Title: A multi-objective dynamic programming-based metaheuristic to solve a bi-objective unit commitment problem using a multi-objective decoder

Authors: Sophie Jacquin; Laetitia Jourdan; El-Ghazali Talbi

Addresses: DOLPHIN Project-team, Inria Lille - Nord Europe, 40 Avenue du Halley, 59650 Villeneuve-d'Ascq, France; Université Lille 1, CRIStAL, UMR CNRS 8022, 40 Avenue du Halley, 59650 Villeneuve-d'Ascq, France ' DOLPHIN Project-team, Inria Lille - Nord Europe, 40 Avenue du Halley, 59650 Villeneuve-d'Ascq, France; Université Lille 1, CRIStAL, UMR CNRS 8022, 40 Avenue du Halley, 59650 Villeneuve-d'Ascq, France ' DOLPHIN Project-team, Inria Lille - Nord Europe, 40 Avenue du Halley, 59650 Villeneuve-d'Ascq, France; Université Lille 1, CRIStAL, UMR CNRS 8022, 40 Avenue du Halley, 59650 Villeneuve-d'Ascq, France

Abstract: The unit commitment problem (UCP) is a heavily constrained scheduling problem, where the on/off scheduling and production amounts of heterogeneous power production units have to be determined for a discrete time horizon. Due to environmental concerns, the traditional UCP to solely minimise the production cost is no longer adequate, and a second objective, to minimise the gas emissions has to be added to properly model reality. In this paper, we propose an efficient metaheuristic to solve this multi-objective version of the UCP. The proposed method, MO-DYNAMOP, is a generalisation of DYNAMOP (DYNAmic programming-based Metaheuristic for Optimisation Problems), a state-of-the-art hybrid optimiser which was successfully applied to the single-objective unit commitment problem. The main difficulty in extending DYNAMOP to the multi-objective UCP is that it uses an indirect representation of solution that gives the on/off scheduling of each unit. The real production amounts are computed by an exact sub-optimiser which minimises the production cost assuming that the on/off scheduling is fixed. Since the sub-optimiser now has to solve a multi-objective problem, each on/off scheduling induces an entire Pareto optimal set of solutions. We handle this complication by assigning an approximation of the corresponding set to each on/off scheduling solution. A comparison study with methods previously proposed in the literature indicates that MO-DYNAMOP performs considerably better on many benchmark instances.

Keywords: hybrid optimisation; indirect representation; multi-objective decoder; multi-objective dynamic programming; multi-objective evolutionary algorithms; unit commitment problem; multi-objective UCP; constrained scheduling; metaheuristics; production costs; greenhouse gases; GHG emissions.

DOI: 10.1504/IJMHEUR.2016.079104

International Journal of Metaheuristics, 2016 Vol.5 No.1, pp.3 - 30

Available online: 12 Sep 2016 *

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