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Black Widow Optimization for Multi Area Economic Emission Dispatch

G. Girishkumar1,*, S. Ganesan2, N. Jayakumar3, S. Subramanian4

1 Department of EEE, Government Polytechnic College, Keelapaluvur, Ariyalur, 621707, Tamil Nadu, India
2 Department of EEE, Government College of Engineering, Salem, 636011, Tamil Nadu, India
3 Department of EEE, Government Polytechnic College, Uthangarai, 635207, Tamil Nadu, India
4 Department of Electrical Engineering, Annamalai University, Annamalai Nagar, 608002, Tamil Nadu, India

* Corresponding Author: G. Girishkumar. Email: email

Intelligent Automation & Soft Computing 2023, 35(1), 609-625.


The optimization field has grown tremendously, and new optimization techniques are developed based on statistics and evolutionary procedures. Therefore, it is necessary to identify a suitable optimization technique for a particular application. In this work, Black Widow Optimization (BWO) algorithm is introduced to minimize the cost functions in order to optimize the Multi-Area Economic Dispatch (MAED). The BWO is implemented for two different-scale test systems, comprising 16 and 40 units with three and four areas. The performance of BWO is compared with the available optimization techniques in the literature to demonstrate the strategy’s efficacy. Results show that the optimized cost for four areas with 16 units is found to be 7336.76$/h, whereas it is 121,589$/h for four areas with 40 units using BWO. It is also noted that optimization algorithms other than BWO require higher cost value. The best-optimized solution for emission is achieved at 9.2784e+06 tones/h, and it is observed that there is a considerable difference between the worst and the best values. Also, the suggested technique is implemented for large-scale test systems successfully with high precision, and rapid convergence occurs in MAED.


Cite This Article

G. Girishkumar, S. Ganesan, N. Jayakumar and S. Subramanian, "Black widow optimization for multi area economic emission dispatch," Intelligent Automation & Soft Computing, vol. 35, no.1, pp. 609–625, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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