This paper introduces a three-phase heuristic approach for a large-scale energy management and maintenance scheduling problem. The problem is concerned with scheduling maintenance and refueling for nuclear power plants up to five years into the future, while handling a number of scenarios for future demand and prices. The goal is to minimize the expected total production costs. The first phase of the heuristic solves a simplified constraint programming model of the problem, the second performs a local search, and the third handles overproduction in a greedy fashion. This work was initiated in the context of the ROADEF EURO Challenge 2010, a competition organized jointly by the French Operational Research and Decision Support Society, the European Operational Research Society, and the European utility company Electricite de France. In the concluding phase of the competition our team ranked second in the junior category and sixth overall. After correcting an implementation bug in the program that was submitted for evaluation, our heuristic solves all ten real-life instances, and the solutions obtained are all within 2.45 of the currently best known solutions. The results given here would have ranked first in the original competition.
Besides the work by very little is published on the topic. Nuclear maintenance and refueling is mentioned by in @cite_8 , but the problem considered is to minimize the environmental impact.
@cite_8
1984494184 2036434250 1499047025 2059695025
Abstract The problem addressed here is scheduling the shutdown for refueling and maintenance of nuclear power plants. The models have up to four reactors requiring of the order of five shutdowns each over a five-year time horizon. The resulting mixed-integer program is large and complex with interesting structure. We show good results using a mixed-integer optimizer taking advantage of a strong linear programming formulation. We address the problem of planning outages of nuclear power plants submitted by EDF (Electricite De France) as the challenge EURO ROADEF 2010. As our team won the first prize of the contest in the senior category, our approach may be of interest: it is conceptually simple, easy to program and computationally relatively fast. We present both our method and some ideas to improve it. This paper introduces a three-phase hybrid heuristic for a large-scale energy management and maintenance scheduling problem. The problem is to schedule maintenance periods and refueling amounts for nuclear power plants with a time horizon of up to five years, and handling a number of scenarios for future demand and prices. The goal is to minimize the expected total production cost. The first phase of the heuristic solves a constraint programming model of a simplified version of the problem, the second performs a local search, and the third handles overproduction in a greedy fashion. This work was initiated in the context of the ROADEF EURO Challenge 2010. In the concluding phase of the competition, our team ranked second in the junior category and sixth overall. After correcting a small implementation bug in the program that was submitted for final evaluation, our solver ranks first in the overall results from the competition. This paper presents a heuristic method based on column generation for the EDF (Electricite 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.