ali hortaçsu, university of chicago steve puller, texas a&m
DESCRIPTION
Testing Models of Strategic Bidding in Auctions: A Case Study of the Texas Electricity Spot Market. Ali Hortaçsu, University of Chicago Steve Puller, Texas A&M. Motivations. “Deregulation” of electricity markets Optimal mechanism for procurement? Empirical auction literature - PowerPoint PPT PresentationTRANSCRIPT
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Testing Models of Strategic Bidding in Auctions:
A Case Study of the Texas Electricity Spot Market
Ali Hortaçsu, University of Chicago
Steve Puller, Texas A&M
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Motivations1. “Deregulation” of electricity markets
– Optimal mechanism for procurement?
2. Empirical auction literature– Bid data + equilibrium model valuation– Analog in “New Empirical IO”
• Eqbm (p,q) data + demand elasticity + behavioral assumption MC
– Can equilibrium models be tested?• Laboratory experiments
– Electricity markets are a great place to study firm pricing behavior
– This paper measures deviations from theoretical benchmark & explores reasons
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Texas Electricity Market
• Largest electric grid control area in U.S. (ERCOT)
• Market opened August 2001• Incumbents
– Implicit contracts to serve non-switching customers at regulated price
• Various merchant generators
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Electricity Market Mechanics• Forward contracting
– Generators contract w/ buyers beforehand for a delivery quantity and price
– Day before production: fixed quantities of supply and demand are scheduled w/ grid operator
– (Generators may be net short or long on their contract quantity)
• Spot (balancing) market– Centralized market to balance realized demand with
scheduled supply– Generators submit “supply functions” to increase or
decrease production from day-ahead schedule
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Balancing Energy Market• Spot market run in “real-time” to balance supply
(generation) and demand (load)– Adjusts for demand and cost shocks (e.g. weather, plant
outage)
• Approx 2-5% of energy traded (“up” and “down”)– “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 “elbow points” (20 up and 20 down)
• Market separated into zones if transmission lines congested – we focus on uncongested hours
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Quantity Traded in Balancing Market
Sample: Sept 2001-January 2003, 6:00-6:15pm, weekdays, no transmission congestion
0.0
001
.00
02.0
003
.00
04D
ensi
ty
-4000 -2000 0 2000 4000Net Volume Traded in Balancing Market (MW)
Mean = -24Stdev = 1068Min = -370025th Pctile = -70975th Pctile = 615Max = 2713
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Who are the Players?
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Incentives to Exercise Market Power• 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 (monopsony)
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Price
Quantity
Sio
(p,QCi)
MCi(q)
QCi
Empirical Strategy
A
D
RD1
MR1
B
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Price
Quantity
Sio
(p,QCi)
MCi(q)
QCi
Empirical Strategy
A
C
D
RD1
RD2
MR1MR2
B
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Price
Quantity
Sio
(p,QCi)
MCi(q)
QCi
Empirical Strategy
A
C
D
RD1
RD2
MR1MR2
Sixpo
(p,QCi)
B
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-2000 -1500 -1000 -500 0 500 1000 15005
10
15
20
25
30
35
40
45
50
Balancing Market Quantity (MW)
Pri
ce (
$/M
Wh
)Reliant on June 4, 2002 6:00-6:15pm
Reliant’sResidual Demand
Reliant’sMC
Reliant’sBid Schedule
Ex Post Optimal Bid
Schedule
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Preview of Results
• Largest firm bids close to benchmarks for optimal bidding
• Small firms significantly deviate, but there’s some evidence of improvement over time
• Efficiency losses from “unsophisticated” bidding at least as large as losses from “market power”
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Methods to Test Expected Profit Maximizing Behavior
Difficult to compare actual to ex-ante optimal bids
– Wolak (2000,2001) solving ex-ante optimal bid strategy (under equilibrium beliefs about uncertainty) is computationally difficult
Options1) Restrict economic environment so ex-post optimal =
ex-ante optimal• Intuitively, uncertainty and private information shift
RD in parallel fashion2) Check (local) optimality of observed bids (Wolak,
2001)• Do bids violate F.O.C. of Eε[π(p,ε)]?
3) Can simple trading rules improve upon realized profits?
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Uniform-Price Auction Model of ERCOT• Setup
– Static game, N firms, costs of generation Cit(q)
– Contract quantity (QCit) and price (PCit)
– Total demand
– Generators bid supply functions Sit(p)
– Note: in “balancing market” terminology, these bids take form of INCrements and DECrements from “day-ahead” scheduled quantities
• Market-clearing price (pc) given by (removing t subscript from now on):
tt DD ~
N
i
ci DpS
1
~)(
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Model (cont’d)
• Ex-post profit:
• Information Structure– Ci(q) common knowledge– Private information:
• QCi
• PCi – but does not affect maximization problem
– is unknown, but this is aggregate uncertainty important sources of uncertainty from perspective of
bidder i• Rival contract positions (QC-i) and total demand (ε)
D~
i ic c
i ic c
i iS p p C S p p P C Q C ( ) ( ( )) ( )
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Sample Genscape Interface
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Characterization of Bayesian Nash Equilibrium
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Equilibrium (cont’d)
B id d e rs ch o o se su p p ly fu n c tio n s to m ax im ize ex p ec ted p ro fits
m ax
If is d iffe ren tiab le , n ecessa ry co n d itio n fo r p o in tw ise
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Equilibrium (cont’d)
C L A IM : If w e re s tric t th e c la ss o f su p p ly fu n c tio n s :
th en (ex a n te ) eq u ilib riu m b id s a re ex p o st b es t re sp o n ses :
w h ere
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R D p
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i i i i
i ii i
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*
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Computing Ex Post Optimal Bids (Prop 3)
Ex post best response is Bayesian Nash Eqbm Uncertainty shifts residual demand parallel in & out
(observed realization of uncertainty provides “data” on RDi'(p) for all other possible realizations)
Can trace out ex post optimal/equilibrium bidpoint for every realization of uncertainty (distribution of uncertainty doesn’t matter)
p M C S pS p Q C
R D pi i
U nknow n
i
U nknow n
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i
( ( ) )( )
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(" in v erse e lastic ity ru le" )
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Do We Expect to See Optimal Bidding?
• First year of market– Some traders experienced while others brought over
from generation and transmission sectors
• Many bidding & optimization decisions being made
• Real-time information?– Frequency charts & Genscape sensor data rival costs– Aggregate bid stacks with 2-3 day lag “adaptive
best-response” bidding?
• Is there enough $$ at stake in balancing market?– Several hundred to several thousand per hour
• “Bounded rationality”
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Sample Bidding Interface
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Sample Bidder’s Operations Interface
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Data (Sept 2001 thru Jan 2003)
• 6:00-6:15pm each day• Bids
– Hourly firm-level bids
• Demand in balancing market – assumed perfectly inelastic
• Marginal Costs for each operating fossil fuel unit• Fuel efficiency – average “heat rates” • Fuel costs – daily natural gas spot prices & monthly
average coal spot prices• Variable O&M• SO2 permit costs
– Each unit’s daily capacity & day-ahead schedule
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Measuring Marginal Cost in Balancing Market
• Use coal and gas-fired generating units that are “on” and the daily capacity declaration
• Calculate how much generation from those units is already scheduled == Day-Ahead Schedule
Total MCBalancing MC
Day-AheadSchedule
Price
MW0
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Reliant (biggest seller) Example
-2000 -1500 -1000 -500 0 500 1000 1500 20000
5
10
15
20
25
30
35
40
45
50
Balancing Market Quantity (MW)
Pri
ce (
$/M
Wh
)Reliant on February 26, 2002 6:00-6:15pm
Residual DemandEx-post optimal bidMC curveActual Bid curve
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TXU (2nd biggest seller) Example
-2000 -1500 -1000 -500 0 500 1000 1500 20000
5
10
15
20
25
30
35
40
45
50
Balancing Market Quantity (MW)
Pri
ce (
$/M
Wh
)TXU on March 6, 2002 6:00-6:15pm
Residual DemandEx-post optimal bidMC curveActual Bid curve
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Guadalupe (small seller) Example
-2000 -1500 -1000 -500 0 500 1000 1500 20000
5
10
15
20
25
30
35
40
45
50
Balancing Market Quantity (MW)
Pri
ce (
$/M
Wh
)
Guadalupe on May 3, 2002 6:00-6:15pm
Residual DemandEx-post optimal bidMC curveActual Bid curve
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Calculating Deviation from Optimal Producer Surplus
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O ptim a l A vo id
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A vo idB A L
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iE P O E P O
i( ) ( )Optimal
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Testing Expected Profit Maximizing Behavior
1) Restrict economic environment so ex-post optimal = ex-ante optimal
2) No restrictions – uncertainty can “shift” and “pivot” RD
1) Can simple trading rules improve upon realized profits?
2) Check (local) optimality of observed bids (Wolak, 2001)
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“Naïve Best Reply Test” of Optimality
• Bidders can see aggregate bids with a few day lag
• Simple trading rule: use bid data from t-3, assume rivals don’t change bids, and find ex post optimal bids (under parallel shift assumption)
• Does this outperform actual bidding?
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Generator’s Ex-Ante Problem
• Max Eε[π(p,ε)]
uncertainty (ε) can enter RD(p,ε) very generally
• Wolak test for (local) optimality:– Ho: Each bidpoint chosen optimally
– Changing the price/quantity of each (pk,qk) will not incrementally increase profits on average
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Test for (Local) Optimality of Bids
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Test for (Local) Optimality of Bids
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What the Traders Say about Suboptimal Bidding
1. Lack of sophistication at beginning of market• Some firms’ bidders have no trading experience; are
employees brought over from generation & distribution
2. Heuristics• Most don’t think in terms of “residual demand”• Rival supply not entirely transparent b/c
• Eqbm mapping of rival costs to bids too sophisticated• Some firms do not use lagged aggregate bid data
• Bid in a markup & have guess where price will be
3. Newer generators• If a unit has debt to pay off, bidders follow a formula
of % markup to add
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What the Traders Say (cont’d)
4. TXU• “old school” – would prefer to serve it’s customers
with own expensive generation rather than buy cheaper power from market
• Anecdotal evidence that relying more on market in 2nd year of market
5. Small players (e.g. munis)• “scared of market” – afraid of being short w/ high
prices• Don’t want to bid extra capacity into market because
they want extra capacity available in case a unit goes down
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Possible Explanations for Deviations from Benchmarks
1. Unmeasured “adjustment costs”2. Transmission constraints3. Collusion / dynamic pricing4. Type of firm5. Stakes matter
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Adjustment Costs?
1. Flexible gas-fired units often are marginal• 70-90% of time for firms serving as own bidders
2. “Bid-ask” spread smaller for firms closer to benchmark• Decreases over time for higher-performing firms
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Transmission Constraints?
• Does bidding strategy from congested hours spillover into uncongested hours?
• 1 std dev increase in percent congestion only 3% ↓Pct Achieved
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Collusion?
• Collusion not consistent with large bid-ask spreads
– Collusion smaller sales than ex-post optimal– Bid-ask spread no sales
• Would be small(!) players - unlikely
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Do Stakes Matter?
Bryan**
Calpine
City of Austin**
City of Garland**
LCRA**
Reliant *
South TX Elec Coop**
TXU*
0.1
.2.3
.4.5
.6.7
.8.9
1F
ract
ion
fro
m N
o B
idd
ing
to O
ptim
al
0 100 200 300 400 500Volume of Optimal Output
* = Investor Owned Utility ** = Municipal Utility/Cooperative
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(4) Own Bidders: 1000MWh increase in sales 86 percentage point increase in Pct Achieved(5) Own Bidders: 1000MWh increase in sales 97 percentage point increase in Pct Achieved
Explaining “Percent Achieved” Across Firms
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Learning?
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Efficiency Losses from Observed Bidding Behavior
• Which source of inefficiency is larger?– Exercise of market power by large firms?
– Bidding “to avoid the market” by “unsophisticated” firms?
– “Strategic” == top 6 in Pct Achieved
– Total efficiency loss = 27%
– Fraction “strategic” = 19% Fraction “unsophisticated”=81%!!
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Conclusions• Electricity markets are a great “field” setting to
understand firm behavior under uncertainty and private information
• Stakes appear to matter in strategic sophistication• Both sophistication (“market power”) and lack of
sophistication (“avoid the market”) contribute to inefficiency in this market
• Equilibrium bidding models – For large firms, models closely predict actual bidding– For small firms/new markets, models less accurate
• Market design– If strategic complexity imposes large participation
costs, may wish to choose mechanisms with dominant strategy equilibrium (e.g. Vickrey auction)
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The End
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Evolution of Bid-Ask Spread