IEFE Seminar Series, 8/10/2010

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Fulvio Fontini

DSE – University of Padua, Italy Eric Guerci

GREQAM – Université Aix-Marseille, France Angelica Gianfreda

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´ Nuclear Power production in Italy: a “new” topic (?) fostered by

the recent government proposal to build new Nuclear Power Plants (l. 23/7/09 n. 99 and ss.) and “strengthened” by ENEL-EDF agreement.

´ Most of the debate focused on a) “traditional” pros and cons

(risk and security issues, costs, needs of incentives) and b) the “effective” possibility of building NPP in Italy.

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´ A common argument brought forward in favor of NPP: It will

reduce the price of electricity (the “typical” reply: depends on the amount of the “extra” costs and the possibility to transfer them to buyers: electricity cost might lower, final price might be higher).

´ There seems to be a “supporters” attitude towards NPP.

´ But … no study yet evaluating the real impact of NPP on the

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´ Evaluating in a “realistic” way the impact of a plausible

scenario about NPP on the electric price, taking into account the effective Italian power production structure: a liberalized market with significant transmission constraints and a large (former monopolistic) operator.

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´ Realistic day-ahead market clearing mechanism (zonal market

splitting mechanism)

´ Equivalent 11 zones Italian transmission grid

´ Detailed agent-based model of the Italian thermal production

pool (158 power-plants).

´ Historical values for maximum grid transmission capacity,

price-inelastic zonal loads and fuel costs.

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´ NPP assumptions:

A) 4 NP Plants (1.25GW each), 2 locations: 1) One Plant per zone in NO, CN, CS, S; 2) Two plants in NO, 2 in CN.

B) no strategic bidding (and get rid of the debate on the effective cost of combustibles): NPPs bid at zero price, maximum capacity.

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( )

(

)

( )

[Euro/h]

[Euro/MWh]

[Euro/h]

i i l i i i i i l i i l i

TC Q

FP

a Q

b

MC Q

FP a

QFC

FP b

=

⋅

⋅ +

=

⋅

=

⋅

Producer i bids:

( )

ˆ ,

ˆ

_{[Euro/MWh], [MWh]}

(

)

i i

P Q

subject to:

Q

i

<

Q

ˆ

i

<

Q

i

and

P

ˆ

i

<

P

*

It is characterized by the following total cost function:

where FP_l [Euro/GJ]

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ˆ [MW] i i i Q < Q < Q , , , , [MW] [MW] l st l st l ts l ts Q Q Q Q ≤ ≤

Active power generation limits:

Active power balance equations for each zone k:

Real power flow limits of lines:

min P ˆ _iQ_i i=1 N

∑

[MW], s.t. Q_i i∈Z

∑

- Q_k_,_load = Q_k_,_inject [MW]

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The solution consists of:

the set of active powers for producer i

the set of zonal prices for zones

*

i

Q

k

ZP

_k

=

_1,...,

_K

The profit per hour for power plant i belonging to zone k is obtained as follows:

( )

* *

[Euro/h]

i k i i i

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SELLERS

´ 5 thermal technologies (74% of the gross national production):

Coal-Fired (CF), Oil-Coal-Fired (OF), Combined Cycle (CC), Turbogas (TG) and Repower (RP).

´ 53 representative sellers (aggregating power-plants for each operator –

technology - zone). The profits are computed as the sum of the profits earned by each power-plant. They always commit themselves (no unit decommitment) Î no strategic behavior (different technologies within zone behaves as different agents even if belong to same owner)

´ Future extension (see later): allow for strategic bidding (capacity

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´ 60 actions per seller (mark-ups, maximum capacity). Simple offers

submitted by sellers (almost 75% of the real market offers are simple)

´ Sellers learn according to a reinforcement learning algorithm (Roth and

Erev 98)

TRANSMISSION NETWORK

´ Equivalent 11 Italian zones (we do not consider foreign and virtual

zones) LOADS

´ Price-inelastic zonal loads faced by thermal power-plants (removed non

dispatchable, export and import and renewables-bilateral contracts of demand side)

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, ( ) (1 ) , 1( ) , ( ) i t i i t i i t i f a = − ⋅r f ₋ a + E a Propensities: Experimentation function: Profits:

( r: recency effect parameter)

( e: experimentation parameter) , , , 1 ( ) (1 ) ( ) ( ) 1 i t i i i t i i t i a e if i plays a E a _e f a otherwise M − Π ⋅ − ⎧ ⎪ = ⎨ ⋅ ⎪⎩ − , , ( ) ( ) i t i i t i i R a a R Π =

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Update probabilities:

Random draw, according to mixed strategy distribution, selects the action at

time (t+1) r e c d 0.6 0.97 0.04 0.035 0.05 where , i t S ( ) ( ) , , , ( ) i t i t i t i t i S a i t i _S _a a e a e λ λ σ =

∑

d t ct λ ₌ −

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´ Updating it !

« Use 2009 data to take into account new power plants, grid extensions

(and reduced market power ?).

´ Assuming more realistic agents:

« The agent can own different technologies in the same zone « The agent can own power-plants in different zones

´ Increasing strategies’ space, allowing for capacity

withholding:

« bidimensional strategy space (price, quantity).

« Evaluating if NP increases MP of “dominant” player

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P

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SMP2 P

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