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Paul Twomey
Richard Green
Karsten Neuhoff
David Newbery
The Cambridge-MITInstitute
Part of the research was funded by the Association of European Transmission Operators ETSO.
A Review of the Monitoring of Market Power
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Outline
• Classifying the various market power detection tools
• Is there evidence for market power?– Latest work from Europe
• Is market power a problem?– Wealth transfer and deadweight loss calculation– Technological Bias
• What to do about market power?• Data issues• Outstanding Issues and Conclusions
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Market Power Detection Tools – Classifications
Different Market Power Detection Tools for
Different Tasks:
• Ex-ante versus ex-post• Long-term analysis versus short-term/real time
analysis• System-level market power versus local market
power• Horizontal market power versus vertical market
power
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Market Power Detection Tools – Classifications
Ex-Ante Ex-Post
Long-Term Analysis
- Structural indices, e.g. Market share, HHI, residual supply index- Simulation models of strategic behaviour
- Competitive benchmark analysis based on historical costs- Comparison of market bids with profit maximizing bids
Short-Term Analysis
- Bid screens comparing bids to references bids- Some use of structural indices such as pivotal supplier indicator and congestion indicators
- Forced outage analysis and audits- Residual demand analysis
Inspired by a similar table in Helman (2004)
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Market Power Detection Tools – Potential Applications
Ex-Ante Ex-Post
Long-Term Analysis
- Merger rulings- Assessing applications for market-based rates - Determining potential must-run generators
- Litigation cases (e.g. California refund case)- Changing market design
Short-Term Analysis
- Spot market bid mitigation- Must-run activation & other system operator contracting
- Short term price re-calculations- Penalties for withholding
Inspired by a similar table in Helman (2004)
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Market Power Detection Tools – List
• Structural Indices and Analysis– Market Share and HHI– Pivotal Supplier Indicator and Residual Supply
Index– Residual Demand Analysis
• Behavioral Indices and Analysis– Bid-Cost Margins (e.g. Lerner Index)– Net Revenue Benchmark Analysis– Withholding Analysis
• Simulation Models– Competitive Benchmark Analysis– Oligopoly Models
• Transmission Related Analysis
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Is there Evidence for Market Power? - State-of-the-Art Examples from Europe
• Müsgens (2004) Ex-post competitive benchmark model of German Electricity Market– Multi-regional approach– Dynamic (including hydro & start-up costs)– >400,000 variables and equations!
• ECN (2004) COMPETES. Ex-ante oligopoly simulation model of Netherlands, Belgium, France and Germany – Cournot and Conjectured Supply Functions (CSF)– Results influential in Nuon-Reliant Merger Case in
the Netherlands (along with Frontier’s SPARK model – a supply function equilibrium model)
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German EEX Prices and Estimated SMC (Müsgens, 2004)
0
10
20
30
40
50
Jun
Jul
Aug
Sep Oct
Nov
Dec Ja
nF
ebM
arA
prM
ay Jun
Jul
Aug
Sep Oct
Nov
Dec Ja
nF
ebM
arA
prM
ay Jun
Jul
Aug
Sep Oct
Nov
Dec Ja
nF
ebM
arA
prM
ay Jun
2000 2001 2002 2003
Monthly Base EEX
Monthly Base SMC
1. Period:Average deviation betweenmarginal costs and pricesabout 2%
2. Period:Average deviation betweenmarginal costs and pricesnearly 50%
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Is Market Power a Problem?
• Can distort efficient dispatch• Can distort long-term investment• Wealth transfers • Potential technological bias
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Wealth Transfers and Deadweight Loss – Back of the Envelope Calculation
• UK 300 TWh per year– wholesale 25£/MWh, retail 50£/MWh– £15 billion retail value– Elasticity on retail demand 0.1, 10%
penalty on wealth transfers from consumers to producers
– Assume 20% wholesale price rise arising from market power
• Welfare loss of £192 million– Equivalent to 1.8 hours lost of total system
at current VOLL– £3 per person
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Market Power and Technological Bias
• Market power is sometimes argued to be tolerated to cover fixed costs and encourage new investment
• Usually assumed that all generators benefits equally from increased prices
• Intermittent generation, with exogenously varying production, benefits less than conventional generation (Twomey and Neuhoff 2004)
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Average Wind Price is Slightly Less than Average Market Price in a Competitive Market
0
20
40
60
80
100
0 10 20 30 40 50
Demand
Pric
e (£
/MW
h)
MC ConventionalGeneration
Demand
Residual ConventionalDemand (Average Wind)
Average Market Price
Volume Weighted Wind Price
DT - QWo DT
Price - Low Wind
Price - High WindMC
High Wind Low Wind
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Average Wind Price is Much Less than Average Market Price in a Monopoly
0
20
40
60
80
100
0 10 20 30 40 50
Demand
Pric
e (£
/MW
h)
MC ConventionalGeneration
Demand
Residual ConventionalDemand (Average Wind)
Monopoly PriceFunction
Average Market Price
Volume Weighted Wind Price
DT - QWo DT
Price - Low Wind
Price - High WindMC
Monopoly Price Function
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Average price and volume weighted price for wind - 90% contract level
0
10
20
30
40
50
60
70
Conv. Wind Conv. Wind Conv. Wind
Pric
e (£
/MW
h)
Market Pow erMargin
PerfectCompetition Price
Monopoly Duopoly w ith Forw ard
Contracting
Duopoly w ith Option
Contracting
Demand Elasticity 0.1, wind 30% market share, wind volatility 100% uniform distribution around average
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So what to do about Market Power?
• Standard Solutions:– Structural
e.g. divestiture, removing entry barriers, transmission expansion, demand responsiveness
– Regulatorye.g. vesting contracts, virtual power plant
auctions, sunshine regulation
– Market Rulese.g. unit-specific bid caps (e.g. AMPS)
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The Big Question
• Do we make best efforts to create a contestable market (e.g. divestiture, functioning contract market) and then let the market operate with only long run monitoring and minimal regulatory interference?
OR• Do we try to micro-manage the market to recreate the
competitive outcome?
• Dangers of too heavy-handed price intervention– Investment problems (Joskow 2003, New England)– Can capacity payments solve this?
• Is ex-ante or ex-post regulation more effective?• Europe versus USA approach
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Market Monitoring Units – Institutional Features
• Market Monitoring Units. Different approaches:– Regulator– Unit attached to Market Operator
• Desirable Features– Forward-looking and preemptive– Support from regulator to respond to
recommendations– Consistent approach to ensure that the market
monitors actions are understood by all participants– Transparent approach to promote confidence in
the operation of the market and allow outside analysis
– Independence from stakeholders
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Data Issues
• TSO/ISO requires all physical transaction data and so is well placed to hold a complete, centrally stored record.
• Data should be held for sufficient time to allow ex-post investigations.
• Homogenous format for data across all nations/states would reduce cost of analysis and increase the integrity of data.
• Access rights to data should be clearly specified. Regulatory authorities should receive access to data either automatically or on request without the need for legal proceedings.
• As much data as possible should be made publicly available to facilitate third party market analysis.
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Conclusions
• Simple MP tools have drawbacks but more complex techniques require considerably more effort to construct and often use difficult-to-obtain data.
• Desirable to employ a range of techniques as no one technique provides definitive results. Further work needs to be conducted on confidence intervals.
• As much data as possible should be published to allow independent analysis to refine techniques for the detection, and hence the deterrence of market power.
• More empirical work needs to be conducted on evaluating the efficiency effects of AMP-type mechanisms, particularly the effect on investment decisions.
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Conclusions
• Ex-ante versus ex-post mitigation issue still to be resolved. Using both is feasible.
• Such work should lead us to a better understanding of the acceptable levels of market power in light of our ability to both detect and mitigate market power.
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Postscript
• Student: “So Master, what is the path of least resistance to electricity deregulation enlightenment”
• Zen Master: “Ohm”