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An advanced Grey Wolf Optimization Algorithm and its application to planning problem in smart grids

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Abstract

Due to the complex mathematical structures of the models in engineering, heuristic methods which do not require derivative are developed. This paper improves recently developed Grey Wolf Optimization Algorithm by extending it with three new features: namely presenting a new formulation for evaluating the positions of search agents, applying mirroring distance to the variables violating the limits, and proposing a dynamic decision approach for each agent either in exploration or exploitation phases. The performance of Advanced Grey Wolf Optimization (AGWO) method is tested using several optimization test functions and compared to several heuristic algorithms. Moreover, a planning problem in smart grids is solved by considering different objective functions using 33 and 141 bus distribution test systems. From the numerical simulation results, we observe that, AGWO is able to find the best results compared to other methods from 10 and 9 out of 13 test functions for 30 and 60 variables, respectively. Similar to this, it finds best function values for 5 out of 10 fixed number of variable test functions. Also, the result of the CEC-C06 2019 benchmark functions shows that AGWO outperforms 8 for optimization problems from 10. In power distribution system planning problem, better objective function values were determined by using AGWO, resulting a better voltage profile, less losses, and less emission costs compared to solutions obtained by Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms.

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Not Applicable

Code availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This research is funded as a part of “117E773 Advanced Evolutionary Computation for Smart Grid and Smart Community” project under the framework of 1001 Project organized by “The Scientific and Technological Research Council of Turkey TUBITAK”.

Funding

This research is funded as a part of “117E773 Advanced Evolutionary Computation for Smart Grid and Smart Community” project under the framework of 1001 Project organized by “The Scientific and Technological Research Council of Turkey TUBITAK”.

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Authors and Affiliations

  1. Department of Electrical Engineering, Istanbul Technical University, Istanbul, Turkey

    Bahman Ahmadi, Soheil Younesi & Aydogan Ozdemir

  2. Management and Information Systems Department, Kadir Has University, Istanbul, Turkey

    Oguzhan Ceylan

  3. Department of Electrical and Electronics Engineering, Marmara University, Istanbul, Turkey

    Oguzhan Ceylan

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  1. Bahman Ahmadi
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  2. Soheil Younesi
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  3. Oguzhan Ceylan
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  4. Aydogan Ozdemir
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Contributions

Bahman Ahmadi has made literature review, worked on software development, prepared the original draft, and visualized the simulation results. Soheil Younesi contributed to literature review and helped on preparing the draft. Oguzhan Ceylan contributed to methodology, data curation, review and editing, and formal analysis. Aydogan Ozdemir contributed to conceptualization and validation. He supervised the study and administered the project.

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Correspondence to Oguzhan Ceylan.

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Ahmadi, B., Younesi, S., Ceylan, O. et al. An advanced Grey Wolf Optimization Algorithm and its application to planning problem in smart grids. Soft Comput 26, 3789–3808 (2022). https://doi.org/10.1007/s00500-022-06767-9

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