary Power Consumption.
4. Operating Heat Rate(Station Heat Rate).
5. Plant Load Factor.
6. Secondary Fuel Oil Consumption.
7. Plant Outages(Forced and Scheduled).
8. Plant Availability.
9. Plant Overhauling time.
Time and again MoP has introduced various awards and recognitions for plants which have
been maintaining either CERC norms or even better performance. There are various
parameters on the basis of which plants are awarded which are :
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PARAMETER CRITERIA FOR ASSIGNING MARKS
Peaking PLF National avg. PLF = 0
>=90%PLF=50
STATION HEAT RATE Deviation of 20% and above from design heat rate = 0
Minimum deviation from design heat rate = 0
AUXILIARY ENERGY CONSUMPTION
Compliance with normative value = 0
Maximum improvement from normative value achieved by any station during the year = 20
SPECIFIC SECONDARY FUEL OIL
CONSUMPTION
Compliance with normative value = 0
Maximum improvement from normative value achieved by any station during the year = 15
TABLE 4 : CRITERIA FOR ASSIGNING AWARDS TO THERMAL POWER
PLANTS7
.
TABLE 5 : WEIGHTAGE OF EACH PARAMETER
There are three awards in each category and in total there are twelve awards that are given
each year.
Other than these three , there are various other criteria on the basis of which power plants are
awarded every then and now , like safety management practises , environment sensitive
practises , quality management practises .The purpose of all these awards is to motivate the
plants to work towards greater performance targets and hence help in efficient energy
generation.
1.7. ORGANISATIONAL PROFILE
Administrative Staff College of India (ASCI) was started jointly by the Government of India
and the representatives of industry as an autonomous institute in the year 1956 to impart training
in the field of management development.
7 Comprehensive award scheme for power sector , Ministry of Power , Gov of India
Central Electricity Authority Report.
PARAMETER WEIGHTAGE
PEAKING PLF 50
SHR 15
SFC 15
AEC 20
TOTAL 100
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It is located at the palace of the erstwhile Prince of Berar known as Bella Vista at Hyderabad.
Initially Government Of India envisaged to set up the college in Britain. The first session was to
commence in 1948 at Henley. However a committee of the All India Council for Technical
Education in 1953 recommended that the Administrative Staff College be established in India.
ASCI specialize in training of civil servants and managers of corporate and government sectors
and urban management. The research and consultancy activities of ASCI were started in 1973
with aid from Ford Foundation.
Ever since it was established in 1956, ASCI fuelled the process of professionalizing management,
by synergizing a symbiotic blend of management development [training], consultancy and
research. This unique blend coupled with information technology pursuit is structured to develop
strategic thinking, reformist leadership, and state-of-the-art skills among practicing managers in
India and the developing world. It thus envisages to achieve competitive dominance by
confronting existing and emerging challenges and effectively managing regulatory, government,
commercial and non-commercial organizations.
Over 75,000 participants from industry, government and non-government organizations in India
and the developing world have taken advantage of nearly 200 management development
programs offered by ASCI every year and over 300 organizations have reaped benefits from its
research and consultancy services.
ASCI provides consultancy to industry, business and government in general management as well
as functional and sectoral areas of the management. The objectives of ASCI Consultancy is to
provide professional services for improving management practices in the organizations leading to
improved economic performance and long-term effectiveness.
The ASCI Consultancy Team generally comprises of faculty members representing different
functional and sectoral areas of management and uses a multi-disciplinary approach to problem
solving. Almost all the faculty members in the College are involved in consulting assignments so
that they will get enough opportunities to experiment with new ideas and approaches in achieving
economic performance and long-term effectiveness in problem-solving for the clients. This
approach also provides an opportunity for the faculty to enrich their teaching inputs.
ASCI have undertaken several consultancy assignments for national and international clients. The
principle objective of this service is to provide a multi-disciplinary approach in finding solutions
to serious problems that plague industry, business, and government. Some of the areas in which
consulting assistance has been provided by ASCI include strategic planning, organizational
restructuring, human resources management and development, restructuring, health management,
organization management, forest management, energy management, business process re-
31 | P a g e
engineering and improving of service delivery of various Government institutions. At any point
of time while the number of projects under implementation is around 50, over 1000 assignments
have been carried out since 1965 till date.
The project clientele of ASCI is a virtual “who‟s who” list, comprising such prestigious agencies
and sponsors as the State and Central Governments, their various ministries/departments,
constituent establishments, public enterprises, statutory organizations as well as autonomous
bodies. ASCI is trusted with work by the International agencies too, like the constituent
organizations of the United Nations, the World Bank Institute, Department for International
Development (DfID, UK), the Japan Bank for International Cooperation (JBIC), etc.
2. LITERATURE REVIEW
As per Electricity Act 2003 Section 7 “Any generating company may establish, operate and
maintain a generating station without obtaining a license.” As a result of this we have power
plants in Central, State as well as private ownerships commonly called independent power
producers and therefore different operation and maintenance practises at differently owned
power plants , but not much literature is available on it because power plant performance is
always looked upon from technical perspective only and is measured on the basis of technical
parameters only .In fact , if we say that managerial practises affect the power plant
performance , it will be something new because we don‟t have the supporting data for the
same. So First of all journals and reports were studied to understand Managerial Efficiency
and the factors affecting the managerial efficiency in context of power plants and at the same
time technical efficiency parameters for thermal power plants were studied .To get an
understanding of the operational parameters of power plants CERC‟s “Norms of operation
for the tariff period 2009-14” was thoroughly studied and analysed.Equally gainful insights
were provided from these reports and journals:
“Manual on best practises in Indian Thermal Power Generating Units” by CII.
“Strengthening Operations and Maintenance Practises in State-Sector Coal-Fired
Power Generation Plants in India.” By The World Bank.
Research Paper titled “Assessment of Managerial Efficiency”; by Janis Vanags and
Ineta Giepele : Riga Technical University.
Working paper on “Operations and Maintenance practises and their impact on Power
Plant performance” , AOM submission number 11129.
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Other than these many other journals and reports were studied which can be found in
bibliography.
3. RESEARCH METHODOLOGY
Figure 5 : FIVE STEP APPROACH ADOPTED FOR THE PREPARATION OF THE REPORT
1.1. DATA COLLECTION
Efforts were done to take at least one plant from every unit size ranging from 20 MW to 600
MW and in total 18 units were taken up for study ranging from state , central to privately
owned thermal power plants , which were as follows :
NAME OF THERMAL POWER
PLANT
UNIT NUMBER TAKEN UNDER
STUDY
CAPACITY (MW)
DATE OF COMMISSIONING
CENTRAL/STATE/PRIVATE
PARLI TPS 2 20 1971 STATE
KORBA EAST TPS 1 50 1966 STATE
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HARDUAGANJ TPS 3 55 1972 STATE
PARAS TPS 1 62.5 1967 STATE
PANIPAT TPS 1 110 1979 STATE
NASIK TPS 1 125 1971 STATE
KORADI TPS 1 105 1974 STATE
UNCHAHAR 4 210 1988 CENTRAL
KORBA WEST TPS 3 210 1983 STATE
IB TPS 2 210 1994 STATE
NASIK TPS 4 210 1979 STATE
PANIPAT TPS 5 250 1989 STATE
YAMUNANAGAR TPS
1 300 2008 STATE
RIHAND TPS 2 500 1989 CENTRAL
SIMHADRI TPS 1 500 2002 CENTRAL
TATA TROMBAY TPS
2 500 1984 PRIVATE
RAJIV GANDHI TPS
1 600 2010 PRIVATE
CHANDRAPUR TPS
2 500 1991 STATE
Table 6 LIST OF UNITS TAKEN UNDER STUDY
3.1.1. IDENTIFICATION AND FILTERATION OF MANAGERIAL
AND OPERATIONAL DATA SETS
On the basis of review of literature for Operational parameters , in which total of nine factors
were identified , three were taken as the indicators of overall plant efficiency. These were :
1. Plant Load Factor
2. Operating Heat Rate/ Station Heat Rate
3. Auxiliary Energy Consumption.
Similarly out of twelve Managerial practises identified for a Thermal Power Plant , five of
them were taken as indicators of efficient plant management practises . These were :
1. Quality Management Practises.
2. Safety Management Practises.
3. Environment Sensitive Practises.
4. HR Practises.
5. Surveillance and Control Practises.
3.1.2. SIGNIFICANCE OF PARAMETERS USED
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1. PLANT LOAD FACTOR : Plant Load Factor is a measure of average capacity
utilization. It is the ratio of actual output of a power plant over a period and its output
if it had operated to full capacity over that period of time.
PLF for the thermal plant lies in the range of 65-90 percent and it can be noticed from
the past data that the average PLF for TPP‟s is increasing over the period .Deeper
insights reveal that the PLF for state sector thermal power plants is lower than the
central and private sector power plants by more than 10 percent. PLF of a plant is
affected by the following factors , namely :
Plant conditions.
Operations and Maintenance practises followed at the plant.
Operating availability of plants.
Vintage of Plant equipments.
2. STATION HEAT RATE : Gross Station Heat Rate (SHR) refers to the heat energy
input (in Kcal) required to generate one Kwh of electrical energy at generator
terminals. It is a measure of power plant efficiency and depends upon the plant
design, operating conditions and the level of electric power output. A lower heat rate
corresponds to better plant efficiency.The heat rate of a plant is affected by the
following factors:
Ageing of the plant
Operating load of the plant
Quality of fuel as compared to design quality
Operating parameters like Main Steam Pressure, Main Steam Temp.,
Reheat Steam Pressure, Reheat Steam Temp. etc.
Condenser vacuum as compared to design vacuum
Feed water inlet temperature as compared to original heat balance
Flue gas outlet temperature as compared to design values
Improper combustion in boiler causing more velocity of flue gas, increased
metal temperatures of reheater tubes which calls for increased reheater spray,
increased unburnt coal in ash, erosion of tubes resulting into more number of
tube leakages etc.
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Leakages in boiler area due to improper sealing etc.
Increase in DM water make-up.
Frequent starts / stops.
3. AUXILIARY ENERGY CONSUMPTION :
The auxiliary power consumption of any unit is based on the following operating
conditions of the plant like :
No. of drives in actual operation as compared to designed drives.
Unique drives typical to a particular power plant as compared to other similar
units.
Ageing of the units.
4. QM PRACTISES : Quality management practises lead to better equipment handling
, timely maintenance and servicing of plant auxiliaries and following a standard for
plant operation and maintenance practises which ultimately should lead to better plant
performance. But while applying these practises at a plant , the role of management is
huge and hence this parameter has been taken as an indicator of plant management
efficiency.
5. SM PRACTISES : Safety management practises lead to lesser accidents and is an
indicator of employee care practise as well. Technical staff as well as non-technical
staff will be more confident in day to day plant activities and operations if they are
sure of their safety .This will ultimately lead to a better run plant with total co-
operation of its manpower.
6. ES PRACTISES : Environment sensitive practises include having a green house gas
emission system , regular overhauling and oiling of plant equipments so that they
produce noise to a limited extent , emit lesser environment pollutant
effluents.Implementing theses also need managerial effectiveness and so these has
been included in parameters.
7. SURVEILLANCE AND CONTROL PRACTISES : Having CCTV cameras
installed throughout the plant premises , PLC and SCADA automated plant control
,an updated website and record keeping , all these factors contribute to the overall
36 | P a g e
plant performance and the onus is on the Manager to implement them , so these has
also been included.
8. HR PRACTISES : Having a full fledged HR management team rather than a
personnel management department in the plant premises goes a long way in catering
to the employee needs and job requirements. It also gives them a sense of belonging
to them. Regular training and development activities , employee promotion all these
play a role in employee motivation and hence heir inclination towards helping the
plant perform to its maximum capability.
3.1.3 DATA SOURCING
To carry out the research, data was collected from secondary data sources like Ministry of
Power , Central Electricity Regulatory Commission, Power plant developers, Central Electricity
Authority are few to name and rest of the input sourced from various reports and publications.
4. DATA ANALYSIS AND INTERPRETATION
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Three step data analysis procedure was followed :
Figure 6 : DATA ANALYSIS STEPS
4.1. QUALITATIVE ANALYSIS
1. On dividing these entire set of plants into three categories namely
20-125 MW
150-300 MW
300-600 MW
And then averaging out the PLF , AEC and SHR following trend was observed :
I. PLF has actually an increasing trend with plant size.
II. While the AEC and SHR both were coming down as the plant size
increased.
2. Plants were definitely better in performance with their increasing size , with Central
and private power plants best performing in each category.
QUALITATIVE ANALYSIS BY PLOTTING GRAPHS AND SCATTER PLOTS.
FORMING MEASUREMENT SCALE AND APPLYING RANK
ANALYSIS
REGRESSION ANALYSIS OF OPERATIONAL PARAMETERS ON
MANAGERIAL PARAMETERS AND VINTAGE OF PLANT.
38 | P a g e
3. At the same time most of the units below 210 MW plants have not been able to
achieve , even the specified CERC norms.
4. The fact that same utilities plants are in best performing category definitely points out
that they follow certain practises that others don‟t and now we will move towards
what these practises are and if these practises are having any effect on their
performance using quantitative tools.
5. We had plants whose age had crossed 30 years like Parli Thermal Power station and
Paras Thermal power station , which have been shut down now , but at the time they
were functional they were performing good when compared to their peer group , when
we tried to find out the reasons , we came across this in one of the reports :
“Dedicated work force of the power station has resulted into a sustained performance of
this power station which is considered to be one of the vintage power stations of
Maharashtra”8
This again pointed towards the fact that the Plant maintenance activities definitely have
significant impact on plant „s operational performance and so we moved towards the
statistical tools to verify the same .
8 Report by MECON ltd titled “Report on achievable Heat Rate and Auxiliary Power Consumption of Thermal
Power Stations” page no 68 .
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Figure 7 : PLF TREND OF TOP PERFORMING UNIT FROM EACH SIZE.
4.2. QUANTITATIVE ANALYSIS USING REGRESSION TOOLS
First step in quantitative analysis was scale formation for each of the operational and
managerial parameters selected. Scale used can be found in Annexure : 5 . Each parameter
was measured on a four point scale . While the operational parameters were split on the
numeric scale , we used point method to form scale for managerial parameters , as explained
below :
1. HR Practises : Four parameters were identified and defined for the same which were
as follows :
Whether the plant was following any HR practise.
ERP SAP was implemented or not.
Training and development activities.
There were many other parameters that could have been taken for scale formation , but for
that lot of time was required , and the project was to be completed in two months span only ,
so we have to suffice with these three.
0
20
40
60
80
100
120
2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12
KORADI TPS ( 105 MW UNIT) KORBA E TPS ( 210 MW MW UNIT)
NTPC SIMHADRI (500 MW U2)
40 | P a g e
For the HR practises prevalent in a plant it was assumed that if a plant has an employee
award system , medical and housing facilities , promotion schemes , any of these pointed
towards an active HR system present in a plant.
Similarly training and development activities were thought to be prerogative of a competent
HR system and so this was also taken as one of the parameters. In a firm it is the HR who
identifies training needs of the organisation and makes arrangement for the same.
Based on the presence of these three, plant was given one point for each and for those having
none of these , zero point was allotted.
2. QM , SM , ES Practises : For these three , following criteria was taken :
If plant is ISO certified for Quality , Safety as well as environment.
If the plant has received any award for the same.
If the plant is having any extra certification.
Point allotment system was same as followed in previous two.
Only an active and efficient management will be willing to take pains to get its plant
registered for awards. Since most of the award schemes require the plants to themselves
apply for the same and in this research it was assumed that if a plant gets any award for any
of the specified practises it tells two things, one that it is really performing well and the other
was that its management is willing to get its plants recognitions which also tells its dedication
towards its plant. And so this was taken as one of the indicators in all three categories i.e.
QM , SM ,ES .
Similarly for the extra certification.
3. SURVEILLANCE AND CONTROL PRACTISES : Following three parameters
were taken as possible indicators for Surveillance and Control :
PLC SCADA AUTOMATED Control system.
CCTV Surveillance system.
Plant specific website.
PLC SCADA control though turned out to be more of a technical factor but still it was kept
on the managerial control side because it was assumed that its implementation is actually
result of the management‟s awareness and inclination towards it.
41 | P a g e
CCTV surveillance at plant is solely management‟s discretion and is not related to technical
aspects even minutely. So this was taken as one of the indicators of control practises.
Plant specific website was taken as one of the indicators because having a plant website and
data updated on it , creates a knowledge bank for the study and research purposes and this
also is on the management‟s discretion.
RANKS: Based on the sum of qualitative and quantitative parameters(can be found in
Annexure :6 ) obtained from applying the measurement scale ranks were defined for each of
the units.For units obtaining same ranks , the one with more vintage was given a higher rank.
This time , analysis revealed following facts :
THERMAL POWER STATION RANK
TROMBAY TPS , U-2, 500 MW , 1984 1
SIMHADRI TPS , U-1 , 500 MW -2002 2
RIHAND TPS , U-1 , 500 MW -1989 3
UNCHAHR TPS U2 , 210 MW - 1988 4
PANIPAT TPS , U-5 , 250 MW 5
IB TPS , U-2 , 210 MW - 1994 6
YAMUNANAGAR TPS U-1 , 300 MW - 2008 7
NASIK TPS , U -4 , 210 MW 8
CHANDRAPUR , U-2 , 500 MW -1991 9
KORADI TPS , U-1 , 105 MW , 1974 10
NASIK TPS , U-1, 125 MW - 1971 12
PARLI TPS U-1 , 20 MW -1971 13
KORBA WEST TPS , U-2, 210 MW , 1983 14
KORBA EAST TPS , U-1 , 50 MW - 1966 15
HARDUAGANJ TPS , U-3 , 55 MW-1972 16
PARAS TPS , 62.5 MW - 1967 17
PANIPAT TPS , U-1,110 MW - 1979 18
Table 7 : RANKING OF PLANTS AFTER QUALITATIVE AND QUANTITATIVE
PARAMETERS WERE TAKEN TOGETHER.
Again same pattern was observed, private plant came out to be best performing plant
followed by central and state generating units.
Qualitative and quantitative analysis were actually giving the same results , but we
still needed some tool which can further verify our findings and can provide a
relationship between managerial and operational parameters and that is where the
project moved towards the regression analysis.
42 | P a g e
REGRESSION ANALYSIS :
1. PLF , AEC AND SHR were averaged out for each of the units.
2. For the units not having any of the three operative variable data , proxy data was used.
e.g. For PLF , PAF was used as a proxy variable , for SHR Specific secondary fuel
oil consumption was used as a variable and for AEC , missing data were replaced with
the average AEC for the unit.
3. For the first part of the regression analysis , operative variables were taken as the
Y(dependent) variable and were regressed on the managerial parameters which were
assumed as X (constant) attributes.
4. Each of the operational variables were first regressed on individual managerial
parameters but it gave no significant result.
5. After that each of the operational parameter was regressed on all the managerial
parameters taken together( result can be found in Annexure : 8,9 and 10) and
following results were observed :
The regression output for PLF due to Managerial factors attributed 11% of the
variability in PLF due to managerial parameters but this was negated because
the alpha value was too high.
The regression output for SHR due to Managerial factors attributed 55.6 % of
the variability in SHR due to Managerial practices and alpha value for this
result was 0.0009 which is less than 0.05, so we can rely on this output and
can say that SHR is affected a great deal by Managerial Practises. But the
question is Why I said great deal and the answer goes like this, because there
are lot of other factors which affect the SHR and since this model have not
been able to take all of them into consideration , we can interpret from the
result that SHR is affected a great deal by Managerial Practises but then , there
might be other factors as well and they might have a diminishing as well as an
enhancing effect on the PLF.
The regression output for AEC due to Managerial Practices attributed 37.2%
of the variability in AEC due to Managerial Practises and alpha value for this
result was 0.02 which again was less than 0.05 i.e. we can rely on this output.
But again there care also lot of other factors which may be the reason behind
43 | P a g e
variability of AEC . We have not been able to take all of them and so we can
say that Managerial Practises have significant effect on the AEC but since our
model is limited , the same can get diminished or enhanced if we assume other
factors as well ; but still the effect will be significant.
6. Afterwards each of the operational parameters were again taken as variables (Y) and
regressed on vintage of the plants i.e. plant age(X).Following were the observations :
Regression output predicts the variability in PLF due to vintage of the plant to
about 7.9% but again this is negated because of high p value.
About 57% of the variability in SHR can be attributed to vintage , p value for
the same is 0.003 which again is less than 0.05 and hence we can rely on this
result.But like we said in case of managerial practices , there are other
variables as well which can have effect on this result. Since we have not been
able to take all the variables into this model , so the result has limitations and
we can say that vintage of the plant has a significant effect on the SHR of the
plant but quantifying it needs more indepth analysis.
Variability in AEC due to vintage of the plant amounts to about 21% as per
the results but again due to high p value this is negated.
5. CONCLUSIONS
1. Managerial practices have a significant effect on Auxiliary Power Consumption as
well as on Station Heat Rate but in case of PLF , there is a relationship between the
two which can be found out by further analysis.
2. Vintage of the plant does lead to higher Station Heat Rate but in case of AEC and
PLF there is a relationship which leaves scope for further analysis. .
3. Lesser capacity units are on the lower side of the performance benchmarks so are the
state GENCOs as compared to Central and Private generating units.
4. Of the technical reasons, low quality of coal has come out to be the major reason
behind non-performance of the utilities.
44 | P a g e
6. RECOMMENDATIONS
1. A power plant has always been looked on from a technical perspective , but the
results of the analysis confirm that managerial practises does lead to a more
technically efficient plant. So the report suggest that certain maintenance practises
be made compulsory in power plants just like we have technical norms , we should
have management/maintenance norms as well which should be reviewed on an
annual basis.
2. Most difficult part of this report was getting the data for individual units. While we
have data for whole of the power station in CEA‟s annual reviews, there is hardly
any platform from where we can get the data for individual units. This despite the
fact that performance of a non-performing unit can be easily hidden with high
performing units of a power station when the result is collective rather than
segregated .This will not only help the plants to discipline its non-performing units
but will also help in research purposes. So I recommend a system where individual
unit data should also be taken from the power generating utilities.
3. For future purposes instead of setting up a below 200 MW Thermal power
generating unit , same can be done with other better efficient technologies like
IGCC , CFBT etc.
4. There are many other variables when it comes to Management practises in a power
plant like Manpower strength , number of permanent and contractual employees ,
employee job satisfaction , leadership skills of the plant manager ,strategic planning
, knowledge management etc which can be taken up for further study.
5. Forced outages , Plant availability and Planned outages are also indicators of an
efficient plant but because CEA has put more emphasis on PLF and AEC , therefore
maximum plants have released their PLF , AEC and SHR data but the other data are
more or less unavailable despite being significant indicators of plant management
efficiency.I therefore recommend a platform where plants are required to submit
individual unit wise details for the same.
45 | P a g e
6. As the report suggest that the vintage of the plant has an effect on the performance
parameter of the plant . Therefore plants should be subjected to timely renovation
and modernization practises.
7. LIMITATIONS
1. Though the results are significant but the model is limited in the sense that for each
operational variable considered there are many other variables which affect them and
which have not been taken into this model because of time consideration and
unavailability of data.
2. Presence of other variables may diminish or enhance the correlation obtained but still
it will be significant, and that‟s why this model is correct but it calls for further study
and we may get more significant results.
3. Logic tells that Plant Availability Factor and Forced Outages should be more
dependent on plant management practises but due to data unavailability , these two
could not be taken into study.
4. All the data have been taken through various reports and data released by utilities and
CEA from time to time , and therefore data is limited. A much better approach could
have been Questionnaire survey taken from all the utilities and then applying the tools
which would have given more accurate and much more reliable results.
46 | P a g e
8. BIBLIOGRAPHY
1. Sustainable Energy Supply in Asia ; Volume 1 : Pradeep Chaturvedi
2. India Power Projects ; Volume 1 : Dimple Shahi Bath
3. Power Management in India : A.K Sah
4. Statistics for Management : Richard I Lewis
5. Operations Research Techniques for Management : B. Banerjee
6. Statistical Methods : S.P Gupta
7. Operations and Maintenance Practices and their Impact on Power Plant Performance : AOM
Submission No 11129
8. Growth of Electricity Sector in India 1947-2011 : GoI , MoP , CEA JUNE 2011
9. Performance of Generating Plant : Managing the Changes : Report by World Energy Council ,
2007.
10. Maintenance Work Management : Best Practices Guidelines by Electric Power Research
Institute.
11. www.mahagenco.in
12. www.mercindia.org.in
13. www.uprvunl.org
14. www.uperc.org.in
15. www.ntpc.co.in
16. www.cseb.gov.in
17. www.cserc.gov.in
18. www.wbpdcl.co.in
19. www.tatapower.com
20. www.planningcommission.nic.in
21. www.cea.gov.in
22. www.cercind.gov.in
23. www.powermin.gov.in
24. http://hpgcl.gov.in
25. www.indiastats.co.in
47 | P a g e
9. ANNEXURES :
Annexure 1 : PLF EXCEL ANALYSIS SHEET
Annexure 2 : AEC EXCEL ANALYSIS SHEET
PLFs
KORBA
50 MW
HARDUA
GANJ 55
MW
PARAS
62.5 MW
KORADI
105 MW
PANIPAT
110 MW
NASIK
125 MW
UNCHAH
AR 210
MW
KORBA
210 MW
IB TPS
210 MW
NASIK
210 MW
PANIPAT
250 MW
YAMUNA
NAGAR
300 MW
RIHAND
500 MW
SIMHADR
I 500 MW
TROMBA
Y 500
MW
CHANDR
APUR 500
MW
2001-02 81.29 52.25 75.34 59.7 30.77 70 78.69 70.64 85.15 - 75.68
2002-03 91.03 53.19 71.68 64.3 58.09 67.75 74.57 71.24 83.13 87.05 63.76
2003-04 46.44 37.13 58.65 59.1 63.09 65.54 83.58 75.44 81.6 65.32 88.48 87.21 80.98 75.89
2004-05 93.16 31.83 82.83 64.56 52.09 72.5 87.44 73.88 86.04 86.25 79.76 90.58 87.91 92.56 62.48
2005-06 92.15 24.71 77.37 77.76 59.4 60.09 92.16 83.96 84.12 83.07 79.73 91.19 92.73 81.27
2006-07 95.57 16.5 94.39 64.5 62.63 68.04 95.69 84.62 90.18 84.47 91.55 84.86 88.37 86.0 78.32
2007-08 85.82 27.85 83.65 72.6 25.29 95.54 84.5 82.6 84.31 96.23 91.9 92.1 89.0
2008-09 82.07 35.31 55.5 28.94 90.88 90.45 86.72 76.12 94.27 69.05 81.4 89.66 91.0
2009-10 80.63 36.13 43.3 79.08 93.14 80.48 84.17 79.06 81.35 89 97.27 92.0
2010-11 73.3 32.82 48.9 90.95 86.56 63 83.91 73.85 93.15 96.08
2011-12 77.93 35.31 79.18 92.14 79.57 70.8 79.52 72.63 92.09 92.78
Avg PLF 81.76 34.82 77.70 62.37 53.41 67.32 90.88 83.85 81.80 78.71 85.50 74.22 89.18 91.57 86.78 72.34
RED- LOWEST PLF
GREEN- SECOND LOWEST PLF
GREEN FILL - PLF BELOW CERC NORM
RED FILL- AVG PLF BELOW CERC NORMS
BLUE FILL - CENTRAL RUN TPS
48 | P a g e
AEC ParliKORBA
50 MW
HARDUA
GANJ 55
MW
PARAS
62.5 MW
KORADI
105 MW
PANIPAT
110 MW
NASIK
125 MW
UNCHAH
AR 210
MW
KORBA
210 MW
IB TPS
210 MW
NASIK
210 MW
PANIPAT
250 MW
YAMUNA
NAGAR
300 MW
RIHAND
500 MW
SIMHADR
I 500 MW
TROMBA
Y 500
MW
CHANDR
APUR 500
MW
2000-01 12.48 15.89 9.94 12.69 9.74 10.58 7.25 - 5.7
2001-02 12.58 17.03 10.34 12.39 8.6 9.67 10.69 7.7 - 5.2 6.99
2002-03 9.25 12.76 16.99 10.8 9.55 11.66 8.55 9.41 11.06 9.12 8.7 10 5.4 6.93
2003-04 10.07 11.44 15.89 10.32 9.83 11.05 8.15 8.76 9.43 10.94 8.99 8.03 6.01 5.04 7.1
2004-05 9.2 11.51 16.68 10.5 9.42 12.13 8.57 8.93 9.99 10.34 9.65 9.21 7.65 6.18 5.3 6.6
2005-06 8.94 10.3 15.12 9.58 9.35 11.75 8.11 8.68 9.71 10.23 9.16 8.9 7.98 5.65 5 7.1
2006-07 9.13 10.18 16.67 10.22 9.6 11.59 8.4 8.48 9.29 10.15 9.61 9.26 7.3 5.65 5.12
2007-08 10.16 18.46 12.13 8.34 9.12 10.11 9.34 6.49 5.77 5.14
2008-09 10.8 11.3 8.62 8.63 10.34 9.87 9.33 7.32 7.5 5.16
2009-10 11.4 10.37 8.72 10.44 10.77 9.29 5.5
2010-11 - 11.44 10.71 8.55 10.51 10.49 9.73 6
2011-12 - 12.04 11.38 10.04 10.44 11.6 9.49
Avg AEC 9.3 11.4 16.6 10.2 9.6 11.6 8.6 9.4 10.5 9.3 9.9 9.5 7.6 6.6 5.3 6.9
GREEN FILL- CERC NORMS ACHIEVED
RED FILL- CERC NORMS NOT ACHIEVED
RED- HIGHEST AEC
GREEN - SECOND HIGHEST AEC
PINK FILL-LOWEST PLF AND HIGHEST AEC
SKY BLUE FILL- PLF AND AEC BOTH NOT AS PER CERC NORM
49 | P a g e
Annexure 3 : SHR ANALYSIS EXCEL SHEET
ParliKORBA
50 MW
PARAS
62.5 MW
KORADI
105 MW
PANIPAT
110 MW
NASIK
125 MW
UNCHAH
AR 210
MW
KORBA
210 MW
NASIK
210 MW
PANIPAT
250 MW
YAMUNA
NAGAR
300 MW
RIHAND
500 MW
SIMHADRI
500 MW
TROMBA
Y 500
MW
CHANDR
APUR 500
MW
2000-01 - 3177.4 3642 2650 2503 2459
2001-02 3366 2833 3847 2497 2761 2553 2451 2494
2002-03 3194 - 3271 2967 3741 2528 2459 2896 2561 2392 2438 2403 2508
2003-04 3210 3007 3121 3002 3497 2589 2458 2577 2385 2404 2428 2544
2004-05 3312 2918 3340 2995 3554 2703 2451 2592 2973 2376 2375 2414 2553
2005-06 3185 - 3197 3046 3508 2720 2430 2645 2758 2349 2361 2469 2606
2006-07 3264 - 3260 3342 2410 2827 2705 2358 2355 2456
2007-08 - 3480 2440 2674 2720 2450 2369 2378 2458
2008-09 - 3480 2653 2689 2387 2450
2009-10 - 3047 2780 2810 2479 2424 2554
2010-11 - - 3112 2675 2406 2512
2011-12 - - 2886
Avg SHR 3233.0 2962.5 3247.5 2968.6 3428.0 2614.5 2441.3 2727.7 2585.6 2761.4 2430.5 2371.5 2390.7 2459.5 2541.0
GREEN FILL - CERC NORM ACHIEVED
RED - LOWEST SHR
50 | P a g e
Annexure 4 : QUALITATIVE MEASUREMENT DATA SHEET
Annexure 5 : MEASUREMENT SCALES
SiteCCTV
cameras
PLC/SCA
DA
Control
HR Policy ERP SAP T&D QMPQM
AwardsQA EC SM P
SM
AwardsSM EC EM P
EM
AwardsEM EC
Y N N N N(2010) N Y N N Y N N Y N N
N N N N N N Y N N Y N N Y N N
0 0 0 0 0 0 1 0 0 1 0 0 1 0 0
N N N y N Y N N N N N N
0 0 0 1 0 1 0 0 0 0 0 0 0 0 0
N N N Y N(2010) N N N N N N N
0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
N N(2011) N Y N Y Y N N Y N N Y N N
0 0 0 1 0 1 1 0 0 1 0 0 1 0 0
Y N Nov-11 N N(2010) Y Y N N Y N N Y N N
1 0 0 0 0 1 1 0 0 1 0 0 1 0 0
N (N)2011 N Y N N Y N N Y N N Y N N
0 0 0 1 0 0 1 0 0 1 0 0 1 0 0
Y N N Y N(2010) Y Y N Y Y N N Y N N
1 0 0 1 0 1 1 0 1 1 0 0 1 0 0
N N Y Y Y Y Y N N Y N Y N N
0 0 1 1 1 1 1 0 0 1 0 0 1 0 0
2012 Y N Y 2008 N Y Y N Y Y N Y Y N
0 1 0 1 0 0 1 1 0 1 1 0 1 1 0
Y N Nov-11 Y N N Y Y N Y N N Y N N
1 0 0 1 0 0 1 1 0 1 0 0 1 0 0
N Y Y Y Y N Y N N Y N N Y N N
0 1 1 1 1 0 1 0 0 1 0 0 1 0 0
N Y Y Y Y Y Y Y N Y Y N Y Y N
0 1 1 1 1 1 1 1 0 1 1 0 1 1 0
N Y Y Y Y Y Y Y N Y Y N Y Y N
0 1 1 1 1 1 1 1 0 1 1 0 1 1 0
N Y Y Y Y Y Y Y Y Y Y Y Y Y Y
0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
N N Nov-11 Y N Y Y N N Y N N Y N N
0 0 0 1 0 1 1 0 0 1 0 0 1 0 0
N Y Y Y
0 1 1 1 0 0 0 0 0 0 0 0 0 0 0
0 00 0 0 0 01 1 10
KORBA EAST THERMAL POWER
STATION UNIT 1 50 MW
HARDUAGANJ THERMAL
POWER STATION 55 MW(U-3) -
PARAS THERMAL POWER
STATION 62.5 MW
PANIPAT THERMAL POWER
STATION 110 MW(U1) - 1979
PARLI THERMAL POWER
STATION 20 MW (U1,2) - 1971 1 0 0 0
NASIK TPS U 1 125 MW - 1971
PANIPAT TPS 250 MW UNIT
YAMUNANAGAR THERMAL
POWER STATION 2*300 MW -
KORADI THERMAL POWER
STATION 105MW(U-1)
UNCHAHAR THERMAL POWER
STATION 5*210 MW
IB TPS 2*210 MW
NASIK THERMAL POWER
STATION 210 MW(U-4)
RIHAND THERMAL POWER
STATION 4*500 MW(Rs. 23900
SIMHADRI THERMAL POWER
STATION 2*500 MW
TROMBAY TPS U 5 - 500 MW
CHANDRAPUR THERMAL
POWER STATION 500 MW (U-
RAJIV GANDHI THERMAL
POWER PROJECT 2*600 MW
PLF SCALE :
VALUE RANK/POINTS
0-49.9 0
50-79.9 1
80-89.9 2
90+ 3
51 | P a g e
PLANT SURVELLIANCE SCALE :
THINGS TO BE CONSIDERED : 1.CCTV SURVELLIANCE 2.PLC/SCADA CONTROL
SHR SCALE :
VALUE RANK/POINTS
20-125 MW>3500 0
3500-2999 1
3000-2599 2
<2600 3
VALUE RANK/POINTS
200-300 MW >3500 0
3500-2999 1
3000-2499 2
<2500 3
VALUE RANK/POINTS
500 MW and above >3000 0
3000-2699 1
2700-2449 2
>2450 3
AEC SCALE :
VALUE RANK/POINTS
>11 0
08-11.00 1
8-6.5 2
<6.5 3
QM SCALE : SM SCALE :
CRITERIA RANKS/POINTS CRITERIA RANKS/POINTS
NO QM PRACTISES 0 NO SM PRACTISES 0
QM PRACTISES FOLLOWED 1 QM PRACTISES FOLLOWED 1
AWARDS RECEIVED 2 AWARDS RECEIVED 2
ADDITIONAL CERTIFICATION 3 ADDITIONAL CERTIFICATION 3
52 | P a g e
3.WEBISTE
CRITERIA RANKS/POINTS
NONE 0
ANY ONE 1
ANY TWO 2
ALL THREE 3
ESP SCALE :
CRITERIA RANKS/POINTS
NO ESP PRACTISES 0
QM PRACTISES FOLLOWED 1
AWARDS RECEIVED 2
ADDITIONAL CERTIFICATION 3
53 | P a g e
Annexure 6 : RANK WISE SEGREGATION OF UNITS.
QM SM SURV.. HR ES
AVG.
VALUEPOINTS
AVG.
VALUEPOINTS
AVG.
VALUEPOINTS POINTS POINTS POINTS POINTS POINTSTOTAL POINT RANK PLANT AGE TIEBREAK
PARLI TPS U-1 , 20 MW -1971 83.6 2 3233.0 1 9.3 1 1 1 1 0 1 8 13 41 13
KORBA EAST TPS , U-1 , 50 MW - 1966 81.8 2 3173.6 1 11.4 0 1 1 0 0 1 6 15 46 15
HARDUAGANJ TPS , U-3 , 55 MW-1972 31.7 0 3 14.8 0 0 0 0 2 0 5 16 40 16
PARAS TPS , 62.5 MW - 1967 77.7 1 3247.5 1 10.2 1 0 0 0 1 0 4 17 45 17
PANIPAT TPS , U-1,110 MW - 1979 48.5 0 3519.8 0 11.8 0 0 1 0 2 1 4 17 33 18
NASIK TPS , U-1, 125 MW - 1971 67.3 1 2614.5 2 8.4 1 1 1 1 1 1 9 12 41 12
KORADI TPS , U-1 , 105 MW , 1974 64.1 1 2968.6 2 9.6 1 1 1 1 2 1 10 10 38 10
UNCHAHR TPS U2 , 210 MW - 1988 90.9 3 2441.3 3 8.6 1 1 1 1 3 1 14 4 24 4
KORBA WEST TPS , U-2, 210 MW , 1983 83.8 2 2727.7 2 9.4 1 1 1 0 0 1 8 13 29 14
IB TPS , U-2 , 210 MW - 1994 78.9 1 1.5 2 10.6 1 2 2 1 1 2 12 5 18 6
NASIK TPS , U -4 , 210 MW 77.9 1 2585.6 3 9.3 1 2 1 1 1 1 11 8 40 8
PANIPAT TPS , U-5 , 250 MW 85.5 2 2761.4 2 9.9 1 1 3 0 2 1 12 5 33 5
YAMUNANAGAR TPS U-1 , 300 MW - 2008 74.2 1 2430.5 3 9.5 1 1 1 2 2 1 12 5 4 7
RIHAND TPS , U-1 , 500 MW -1989 89.2 2 2371.5 3 7.6 2 2 2 2 3 2 18 3 23 3
SIMHADRI TPS , U-1 , 500 MW -2002 91.6 3 2390.7 3 6.6 2 2 2 2 3 2 19 2 10 2
TROMBAY TPS , U-2, 500 MW , 1984 87.1 2 2454.2 3 5.3 3 3 3 2 3 3 22 1 28 1
CHANDRAPUR , U-2 , 500 MW -1991 72.3 1 2541.0 3 6.9 2 1 1 0 2 1 11 8 21 9
RAJIV GANDJI TPS , U-1 , 600 MW- 2010 52.2 1 2596.5 3 6.1 3 0 0 2 1 0 10 10 2 11
PLF SHR AEC
54 | P a g e
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.3318192
R Square 0.110104
Adjusted R Square 0.04654
Standard Error 15.668726
Observations 16
ANOVA
df SS MS F Significance F
Regression 1 425.26446 425.2645 1.732175 0.209276311
Residual 14 3437.125848 245.509
Total 15 3862.390308
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Lower 95.0%Upper 95.0%
Intercept 64.642295 8.838353724 7.313839 3.83E-06 45.68591137 83.59868 45.68591 83.59868
14 1.8537747 1.408513764 1.316121 0.209276 -1.167186912 4.874736 -1.16719 4.874736
PLF = 64.64 + 1.85MP
RESIDUAL OUTPUT
ObservationPredicted 87.1411111111111Residuals Standard Residuals
1 85.033816 6.533961751 0.431643
2 85.033816 4.172433974 0.275637
3 77.618717 13.26294927 0.876169
4 77.618717 -29.14071739 -1.92508
5 79.472492 -0.602492051 -0.0398
6 77.618717 -3.398717392 -0.22452
7 75.764943 2.145771552 0.141753
8 73.911168 -1.567168075 -0.10353
9 75.764943 -11.61694273 -0.76743
10 73.911168 -6.591168075 -0.43542
11 72.057393 11.51060658 0.760407
12 70.203619 13.64547215 0.901439
13 70.203619 13.64547215 0.901439
14 68.349844 -36.65117743 -2.42123
15 66.496069 11.20535913 0.740242
16 72.057393 13.44635658 0.888285
PLF and Managerial factors Regression analysis :
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SUMMARY OUTPUT
Regression Statistics
Multiple R 0.610542
R Square 0.372761
Adjusted R Square 0.327958
Standard Error 1.639533
Observations 16
ANOVA
df SS MS F Significance F
Regression 1 22.36486 22.36486 8.320049 0.012005
Residual 14 37.63295 2.688068
Total 15 59.9978
CoefficientsStandard Error t Stat P-value Lower 95%Upper 95%Lower 95.0%Upper 95.0%
Intercept 12.0183 0.924821 12.99527 3.34E-09 10.03476 14.00185 10.03476 14.00185
14 -0.42512 0.147383 -2.88445 0.012005 -0.74122 -0.10901 -0.74122 -0.10901
RESIDUAL OUTPUT
Observation Predicted 5.256ResidualsStandard Residuals
1 7.341995 -0.74699 -0.47161
2 7.341995 0.260227 0.164291
3 9.042471 -0.40747 -0.25725
4 9.042471 0.887529 0.560331
5 8.617352 1.978273 1.248958
6 9.042471 0.417529 0.263602
7 9.467589 -0.16159 -0.10202
8 9.892708 -2.94871 -1.86163
9 9.467589 0.082411 0.052029
10 9.892708 -1.49604 -0.94451
11 10.31783 -0.99983 -0.63123
12 10.74295 -1.38461 -0.87416
13 10.74295 0.68122 0.43008
14 11.16807 3.674876 2.320087
15 11.59318 -1.35033 -0.85251
16 10.31783 1.513506 0.955533
AEC and Managerial parameters Regression analysis :
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SUMMARY OUTPUT
Regression Statistics
Multiple R 0.745804
R Square 0.556224
Adjusted R Square 0.524525
Standard Error 253.147
Observations 16
ANOVA
df SS MS F Significance F
Regression 1 1124499 1124499 17.54742 0.00091
Residual 14 897167.9 64083.42
Total 15 2021667
CoefficientsStandard Error t Stat P-value Lower 95%Upper 95%Lower 95.0%Upper 95.0%
Intercept 3340.814 142.7942 23.39601 1.27E-12 3034.551 3647.077 3034.551 3647.077
14 -95.325 22.75623 -4.18896 0.00091 -144.132 -46.5178 -144.132 -46.5178
RESIDUAL OUTPUT
ObservationPredicted 2454.15384615385ResidualsStandard Residuals
1 2292.239 98.47529 0.402658
2 2292.239 79.26101 0.324092
3 2673.539 -232.206 -0.94947
4 2673.539 87.88947 0.359373
5 2578.214 88.78593 0.363039
6 2673.539 -243.039 -0.99377
7 2768.864 -183.264 -0.74935
8 2864.189 -323.189 -1.3215
9 2768.864 199.7359 0.816704
10 2864.189 -249.689 -1.02096
11 2959.514 273.4858 1.118262
12 3054.839 -327.125 -1.33759
13 3054.839 118.7608 0.485604
14 3150.164 49.83577 0.203775
15 3245.489 1.996455 0.008163
16 2959.514 560.2858 2.290965
SHR and managerial parameters Regression analysis :
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VINTAGE REGRESSION ANALYSIS FOR PLF:
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.282641795
R Square 0.079886384
Adjusted R Square 0.014163983
Standard Error 15.93253166
Observations 16
ANOVA
df SS MS F Significance F
Regression 1 308.5524 308.5523965 1.215512 0.288836
Residual 14 3553.8379 253.8455651
Total 15 3862.3903
Coefficients Standard Error t Stat P-value Lower 95%Upper 95% Lower 95.0%Upper 95.0%
Intercept 85.9716773 10.660405 8.06457922 1.25E-06 63.10738 108.836 63.10738346 108.836
28 -0.359650312 0.3262126 -1.102502726 0.288836 -1.05931 0.340006 -1.059306814 0.340006
RESIDUAL OUTPUT
Observation Predicted 87.1411111111111ResidualsStandard Residuals
1 82.37517419 9.1926036 0.59722139
2 77.69972014 11.50653 0.747551626
3 77.34006983 13.541597 0.879765043
4 74.46286734 -25.98487 -1.688174459
5 79.4979717 -0.627972 -0.040797814
6 84.53307606 -10.31308 -0.670015797
7 71.58566484 6.3250494 0.41092328
8 78.41902076 -6.075021 -0.394679517
9 72.30496547 -8.156965 -0.529938467
10 71.22601453 -3.906015 -0.253764389
11 71.22601453 12.341985 0.801829171
12 75.54181827 8.3072726 0.539703563
13 69.42776297 14.421328 0.93691906
14 71.58566484 -39.887 -2.59136253
15 69.78741329 7.9140153 0.514154577
16 74.10321702 11.400533 0.740665263
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VINTAGE REGRESSION ANALYSIS (SHR) :
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.685724
R Square 0.470218
Adjusted R Square 0.432376
Standard Error 276.5919
Observations 16
ANOVA
df SS MS F Significance F
Regression 1 950623.8 950623.8222 12.42596 0.003364
Residual 14 1071043 76503.07733
Total 15 2021667
CoefficientsStandard Error t Stat P-value Lower 95%Upper 95%Lower 95.0%Upper 95.0%
Intercept 2199.49 185.0667 11.88484943 1.06E-08 1802.562 2596.419 1802.562 2596.419
28 19.96275 5.663116 3.525046856 0.003364 7.816573 32.10892 7.816573 32.10892
RESIDUAL OUTPUT
Observation Predicted 2454.15384615385Residuals Standard Residuals
1 2399.118 -8.40341 -0.031448326
2 2658.633 -287.133 -1.074548193
3 2678.596 -237.263 -0.887915972
4 2838.298 -76.8696 -0.287671379
5 2558.82 108.1803 0.404846543
6 2279.341 151.1588 0.565686199
7 2998 -412.4 -1.54333767
8 2618.708 -77.7079 -0.290808735
9 2958.075 10.52536 0.039389387
10 3017.963 -403.463 -1.509891517
11 3017.963 215.0371 0.804740004
12 2778.41 -50.6956 -0.189719771
13 3117.777 55.82338 0.20890954
14 2998 201.9999 0.755950296
15 3097.814 149.6718 0.560121519
16 2858.261 661.5391 2.475698075
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VINTAGE REGRESSION ANALYSIS (AEC) :
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.461289
R Square 0.212788
Adjusted R Square0.156558
Standard Error1.836748
Observations 16
ANOVA
df SS MS F Significance F
Regression 1 12.76679 12.76679 3.784273378 0.0721
Residual 14 47.23101 3.373644
Total 15 59.9978
CoefficientsStandard Error t Stat P-value Lower 95%Upper 95%Lower 95.0%Upper 95.0%
Intercept 7.409433 1.228962 6.029016 3.09657E-05 4.773571 10.04529 4.773571 10.04529
28 0.073157 0.037607 1.945321 0.0720998 -0.0075 0.153816 -0.0075 0.153816
RESIDUAL OUTPUT
ObservationPredicted 5.256ResidualsStandard Residuals
1 8.141004 -1.546 -0.87125
2 9.092047 -1.48983 -0.83959
3 9.165205 -0.5302 -0.2988
4 9.750462 0.179538 0.101179
5 8.726262 1.869363 1.053479
6 7.702061 1.757939 0.990685
7 10.33572 -1.02972 -0.5803
8 8.945733 -2.00173 -1.12808
9 10.1894 -0.6394 -0.36034
10 10.40888 -2.01221 -1.13398
11 10.40888 -1.09088 -0.61476
12 9.53099 -0.17266 -0.0973
13 10.77466 0.649505 0.366028
14 10.33572 4.507222 2.540042
15 10.7015 -0.45865 -0.25847
16 9.823619 2.007714 1.131446
