Transcript Good Afternoon

Agent-based Models for
Electricity Policy Design
Derek W. Bunn
Augusto Rupérez Micola
London Business School
Background
o Series of 5 PhD dissertations at LBS since 1994
applying ABS to competitive pricing behaviour in
electricity markets
o Each addressed Specific Questions for sponsoring
companies and government inquiries
– Normative company insight for business strategy and
risk management
– Normative institutional insight into market reforms and
regulation
o What follows is a selective, critical review
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Business Strategy Questions
o How do players with substantial market
power maximise returns over time in both
spot and forward contract markets?
o How can a company understand the new
trading risks for an impending spot-market
rule change?
o As market structures evolve through M &A
activities: How should companies position
themselves horizontally and vertically?
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Institutional Design Questions
o How much divestiture should be required of dominant
incumbents?
o Will a market rule change from a day-ahead Pool
auction to Continuous Bilateral Trading impede the
exercise of market power?
o Does a particular generator have the ability to move
the market?
o What is the effect of an increasing tolerance for crossholdings and vertical integration?
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Why Should ABS Be More Useful?
o Electricity markets are too complex and the
required answers too subtle for analytical
methods:
–
–
–
–
–
Imperfect competition
Very low demand elasticity to price
Discontinuously convex supply functions
High-frequency repeated game
Heterogeneous agents
•
•
•
•
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Size
Portfolios
Technologies
Cross-Ownerships
5
Typical ABS Models of the Electricity
Wholesale Market
Start with detailed representation of the
generators’ marginal-cost supply function
50
40
30
P ric e
(£ /M W /h r)
20
10
0
0
5000
10000
15000
20000
25000
30000
35000
40000
C u m u la tiv e C a p a c ity (M W )
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Days
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20th January
12th February
11th February
10th February
9th February
8th February
7th February
6th February
5th February
4th February
3rd February
29th January
28th January
27th January
26th January
25th January
24th January
23rd January
22nd January
21st January
Price
(£/MWHr)
Supply Functions and Dynamic Learning:
Offers are not at Marginal Cost
60
50
40
30
20
10
0
30500
23000
15500
8000
500
Capacity
(MW)
7
Typical ABS Models of the Electricity
Wholesale Market
o Artificial Agents similarly learn to make offers to
the market above their marginal costs
o The demand side is represented via a function or
more explicitly by a double-sided auction
o We have found that both best-response and
simple reinforcement learning work well, and can
produce credible results
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Partial Best-Response Learning and
Supply-Function Evolution
60
50
40
Price
30
(£/MWHr)
20
10
33000
26500
7000
500
170
171
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13500
172
Days
20000
174
173
190
189
188
187
186
185
184
183
182
181
180
179
178
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175
0
Capacity
(MW)
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Validation Via Theoretical SupplyFunction Equilibrium
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Model Calibration:
Steep Demand Function
o Whether ABS or analytical, avoiding very
high prices in profit-maximising models of
electricity auctions is a pervasive problem
o In reality high price spikes occur at times of
scarcity, but most of the time generators do
not exert their full market power to raise
prices
– A “long-term” elasticity is often used, as a
calibrated surrogate for the repeated game
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Local Search Works Better
Global Search => Cycles
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Avg. Percentage Bid Above Marginal Cost
Divestiture and Contract Cover
70%
60%
Existing
Existing (No Earn-Out)
50%
25% Divested (No Earn-Out)
50% Divested (No Earn-Out)
40%
30%
20%
10%
0%
75%
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80%
85%
Percentage of Contract Cover
90%
95%
13
Calibration through Utilisation
In order to use Realistic Short-term Elasticity
—another “calibration parameter” is needed
A “Utilisation” factor can be introduced as a target alongside daily
profit maximisation
This has been framed as a corporate, market-share objective in
several reinforcement-learning models
(market-power studies for UK, Germany, Italy, Russia)
And as regulatory, availability constraint in a best-response learning
model for the UK.
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Profits and Utilisation
1. Market Share
Objective
YES
Did EACH plant reach
target utilisation rate at
time T -1 ?
2. Profit Objective
YES
REPEAT
All T -1
bids by
random %
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TOTAL profit at time T - 1
exceed that at time T -2 ?
NO
NO
LOWER
Plant’s T 1 bid by
random %
CHANGE
All T -1
bids by
random %
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One approach is to see if historical data on utilisation
allows the model to fit…..
Mean Simulated and Actual SMP (1 December 1997)
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We then used this model for UK market rule changes
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Prices were Higher under the Bilateral
Model than in the Pool
Pool versus Bilateral Model Simulated-Clearing
Prices
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Another approach is to calibrate the model to
historical data via utilisation…..
35.00
30.00
Price (Euro/MWh)
25.00
20.00
15.00
10.00
5.00
0.00
March
May
August
November
Months
Simulated On-peak
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Simulated Off-peak
Actual CEPI On-peak
Actual CEPI Off-peak
We then used this model for German mergers
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The Effect of Four Big Mergers
80.00
70.00
Price (Euro/MWh)
60.00
50.00
40.00
30.00
20.00
10.00
0.00
March
May
August
November
Months
On-Peak Base Case
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Off-Peak Base Case
On-Peak 4 Mergers
Off-Peak 4 Mergers19
An increase in Target Utilisation Rate increases
Agent Rivalry, and Prices Fall
80.00
70.00
Price (Euro/MWh)
60.00
50.00
40.00
30.00
20.00
10.00
0.00
March
May
August
November
Months
100% Utilisation
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70% Utilisation
65% Utilisation
60% Utilisation
55% Utilisation
Which is why incumbents are reluctant to sell
plant to new entrants
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Reserve As Utilisation
An equivalent effect follows from reserve
capacity
We used this in an antitrust-case model
Whether two “minor” players could move the market
price by capacity withdrawals
Using a double-sided reinforcement model
Evidence
Individually they could not
Collusively they could
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ABS in Adversity
• As a basis for expert testimony, ABS results can be
fragile:
– Statistical and equilibrium uncertainties can be easily
challenged (lawyers don’t like t-tests)
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Price (£/MWh)
70
45GW demand, full capacity
60
average
50
maximum
40
minimum
30
difference
20
10
0
400 trading days
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Market Structure and Ownership
o Power plants and energy companies are
actively traded
– This “market” determines the behaviour in the spot
o The effects of horizontal concentration
amongst generators have been wellresearched
o Recently we have used ABS to look at:
- Cross-holdings
- Vertical relationships
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Crossholdings and Coordination
P
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Cross-holdings
Collusive Value of Transparency
PPUB – PPRIV
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Cross-holdings
Gas-Power Value Chain
Gas Shipping
Electricity
Generation
Electricity
Retailing
Modelling Two Simultaneous Spot Markets
Presents an extra modelling challenge:
Sequencing the Clearing
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A Trading Perspective on Clearing
SG
SE
PE
PG
DG
QG = Q E = Q R
Wholesale
Gas Market
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SR

PR
DR
DE
Q E = QR
Wholesale
Electricity Market
QR
Retail
Electricity Market
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Making Agent-based Models Work:
Some Open Questions
• Parameterisation for Energy Policy
– Role of elasticity
• Long-term / short-term
– Excess capacity and capacity utilisation
– Auction type
• Double- / single-sided
– Market clearing in sequential markets
• Supply chain / Trading netback / Simultaneous
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References
o Competition Commission. 2001. AES and British Energy: A report made
under section 12 of the Electricity Act 1989. http://www.competitioncommission.gov.uk/reports/2000.htm#2001
o Divestiture of Generation Assets in the England and Wales Electricity
Market: A Computational Approach to Modelling Market Power. (with C.Day)
Journal of Regulatory Economics, 2001
o Imperfect competition in uniform-price and discriminatory auctions for
electricity. (with J. Bower) Journal of Economic Dynamics and Control,
2001
o Agent-based Simulation: Modelling the New Electricity Trading
Arrangements of England and Wales. (with F. Oliveira) IEEE Trans on
Evolutionary Computation, 2001
o A Model Based Comparison of Strategic Consolidation in the German
Electricity Industry. (with J. Bower), Energy Policy , 29 , pp 987-1005, 2001
o Current working papers at www.london.edu/ds
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