Solving the electricity production planning problem by a column generation based heuristic

Authors:
Antoine Rozenknop;Roberto Wolfler Calvo;Laurent Alfandari;Daniel Chemla;Lucas Létocart
Affiliations:
LIPN, CNRS (UMR7030), Université Paris, 13, Sorbonne Paris Citè, Villetaneuse, France 93430;LIPN, CNRS (UMR7030), Université Paris, 13, Sorbonne Paris Citè, Villetaneuse, France 93430;ESSEC Business School, Cergy-Pontoise, France 95021;LIPN, CNRS (UMR7030), Université Paris, 13, Sorbonne Paris Citè, Villetaneuse, France 93430 and CERMICS École des Ponts ParisTech, Marne la Vallée Cedex 2, France 77455;LIPN, CNRS (UMR7030), Université Paris, 13, Sorbonne Paris Citè, Villetaneuse, France 93430
Venue:
Journal of Scheduling
Year:
2013

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Abstract

This paper presents a heuristic method based on column generation for the EDF (Electricité De France) long-term electricity production planning problem proposed as subject of the ROADEF/EURO 2010 Challenge. This is to our knowledge the first-ranked method among those methods based on mathematical programming, and was ranked fourth overall. The problem consists in determining a production plan over the whole time horizon for each thermal power plant of the French electricity company, and for nuclear plants, a schedule of plant outages which are necessary for refueling and maintenance operations. The average cost of the overall outage and production planning, computed over a set of demand scenarios, is to be minimized. The method proceeds in two stages. In the first stage, dates for outages are fixed once for all for each nuclear plant. Data are aggregated with a single average scenario and reduced time steps, and a set-partitioning reformulation of this aggregated problem is solved for fixing outage dates with a heuristic based on column generation. The pricing problem associated with each nuclear plant is a shortest path problem in an appropriately constructed graph. In the second stage, the reload level is determined at each date of an outage, considering now all scenarios. Finally, the production quantities between two outages are optimized for each plant and each scenario by solving independent linear programming problems.