IEFE Seminar Series, 8/10/2010

Transcription

1 Fulvio Fontini DSE University of Padua, Italy Eric Guerci GREQAM Université Aix-Marseille, France Angelica Gianfreda DSE - University of Verona, Italy 1

2 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. 2

3 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 electricity price in Italy 3

4 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. 4

5 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. Empirical validation performed for December 20,

6 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. Compare Status Quo with NPP scenarios (2 configurations) 6

7 Producer i bids: ( ) ( ) subject to: TC Q = FP a Q + b ( ) ( ) i i l i i i MC Q = FP a QFC ( ) i i l i = FP b i l i PQ ˆ, ˆ [Euro/MWh], [MWh] i i Q < Qˆ < Q and Pˆ < P i i i i It is characterized by the following total cost function: where FP l [Euro/GJ] NPP bids: ( NP 0, Q ) max [Euro/h] [Euro/MWh] [Euro/h] * 7

8 N min P ˆ i Q i [MW], s.t. i=1 Active power generation limits: Q < Q < Qˆ [MW] i i i Active power balance equations for each zone k: i Z Q i -Q k,load =Q k, inject [MW] Real power flow limits of lines: Q Q Q lst, lst, Q lts, lts, [MW] [MW] 8

9 The solution consists of: * Q i the set of active powers for producer i ZPk the set of zonal prices for zones k = 1,..., K The profit per hour for power plant i belonging to zone k is obtained as follows: ( ) R = ZP Q TC Q * * i k i i i [Euro/h] 9

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11 SELLERS 5 thermal technologies (74% of the gross national production): Coal- Fired (CF), Oil-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) 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 withholding) across technologies within zones. 11

12 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) EXPERIMENTS 15 experiments (1 experiment = 10,000 runs) 12

13 Propensities: ( r: recency effect parameter) f ( a ) = (1 r) f ( a ) + E ( a ) it, i it, 1 i it, i Experimentation function: ( e: experimentation parameter) Π ( a ) (1 e) if i plays a it, i i Eit, ( ai) = e it, 1( i) f a otherwise M 1 Profits: Π ( a ) = it, i R it, ( a ) R i i where R i is the maximum profit achievable by agent i. 13

14 Update probabilities: where λ t = ct d σ, ( a ) = i S it, ( a ) t i it, ( ) it i S a a e e λ λ t i Random draw, according to mixed strategy distribution, selects the action at time (t+1) S it, Table with the parameters value r e c d

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22 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 Adopting genetic algorithms to model individual behavior 22

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24 P SMP2 MWh 24

25 P SMP2 SMP1 MWh 25

26 I: number of agents T: number of runs G: number of generations per run P: population size 26

27 THANK YOU! 27