testing strategic models of firm behavior in restructured electricity markets: a case study of ercot...
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Testing Strategic Models of Firm Behavior in Restructured
Electricity Markets:A Case Study of ERCOT
Ali Hortacsu, University of Chicago
Steve Puller, Texas A&M
Motivation• Can we measure how effective bids are at…
– Maximizing profit?– Achieving least cost system dispatch?
• Auction theory has predictions– Can we test if bids are close to “optimal” bids?– Complications: uncertainty and private information
difficult to calculate the equilibrium
• This paper…– Derives model of bidding that analysts can use to
calculate optimal bids– Compares optimal bids to actual bids Technique market monitors can use to evaluate
competitiveness of balancing markets
Texas Electricity Market
• ERCOT is largest grid control area in U.S. – Currently has “excess capacity”
(2002: 77 GW installed capacity vs. peak of 56 GW)
• Market opened August 2001• Bilateral transactions scheduled daily through
the system operator plus a “balancing market”• Players
– Incumbents (e.g. Reliant, TXU…) with implicit vesting contracts to serve non-switching customers at price-to-beat
– Various merchant generators (e.g. Calpine)
Balancing Energy Market
• Bilateral trades scheduled in day-ahead– may be long or short on contract position
• Balancing market is approx. 2-5% of energy traded– “up” bidding price to receive to produce more– “down” bidding price to pay to produce less
• Uniform-price auction using hourly portfolio bids that clear every 15-minute interval
• Bids: monotonic step functions with up to 40 steps• Zonal pricing of congestion – we focus on
uncongested hours
Quantity Traded in Balancing Market
0.0
00
1.0
00
2.0
00
3.0
00
4D
en
sity
-4000 -2000 0 2000 4000Net MW in BES Market
Mean = -257Stdev = 1035Min = -370025th Pctile = -96475th Pctile = 390Max = 2713
Sample: Sept 2001-July 2002, 6:00-6:15pm, weekdays, no transmission congestion
Who are the Players? Generator Average Balancing
Sales** (MWh)% of Installed
Capacity
TXU Electric 156 24
Reliant Energy 473 18
City of San Antonio Public Service * 8
Central Power & Light 28 7
City of Austin 40 6
Calpine 78 5
Lower Colorado River Authority * 4
Lamar Power Partners 23 4
Guadalupe Power Partners 8 2
West Texas Utilities 10 2
Midlothian Energy * 2
Dow Chemical * 1
Brazos Electric Power Coop 5 1
Others * 16* Cannot uniquely identify the bids ** Sales in zones where bids can be uniquely identified
Bidding Incentives• Suppose no further contract obligations
upon entering balancing market• INCremental demand periods
– Bid above MC to raise revenue on inframarginal sales
– Just “monopolist on residual demand”
• DECremental demand periods– Bid below MC to reduce output– Make yourself “short” but drive down the
price of buying your short position
Price
Quantity
RDi(p)
Si (p)
MCi(q)
MRi(p)
QCi
A
B
C
E
D
Overview of Model
• Assume– Static one-shot game– Marginal Costi is public information– Contract quantity (QCi) and price (PCi) are private
information– Generators bid supply functions Si(p,QCi)
• Sources of uncertainty– Total load stochastic (Klemperer & Meyer)– Rivals’ bids S-i(QC-i)Market clearing price is uncertain (application of Wilson’s 1979 share auction)
Solving for Equilibrium Bids
Ex ante problem:
If supply functions take form: Si(p,QCi)=αi(p)+βi(QCi)
Then ex post best response is a (Bayesian Nash) equilibrium
Uncertainty shifts residual demand parallel in & out Can trace out ex post optimal / equilibrium bids
M axS p( )
E [M ark e t p ro fits (p * ) - Im b a lan ce o n C o n trac ts (p * )]p *(S (p ))
p M C S pS p Q C
R D pi i
U nknow n
i
U nknow n
i
i
( ( ) )( )
( )*
*
(" in v erse e lastic ity ru le" )
Do We Expect to See Optimal Bidding?
• First year of market’s operation
• Different levels of sophistication– Some firms hired experienced traders and some didn’t
• Real-time information?– Frequency charts & Genscape sensor data cost data
– Aggregate bids with day or two lag
• Is there enough $$ at stake in balancing market?– Several hundred to several thousand per hour
Data (Sept 2001 thru July 2002)
• Bids– Hourly firm-level (“portfolio”) bids into Balancing
Market
• Marginal Costs for fossil fuel units• Fuel efficiency (“Heat rates”)
• Spot Fuel costs – gas & coal
• Variable O&M
• SO2 permit prices
– Generating unit-level day-ahead scheduled generation
• Periods analyzed: 6:00-6:15pm when no interzonal congestion
Measuring “Residual” Marginal Cost• Use coal and gas-fired generating units that are “on” that hour
and the daily capacity declaration (Nukes, Wind, Hydro may not have ability to INC or DEC)
• Calculate MC (using heat rates, fuel spot prices, VOM similar to Wolfram, BBW, Joskow&Kahn, Mansur…)
• Calculate how much generation from those units is already scheduled == Day-Ahead Schedule
Total MCResidual MC
Day-AheadSchedule
Price
MW
Reliant (biggest seller) Example
TXU (2nd biggest seller) Example
"Bid-Ask" Spread For Largest Sellers
0
10
20
30
40
50
Jul-01 Sep-01 Nov-01 Dec-01 Feb-02 Apr-02 May-02 Jul-02 Sep-02
$/M
Wh
Reliant
Calpine
TXU
Difference betw een INC and DEC bid prices at q=0, excluding hockey stick hours.
Calpine (3rd biggest seller) Example
Guadalupe (small seller) Example
Calculating Deviation from Optimal Profits
(2 ) P ercent A chieved
A ctua l A vo id
O ptim a l A vo id
P ro fit P T C P PC Q CA vo idB A L
A vo idB A L
i0 0( ) ( )
P ro fit P q T C q P PC Q CB A LiB A L
iB A L B A L
i( ) ( )
P ro fit P q T C q P PC Q CE P OiE P O
iE P O E P O
i( ) ( )Optimal
Actual
Avoid
$
(1 ) F o rego ne P ro fits O ptim a l A ctua l
Measures of Foregone ProfitsPercent Achieved
$/MWh $/hour-dayReliant 3.75$ 1,295$ 83%City of Bryan 3.47$ 119$ 66%TXU 3.25$ 2,240$ 45%BP 3.12$ 46$ 43%Mirant 4.82$ 46$ 33%City of Austin 3.76$ 1,111$ 24%Calpine 2.93$ 1,573$ 22%West Texas Utilities 5.29$ 1,593$ 15%Central Power and Light 4.55$ 2,027$ 15%Lamar 5.05$ 1,198$ 8%
Pct Achieved is percent of potential improvement over not bidding.
Foregone Profits
Percent of Potential Gains Achieved vs. Size
Calpine
Bryan
TXU
Austin
Mirant
WTU
Lamar
TXU
Lamar
WTU
Bryan
CalpineAustin Calpine
Lamar
TXU
WTU
Bryan
Austin
WTU
Lamar
TXU
Bryan
CalpineAustinAustin
Reliant
WTU CPLLamar
Calpine
TXU
Bryan
TXU
CalpineAustin
Bryan
WTU CPLLamar
Reliant
Mirant
Austin
TXU
CPL
BP
Calpine
Reliant
Lamar
Bryan
WTU
Reliant
WTU
BP
CPL
Bryan
TXU
Calpine
Lamar
Austin
Bryan
WTUCalpine
Lamar
Reliant
Austin
CPL
TXUTXU
Reliant
Bryan
Calpine
CPLWTU
Austin
Lamar
BP
Mirant
0.1
.2.3
.4.5
.6.7
.8.9
1F
ract
ion
fro
m N
o B
idd
ing
to
Op
tima
l
0 100 200 300 400 500 600 700Absolute Value of Optimal Output
Smaller Players• Appear to bid to “withhold capacity” to avoid the
balancing market productive inefficiencies
• Not market power because markups/markdowns are too large given their small inframarginal sales
• Policy implications:– Fixed costs to participation?
– But some small players are closer to optimal• Sticky market for managerial efficiency?
• Incentives in compensation packages
Possible Explanations for Suboptimal Bidding
1. Not enough $$ at stake avoid the balancing market– Potential profits for each 6-7pm
• Reliant = $6,165• Lamar Power Partners = $1,391• But Bryan = $315!!
2. Learning– Decrease in bid-ask spread– Profitability over time– Use more bid points over time
Learning by Larger Players?
Calpine
TXUAustin
WTULamar
TXU
Lamar
WTUCalpine
Austin
Calpine
Lamar
TXU
WTU
Austin
WTU
Lamar
TXU
Calpine
Austin
Austin
Reliant
WTUCPLLamar
Calpine
TXU
TXUCalpineAustin
WTUCPL
Lamar
Reliant
Austin
TXU
CPL
Calpine
Reliant
Lamar
WTU
Reliant
WTU
CPL
TXUCalpine
Lamar
Austin
WTUCalpine
Lamar
Reliant
AustinCPL
TXU
TXUReliant
Calpine
CPL
WTU
Austin
Lamar
-.5
0.5
1F
ract
ion
fro
m N
o B
idd
ing
to
Op
tima
l
2 4 6 8 10 12Months Between Aug 2001 and July 2002
Average Bid Points Used Per PeriodFor Three Largest Sellers
0
5
10
15
20
25
Sep-01
Oct-01
Nov-01
Dec-01
Jan-02
Feb-02
Mar-02
Apr-02
May-02
Jun-02
Jul-02
Avg
. N
um
ber
of
Bid
Po
ints Reliant
Calpine
TXU
Possible Explanations for Suboptimal Bidding
3. Adjustment costs?• Marginal generating unit most often is gas (very
flexible)• Incremental HR Avg HR “locally”
4. Is transmission congestion important?– We analyze only periods with no interzonal
transmission congestion b/c congestion changes residual demand
– Does congestion “spillover” to uncongested hours?
5. Collusion?• Would be small(!) players – seems unlikely
Conclusions• Market power on DEC side can be inefficient
just as on INC side (“prices can be too low”)• Stakes appear to matter in strategic sophistication• Both sophistication (“market power”) and lack of
sophistication (“avoid the market”) contribute to inefficiency in this market
• Methodology allows calculation of dispatch costs and compare Actual bidding to:
(1) Unilateral Best-Reply (Uniform-price auction)(2) Competitive bidding (Vickrey multiunit auction)(3) "Large Unilateral" and "Small Competitive"