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Journals / Turkish Journal of Electrical Engineering and Computer Science / 2013 / Cilt: 21 - Sayı: Sup.1

Estimation of fuel cost curve parameters for thermal power plants using the ABC algorithm

Yusuf SÖNMEZ
Pages
1827–1841
Publication Date
2012-12-01
DOI
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AI Summary & Key Takeaways

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

The results show that the ABC algorithm is stronger than the others at solving such a problem.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The solution accuracy of economic dispatch problems is associated with the accuracy of the fuel cost curve parameters.
📊
Key Findings & Data:In the estimation problem, 1st-, 2nd-, and 3rd-order fuel cost functions are used, and the estimation problem is formulated as an optimization one.
💡
Conclusion & Impact:The results show that the ABC algorithm is stronger than the others at solving such a problem.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Fuel cost curve · parameter estimation · artificial bee colony algorithm

Abstract

The solution accuracy of economic dispatch problems is associated with the accuracy of the fuel cost curve parameters. Therefore, updating of these parameters is a very important issue to further improve the final accuracy of economic dispatch problems. Estimating the parameters of the fuel cost curve may be the best solution for this issue. This paper presents an application of the artificial bee colony (ABC) algorithm to estimate the fuel cost curve parameters of thermal power plants. In the estimation problem, 1st-, 2nd-, and 3rd-order fuel cost functions are used, and the estimation problem is formulated as an optimization one. The ABC algorithm is used to solve this optimization problem by minimizing the total error in the estimated parameters. In this study, in order to evaluate the performance of the ABC algorithm, it is tested on 3 different cases that have 3 different fuel cost types, such as coal, oil, and gas. The results obtained from the proposed method are compared with the genetic algorithm, particle swarm optimization, and least square error methods reported previously in the literature. The results show that the ABC algorithm is stronger than the others at solving such a problem.

Özet

The solution accuracy of economic dispatch problems is associated with the accuracy of the fuel cost curve parameters. Therefore, updating of these parameters is a very important issue to further improve the final accuracy of economic dispatch problems. Estimating the parameters of the fuel cost curve may be the best solution for this issue. This paper presents an application of the artificial bee colony (ABC) algorithm to estimate the fuel cost curve parameters of thermal power plants. In the estimation problem, 1st-, 2nd-, and 3rd-order fuel cost functions are used, and the estimation problem is formulated as an optimization one. The ABC algorithm is used to solve this optimization problem by minimizing the total error in the estimated parameters. In this study, in order to evaluate the performance of the ABC algorithm, it is tested on 3 different cases that have 3 different fuel cost types, such as coal, oil, and gas. The results obtained from the proposed method are compared with the genetic algorithm, particle swarm optimization, and least square error methods reported previously in the literature. The results show that the ABC algorithm is stronger than the others at solving such a problem.

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Cite This Article

SÖNMEZ, Y. (2012). Estimation of fuel cost curve parameters for thermal power plants using the ABC algorithm. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 21 - Sayı: Sup.1, 1827–1841. https://ajindex.com/articles/188585

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