The demand for electrical energy is globally growing very quickly. For this reason, the optimization of power plant productions and power plant maintenance scheduling have become important research topics. A Large Scale Energy Management (LSEM) problem is studied in this paper. Two types of power plants are considered: power plants of type 1 can be refueled while still operating. Power plants of type 2 need to be shut down from time to time, for refueling and ordinary maintenance (these are typically nuclear plants). Considering these two types of power plants, LSEM is the problem of optimizing production plans and scheduling of maintenances of type 2 plants, with the objective of keeping the production cost as low as possible, while fulfilling the customers demand. Uncertainty about the customers demand is taken into account in the model considered. In this article, a matheuristic optimization approach based on problem decomposition is proposed. The approach involves mixed integer linear programming and simulated annealing optimization methods. Computational results on some realistic instances are presented.

A matheuristic algorithm for a large-scale energy management problem

ANGHINOLFI, DAVIDE;NATTERO, CRISTIANO;PAOLUCCI, MASSIMO;
2012

Abstract

The demand for electrical energy is globally growing very quickly. For this reason, the optimization of power plant productions and power plant maintenance scheduling have become important research topics. A Large Scale Energy Management (LSEM) problem is studied in this paper. Two types of power plants are considered: power plants of type 1 can be refueled while still operating. Power plants of type 2 need to be shut down from time to time, for refueling and ordinary maintenance (these are typically nuclear plants). Considering these two types of power plants, LSEM is the problem of optimizing production plans and scheduling of maintenances of type 2 plants, with the objective of keeping the production cost as low as possible, while fulfilling the customers demand. Uncertainty about the customers demand is taken into account in the model considered. In this article, a matheuristic optimization approach based on problem decomposition is proposed. The approach involves mixed integer linear programming and simulated annealing optimization methods. Computational results on some realistic instances are presented.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/532695
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