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Newly-Developed Swarm Intelligence Algorithms Applied to Renewable Energy-Based Load Dispatch Real-World Problems

Newly-Developed Swarm Intelligence Algorithms Applied to Renewable Energy-Based Load Dispatch Real-World Problems

Sunanda Hazra, Provas Kumar Roy
Copyright: © 2020 |Pages: 26
ISBN13: 9781799832225|ISBN10: 1799832228|ISBN13 Softcover: 9781799832232|EISBN13: 9781799832249
DOI: 10.4018/978-1-7998-3222-5.ch001
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MLA

Hazra, Sunanda, and Provas Kumar Roy. "Newly-Developed Swarm Intelligence Algorithms Applied to Renewable Energy-Based Load Dispatch Real-World Problems." Handbook of Research on Advancements of Swarm Intelligence Algorithms for Solving Real-World Problems, edited by Shi Cheng and Yuhui Shi, IGI Global, 2020, pp. 1-26. https://doi.org/10.4018/978-1-7998-3222-5.ch001

APA

Hazra, S. & Roy, P. K. (2020). Newly-Developed Swarm Intelligence Algorithms Applied to Renewable Energy-Based Load Dispatch Real-World Problems. In S. Cheng & Y. Shi (Eds.), Handbook of Research on Advancements of Swarm Intelligence Algorithms for Solving Real-World Problems (pp. 1-26). IGI Global. https://doi.org/10.4018/978-1-7998-3222-5.ch001

Chicago

Hazra, Sunanda, and Provas Kumar Roy. "Newly-Developed Swarm Intelligence Algorithms Applied to Renewable Energy-Based Load Dispatch Real-World Problems." In Handbook of Research on Advancements of Swarm Intelligence Algorithms for Solving Real-World Problems, edited by Shi Cheng and Yuhui Shi, 1-26. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-3222-5.ch001

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Abstract

Swarm intelligence is a promising field of biologically-inspired artificial intelligence, which is based on the behavioral models of social insects. This article covers Swarm Intelligence Algorithm, i.e., grasshopper optimization algorithm (GOA) which is based on the social communication nature of the grasshopper, applied to renewable energy based economic and emission dispatch problems. Based on Weibull probability density function (W-pdf), the stochastic wind speed including optimization problem is numerically solved for a 2 renewable wind energy incorporating 6 and 14 thermal units for 3 different loads. Moreover, to improve the solution superiority and convergence speed, quasi oppositional based learning (QOBL) is included with the main GOA algorithm. The performance of GOA and QOGOA is evaluated and the simulation results as well as statistical results obtained by these methods along with different other algorithms available in the literature are presented to demonstrate the validity and effectiveness of the proposed GOA and QOGOA schemes for practical applications.

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