Optimized Economic Load Dispatch with Multiple Fuels and Valve-Point Effects Using Hybrid Genetic–Artificial Fish Swarm Algorithm

Sustainability

Fahad R. Albogamy¹  Abdulrashid Muhammad Kabir²  Zahid Ullah³  Faizan Mehmood⁴  Fiaz Ahmad⁵  Ghulam Hafeez⁶  Mohsin Kamal⁷ 

[1] Computer Sciences Program, Turabah University College, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;Department of Electrical Engineering, Kebbi State University of Science and Technology, Aliero 863104, Nigeria;Department of Electrical Engineering, Sialkot Campus, University of Management and Technology (Lahore), Sialkot 51310, Pakistan;Department of Electrical Engineering, University of Engineering and Technology, Taxila 47050, Pakistan;Department of Electrical and Computer Engineering, Air University Islamabad, Islamabad 44000, Pakistan;Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad 44000, Pakistan;KIOS Research and Innovation Center of Excellence, University of Cyprus, Nicosia 2109, Cyprus;

关键词: artificial fish swarm algorithm;    economic load dispatch;    genetic algorithm;    hybrid genetic–artificial fish swarm algorithm;    multi-objective optimization;    sustainable power generating system;   

DOI  :  10.3390/su131910609

来源: DOAJ

【 摘 要 】

Economic Load Dispatch (ELD) plays a pivotal role in sustainable operation planning in a smart power system by reducing the fuel cost and by fulfilling the load demand in an efficient manner. In this work, the ELD problem is solved by using hybridized robust techniques that combine the Genetic Algorithm and Artificial Fish Swarm Algorithm, termed the Hybrid Genetic–Artificial Fish Swarm Algorithm (HGAFSA). The objective of this paper is threefold. First, the multi-objective ELD problem incorporating the effects of multiple fuels and valve-point loading and involving higher-order cost functions is optimally solved by HGAFSA. Secondly, the efficacy of HGAFSA is demonstrated using five standard generating unit test systems (13, 40, 110, 140, and 160). Finally, an extra-large system is formed by combining the five test systems, which result in a 463 generating unit system. The performance of the developed HGAFSA-based ELD algorithm is then tested on the six systems including the 463-unit system. Annual savings in fuel costs of $3.254 m, $0.38235 m, $2135.7, $9.5563 m, and $1.1588 m are achieved for the 13, 40, 110, 140, and 160 standard generating units, respectively, compared to costs mentioned in the available literature. The HGAFSA-based ELD optimization curves obtained during the optimization process are also presented.

【 授权许可】

Unknown