PhD Thesis Presentation hold on 4.25.2008 in Argentina

1. Doctoral Examination
Assessment of Short-term Strategic Behavior
f h i h i
in Electricity Markets
Introduction
Analysis
Modeling
Results
Conclusions
Ing. Pablo Frezzi
San Juan, 25/04/2008

2. Introduction 1
Motivation (1)
Characteristics of the present electricity markets
Impossibility to store economically large amounts of electricity
Low price elasticity of demand
Repeated interaction
Significant economies of scale
Transmission constraints
Market
M k t concentration as a consequence of i ffi i t di tit
t ti f inefficient divestiture and
d
consolidations
Electricity markets are not perfectly competitive
In contrast to perfectly competitive markets, market participants do not play a
passive role as „price takers“
Price and market dynamic depend on market participants‘ strategies to maximize
profits
Strategic behavior: individual or group action to increase profits by means of
overt or tacit agreements influencing the market variables
Market po e individual profit-maximizing action
a ke power: d dua p o ax g ac o
Collusion: cooperative profit-maximizing action

3. Introduction 2
Motivation (2)
Consequences of strategic behavior:
Wealth transfer from customers to producers
p
Deadweigh loss and and reduction of social welfare
Supply shortages pursue
Price volatility
l l
Distortion of price signals which may lead to inefficient investments
Physical ithh ldi
Ph i l withholding Economic withholding
E i ithh ldi
Wealth transfer Demand Offer Wealth transfer Demand Offer
Price Price
Costs Costs
PSM PSM
PVM PVM
Deadweigh loss Deadweigh loss
Withheld Withheld
capacity capacity
0 QSM QVM Quantity 0 QSM QVM Quantity
Strategic behavior may affect the benefits pursued by liberalizing processes
Need of models to reproduce actual strategic behavior in electricity markets

4. Introduction 3
Research Aim
Development of a simulation model of electricity markets to reproduce and
p y p
assess the strategic behavior of market participants
Specific aims:
f
Indentification and proof of exercise of strategic behavior in electricity markets
Quantification of the influence of strategic behavior on the electricity price
Analysis of the influence of individual behavior on the short-term dynamic of
electricity markets, specially with signs of concentration
y , p y g
Identification of the most relevant causes of strategic behavior
Analysis of the influence of transmission constraints on the individual behavior of
the market participants and on the exercise of strategic behavior
Application field
pp
Competition authorities
Regulators

5. Analysis 4
Strategic Behavior
Market power
p
Maximization of benefits by means of exploitation of market dominance
Static context
Unilateral d i d
U il t l and independent behavior
d tb h i
Own theory and well understood
Comprehensively researched
o pe e ey e e e
Well defined indices to quantify market power potential
Tacit collusion
Maximization of benefits by means of tacit coordination of strategies
Dynamic context
Multilateral and interdependent behavior
No own theory
Not enough researched
Hardly any successful prosecution of tacit collusion due to lack of analysis models
Tacit collusion has not been comprehensively researched in electricity markets
p y y
yet
Need of models to detect and assess tacit collusion in electricity markets

6. Analysis 5
Tacit Collusion (1)
Necessary conditions
y
Market concentration
• Easy to coordinate and reach a tacit agreement
• T an mi ion con t aint inc ea e market concentration
Transmission constraints increase ma ket concent ation
Repeated interaction
• Coordination of strategies by means of learning processes
• Daily repetition intensify the learning process
Barriers to entry and exit
• „sunk costs“ nonreversible investments
sunk costs
• No contestable market
Coordination capacity
• Coordination on specific collusive equilibria
Punishment of deviation from collusive agreements
• Discouragement of deviations from collusive agreements
Electricity markets fulfill the necessary conditions for a tacitly collusive
agreement to emerge and remain stable over time

7. Analysis 6
Tacit Collusion (2)
Facilitating factors
Symmetrical firms
• Easy to achieve collusive agreement among firms with similar production costs
Homogeneous product
• Product variety reduces competition and thus increases concentration and
coordination
Transparency
• Increases coordination and detection of deviations from collusive equilibria
Stable and predictable demand
• Revisionary processes with decreasing prices
• Low price elasticity of demand
Fragmented demand-side
• Small and frequent orders
ll d f d
• Less incentives to defect
• Short time-lags encourage coordination among market participants
Uniform-price auction
• Difficult detection of collusion
Present electricity markets fulfill necessary conditions and facilitating factors
and are thus prone to tacit collusion

8. Analysis 7
Tacit Collusion (3)
Learning
Learning abilities of agents p
process Collusion
Dynamic of electricity markets
• Repeated interaction
• Short-time lags
• Adaptable behavior Punishment Deviation
Agents Bids Results
Market
Reward
Market participants learn the market dynamic and adapt their behavior

9. Analysis 8
Tacit Collusion in Liberalized Electricity Markets
England & Wales
g
Tacit collusion between the two biggest generation companies in the 1990‘s
90% of the time, the price was set by the two biggest generation companies
California
Californian energy crisis between 1998 and 2001
Economic withholding exercised 60% of the time
ld d f
Germany
High level of market concentration
Some research reports prices much higher than cost estimators as a consequence
of tacit collusion
European Transmission System Operators (ETSO)
Advice about the importance of market monitoring in Europe in order to ensure
adequate market conditions
Tacit collusion has become a worldwide problem

10. Modeling 9
Description of the Model
Classic oligopolistic models
g p
Identification of equilibria, i.e. Nash equilibria
Quantity and price competition
Static d i l
St ti and single-period models
i d d l
For market power assessment suitable
Repeated games with imperfect public information
d ihi f bli i f i
Dynamic coordination among market participants
Imperfect public information:
• Price and quantity
Non-public information:
• Cost structure and past actions
Present actions depend on public and non-public information
Strategy function: dynamic behavior of market participants
Repeated games with imperfect public information are adequate to
reproduce tacit collusion

11. Modeling 10
Simulation Model
Hourly assessment of tacit collusion on the generarion-side
Availability of generation units Transmission constraints
Fuel prices Regulatory framework
Thermal efficiencies Mean nodal demand
Generation portfolios
G i f li
Generation Agent Market Agent
Decision-making: Generation scenarios Demand scenarios
Maximization f benefits
M i i ti of b fit Offers
Off
Minimization of
policy function generation costs
iterative
repetition Results
Assessment of rewards: Market settlement
reward function
Updating of information
action-value function Database
Time limits
Simulation horizon: 1 month – 1 year
Periodicity: 1 hour

12. Modeling 11
Decision-making
Decision making of Generation Agents
Portfolios with thermal plants
Different thermal generation technologies
Fuel prices exogenous variables
Startup costs
Objective Function
max [ Earnings from energy sales – Variable costs ]
Assessment of rewards
Short-time uncertainties
Availability f
A il bilit of generating units
ti it
120
Stochastic fluctuations of demand Supply function
[€/MWh]
Decision of other generation agents
g g Marginal cost curve
Strategy 80
Price competition (Bertrand competition) 60
Percent increase of the supply f
f l function 40
Price increase = 0 „price taker“ 20
0
0 100 200 [MW] 400
Generation capacity

13. Modeling 12
Strategy Actualization
Game theory with artificial intelligence (Reinforcement-Learning)
Efficient
Effi i t appraisal of optimal strategies t maximize profits
i l f ti l t t i to i i fit
Consideration of the characteristics of social behavior:
• Exploitation of past actions
p p
• Exploration of new actions
• Recency
Strategy actualization
act ali ation Softmax algorithm f(S)
π(o)=σ
Probability
Agent function
Strategy
Soptimal Strategies
Information
I f ti Policy function Action
A ti π: P li function
Policy f ti
o: Vector of information
Reward σ: Strategy mix
Environment
The policy function and strategy actualization allow to reproduce the actual
behavior of generation agents

14. Modeling 13
Short-term
Short term Uncertainties
Availability of g
y generating units
g
Two-state Markov model
λ Failure
Unit Unit
operable μ Reparation failed
So
Stochastic determination of generation scenarios
ee o o ge e o e o
45
[GW]
Generation 43
capacity 42
41
40
0
1st w 2nd w 3rd w 4th w 40
Stochastic demand scenarios [GW]
Demand
Stochastic Gauss-Markov model 20
Statistical information from January 10 Working
system July
day
d
Saturday Sunday
0
1 7 13 19 1 7 13 19 1 7 [h] 19
hour

15. Modeling 14
Market Agent
Spot market
p
Opening of market and reception of energy bids from generation agents
Hourly bids
y
Demand scenarios
stochastic Gauss-Markov model
Gauss Markov
Clearance of the market and calculation of the hourly price through minimization
of generation costs considering generation and transmission constraints
Lagrange Relaxation:
min L = min [ ∑(Generation costs) +
i i ∑(G i )
β [ ∑(Demand) + ∑(Losses) - ∑(Generation) ] +
∑ ŋ (Transmission constraints) + ∑ ε (Generation constraints) ]
Price calculation
Price = β [ node factor ] - ∑ ŋ [ PTDF ]
Losses Transmission constraints

16. Results 15
Model System
6 thermal generation technologies
100 generation plants with usual capacities in actual systems
Total installed capacity 44,4 GW
Emissions certificate 12 €/EUA
3 market concentration levels
100 GA: unconcentrated
10 GA: moderately concentrated Generation marginal cost curve
Generation 120
5 GA: highly concentrated
costs
[€/MWh]
Generation Technology Mix
Zusammensetzung des Kraftwerksparks
80
Lignite Hard coal
60
CCGT (gas/oil) 40
Steam turbine (gas/oil) 20
Nuclear
Gas turbine (Gas/oil)
( / )
0
0 10 20 30 [GW] 50
Aggregate generation capacity

17. Results 16
Simulated Hourly Prices (1)
a) Constant available generation capacity and deterministic demand
g p y
January July
120 120
[€/MWh] [€/MWh]
80 80
60 60
Price
Price
40 40
20 Working 20 Working
day Saturday Sunday day Saturday Sunday
0 0
1 7 13 19 1 7 13 19 1 7 [h] 19 1 7 13 19 1 7 13 19 1 7 [h] 19
hour hour
PCM 100 GA 10 GA 5 GA
Simulated prices considering coordination abilities are higher than generation
marginal costs
The higher the market concentration is, the higher prices are

18. Results 17
Simulated Hourly Prices (2)
b) Stochastic availability of the g
y generating units and deterministic demand
g
January July
120 120
[€/MWh] [€/MWh]
80 80
60 60
Price
Price
40 40
20 Working 20 Working
day Saturday Sunday day Saturday Sunday
0 0
1 7 13 19 1 7 13 19 1 7 [h] 19 1 7 13 19 1 7 13 19 1 7 [h] 19
hour hour
PCM 100 GA 10 GA 5 GA
Simulated prices considering coordination abilities are higher than generation
marginal costs
g
The higher the market concentration is, the higher prices are

19. Results 18
Simulated Hourly Prices (3)
c) Stochastic availability of the g
y generating units and demand fluctuations
g
January July
120 120
[€/MWh] [€/MWh]
80 80
60 60
Price
Price
40 40
20 Working 20 Working
Saturday Sunday day Saturday Sunday
day
0 0
1 7 13 19 1 7 13 19 1 7 [h] 19 1 7 13 19 1 7 13 19 1 7 [h] 19
hour hour
PCM 100 GA 10 GA 5 GA
Price differences are reduced due to information uncertainties
Information uncertainties restrain influence of market concentration

20. Results 19
Comparative Analysis of Results (1)
Monthly revenues and producer surpluses
January July
1800 1800
[Mio. €] [Mio. €]
1400 1400
1200 1200
1000 1000
800 800
600 600
400 400
200 200
0 0
a b c a b c a b c a b c a b c a b c a b c a b c
PCM 100 GA 10 GA 5 GA PCM 100 GA 10 GA 5 GA
a) Constant available generation capacity and deterministic demand
Producer surpluses
b) Stochastic availability of the generating units and deterministic demand
h l bl f h dd d d Generation costs
c) Stochastic availability of the generating units and demand fluctuations
Market concentration and information uncertainties play a key role when
tacit collusion occurs

21. Results 20
Comparative Analysis of Results (2)
Assessment of collusion by means of the Lerner Index
Lerner I d
L Index=(Price-Marginal generation cost)/Price
(P i M i l ti t)/P i
January July
0,5 0,5
Lerner
Lerner- Lerner
Lerner-
Index Index
0,4 0,4
0,3 0,3
0,2 0,2
0,1 0,1
0 0
PCM 100 GA 10 GA 5 GA PCM 100 GA 10 GA 5 GA
Scenario a
S i Scenario b
S i Scenario c
S i
Tacit collusion even with low levels of concentration
Information uncertainties reduce extraordinary surpluses

22. Conclusions 21
Conclusions
Research aim:
Development of a simulation model of electricity markets to reproduce and assess the
strategic behavior of market participants
Analysis
Characteristics and consequences of strategic behavior in electricity markets
Necessary conditions and facilitating factors of tacit collusion
Electricity markets are prone to suffer tacit collusion
Modeling
Mixed Model:
• Game theory: repetitive game with imperfect public information
• Artificial intelligence: Reinforcement Learning
Results:
Market concentration and information uncertainties play a key role in cases of
tacit collusion
Tacit collusion even with low concetration levels
Main contributions
Comprenhensive analysis of tacit collusion in electricity markets and its dynamic
Identification of main influencing factors and their assessment on the market
The simulation model is suitable to reproduce short-term strategic behavior

23. Doctoral Examination
Assessment of Short-term Strategic Behavior
in Electricity Markets
Ing. Pablo Frezzi
San Juan, 25/04/2008