energy leader consulting generation evaluator ( ege ) shirley :
as requested , herein is information regarding the meeting with vince kaminski .
the presentation on the ege tool ' s applications and the allegheny energy case study is timed to take an hour . if the meeting is most conveniently scheduled for tuesday , may 29 , might i request it be set for late afternoon ( as my other appointments are the next day ) .
and , as vince will recall , i was co - leader of the energy consulting business of phb hagler bailly , and developer of the ramp up , real time , 75 check and electric strategy tools . presently , i am ceo of energy leader consulting ( elc ) .
background
the u . s . power generation industry has become increasingly efficient in recent years . rapidly growing new entrants seek profit maximization aggressively . utilities , who still control most power plants , endeavor to adopt the entrants ' methods . yet , inefficiency among many utilities remains widespread .
utility inefficiency arises from adherence to decades - old habits and in unit commitment and dispatch and planned maintenance scheduling . many utilities , notwithstanding the industry - wide trend towards profit maximization , cling to ingrained routines .
inefficiency can also arise from the diseconomies of small scale . a utility may operate a relatively small system ( fewer than a dozen plants ) . a small system lacks portfolio diversification and perspective in its focus on its regulated customers , playing the wholesale market at the margin .
for a variety of reasons , utilities are reluctant to cut back the starts of their generating units , let alone shut down any ( even temporarily or seasonally ) . economically inefficient units continue to be committed , week after week , and run in the half - load range .
ege objectives
ege identifies and assesses generating units of a utility with questionable commitment routines . taking into account transmission and reliability factors , the procedure points towards profit opportunities that may be exploited by another industry participant .
i . an industry participant can use ege as a basis for a medium or long - term wholesale power transaction with a utility ; or to price wholesale power more aggressively , to take market share from the utility ( i . e . , compel changes in unit commitment habits ) .
ii . an industry participant can use ege to spot and quantify efficiencies that would come from a merger or acquisition .
iii . a power plant developer can use ege to estimate the incremental value a new plant will enjoy when the target utility ' s unit commitment routines inevitably become rationalized .
specific ege concepts
ege reduces and analyses the extraordinary but unwieldy continuous emission monitoring data base intelligently focusing on profit opportunities .
it produces indicative statistics such as :
a . the frequency distribution of starts per week ;
b . the frequency distribution of starts by day / 15 - minute segment during the week ;
c . the frequency distribution of load level ;
d . the frequency distribution of hours of operation per start ;
e . average heat rate and approximate fully - allocated cost in the half - load range ;
f . average ramp rate from the half - load range ;
g . the frequency distribution of unused connected capacity during the highest demand hours ; and
h . forced - off maintenance outage rate ( where indicated ) .
indicative statistics are generally aggregated by month / year ; in some cases , by temperature range . ( they can be by regional wholesale prices as well . ) ege establishes if the target utility has changed unit commitment routines significantly in recent years .
ege is based upon uniquely timely actual hourly operating data . ege is now updated for the 4 th quarter 2000 ( through december 31 , 2000 ) . ege lst quarter 2000 ( through march 31 , 2001 ) will be available approximately june 15 , 2001 .
ege also compares and ranks generating units ' commitment and dispatch with that of similar units operated by the target utility ( as well as other regional generators ) . some utilities operate a group of economically marginal units at the half - load level for lengthy time periods ( without an apparent reliability basis ) , splitting the limited economic demand for power among the units .
other ege supporting data :
i . planned maintenance schedule ( where indicated ) ;
j . actual maximum generating capacity ;
k . actual minimum generating capacity ( actual maximums and minimums can differ significantly from government - reported values ) ;
l . average heat rate in the full - load range ; and
m . average heat rate in the three - quarter - load range .
with respect to a generating units ' average heat rate in the half - load , three - quarter - load and full - load ranges , it can be instructive to rank these relative to similar generating units within a region . it can also be of interest to identify significant seasonal variations in average heat rates and maximum capacities , and changes in recent years in these parameters .
the real - world example of allegheny energy
allegheny energy can serve as a case study to illustrate the application of ege . in the 4 th quarter 2000 , for instance , one high - cost generating unit was started virtually every weekday morning ( 52 times ) and committed for the whole day ( in all but two cases ) . arguably , there are power products that could substitute for this routine ( in part at least ) at a profit to the seller of the product and allegheny energy .
another high - cost allegheny energy generating unit was started virtually every weekend during the autumn ( nine times ) and committed for most of the coming week . at another plant , two high - cost units were operated too often in the expensive half - load range ( some 550 hours ) and three - quarter - load range ( another 400 to 600 hours ) ; they were seldom called upon to run at higher levels . again , there are power products that that address these practices and might appeal to allegheny energy .
offering of energy leader consulting ( elc )
ege is a procedure , not a software package or data base . elc believes this format is more effective in arming clients with the information they need to act upon profit opportunities .
elc transfers its " knowledge " about the ege procedure and the supporting data methods in a straight - forward four - step process :
1 . enron would select one to three target utilities .
2 . elc would perform the ege procedure on the target utilities .
3 . employing this real - world analysis as a pedagogic tool , elc , in a one - day seminar with enron personnel , would instruct how to perform the procedure in the future ( without the assistance of elc ) .
4 . optionally , elc would provide ege supporting data , quarterly , to enron .
the basic ege supporting data set is national including all generating units under the continuous emission monitoring program ( virtually all fossil fuel units ) . parameters that are incorporated , and the data set format , will be specified upon request . custom modifications will be considered .
steven a . mitnick
chief executive officer
energy leader consulting
4807 41 st street , nw
washington , dc 20016
( 202 ) 997 - 0924 voice
( 202 ) 537 - 0906 fax
smitnick @ energyleader . com
energy leader consulting generation evaluator ( ege )
shirley :
as requested , herein is information regarding the meeting with vince kaminski .
the presentation on the ege tool ' s applications and the allegheny energy case study is timed to take an hour . if the meeting is most conveniently scheduled for tuesday , may 29 , might i request it be set for late afternoon ( as my other appointments are the next day ) .
and , as vince will recall , i was co - leader of the energy consulting business of phb hagler bailly , and developer of the ramp up , real time , 75 check and electric strategy tools . presently , i am ceo of energy leader consulting ( elc ) .
background
the u . s . power generation industry has become increasingly efficient in recent years . rapidly growing new entrants seek profit maximization aggressively . utilities , who still control most power plants , endeavor to adopt the entrants ' methods . yet , inefficiency among many utilities remains widespread .
utility inefficiency arises from adherence to decades - old habits and in unit commitment and dispatch and planned maintenance scheduling . many utilities , notwithstanding the industry - wide trend towards profit maximization , cling to ingrained routines .
inefficiency can also arise from the diseconomies of small scale . a utility may operate a relatively small system ( fewer than a dozen plants ) . a small system lacks portfolio diversification and perspective in its focus on its regulated customers , playing the wholesale market at the margin .
for a variety of reasons , utilities are reluctant to cut back the starts of their generating units , let alone shut down any ( even temporarily or seasonally ) . economically inefficient units continue to be committed , week after week , and run in the half - load range .
ege objectives
ege identifies and assesses generating units of a utility with questionable commitment routines . taking into account transmission and reliability factors , the procedure points towards profit opportunities that may be exploited by another industry participant .
i . an industry participant can use ege as a basis for a medium or long - term wholesale power transaction with a utility ; or to price wholesale power more aggressively , to take market share from the utility ( i . e . , compel changes in unit commitment habits ) .
ii . an industry participant can use ege to spot and quantify efficiencies that would come from a merger or acquisition .
iii . a power plant developer can use ege to estimate the incremental value a new plant will enjoy when the target utility ' s unit commitment routines inevitably become rationalized .
specific ege concepts
ege reduces and analyses the extraordinary but unwieldy continuous emission monitoring data base intelligently focusing on profit opportunities .
it produces indicative statistics such as :
a . the frequency distribution of starts per week ;
b . the frequency distribution of starts by day / 15 - minute segment during the week ;
c . the frequency distribution of load level ;
d . the frequency distribution of hours of operation per start ;
e . average heat rate and approximate fully - allocated cost in the half - load range ;
f . average ramp rate from the half - load range ;
g . the frequency distribution of unused connected capacity during the highest demand hours ; and
h . forced - off maintenance outage rate ( where indicated ) .
indicative statistics are generally aggregated by month / year ; in some cases , by temperature range . ( they can be by regional wholesale prices as well . ) ege establishes if the target utility has changed unit commitment routines significantly in recent years .
ege is based upon uniquely timely actual hourly operating data . ege is now updated for the 4 th quarter 2000 ( through december 31 , 2000 ) . ege lst quarter 2000 ( through march 31 , 2001 ) will be available approximately june 15 , 2001 .
ege also compares and ranks generating units ' commitment and dispatch with that of similar units operated by the target utility ( as well as other regional generators ) . some utilities operate a group of economically marginal units at the half - load level for lengthy time periods ( without an apparent reliability basis ) , splitting the limited economic demand for power among the units .
other ege supporting data :
i . planned maintenance schedule ( where indicated ) ;
j . actual maximum generating capacity ;
k . actual minimum generating capacity ( actual maximums and minimums can differ significantly from government - reported values ) ;
l . average heat rate in the full - load range ; and
m . average heat rate in the three - quarter - load range .
with respect to a generating units ' average heat rate in the half - load , three - quarter - load and full - load ranges , it can be instructive to rank these relative to similar generating units within a region . it can also be of interest to identify significant seasonal variations in average heat rates and maximum capacities , and changes in recent years in these parameters .
the real - world example of allegheny energy
allegheny energy can serve as a case study to illustrate the application of ege . in the 4 th quarter 2000 , for instance , one high - cost generating unit was started virtually every weekday morning ( 52 times ) and committed for the whole day ( in all but two cases ) . arguably , there are power products that could substitute for this routine ( in part at least ) at a profit to the seller of the product and allegheny energy .
another high - cost allegheny energy generating unit was started virtually every weekend during the autumn ( nine times ) and committed for most of the coming week . at another plant , two high - cost units were operated too often in the expensive half - load range ( some 550 hours ) and three - quarter - load range ( another 400 to 600 hours ) ; they were seldom called upon to run at higher levels . again , there are power products that that address these practices and might appeal to allegheny energy .
offering of energy leader consulting ( elc )
ege is a procedure , not a software package or data base . elc believes this format is more effective in arming clients with the information they need to act upon profit opportunities .
elc transfers its " knowledge " about the ege procedure and the supporting data methods in a straight - forward four - step process :
1 . enron would select one to three target utilities .
2 . elc would perform the ege procedure on the target utilities .
3 . employing this real - world analysis as a pedagogic tool , elc , in a one - day seminar with enron personnel , would instruct how to perform the procedure in the future ( without the assistance of elc ) .
4 . optionally , elc would provide ege supporting data , quarterly , to enron .
the basic ege supporting data set is national including all generating units under the continuous emission monitoring program ( virtually all fossil fuel units ) . parameters that are incorporated , and the data set format , will be specified upon request . custom modifications will be considered .
steven a . mitnick
chief executive officer
energy leader consulting
4807 41 st street , nw
washington , dc 20016
( 202 ) 997 - 0924 voice
( 202 ) 537 - 0906 fax
smitnick @ energyleader . com