Diapositiva 1 - Ceris-CNR

How simulators can be
useful in scenario
analyses assessing the
impact of network
constraints on market
prices
M. Gallanti, G. Migliavacca
CESI RICERCA
CESI RICERCA
Electricity market performance under physical constraints - September, 25th 2007
Index
• Introduction of the Italian market (IPEX): the PUN and its effects
• Impact of congestion on market efficiency and market power
• Effects of increasing transmission capacity between zones
• Aims and methodologies of market simulation
• SREMS: a tool to investigate strategic competition
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The Italian day-ahead electricity market
• The Italian Power EXchange (IPEX) is operative since April 1st, 2004
and features three physical markets (day-ahead, adjustment, ancillary
services).
• The day-ahead is zonal, structured in 22 zones (7 geographical, 6
limited production poles and 9 border virtual zones). Market splitting is
used for congestion management.
• It is a non-compulsory (mixed) market: both spot market and bilateral
physical contracts are allowed.
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The Italian day-ahead electricity market
• Zonal sell prices in the IPEX in the last year
Source: GME monthly report - July 2007
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The Uniform Purchase Price (PUN)
• Unique feature: generated power paid at the zonal price, while
purchase price is uniform on the national territory (PUN = Prezzo
Unico Nazionale). The PUN is obtained as an average of all zonal sale
prices weighed with the purchase volumes.
Q p
PUN 
Q
Cz
z
z
Cz
z
Inter-zonal transmission
constraints respected?
Calculation of energy
exchanges between
zones
Sale and purchase
bids
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Yes
National price
No
Separation of the
market in zones
Injection and
withdrawal programs
compatible with the
constraints
• zonal sale prices
• uniform purchase price
Some important side-effects of the PUN
•
The PUN destroys any locational signals given to the load (while they remain
on generation).
•
With the PUN the concept of “congestion rent” becomes opaque and difficult to
understand by market operators.
•
The grid structure and power plants distribution among generating companies
in the different areas may determine market concentration in some areas.
– Local Market concentration may turn out in local market power
– In Italy the incumbent generation share is very significant in the South
•
The PUN introduces further incentives to exercise local market power:
– Suppose a zonal system with one exporting zone (A) and one importing zone (B).
Demand elasticity and competition would act discouraging GenCos from raising
prices in B, even if pivotal: a GenCo that is not monopolist in B by raising prices
would give up part of its production in favor of its competitors, with a possible loss of
profits.
– The PUN weakens this effect: loads in B pay the PUN, that is lower than the
relevant zonal price.
– The greater the demand in A wrt B, the weaker the influence of zonal price in B onto
the PUN.
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Effects of network congestion on prices
• Since the opening of the Italian market, CESI RICERCA has studied
the effects of network congestion on the electricity prices
• The economic impact of the unavailability of the Matera – S. Sofia line
was assessed through a market simulator (fall 2004) .
Price duration curve (simulated) in the South zone without the Matera - S. Sofia line
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Effects of network congestion on prices
Price reduction duration curve (simulated) in the South zone due to the
presence of the Matera - S. Sofia line.
– The availability of the Matera – S. Sofia line made it possible to increase the
the transits capacity among the zones
The return for the consumers would have been an annual cost reduction of
40 M€. The total cost of the Matera - S. Sofia line was estimated around
100M€.
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Congestion and social welfare in zonal markets
NC
NG
i 1
j 1
SW   QCi PoffCi   QGj PoffGj
Load offer prices are indicative
of the value of the goods produced
Generators bid prices should be
indicative of the costs for producing
energy
• The merchandise surplus corresponds
to the congestion rent extracted by the
TSO.
• The dead-weight loss is an indicator of
efficiency loss in the market due to
congestion.
Congestion means lower
market efficiency
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Effects of an increase of interzonal capacity
• Increasing the capacity of
congested tie-lines decreases the
dead-weight loss and increases the
social welfare. The market solution
is more efficient.
• However, the increase of
p
p
B
A
social welfare (A>B) is not
necessarily matched with a
reduction of loads payments.
In this example:
p3
p2
p1
Export zone
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q
Import zone
q
– if pure zonal market: loads
in the export zone pay price
p2 >p1, those in import zone
pay the same;
– if PUN is introduced: all the
loads pay more (the weighed
average grows).
Congestions and market power
• Market power can be exercised both at global (market) level and locally.
• Textbook definition of local market power consists in a producer actuating bidding
behaviors such as to artificially cause congestion between market zones. In this
way, he prevents the import of cheaper energy from other zones and creates
pivotality conditions for its own local generators in the importer zone. The
action of creating congestion may be not profitable in itself, but the sum with the
exercise of market power by local generators turns up to be very profitable.
• This kind of local market power may only be exercised in meshed networks,
not in tree-like ones (as the Italian market)
A
1. Create a
saturation
between A
and B
B
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A
C
2. C can’t import
any longer
(parallel flows);
local generators
are pivotal
B
C
By saturating A-B, B receives more power. Either it
uses it locally or exports it to C. In both cases, C
imports the same power or more and this reduces
(or doesn’t increase) the pivotality of local
generators.
Congestions and market power
• However, this does not mean that a producer with market power on the whole
national territory cannot find it profitable to congest one or several
connections. By comparing the results of perfect and strategic competition in
simulations carried out with SREMS, we see there are several hours in which
the leader could strategically congests the tie line between Sicily and
continent.
One example:
Perfect competition:
P = 48.61 €/MWh
DL=-186 MW
DF=+229 MW
+43
Strategic competition:
P = 50.34 €/MWh
P(Sicily)=59.31 €/MWh
The leader increases his
surplus by: 60.8 k€
DQ=+43
DL=0
DF=0
DL=0
DF=0
DL=-43
DF=0
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+43 (saturated)
What are market simulators
• Electricity market simulators are used to forecast market results
under given scenario hypotheses, like:
– fuel prices
– characteristics of generators, distribution on the territory and
among the generation firms
– characteristics of the transmission network (transit limits)
– legislation (e.g. emissions of pollutants)
• Simulators are classified by their reference time horizon:
1 hour
1 week
Short term
• Detailed description
• Hourly bidding optimization
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1 month
Miedium term
• Average detail level
• Scenario analyses
20 years
1 year
Long term
• Low detail level
• Analysis of evolution of
generation set
Who uses market simulators
Analysis of bid strategy
Producers
1 hour
Short term
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1 week
Scenario analysis
Regulatory impact
analysis
Research centers
and stakeholders
Regulators
1 month
Medium term
20 years
1 year
Long term
Market analysis tools
Data analysis
to examine past market behavior
by means of indices and
different data aggregations
Price forecasting tools
extrapolating market results
to the future (ARMAX, etc)
Optimization tools
least cost generation decisions,
once provided supply and
demand schedules,
keeping into account
operational constraints
Suppliers’ strategy models
simulation using games theory
(Cournot, Bertrand, Stackelberg,
Supply Function Equilibrium,
conjectural variations)
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Main features of SREMS
 SREMS is a short-medium run electricity market simulator based on game
theory.
 SREMS calculates price makers’ hourly strategic bids, supposing they
actuate both a bid-up and a capacity withholding strategy. Additionally, it allows to
define a certain number of price takers.
 Demand is inelastic, defined hour by hour and zone by zone.
 The network is supposed non-meshed (tree-like). Nodes represent market
zones separated by interconnectors with min/max transit limits.
 The electricity market is modeled with hourly detail, but the scheduling of
reservoir hydro and pumping power plants is carried out monthly.
 Characteristics of thermal power plants are taken into account with a high
level of realism (quadratic cost curves, maintenance periods, accidental
outages).
 Each producer can appoint a percentage of his power to physical bilateral
contracts, function of total load, market share and attitude towards the risk.
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Input XLS file:
• load, limits of transit
• generators characteristics
• calendar of maintenances
• monthly production and
pumping of hydro power plants
• overall bilaterals percentage
Input reading
and internal database build-up
M=1
Unavaliability and maintenance
Preferential transactions (CIP6,
import) and hydro plants
Unit commitment (twice a week)
Physical bilateral contracts
COMPETITIVE LOAD
H=1
Perfect competition
Strategic competition
H+
M+
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no
Month end
yes
Output XLS file:
• thermoelectric generation
• transits between zones
• zonal prices
• producer surplus
Preferential transactions and hydro plants
Leq  L( t )  PR ( t )  PCIP 6 ( t )  PImp ( t )  PRe s ( t )  PP ( t )
L
PSmax
Run-of-the-river
ESmax
Power constraint active
CIP6
Import
Power constraint not active
Power constraint not active
Power constraint active
PTma x
ET
Ppmax
Epmax
L( h* )
h(t)
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Unit Commitment of thermal power plants
• Setting up a UC strategy for a producer is very complex. Many aspects
have to be considered:
– If the sum of minima of all committed generators is higher than the load,
price is zero (even variable costs are not recovered) and the generators
must bid in the adjustment market.
– In general, too much committed power generates low prices. However,
generators are also interested to have many plants committed to maximize
produced power.
– Load changes along the day: during the night few plants should be on
(sum of technical minima must stay below the load), while during the day
power should be available to catch peaking prices. However, flexibility to
switch on and off is limited and depends from the plant technology.
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Strategic competition: the algorithm Vampiro
Let’s suppose a leader knows the bids of all his competitors and, on
the basis of them he wants to calculate the most favorable market
solution (highest producer surplus), in terms of produced quantity
and zonal prices.
Vampiro solves this problem resorting to the following logical steps:
–
–
–
–
write market clearing problem,
translate it into a set of equilibrium conditions,
write producer’s problem,
solve the latter adding the equilibrium conditions of the market
clearing problem as a set of additional constraints.
The resulting optimization problem is not
convex: local minima do exist!
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Strategic competition: calculating bids
•
Then, it is necessary to translate the information on leader’s optimal
quantities into an optimal bidding strategy (price-quantity) capable to
induce the optimal market clearing.
P
P
Pz
Pz


1
Bids
Ci
Ci
Bids
0
q
Omothetic shift
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q
Aggressiveness tuning
Typical trend of one week of zonal hourly prices
Perfect competition
Strategic competition
Market outcome
400
350
300
250
200
150
100
50
0
1
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12 23
34
45
56 67
78
89 100 111 122 133 144 155 166
A simulation of the Italian market
• Aim of the simulation is to assess the economical benefits that an
enlargement by 300MW of the maximum allowed transit on the
connector between Sicily and continent.
• Benefits are measured in terms of total saving of the customers against
value of the investment in new infrastructures (1.8 M€/km x 40 km = 72
M€ + additional expenditures = 80 M€).
• The simulations have been built upon a scenario 2005 of the Italian
market:
–
–
–
–
–
–
19 GenCos, 170 thermal units, 53 hydro units;
4 macro-zones (Nord, Centro-Sud, Sicilia, Sardegna);
fuel prices: 26.14 €/Gcal (oil), 31.55 €/Gcal (methane);
real hourly load assigned to each macrozone;
max bid price tuned on real market price peaks;
monthly CIP6 and import provided as input data.
• Preliminary simulation results show that investment costs could be
recovered in less than two years. However, the non-convexity of the
problem solved by Vampiro invites to some precaution in reading the
results of comparative simulations (like this one).
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Conclusions
• SREMS is a reliable tool allowing to perform scenario analyses on real
electricity markets over a short-medium run time horizon and to assess the
capability of the market leaders to exercise market power, both market-wide and
at local level. The true challenge would be to incorporate real strategies instead
of sheer profit maximization, to make the market equilibrium model acquire a true
predictive role. No literature paper has tackled the problem yet.
• On the basis of simulations, building inter-zonal tie lines to decongest the
most critical connectors would probably create an amount of benefit sufficient
to recover investment costs in few years. Increasing TTC between zones would
also make the market more efficient and the exercise of market power more
difficult.
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Thank you for your attention...
Massimo Gallanti
Gianluigi Migliavacca
CESI RICERCA
via Rubattino,54
20134 Milano (Italy)
E-mail: [email protected]
[email protected]
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