Optimal Power Flow using Hybrid Metaheuristic Algorithms with RES and EV Integration

Chetan Bariya

doi:10.31127/tuje.1840309

Optimal Power Flow using Hybrid Metaheuristic Algorithms with RES and EV Integration

Abstract

The growing integration of renewable energy sources (RES) and electric vehicles (EVs) in modern power systems introduces new challenges in solving Optimal Power Flow (OPF) problems due to the uncertainty and variability of generation and demand. This paper proposes a novel hybrid metaheuristic algorithm based on Harris Hawks Optimization (HHO) and Salp Swarm Algorithm (SSA) to solve the OPF problem in systems with wind, solar, and V2G-enabled EVs. The objective is to minimize total fuel cost, reduce emissions, and improve voltage profile while satisfying system operational constraints such as power balance, generator limits, voltage bounds, and line flow limits. The performance of the proposed method is tested on standard and modified IEEE 57-bus systems. Simulation results demonstrate that the hybrid HHO-SSA algorithm outperforms individual HHO, SSA optimization techniques and conventional optimization methods such as GA and PSO in terms of convergence speed, cost reduction, and voltage stability, especially under high RES and EV penetration scenarios. Results obtained with the proposed strategy shows 14.8%, 19.7% and 4.9% improvement in minimization of total fuel cost, emission reduction and voltage profile improvement respectively compared to current ongoing research.

Keywords

Supporting Institution

Government Engineering Collge, Modasa and. Gujarat Technological University.

Project Number

NIL

Ethical Statement

I certify that my work is original and is not published or under publication consideration elsewhere. In addition, I confirm that submitted papers have not been copied or plagiarized, in whole or in part, from other papers or studies.

Thanks

Special thanks to electrical department, Government Engineering College, Modasa.

References

Ullah, Z., Kotb, K. M., Elkadeem, M. R., Yan, L., Khan, T., Qazi, H. S., Badshah, F., & Abido, M. A. (2025). Integrating electric vehicles and renewable energy in modern power systems: A review of EV charging station design, control strategies, and emerging trends. Ain Shams Engineering Journal, 16(10), 103592.
Suraparaju, S. K., Samykano, M., Vennapusa, J. R., Rajamony, R. K., Balasubramanian, D., Said, Z., & Pandey, A. K. (2025). Challenges and prospectives of energy storage integration in renewable energy systems for net zero transition. Journal of Energy Storage, 125, 116923. https://doi.org/10.1016/J.EST.2025.116923
Kumar, P., Channi, H. K., Kumar, R., Rajiv, A., Kumari, B., Singh, G., Singh, S., Dyab, I. F., & Lozanović, J. (2025). A comprehensive review of vehicle-to-grid integration in electric vehicles: Powering the future. Energy Conversion and Management: X, 25, 100864. https://doi.org/10.1016/J.ECMX.2024.100864
Risi, B. G., Riganti-Fulginei, F., & Laudani, A. (2022). Modern Techniques for the Optimal Power Flow Problem: State of the Art. Energies 2022, Vol. 15, Page 6387, 15(17), 6387. https://doi.org/10.3390/EN15176387
Ehsan, A., & Yang, Q. (2018). Optimal integration and planning of renewable distributed generation in the power distribution networks: A review of analytical techniques. Applied Energy, 210, 44–59. https://doi.org/10.1016/j.apenergy.2017.10.106
Bakır, H. (2024). Optimal power flow analysis with circulatory system-based optimization algorithm. Turkish Journal of Engineering, 8 (1), 92-106
Guven, A. F. (2024). Integrating electric vehicles into hybrid microgrids: A stochastic approach to future-ready renewable energy solutions and management. Energy, 303, 131968. https://doi.org/10.1016/J.ENERGY.2024.131968
Wang, Z., Younesi, A., Liu, M. V., Guo, G. C., & Anderson, C. L. (2023). AC Optimal Power Flow in Power Systems with Renewable Energy Integration: A Review of Formulations and Case Studies. IEEE Access, 11, 102681–102712. https://doi.org/10.1109/ACCESS.2023.3314330.

Çelik, İ., Yıldız, C., & Şekkeli, M. (2021). Wind power plant layout optimization using particle swarm optimization. Turkish Journal of Engineering, 5(2), 89–94. https://doi.org/10.31127/TUJE.698856
Patel, N. M., Thangadurai, N., & Patel, C. (2024). Analysis of Optimal Power Flow Using Combined PSO-GSA Technique. ITM Web of Conferences, 65, 04003.https://doi.org/10.1051/itmconf/20246504003
Hasanien, H. M., Alsaleh, I., & Alassaf, A. (2024). Impact of electric vehicles and wave energy systems on OPF of power networks using hybrid Osprey-PSO approach. Energy, 308, 132818. https://doi.org/10.1016/J.ENERGY.2024.132818
Paul, C., Sarkar, T., Dutta, S., Hazra, S., & Roy, P. K. (2024). Optimal Power Flow of Multi-objective Combined Heat and Power with Wind-Solar-Electric Vehicle-Tidal Using Hybrid Evolutionary Approach. Process Integration and Optimization for Sustainability, 8(5), 1337–1367. https://doi.org/10.1007/S41660-024-00431-5 Azadikhouy, M. (2025). Multi objective moth swarm algorithm for optimizing electric vehicle integration in distribution grids. Scientific Reports, 15(1), 25320. https://doi.org/10.1038/S41598-025-10849-7
Ghasemi, M., Ghavidel, S., Ghanbarian, M. M., Gharibzadeh, M., & Azizi Vahed, A. (2014). Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm. Energy, 78, 276–289. https://doi.org/10.1016/J.ENERGY.2014.10.007
Alexakis, K., Benekis, V., Kokkinakos, P., & Askounis, D. (2025). Genetic algorithm-based multi-objective optimisation for energy-efficient building retrofitting: A systematic review. Energy and Buildings, 328. https://doi.org/10.1016/J.ENBUILD.2024.115216
Majed, Roy, N. K., & Ahmed, A. (2025). Optimizing the load curve through V2G technology for sustainable energy management. Energy Reports, 13, 5848–5863. https://doi.org/10.1016/J.EGYR.2025.04.066
Mostafa, R. R., Khedr, A. M., AL Aghbari, Z., Afyouni, I., Kamel, I., & Ahmed, N. (2025). A multi-strategy improved electric eel foraging optimization algorithm: continuous and binary variants for solving optimization problems. International Journal of Machine Learning and Cybernetics, 16(9), 5985–6030. https://doi.org/10.1007/S13042-025-02609-W
Alorf, A. (2023). A survey of recently developed metaheuristics and their comparative analysis. Engineering Applications of Artificial Intelligence, 117.https://doi.org/10.1016/J.ENGAPPAI.2022.105622
Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawk optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849–872. https://doi.org/10.1016/J.FUTURE.2019.02.028
El-Shorbagy, M. A., & Nasar, I. (2025). Salp Swarm Optimization: A Comprehensive Review of Recent Advances, Variants, Applications, and Future Research Directions. European Journal of Pure and Applied Mathematics, 18(4), 7030. https://doi.org/10.29020/NYBG.EJPAM.V18I4.7030
Abualigah, L. G.;, Zitar, A., Hashim, R. ;, Amin, F. A. ;, Saber, M. ;, Almotairi, A. ;, Gandomi, K. H. ;, Hussien, A. G., (2022). Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications. Electronics 2022, Vol. 11, Page 1919, 11(12), 1919. https://doi.org/10.3390/ELECTRONICS11121919
Zhu, L., Xu, B., Wang, X., Yue, H., Mo, R., Wang, S., Zhao, Z., & Lu, P. (2025). Stochastic simulation framework for renewable power output: Integrating hybrid discrete-continuous distributions with vine copula function. Renewable Energy, 251, 123444. https://doi.org/10.1016/J.RENENE.20
Brahmbhatt, B., Sarda, J., Bariya, C., Choi, S. Y., Hui, K. L., & Sain, M. (2025). An efficient real-time model of a five-level modular multilevel converter with modified pulse width modulation technique for reduced switching frequency. Measurement and Control (United Kingdom). https://doi.org/10.1177/00202940251313694;WGROUP:STRING:PUBLICATION.
Tan, W. S., Shaaban, M., & Ab Kadir, M. Z. A. (2019). Stochastic generation scheduling with variable renewable generation: Methods, applications, and future trends. IET Generation, Transmission and Distribution, 13(9), 1467–1480. https://doi.org/10.1049/IET-GTD.2018.6331
Abaci, K., Yetgin, Z., Yamacli, V., & Isiker, H. (2024). Modified effective butterfly optimizer for solving optimal power flow problem. Heliyon, 10(12), e32862.https://doi.org/10.1016/J.HELIYON.2024.E32862
Banker, S., Chakravorty, J., Bariya, C., … Chaudhary, T. (2025). Enhancing Aggregator profitability in the Electricity Market using modern metaheuristic optimization techniques. Turkish Journal of Engineering, 9(4), 661-669.
Kumar Easwaramoorthy, N., & Dhanasekaran, R. (2012). Solution of Optimal Power Flow Problem Incorporating Various FACTS Devices. International Journal of Computer Applications, 55(4), 38–44. https://doi.org/10.5120/8746-2633
Sisteminde, G., Algoritmalarla, S., Ekonomi, S., Maliyet, A., Serkan İşcan, O., & Lokman, G. (2024). Cost Optimization for Sustainable Economy with Heuristic Algorithms in Power System. Turkish Journal of Engineering Research and Education, 3(1),26–37. https://dergipark.org.tr/en/pub/tmaed/issue/84804/1402629
Mochi, P., Pandya, K. S., Faia, R., Dabhi, D., Soares, J., & Vale, Z. (2024). Empowering customers in local electricity market: A prosumer segmentation and operating envelope strategy for joint cost reduction and profit maximization. Electric Power Systems Research, 226. https://doi.org/10.1016/J.EPSR.2023.109908
Elnady, M., & Ozana, S. (2025). Artificial Intelligence techniques in Vehicle-to-Grid (V2G) systems: A review, comparative study, and model evaluation. Journal of Energy Storage, 135, 118155. https://doi.org/10.1016/J.EST.2025.118155
Yang, H. T., Yang, P. C., & Huang, C. L. (1996). Evolutionary programming based economic dispatch for units with non-smooth fuel cost functions. IEEE Transactions on Power Systems, 11(1), 112–118. https://doi.org/10.1109/59.485992
Wang, C., Liu, W., Zhang, J., & Liu, H. (2023). Potential Development and Planning Method of Industrial Load Regulation. Lecture Notes in Electrical Engineering, 1054 LNEE, 1195–1203. https://doi.org/10.1007/978-981-99-3408-9_107
Bakır, H., Duman, S., Guvenc, U., & Kahraman, H. T. (2023). A novel optimal power flow model for efficient operation of hybrid power networks. Computers and Electrical Engineering, 110, 108885. https://doi.org/10.1016/J.COMPELECENG.2023.108885
Barnawi, A. B. (2024). Development and analysis of AC optimal power flow optimization algorithms for minimization of cost and emissions with stochastic renewables. Energy Reports, 11, 2059–2076. https://doi.org/10.1016/J.EGYR.2024.01.052
Basak, S., Bhattacharyya, B., & Dey, B. (2022). Dynamic economic dispatch using hybrid CSAJAYA algorithm considering ramp rates and diverse wind profiles. Intelligent Systems with Applications, 16, 200116. https://doi.org/10.1016/J.ISWA.2022.200116
Trivedi, I. N., Bhoye, M., Jangir, P., Parmar, S. A., Jangir, N., & Kumar, A. (2016). Voltage stability enhancement and voltage deviation minimization using BAT optimization algorithm. 2016 3rd International Conference on Electrical Energy Systems, ICEES 2016, 112–116. https://doi.org/10.1109/ICEES.2016.7510626.
Naidu, T. P., Balasubramanian, G., & Bathina, V. R. (2024). Weighted sum method based multi-objective optimal power flow considering various objectives: An application of whale optimization algorithm. International Journal of Applied Power Engineering, 13(4), 963–972. https://doi.org/10.11591/IJAPE.V13.I4.PP963-972
Junlakarn, S., Diewvilai, R., & Audomvongseree, K. (2022). Stochastic Modeling of Renewable Energy Sources for Capacity Credit Evaluation. Energies, 15(14). https://doi.org/10.3390/EN15145103
Tosun, S., Öztürk, A., & Hasırcı, U. (2018). THE EFFECTS OF RENEWABLE ENERGY SOURCES ON VOLTAGE STABILITY. Technological Applied Sciences, 13(2), 67–74. https://doi.org/10.12739/NWSA.2018.13.2.2A0137
Sarda, J., Pandya, K., & Lee, K. Y. (2021). Dynamic optimal power flow with cross entropy covariance matrix adaption evolutionary strategy for systems with electric vehicles and renewable generators. International Journal of Energy Research, 45(7), 10869–10881. https://doi.org/10.1002/ER.6571
Aljeddani, S. M. A., & Mohammea, M. A. (2023). A novel approach to Weibull distribution for the assessment of wind energy speed. Alexandria Engineering Journal, 78, 56–64. https://doi.org/10.1016/J.AEJ.2023.07.027
Sumit Banker. (2024). Metaheuristic optimization algorithms comparison adopted for the profit maximization of electricity market participants. Journal of Electrical Systems, 20(6s), 1032–1042. https://doi.org/10.52783/JES.2835
Kantoğlu, B., & Argun, İ. D. (2023). Evaulation of renewable energy source alternatives prioritization. Turkish Journal of Engineering, 7(1), 1–8. https://doi.org/10.31127/TUJE.1001488
Bamisile, O., Acen, C., Cai, D., Huang, Q., & Staffell, I. (2025). The environmental factors affecting solar photovoltaic output. Renewable and Sustainable Energy Reviews, 208, 115073. https://doi.org/10.1016/J.RSER.2024.115073
Sarda, J., Pandya, K., & Lee, K. Y. (2023). Hybrid cross entropy—cuckoo search algorithm for solving optimal power flow with renewable generators and controllable loads. Optimal Control Applications and Methods, 44(2), 508–532. https://doi.org/10.1002/OCA.2759;WGROUP:STRING:PUBLICATION
Refaat, S. S., Mohammed, A., Foqha, T., Syed, A., Alsadi, S., & Farrag, M. (2025). Electric Vehicle Technologies in the Smart Grid Era: A Comprehensive Review. IET Electrical Systems in Transportation, 2025(1), 3139124. https://doi.org/10.1049/ELS2/3139124
Niu, Z., An, K., & Ma, W. (2024). Vehicle-to-grid enabled charging infrastructure planning and operations considering demand uncertainties. Transportation Research Part D: Transport and Environment, 127, 103918. https://doi.org/10.1016/J.TRD.2023.103918
Chetan Bariya. (2025). A Novel Harris Hawks Optimization Algorithm for Optimal Power Flow in Power Systems with Renewable Energy Integration. Journal of Information Systems Engineering and Management, 10(51s), 215–225. https://doi.org/10.52783/JISEM.V10I51S.10380
Shehab, M., Mashal, I., Momani, Z., Shambour, M. K. Y., AL-Badareen, A., Al-Dabet, S., Bataina, N., Alsoud, A. R., & Abualigah, L. (2022). Harris Hawks Optimization Algorithm: Variants and Applications. Archives of Computational Methods in Engineering 2022 29:7, 29(7), 5579–5603. https://doi.org/10.1007/S11831-022-09780-1
Kundu, T., Deepmala, & Jain, P. K. (2022). A hybrid salp swarm algorithm based on TLBO for reliability redundancy allocation problems. Applied Intelligence (Dordrecht, Netherlands), 52(11), 12630. https://doi.org/10.1007/S10489-021-02862-W
Yang, C., Sun, Y., Zou, Y., Zheng, F., Liu, S., Zhao, B., Wu, M., & Cui, H. (2023). Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods. Energies 2023, Vol. 16, Page 5974, 16(16), 5974. https://doi.org/10.3390/EN16165974.
Zile, M. (2020). OPTIMAL DESIGN OF POWER TRANSFORMER TANK USING ANT/FIREFLY HYBRID HEURISTIC ALGORITHM. Turkish Journal of Engineering, 4(1), 17–22. https://doi.org/10.31127/TUJE.583975

Details

Primary Language

English

Subjects

Electrical Energy Transmission, Networks and Systems, Electrical Energy Generation (Incl. Renewables, Excl. Photovoltaics)

Journal Section

Research Article

Authors

Chetan Bariya ^*
0009-0005-6388-3765
India

Publication Date

May 1, 2026

Submission Date

December 11, 2025

Acceptance Date

March 4, 2026

Published in Issue

Year 2026 Volume: 10 Number: 2

DOI

https://doi.org/10.31127/tuje.1840309

IZ

https://izlik.org/JA64FR36PB

Cite

RIS / Bibtex

APA

Bariya, C. (2026). Optimal Power Flow using Hybrid Metaheuristic Algorithms with RES and EV Integration. Turkish Journal of Engineering, 10(2), 676-688. https://doi.org/10.31127/tuje.1840309

AMA

1.Bariya C. Optimal Power Flow using Hybrid Metaheuristic Algorithms with RES and EV Integration. TUJE. 2026;10(2):676-688. doi:10.31127/tuje.1840309

Chicago

Bariya, Chetan. 2026. “Optimal Power Flow Using Hybrid Metaheuristic Algorithms With RES and EV Integration”. Turkish Journal of Engineering 10 (2): 676-88. https://doi.org/10.31127/tuje.1840309.

EndNote

Bariya C (May 1, 2026) Optimal Power Flow using Hybrid Metaheuristic Algorithms with RES and EV Integration. Turkish Journal of Engineering 10 2 676–688.

IEEE

[1]C. Bariya, “Optimal Power Flow using Hybrid Metaheuristic Algorithms with RES and EV Integration”, TUJE, vol. 10, no. 2, pp. 676–688, May 2026, doi: 10.31127/tuje.1840309.

ISNAD

Bariya, Chetan. “Optimal Power Flow Using Hybrid Metaheuristic Algorithms With RES and EV Integration”. Turkish Journal of Engineering 10/2 (May 1, 2026): 676-688. https://doi.org/10.31127/tuje.1840309.

JAMA

1.Bariya C. Optimal Power Flow using Hybrid Metaheuristic Algorithms with RES and EV Integration. TUJE. 2026;10:676–688.

MLA

Bariya, Chetan. “Optimal Power Flow Using Hybrid Metaheuristic Algorithms With RES and EV Integration”. Turkish Journal of Engineering, vol. 10, no. 2, May 2026, pp. 676-88, doi:10.31127/tuje.1840309.

Vancouver

1.Chetan Bariya. Optimal Power Flow using Hybrid Metaheuristic Algorithms with RES and EV Integration. TUJE. 2026 May 1;10(2):676-88. doi:10.31127/tuje.1840309

Cited By

TAHO: tri-swarm adaptive hybrid optimizer for optimal power flow in integrated transmission-distribution systems

AIMS Mathematics

https://doi.org/10.3934/math.2026683