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Séance TA2 - Production d'énergie I / Energy Production I

Jour mardi, le 05 mai 2009
Salle Van Houtte
Président David J. Fuller

Présentations

10h30-
10h55
Pricing of Binary Variables in Energy Market Equilibrium Models
  David J. Fuller, University of Waterloo, Management Sciences, Waterloo, Ontario, Canada, N2L 3G1

In market equilibrium models with continuous variables, prices are usually extracted as dual variables of constraints. However, many models require some binary variables -- e.g., build/don't build decisions in capacity planning models. There is no established way to define prices of discrete activities, so equilibrium cannot be defined. A two-part pricing approach can resolve some difficulties.


10h55-
11h20
Natural Inflow Computed at the Hourly Rate
  Stéphane Alarie, IREQ, Canada

The natural inflow to a hydroelectric reservoir is computed using the water balance equation. At the hourly rate, the resulting inflow appears quite noisy. To improve the inflow evaluation, a linear programming approach is proposed, which consists in disaggregating the information from longer time scales.


11h20-
11h45
Optimal Production and Storage of Hydro-Power
  Michel Denault, GERAD, HEC Montréal, Méthodes quantitatives de gestion, 3000, chemin de la Côte-Sainte-Catherine, Montréal, Québec, Canada, H3T 2A7
Mathieu Rousseau, HEC Montréal

We consider the hydro-power "generate vs store" problem. At each moment, a manager can generate power, or store energy for future use. One main difficulty is to correctly account for the inherent value of optionality while modelling the forward-looking storage constraint. The control problem is solved by adapting Longstaff and Schwartz's popular "simulations/regressions" option pricing technique.


11h45-
12h10
Heuristique de décomposition de la fonction de Bellman d'un problème de programmation dynamique stochastique sous contrainte d'équilibre
  Kengy Barty, EDF, OSIRIS, 1 avenue du Général de Gaulle BP 408, Clamart Cedex, France, 92141

La fonction de Bellman solution d'une équation de programmation dynamique n'est pas généralement décomposable. Nous proposons une méthode combinant à la fois les techniques de décomposition en optimisation et de régression en statistique afin de contourner ce problème. Nous appliquons notre approche à la gestion d'équilibre offre demande en électricité.


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