Testing Models of Strategic Bidding in Auctions:

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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 AM

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Motivations

• Deregulation of electricity markets
• Optimal mechanism for procurement?
• 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
Mean -24 Stdev 1068 Min -3700 25th Pctile
-709 75th Pctile 615 Max 2713
Sample Sept 2001-January 2003, 600-615pm,
weekdays, no transmission congestion
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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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Empirical Strategy
Price
MR1
Sio (p,QCi)
B
D
MCi(q)
A
RD1
Quantity
QCi
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Empirical Strategy
Price
MR1
MR2
Sio (p,QCi)
B
D
MCi(q)
C
A
RD1
RD2
Quantity
QCi
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Empirical Strategy
Price
Sixpo (p,QCi)
MR1
MR2
Sio (p,QCi)
B
D
MCi(q)
C
A
RD1
RD2
Quantity
QCi
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Reliants Residual Demand

Reliants MC
Ex Post Optimal Bid Schedule
Reliants Bid Schedule
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Preview of Results

• Largest firm bids close to benchmarks for optimal
  bidding
• Small firms significantly deviate, but theres
  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
• Options
• Restrict economic environment so ex-post optimal
  ex-ante optimal
• Intuitively, uncertainty and private information
  shift RD in parallel fashion
• Check (local) optimality of observed bids (Wolak,
  2001)
• Do bids violate F.O.C. of Eep(p,e)?
• 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)

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Model (contd)

• 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
  (e)

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Sample Genscape Interface
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Characterization of Bayesian Nash Equilibrium
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Equilibrium (contd)
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Equilibrium (contd)
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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 doesnt matter)

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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 Bidders Operations Interface
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Data (Sept 2001 thru Jan 2003)

• 600-615pm 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 OM
• SO2 permit costs
• Each units 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

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Reliant (biggest seller) Example
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TXU (2nd biggest seller) Example
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Guadalupe (small seller) Example
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Calculating Deviation from Optimal Producer
Surplus
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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
3. Can simple trading rules improve upon realized
   profits?
4. 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 dont change bids, and find ex post
  optimal bids (under parallel shift assumption)
• Does this outperform actual bidding?

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Generators Ex-Ante Problem

• Max Eep(p,e)
• uncertainty (e) can enter RD(p,e) 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
Moment condition for each bidpoint on day t
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Test for (Local) Optimality of Bids
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What the Traders Say about Suboptimal Bidding

• Lack of sophistication at beginning of market
• Some firms bidders have no trading experience
  are employees brought over from generation
  distribution
• Heuristics
• Most dont 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
• 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 (contd)

• TXU
• old school would prefer to serve its
  customers with own expensive generation rather
  than buy cheaper power from market
• Anecdotal evidence that relying more on market in
  2nd year of market
• Small players (e.g. munis)
• scared of market afraid of being short w/
  high prices
• Dont 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 constraints
3. Collusion / dynamic pricing
4. Type of firm
5. Stakes matter

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Adjustment Costs?

• Flexible gas-fired units often are marginal
• 70-90 of time for firms serving as own bidders
• 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?
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Explaining Percent Achieved Across Firms
(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
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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
  unsophisticated81!!

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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