Enhanced generator scheduling through the lambda iteration method with clone-based optimization and Lévy flight integration

Authors

  • Rifki Rahman Nur Ikhsan School of Electrical Engineering, Telkom University, Bandung 40257, Indonesia; Center of Excellence of Sustainable Energy and Climate Change, Telkom University, Bandung 40257, Indonesia https://orcid.org/0009-0002-7873-0049
  • Jangkung Raharjo School of Electrical Engineering, Telkom University, Bandung 40257, Indonesia; Center of Excellence of Sustainable Energy and Climate Change, Telkom University, Bandung 40257, Indonesia https://orcid.org/0000-0001-7082-5583
  • Ardiansyah Ramadhan School of Electrical Engineering, Telkom University, Bandung 40257, Indonesia; Center of Excellence of Intelligent Sensing-IoT, Telkom University, Bandung 40257, Indonesia https://orcid.org/0009-0005-5126-1176
  • Lindiasari Martha Yustika School of Electrical Engineering, Telkom University, Bandung 40257, Indonesia; Center of Excellence of Sustainable Energy and Climate Change, Telkom University, Bandung 40257, Indonesia https://orcid.org/0009-0004-4281-048X
Article ID: 735
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DOI:

https://doi.org/10.18686/cest735

Keywords:

artificial intelligence; calculus-based; cloning; generator scheduling; Lévy flight

Abstract

One of the hardest parts of running a power system is economic dispatch (ED), which means finding the cheapest way to meet demand and operational limits. To alleviate these limitations, this study presents a hybrid methodology known as Vectorized Lambda Iteration Method–Clone-Based Optimization with Lévy flight (VLIM-CBO-LF). The contribution comprises the synchronized amalgamation of three complementary strategies. The VLIM-CBO-LF improves the accuracy of dispatch calculations, clone-based optimization improves local search by using adaptive cloning, selection, and Lévy flight improves global exploration by letting movements happen over long distances from time to time. The suggested method was tested on a 42-unit economic dispatch system over a 24-h period, running it 30 times to make sure it worked each time. The results show that VLIM-CBO-LF has the lowest average daily fuel cost of IDR 3.9096 × 1010. This is better than the artificial bee colony, particle swarm optimization, grey wolf optimization, and whale optimization algorithm. The cost goes down from IDR 1.050 × 10⁹ to 3.666 × 10⁹ per day, but the performance stays the same, with a standard deviation of 6.50 × 10⁷ IDR. The ANOVA test (F = 19.004, p = 1.820 × 10⁻⁷) and the Friedman test (χ2 = 60, p = 9.358 × 10⁻14) both show that the results are statistically significant. The Wilcoxon signed-rank test (W⁺ = 0) shows that the results are strictly better than all other benchmark methods. Future research may investigate the applicability of the VLIM-CBO-LF model to more complex electrical market mechanisms.

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Published

2026-06-05

How to Cite

Ikhsan, R. R. N., Raharjo, J., Ramadhan, A., & Yustika, L. M. (2026). Enhanced generator scheduling through the lambda iteration method with clone-based optimization and Lévy flight integration. Clean Energy Science and Technology, 4(3). https://doi.org/10.18686/cest735

Issue

Vol. 4 No. 3 (2026): In progress

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Article

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Copyright (c) 2026 Rifki Rahman Nur Ikhsan, Jangkung Raharjo, Ardiansyah Ramadhan, Lindiasari Martha Yustika

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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