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Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Optimized Economic Load Dispatch with Multiple Fuels and Valve-Point Effects Using Hybrid Genetic–Artificial Fish Swarm Algorithm
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.
Optimized Economic Load Dispatch with Multiple Fuels and Valve-Point Effects Using Hybrid Genetic–Artificial Fish Swarm Algorithm
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.
Optimized Economic Load Dispatch with Multiple Fuels and Valve-Point Effects Using Hybrid Genetic–Artificial Fish Swarm Algorithm
Abdulrashid Muhammad Kabir (Autor:in) / Mohsin Kamal (Autor:in) / Fiaz Ahmad (Autor:in) / Zahid Ullah (Autor:in) / Fahad R. Albogamy (Autor:in) / Ghulam Hafeez (Autor:in) / Faizan Mehmood (Autor:in)
2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
artificial fish swarm algorithm , economic load dispatch , genetic algorithm , hybrid genetic–artificial fish swarm algorithm , multi-objective optimization , sustainable power generating system , Environmental effects of industries and plants , TD194-195 , Renewable energy sources , TJ807-830 , Environmental sciences , GE1-350
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