Journals / Electrica / 2018 / Cilt: 18 - Sayı: 2

New Optimization Algorithms for Application to Environmental Economic Load Dispatch in Power Systems

Journal
Electrica
Pages
133–142
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results demonstrate that the proposed new methods have better economic and environmental distribution performances.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The determination of the most economical generation dispatch in an electrical power system has become a very important issue globally.
📊
Key Findings & Data:The results demonstrate that the proposed new methods have better economic and environmental distribution performances.
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Conclusion & Impact:Accordingly, it can be concluded that the new methods are more effective and should be adopted.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Özet

The determination of the most economical generation dispatch in an electrical power system has become a very important issue globally. However, economical dispatch can no longer be considered alone because of environmental problems that are derived from emissions such as nitrogen oxide, carbon dioxide, and sulfur dioxide. In this study, the problem of environmental economic load dispatch (EELD) in a power system of six generators is addressed both by neglecting and including line transmission losses using a modified genetic algorithm and a modified artificial bee colony optimization method. The results of these modified algorithms are compared with those of the unmodified versions. The results demonstrate that the proposed new methods have better economic and environmental distribution performances. Accordingly, it can be concluded that the new methods are more effective and should be adopted.

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Cite This Article

AKKAŞ, Ö. P., ÇAM, E., EKE, İ., ARIKAN, Y. (2018). New Optimization Algorithms for Application to Environmental Economic Load Dispatch in Power Systems. Electrica, Cilt: 18 - Sayı: 2, 133–142. https://ajindex.com/articles/153538

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