Quantum-Inspired Evolutionary Algorithms for Solving Large-Scale, Non-Convex Optimal Power Flow Problems in Complex Smart Grid Systems with Renewable Integration

Yongchuang Zhou

Abstract


The Optimal Power Flow (OPF) problem is critical to the safe and profitable operation of smart grids that employ renewable energy (RE) like solar and wind. Conventional metaheuristic optimization algorithms struggle to solve large-scale, nonlinear, nonconvex, and high-dimensional OPF problems. This leads to problems like premature convergence, insufficient global search, high computational complexity, and unstable performance in the face of variable renewable conditions. This research proposes a Golden Sine-tuned Quantum-Inspired Bacterial Foraging Algorithm (GS-QIBFA) for solving renewable-integrated OPF problems in complex smart grid environments. While preserving dependable and stable grid operation, the suggested approach seeks to reduce generation cost, transmission power loss, voltage variation, and constraint violations. Because of its medium-scale architecture, various generator interactions, and suitability for assessing renewable-integrated OPF scenarios under dynamic operating conditions, the IEEE 39-bus system is chosen as the benchmark network. The GS-QIBFA integrates GS to enhance the exploration–exploitation balance and accelerate convergence. The QIBFA improves global search capability through quantum probability representation and evolutionary updating. Together, they improve solution refinement, diversity maintenance, and constraint handling for efficient OPF optimization. The proposed model is implemented in a MATLAB-based smart grid simulation environment with renewable uncertainty modeling and nonlinear OPF constraint analysis. According to experimental data, GS-QIBFA outperforms current OPF optimization techniques with a minimal power loss of 9.7 MW, fuel cost of 760.2 $/h, total emission of 0.189, and voltage stability index of 0.170. All things considered, GS-QIBFA is a dependable and efficient technique for resolving renewable-integrated OPF issues in smart grid systems.


Keywords


Transmission Loss Reduction; Voltage Stability; Smart Grid Optimization; Energy Management; Economic Dispatch; IEEE 39-Bus System

Full Text:

PDF

References


Abbassi, R., Trojovský, P., Mansor, Z., Trojovská, E., Kchaou, M., Zuš?ák, T., Jerbi, H. and Naveen, P., 2025. Cuckoo optimization algorithm via Grey Wolf Optimizer for usage in engineering optimization and optimal power flow with renewable energy sources. Scientific Reports, 15(1), p.37629. https://doi.org/10.1038/s41598-025-21515-3

Sulaiman, M.H. and Mustaffa, Z., 2024. Hyper-heuristic strategies for optimal power flow problem with FACTS devices allocation in wind power integrated system. Results in Control and Optimization, 14, p.100373. https://doi.org/10.1016/j.rico.2024.100373

Li, N., Zhou, G., Zhou, Y., Deng, W. and Luo, Q., 2023. Multi-objective pathfinder algorithm for multi-objective optimal power flow problem with random renewable energy sources: wind, photovoltaic and tidal. Scientific Reports, 13(1), p.10647. https://doi.org/10.1038/s41598-023-37635-7

Trojovský, P., Trojovská, E. and Akbari, E., 2024. Economical-environmental-technical optimal power flow solutions using a novel self-adaptive wild geese algorithm with stochastic wind and solar power. Scientific Reports, 14(1), p.4135. https://doi.org/10.1038/s41598-024-54510-1

Hassan, M.H., Kamel, S., Alateeq, A., Alassaf, A. and Alsaleh, I., 2024. Optimal power flow in hybrid Wind-PV-V2G systems with dynamic load demand using a Hybrid MRFO-AHA Algorithm. IEEE Access, 12, pp.174297-174329. https://doi.org/10.1109/ACCESS.2024.3496123

Wu, H., Chen, Y., Cai, Z., Heidari, A.A., Chen, H. and Liang, G., 2024. Dual-weight decay mechanism and Nelder-Mead simplex boosted RIME algorithm for optimal power flow. Journal of Big Data, 11(1), p.172. https://doi.org/10.1186/s40537-024-01034-0

Almutairi, S.Z. and Shaheen, A.M., 2026. An Adaptive Fitness-Guided Starfish Optimization Framework for Optimal Power Flow Operation. Mathematics, 14(5), p.909. https://doi.org/10.3390/math14050909

Adhikari, A., Jurado, F., Naetiladdanon, S., Sangswang, A., Kamel, S. and Ebeed, M., 2023. Stochastic optimal power flow analysis of power system with renewable energy sources using Adaptive Lightning Attachment Procedure Optimizer. International Journal of Electrical Power & Energy Systems, 153, p.109314. https://doi.org/10.1016/j.ijepes.2023.109314

Mohamed, A.A., Kamel, S., Hassan, M.H. and Domínguez-García, J.L., 2024. Optimal power flow incorporating renewable energy sources and FACTS devices: A chaos game optimization approach. IEEE Access, 12, pp.23338-23362. https://doi.org/10.1109/ACCESS.2024.3363237

Khunkitti, S., Premrudeepreechacharn, S. and Siritaratiwat, A., 2023. A two-archive Harris Hawk optimization for solving many-objective optimal power flow problems. Ieee Access, 11, pp.134557-134574. https://doi.org/10.1109/ACCESS.2023.3337535

Adam, A.H., Kamel, S., Hassan, M.H. and Mustafa, G.I., 2026. Optimal power flow of hybrid wind/solar/thermal energy integrated power systems considering renewable energy uncertainty via an enhanced weighted mean of vectors algorithm. Plos one, 21(2), p.e0336157. https://doi.org/10.1371/journal.pone.0336157

Hassan, M.H., Mohamed, E.M., Kamel, S. and Ardjoun, S.A.E.M., 2024. Stochastic optimal power flow integrating with renewable energy resources and V2G uncertainty considering time-varying demand: hybrid GTO-MRFO algorithm. IEEE Access, 12, pp.97893-97923. https://doi.org/10.1109/ACCESS.2024.3425754

Farhat, M., Kamel, S., Elseify, M.A. and Abdelaziz, A.Y., 2024. A modified white shark optimizer for optimal power flow considering uncertainty of renewable energy sources. Scientific Reports, 14(1), p.3051. https://doi.org/10.1038/s41598-024-53249-z

Emam, M.M., Houssein, E.H., Tolba, M.A., Zaky, M.M. and Hamouda Ali, M., 2023. Application of modified artificial hummingbird algorithm in optimal power flow and generation capacity in power networks considering renewable energy sources. Scientific Reports, 13(1), p.21446. https://doi.org/10.1038/s41598-023-48479-6

Ebeed, M., Abdelmotaleb, M.A., Khan, N.H., Jamal, R., Kamel, S., Hussien, A.G., Zawbaa, H.M., Jurado, F. and Sayed, K., 2024. A modified artificial hummingbird algorithm for solving optimal power flow problem in power systems. Energy Reports, 11, pp.982-1005. https://doi.org/10.1016/j.egyr.2023.12.053

Mayouf, C., Salhi, A., Haidara, F., Aroua, F.Z., El-Sehiemy, R.A., Naimi, D., Aya, C. and Kane, C.S.E., 2024. Solving optimal power Flow using new efficient hybrid jellyfish search and Moth flame optimization algorithms. Algorithms, 17(10), p.438. https://doi.org/10.3390/a17100438

Ali, A., Hassan, A., Keerio, M.U., Mugheri, N.H., Abbas, G., Hatatah, M., Touti, E. and Yousef, A., 2024. A novel solution to optimal power flow problems using composite differential evolution integrating effective constrained handling techniques. Scientific reports, 14(1), p.6187. https://doi.org/10.1038/s41598-024-56590-5

Shaheen, A.M., El-Sehiemy, R.A., Hasanien, H.M. and Ginidi, A., 2024. An enhanced optimizer of social network search for multi-dimension optimal power flow in electrical power grids. International Journal of Electrical Power & Energy Systems, 155, p.109572. https://doi.org/10.1016/j.ijepes.2023.109572

Ahmadipour, M., Othman, M.M., Bo, R., Javadi, M.S., Ridha, H.M. and Alrifaey, M., 2024. Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer. Expert Systems with Applications, 235, p.121212. https://doi.org/10.1016/j.eswa.2023.121212

Bathina, V., Devarapalli, R. and Garcia Marquez, F.P., 2023. Hybrid approach with combining cuckoo-search and grey-wolf optimizer for solving optimal power flow problems. Journal of Electrical Engineering & Technology, 18(3), pp.1637-1653. https://doi.org/10.1007/s42835-022-01301-1

Yi, W., Lin, Z., Lin, Y., Xiong, S., Yu, Z. and Chen, Y., 2023. Solving optimal power flow problem via improved constrained adaptive differential evolution. Mathematics, 11(5), p.1250. https://doi.org/10.3390/math11051250

Su, H., Niu, Q. and Yang, Z., 2023. Optimal power flow using improved cross-entropy method. Energies, 16(14), p.5466. https://doi.org/10.3390/en16145466

Akbari, E., Khodabakhshian, A., Rahimnejad, A. and Gadsden, S.A., 2025. Stable Matching-Enhanced MOEA/D for Solving Multi-Objective Optimal Power Flow Problems. Results in Engineering, 27, p.106520. https://doi.org/10.1016/j.rineng.2025.106520

Daqaq, F., Hassan, M.H., Kamel, S. and Hussien, A.G., 2023. A leader supply-demand-based optimization for large scale optimal power flow problem considering renewable energy generations. Scientific Reports, 13(1), p.14591. https://doi.org/10.1038/s41598-023-41608-1

ElMessmary, M.H., Diab, H.Y., Abdelsalam, M. and Moussa, M.F., 2024. A novel optimization algorithm inspired by Egyptian stray dogs for solving multi-objective optimal power flow problems. Applied System Innovation, 7(6), p.122. https://doi.org/10.3390/asi7060122

Pulluri, H., Devi, T.A., Kuppireddy, N.R., Dahiya, P., Naga sai kalyan, C., Goud, B.S., Shorfuzzaman, M., ELrashidi, A., Nureldeen, W. and Reddy, C.R., 2025. Solution of optimal power flow with wind power uncertainty using hybridized self-adaptive differential evolution. Scientific Reports, 15(1), p.26206. https://doi.org/10.1038/s41598-025-06555-z

Wu, H., Chen, Y., Cai, Z., Heidari, A.A., Chen, H. and Liang, G., 2024. Gradient pyramid mechanism and Nelder-Mead simplex enhanced Colony Predation Algorithm for optimal power flow problems. Energy Reports, 11, pp.2901-2920. https://doi.org/10.1016/j.egyr.2024.02.038

Al Butti, O.S.T., Burunkaya, M., Rahebi, J. and Lopez-Guede, J.M., 2024. Optimal power flow using PSO algorithms based on artificial neural networks. IEEE Access, 12, pp.154778-154795. https://doi.org/10.1109/ACCESS.2024.3479097

Mohamed, S.A., Anwer, N. and Mahmoud, M.M., 2025. Solving optimal power flow problem for IEEE-30 bus system using a developed particle swarm optimization method: towards fuel cost minimization. International Journal of Modelling and Simulation, 45(1), pp.307-320. https://doi.org/10.1080/02286203.2023.2201043

Alsokhiry, F., 2024. Leveraging Harris Hawks Optimization for Enhanced Multi-Objective Optimal Power Flow in Complex Power Systems. Energies, 18(1), p.18. https://doi.org/10.3390/en18010018




DOI (PDF): https://doi.org/10.20508/ijrer.v16i3.17158.g9252

Refbacks

  • There are currently no refbacks.


Online ISSN: 1309-0127

Publisher: Gazi University

IJRER is indexed in EI Compendex, SCOPUS, EBSCO, WEB of SCIENCE (Clarivate Analytics)and CrossRef.

IJRER has been indexed in Emerging Sources Citation Index from 2016 in web of science.

WEB of SCIENCE in 2026; 

h=38,

Average citation per item=7.69

Last three Years Impact Factor=(1768+1782+2085)/(146+78+80)=5635/304=18.53

Category Quartile:Q4