Journal
APPLIED THERMAL ENGINEERING
Volume 146, Issue -, Pages 688-700Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.applthermaleng.2018.10.020
Keywords
Environmental-economic dispatch; Power systems; Multi-objective optimization; Reinforcement learning; Genetic algorithm
Funding
- National Council of Scientific and Technologic Development of Brazil - CNPq [303908/2015-7-PQ, 303906/2015-4-PQ, 404659/2016-0-Univ, 405101/2016-3-Univ]
- National Council of Scientific and Technologic Development of Brazil - CNPq (PRONEX 'Fundacao Araucaria') [042/2018]
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This paper presents an improved non-dominated sorting genetic algorithm II (NSGA-II) approach incorporating a parameter-free self-tuning by reinforcement learning technique called learner non-dominated sorting genetic algorithm (NSGA-RL) for the multi-objective optimization of the environmental/economic dispatch (EED) problem. To evaluate the performance features, the proposed NSGA-RL approach is investigated on ten multi objective benchmark functions. Besides, to evaluate the effectiveness of the proposed approach, the standard IEEE (Institute of Electrical and Electronics Engineers) of 30-bus network with six generating units (with/without considering losses) is adopted, with operating cost (fuel cost) and pollutant emission as two conflicting objectives to be optimized at the same time. In comparison to literature, it was observed that the proposed approach provides a better satisfaction level in conflicting objectives with well distributed Pareto front, in comparison with the classical NSGA-II method, and to other existing methods reported in the literature. The NSGA-RL was found to be comparable to them considering the quality of the solutions obtained, with the advantage of non-time spent for parameters tuning.
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