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"