Research Article | ![]()
Application of the JAYA Algorithm for Optimal Power Flow and RES uncertainty with Distributed Generation on the IEEE 30-Bus System
Author(s): Priya Patil1, Dr. Sangamesh Sakri2
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 14, Issue 1
Publisher : FOREX Publication
Published : 10 March 2026
e-ISSN : 2347-470X
Page(s) : 146-153
Abstract
Because they are intermittent and stochastic, Renewable Energy Sources (RES) like wind and solar add a great deal of uncertainty to power systems when integrated. Particularly in large-scale systems like the 220kV IEEE 30-Bus network, these uncertainties make grid stability maintenance challenging. This paper introduces a framework that uses JAYA optimization to reduce the effects of RES uncertainty on grid performance. Power flow, voltage stability, and reactive power support are optimized in variable RES generation situations using the JAYA algorithm, which is renowned for its robust convergence qualities and its simplicity. To keep the system stable, the suggested method makes real-time adjustments to control parameters such reactive power compensators, tap-changing transformers, and generator outputs. When compared to conventional optimization methods, the JAYA algorithm considerably improves voltage profile, decreases power losses, and increases system dependability in simulations run on the IEEE 30-Bus system with high-penetration renewable energy sources added. In view of the increasing impact of renewable energy sources, this study emphasizes the possibility of intelligent metaheuristic algorithms to facilitate robust and stable grid operation. In terms of identifying high-quality, optimally viable solutions, the numerical results demonstrate that the proposed JAYA Optimization outperforms all prior published-results over a range of objective functions with total generation cost reduced values of 747.16 ₹/h according to the data. In addition, the proposed algorithm's superiority then BSA approach.
Keywords: Optimal power flow, Jaya algorithm, Generation cost, Power losses, Voltage stability enhancement, Distributed generation.
Priya Patil, Assistant Professor, Electrical & Electronics Engineering., PDA College of Engineering, Kalaburagi (Gulbarga), Karnataka, India; Email: priyampatil2020@gmail.com
Dr. Sangamesh Sakri, Associate Professor, Electrical & Electronics Engineering., PDA College of Engineering, Kalaburagi (Gulbarga), Karnataka, India; Email: sakripda@gmail.com
-
[1] J. Lakshmi Priya and S. T. Jaya Christa, “An effective hybridized GWO BSA for resolving optimal power flow problem with the inclusion of unified power flow controller,” IETE J. Res., vol. 69, no. 7, pp. 4605–4617, 2023. doi: 10.1080/03772063.2021.1942245
-
[2] A. F. Barakat, R. A. El Sehiemy, M. I. Elsayd, and E. Osman, “An enhanced Jaya optimization algorithm (EJOA) for solving multi-objective ORPD problem,” in Proc. Int. Conf. Innovative Trends Comput. Eng. (ITCE), 2019, pp. 479–484. doi: 10.1109/ITCE.2019.8646363
-
[3] G. Jothi, H. H. Inbarani, A. T. Azar, and K. R. Devi, “Rough set theory with Jaya optimization for acute lymphoblastic leukemia classification,” Neural Comput. Appl., vol. 31, pp. 5175–5194, 2019. doi: 10.1007/s00521-018-3665-7
-
[4] A. Garbaya, M. Kotti, M. Fakhfakh, and B. Benhala, “Comparative study of Jaya metaheuristic to benchmark functions and RF circuits,” in Proc. Int. Conf. Innovative Res. Appl. Sci., Eng. Technol. (IRASET), 2020, pp. 1–5.
-
[5] M. I. Jarrah et al., “A novel explanatory hybrid artificial bee colony algorithm for numerical function optimization,” J. Supercomput., vol. 76, pp. 9330–9354, 2020. doi: 10.1007/s11227-020-03289-z
-
[6] F. Berrouk et al., “A new multi-objective Jaya algorithm for solving the optimal power flow problem,” J. Electr. Syst., vol. 14, no. 3, pp. 165–181, 2018. doi: 10.20528/jesi.2018.03.001
-
[7] K. Aurangzeb et al., “An effective solution to the optimal power flow problem using meta-heuristic algorithms,” Front. Energy Res., vol. 11, Art. no. 1170570, 2023. doi: 10.3389/fenrg.2023.1170570
-
[8] T. H. B. Huy et al., “Performance improvement of multiobjective optimal power flow-based renewable energy sources using intelligent algorithm,” IEEE Access, vol. 10, pp. 48379–48404, 2022. doi: 10.1109/ACCESS.2022.3170738
-
[9] F. Daqaq, M. Ouassaid, and R. Ellaia, “A new meta-heuristic programming for multi-objective optimal power flow,” Electr. Eng., vol. 103, no. 2, pp. 1217–1237, 2021. doi: 10.1007/s00202-020-01118-5
-
[10] K. Sriram, S. P. Mangaiyarkarasi, S. Sakthivel, and L. Jebaraj, “An extensive study using the beetle swarm method to optimize single and multiple objectives of various optimal power flow problems,” Int. Trans. Electr. Energy Syst., vol. 2023, Art. no. 5779700, 2023. doi: 10.1002/2050-7038.5779700
-
[11] C. T. Hien, M. P. Duong, and L. H. Pham, “Skill optimization algorithm for solving optimal power flow problem,” Bull. Electr. Eng. Inform., vol. 13, no. 1, pp. 12–19, 2024. doi: 10.11591/eei.v13i1.4354
-
[12] O. Akdag, “A improved Archimedes optimization algorithm for multi/single-objective optimal power flow,” Electr. Power Syst. Res., vol. 206, Art. no. 107796, 2022. doi: 10.1016/j.epsr.2022.107796
-
[13] M. Al-Kaabi, J. Al Hasheme, and L. Al-Bahrani, “Improved Differential Evolution Algorithm to solve multi-objective of optimal power flow problem,” Arch. Electr. Eng., pp. 641–657, 2022. doi: 10.24425/aee.2022.142244
-
[14] A. L. Layth, A. L. Murtadha, and A. L. Jaleel, “Solving optimal power flow problem using improved differential evolution algorithm,” Int. J. Electr. Electron. Eng. Telecommun., vol. 11, no. 2, pp. 146–155, 2022.
-
[15] S. P. Dash, K. R. Subhashini, and P. Chinta, “Development of a Boundary Assigned Animal Migration Optimization algorithm and its application to optimal power flow study,” Expert Syst. Appl., vol. 200, Art. no. 116776, 2022. doi: 10.1016/j.eswa.2022.116776
-
[16] N. A. Nguyen, D. N. Vo, T. T. Nguyen, and T. L. Duong, “An improved equilibrium optimizer algorithm for solving optimal power flow problem with penetration of wind and solar energy,” Int. Trans. Electr. Energy Syst., vol. 2022, Art. no. 7827164, 2022. doi: 10.1002/2050-7038.7827164
-
[17] A. M. Shaheen et al., “multi-dimensional energy management based on an optimal power flow model using an improved quasi-reflection jellyfish optimization algorithm,” Eng. Optim., vol. 55, no. 6, pp. 907–929, 2023. doi: 10.1080/0305215X.2022.2144034
-
[18] S. Mouassa, A. Althobaiti, F. Jurado, and S. S. M. Ghoneim, “Novel design of slim mould optimizer for the solution of optimal power flow problems incorporating intermittent sources: A case study of Algerian electricity grid,” IEEE Access, vol. 10, pp. 22646–22661, 2022. doi: 10.1109/ACCESS.2022.3154639
-
[19] A. S. Alghamdi, “Optimal power flow of renewable-integrated power systems using a Gaussian bare-bones levy-flight firefly algorithm,” Front. Energy Res., vol. 10, Art. no. 921936, 2022. doi: 10.3389/fenrg.2022.921936
-
[20] S. Banerjee, P. K. Roy, and P. K. Saha, “A Novel Trigonometric Mutation-Based Backtracking Search Algorithm for Solving Optimal Power Flow Problem Considering Renewable Energy Sources,” in Proc. Int. Conf. Comput. Intell. Commun. Bus. Anal., Cham, Switzerland: Springer, 2023, pp. 171–185. doi: 10.1007/978-3-031-23456-7_14
-
[21] S. Gupta et al., “A Robust Optimization Approach for Optimal Power Flow Solutions Using Rao Algorithms,” Energies, vol. 14, Art. no. 5449, 2021. doi: 10.3390/en14175449
-
[22] M. Ghasemi, M. Zare, S. K. Mohammadi, and S. Mirjalili, “Applications of whale migration algorithm in optimal power flow problems of power systems,” in Handbook of Whale Optimization Algorithm, Academic Press, 2024, pp. 347–364.
-
[23] M. H. Hassan et al., “Developing chaotic Bonobo optimizer for optimal power flow analysis considering stochastic renewable energy resources,” Int. J. Energy Res., vol. 46, no. 8, pp. 11291–11325, 2022. doi: 10.1002/er.7700
-
[24] M. Al-Kaabi, J. Al Hasheme, V. Dumbrava, and M. Eremia, “Application of Harris Hawks Optimization (HHO) Based on Five Single Objective Optimal Power Flow,” in Proc. Int. Conf. Electron., Comput. Artif. Intell. (ECAI), 2022, pp. 1–8. doi: 10.1109/ECAI55653.2022.9946893
-
[25] R. K. Avvari, “Optimal Power Flow Using Improved Grey Wolf Optimization Algorithm,” in Proc. Int. Conf. Smart Technol. Power Renew. Energy (SPECon), 2024, pp. 1–5.

I. J. of Electrical & Electronics Research