Parallel Multi-Objective Genetic Algorithm for Short-Term Economic Environmental Hydrothermal Scheduling

Zhong-Kai Feng, Wen-Jing Niu, Jian-Zhong Zhou, Chun-Tian Cheng, Hui Qin and Zhi-Qiang Jiang
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Zhong-Kai Feng: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Wen-Jing Niu: Institute of Hydropower System & Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Jian-Zhong Zhou: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Chun-Tian Cheng: Institute of Hydropower System & Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Hui Qin: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Zhi-Qiang Jiang: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2017, vol. 10, issue 2, 1-22

Abstract: With the increasingly serious energy crisis and environmental pollution, the short-term economic environmental hydrothermal scheduling (SEEHTS) problem is becoming more and more important in modern electrical power systems. In order to handle the SEEHTS problem efficiently, the parallel multi-objective genetic algorithm (PMOGA) is proposed in the paper. Based on the Fork/Join parallel framework, PMOGA divides the whole population of individuals into several subpopulations which will evolve in different cores simultaneously. In this way, PMOGA can avoid the wastage of computational resources and increase the population diversity. Moreover, the constraint handling technique is used to handle the complex constraints in SEEHTS, and a selection strategy based on constraint violation is also employed to ensure the convergence speed and solution feasibility. The results from a hydrothermal system in different cases indicate that PMOGA can make the utmost of system resources to significantly improve the computing efficiency and solution quality. Moreover, PMOGA has competitive performance in SEEHTS when compared with several other methods reported in the previous literature, providing a new approach for the operation of hydrothermal systems.

Keywords: parallel computing; economic environmental hydrothermal scheduling; multi-objective optimization; multi-objective genetic algorithm; constraint handling method (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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