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A Memetic Evolutionary Multi-Objective Optimization Method for Environmental Power Unit Commitment

Abstract : A multi-objective power unit commitment problem is framed to consider simultaneously the objectives of minimizing the operation cost and minimizing the emissions from the generation units. To find the solution of the optimal schedule of the generation units, a memetic evolutionary algorithm is proposed, which combines the non-dominated sorting genetic algorithm-II (NSGA-II) and a local search algorithm. The power dispatch sub-problem is solved by the weighed-sum lambda-iteration approach. The proposed method has been tested on systems composed by 10 and 100 generation units for a 24 hour demand horizon. The Pareto-optimal front obtained contains solutions of different trade off with respect to the two objectives of cost and emission, which are superior to those contained in the Pareto-front obtained by the pure NSGA-II. The solutions of minimum cost are shown to compare well with recent published results obtained by single-objective cost optimization algorithms.
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Submitted on : Wednesday, January 16, 2013 - 5:07:27 PM
Last modification on : Saturday, May 8, 2021 - 3:26:54 AM
Long-term archiving on: : Saturday, April 1, 2017 - 6:24:27 AM


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  • HAL Id : hal-00777057, version 1


Yan-Fu Li, Nicola Pedroni, Enrico Zio. A Memetic Evolutionary Multi-Objective Optimization Method for Environmental Power Unit Commitment. IEEE Transactions on Power Systems, Institute of Electrical and Electronics Engineers, 2013, pp.1-10. ⟨hal-00777057⟩



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