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

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

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

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

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

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

Who are the Players?

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)

Price

Quantity

Sio

(p,QCi)

MCi(q)

QCi

Empirical Strategy

A

D

RD1

MR1

B

Price

Quantity

Sio

(p,QCi)

MCi(q)

QCi

Empirical Strategy

A

C

D

RD1

RD2

MR1MR2

B

Price

Quantity

Sio

(p,QCi)

MCi(q)

QCi

Empirical Strategy

A

C

D

RD1

RD2

MR1MR2

Sixpo

(p,QCi)

B

-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

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”

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?

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

~)(

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 ( ) ( ( )) ( )

Sample Genscape Interface

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

o p tim a lity o f :

N o te : a lso h o ld s u n d e r r isk av e rs io n (m ax im iz in g

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p S p C S p p P C Q C d H p S p Q C

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S p

p C S p S p Q CH p S p Q C

H p S p Q C

E U

w h ere U 0 )

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

S p p Q C

p C S pR D p Q C

R D p

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i i i i

i ii i

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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

i

i

( ( ) )( )

( )*

*

(" in v erse e lastic ity ru le" )

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”

Sample Bidding Interface

Sample Bidder’s Operations Interface

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

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

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

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

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

Calculating Deviation from Optimal Producer Surplus

(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

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)

“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?

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

Test for (Local) Optimality of Bids

QCPCp

pSCppRD

)),((

))),,(((),()),,((),(

),...,( vector bid Choose ,11 KK qpqp

0),(

kqE

?0 day for moments ofVector :H

- ),(),( ),(

o

t

kkk

utk

q

S

S

C

q

p

p

S

S

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p

RD

q

Moment condition for each bidpoint on day t:

ddistribute ])[ ]([ 2

1

11

1

1k

T

ttTT

T

ttTT uSuJT

Test for (Local) Optimality of Bids

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

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

Possible Explanations for Deviations from Benchmarks

1. Unmeasured “adjustment costs”2. Transmission constraints3. Collusion / dynamic pricing4. Type of firm5. Stakes matter

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

Transmission Constraints?

• Does bidding strategy from congested hours spillover into uncongested hours?

• 1 std dev increase in percent congestion only 3% ↓Pct Achieved

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

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

(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

Learning?

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%!!

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)

The End

Evolution of Bid-Ask Spread