Enhanced Frequency Stabilization in Interconnected Power Grids with Wind-PV-BESS Using Metaheuristic-tuned Controllers

Abdelfattah M. Elmoaty; Ahmed M. Othman; Mahdi M. Elarini

doi:10.29284/pr0qv076

Authors

Abdelfattah M. Elmoaty Department of Electrical Power and Machines Engineering, Zagazig University, Egypt
Ahmed M. Othman Department of Electrical Power and Machines Engineering, Zagazig University, Egypt
Mahdi M. Elarini Department of Electrical Power and Machines Engineering, Zagazig University, Egypt

DOI:

https://doi.org/10.29284/pr0qv076

Keywords:

Puma Optimizer; Frequency Regulation; Wind Turbine; Pitch Angle; PIDN Controller.

Abstract

This paper suggests an effective controller for frequency regulation of dual-area power networks that are heavily penetrated by renewable energy resources. The purpose of frequency controllers on variable speed wind turbines is to effectively manage the pitch angle and droop controller for frequency support and release the energy contained in spinning masses. Employing an auxiliary controller, solar PV sources equipped with Battery Energy Storage System (BESS) can influentially contribute to the frequency regulation. The Puma Optimizer (PO) is employed to fine-tune the optimal parameters of proportional integral derivative with filter controllers, as well as droop, pitch angle, and auxiliary storage controllers. Integral Time of Absolute Error (ITAE) is proposed as the fitness cost function of frequency deviation in both areas and tie-line power deviation. The superiority of PO is validated by comparing result with benchmarked algorithms such as Genetic Algorithm (GA), Gorilla Troops Optimizer (GTO), Walrus Optimization Algorithm (WaOA), and Hippopotamus Optimizer (HO). It is observed that PO granted lower ITAE by 2.621%, 24.879%, 21.337%, and 43.164% than WaOA, HO, GTO and GA respectively under +20% step load perturbation (+10% at each area). Furthermore, the performance and feasibility are confirmed under random load perturbation and variable weather conditions.

References

[1] Hussain J, Zou R, Pathak PK, Karni A, Akhtar S. Design of a novel cascade PI-(1+FOPID) controller to enhance load frequency control performance in diverse electric power systems. Electr Power Syst Res. 2025;243:111488. doi:10.1016/j.epsr.2025.111488.

[2] Singh A, Yadav S, Tiwari N, Nishad DK, Khalid S. Optimized PID controller and model order reduction of reheated turbine for load frequency control using teaching learning-based optimization. Sci Rep. 2025;15:3759. doi:10.1038/s41598-025-87866-z.

[3] Veerendar T, Kumar D. Teaching-learning optimizer-based FO-PID for load frequency control of interlinked power systems. Int J Model Simul. 2023;43:683–705. doi:10.1080/02286203.2022.2112009.

[4] Lamba R, Singla SK, Sondhi S. Design of Fractional Order PID Controller for Load Frequency Control in Perturbed Two Area Interconnected System. Electr Power Components Syst. 2019;47:998–1011. doi:10.1080/15325008.2019.1660736.

[5] Singh VP, Kishor N, Samuel P. Improved load frequency control of power system using LMI based PID approach. J Franklin Inst. 2017;354:6805–30. doi:10.1016/j.jfranklin.2017.08.031.

[6] Guha D, Roy PK, Banerjee S. Grasshopper optimization algorithm scaled fractional order PI-D controller applied to reduced order model of load frequency control system. Int J Model Simul. 2020;40:217–42. doi:10.1080/02286203.2019.1596727.

[7] Guha D, Roy PK, Banerjee S. Quasi-oppositional JAYA optimized 2-degree-of-freedom PID controller for load-frequency control of interconnected power systems. Int J Model Simul. 2022;42:63–85. doi:10.1080/02286203.2020.1829444.

[8] Gulzar MM, Iqbal M, Shahzad S, Muqeet HA, Shahzad M, Hussain MM. Load Frequency Control (LFC) Strategies in Renewable Energy-Based Hybrid Power Systems: A Review. Energies. 2022;15. doi:10.3390/en15103488.

[9] El-Rifaie AM. A Novel Lyrebird Optimization Algorithm for Enhanced Generation Rate-Constrained Load Frequency Control in Multi-Area Power Systems with Proportional Integral Derivative Controllers. Processes. 2025;13. doi:10.3390/pr13040949.

[10] Sonker B, Kumar D, Samuel P. Design of two degree of freedom-internal model control configuration for load frequency control using model approximation. Int J Model Simul. 2019;39:27–37. doi:10.1080/02286203.2018.1474027.

[11] Goyal H, Handmandlu M, Kothari DP. A Novel Modelling Technique for Automatic Load Frequency Control of Small Hydro Power Plants. Int J Model Simul. 2007;27:186–92. doi:10.1080/02286203.2007.11442416.

[12] Liu X, Shi K, Zhou K, Wen S, Tang Y, Tang L. Event-triggering-based H∞ load frequency control for multi-area cyber–physical power system under DoS attacks. Franklin Open. 2023;3:100012. doi:10.1016/j.fraope.2023.100012.

[13] Tuan DH, Tran DT, Nguyen Ngoc Thanh V, Huynh V V. Load Frequency Control Based on Gray Wolf Optimizer Algorithm for Modern Power Systems. Energies. 2025;18. doi:10.3390/en18040815.

[14] D R, G MK. Artificial Raindrop Algorithm-Based Load Frequency Control in PV-Thermal-Thermal Interconnected Power System. Electr Power Components Syst. 2023:1–11. doi:10.1080/15325008.2023.2261125.

[15] Chi X, Liu X, Jia X. Multi-rate sampled-data observer-based networked load frequency control for multi-area interconnected power systems under multiple cyber-attacks. J Franklin Inst. 2025;362:107427. doi:10.1016/j.jfranklin.2024.107427.

[16] Thorat R, Bhatt P. Electric vehicle participation for optimized load frequency control in a multi-area restructured power system. Int J Model Simul. 2024:1–14. doi:10.1080/02286203.2024.2327640.

[17] Khokhar B, Parmar KPS. Load frequency control performance enhancement of an electric vehicle integrated time-delay multi-microgrid system. Int J Model Simul. 2024:1–18. doi:10.1080/02286203.2024.2392223.

[18] El-Hameed MA, Elkholy MM, El-Fergany AA. Efficient frequency regulation in highly penetrated power systems by renewable energy sources using stochastic fractal optimiser. IET Renew Power Gener. 2019;13:2174–83. doi:10.1049/iet-rpg.2019.0186.

[19] Ma T, Barajas-Solano DA, Tartakovsky AM. Chance constrained load frequency control of power systems with wind resources. J Franklin Inst. 2025;362:107478. doi:10.1016/j.jfranklin.2024.107478.

[20] Kumar R, Sikander A. A new hybrid approach based on fuzzy logic and optimization for load frequency control. Int J Model Simul. 2024:1–10. doi:10.1080/02286203.2024.2315327.

[21] Dokht Shakibjoo A, Moradzadeh M, Moussavi SZ, Mohammadzadeh A, Vandevelde L. Load frequency control for multi-area power systems: A new type-2 fuzzy approach based on Levenberg–Marquardt algorithm. ISA Trans. 2022;121:40–52. doi:10.1016/j.isatra.2021.03.044.

[22] Cui M, Zhao Y, Cao P, Tang Y, Lu Y. Load frequency control of interconnected hydrothermal power system based on FOPI + FOPD controller. Int J Dyn Control. 2024;12:1073–85. doi:10.1007/s40435-023-01212-7.

[23] Arora K, Kumar A, Kamboj VK, Prashar D, Shrestha B, Joshi GP. Impact of Renewable Energy Sources into Multi Area Multi-Source Load Frequency Control of Interrelated Power System. Mathematics. 2021;9. doi:10.3390/math9020186.

[24] Xie Y, Liao P, Liang Z, Zhou D. State of Change-Related Hybrid Energy Storage System Integration in Fuzzy Sliding Mode Load Frequency Control Power System with Electric Vehicles. Machines. 2025;13. doi:10.3390/machines13010057.

[25] Nayak PC, Umesh Chandra P, Ramesh Chandra P, and Panda S. Imperialist competitive algorithm optimized cascade controller for load frequency control of multi-microgrid system. Energy Sources, Part A Recover Util Environ Eff. 2025;47:5538–60. doi:10.1080/15567036.2021.1897710.

[26] Khadanga RK, Kumar A, Panda S. A novel modified whale optimization algorithm for load frequency controller design of a two-area power system composing of PV grid and thermal generator. Neural Comput Appl. 2020;32:8205–16. doi:10.1007/s00521-019-04321-7.

[27] Guha D, Roy PK, Banerjee S. Whale optimization algorithm applied to load frequency control of a mixed power system considering nonlinearities and PLL dynamics. Energy Syst. 2020;11:699–728. doi:10.1007/s12667-019-00326-2.

[28] Çelik E. Improved stochastic fractal search algorithm and modified cost function for automatic generation control of interconnected electric power systems. Eng Appl Artif Intell. 2020;88:103407. doi:10.1016/j.engappai.2019.103407.

[29] Kumar B, Adhikari S, Datta S, Sinha N. Real time simulation for load frequency control of multisource microgrid system using grey wolf optimization based modified bias coefficient diagram method (GWO-MBCDM) controller. J Electr Eng Technol. 2021;16:205–21. doi:10.1007/s42835-020-00596-2.

[30] Veerendar T, Kumar D, Kumar Gupta A. Quasi-Oppositional African Vultures Optimization-Based PIλDn Plus PIλ Controller for Frequency Control of an Interlinked Hybrid Power System. Electr Power Components Syst. 2023:1–21. doi:10.1080/15325008.2023.2191249.

[31] Persson M, Chen P. Frequency control by variable speed wind turbines in islanded power systems with various generation mix. IET Renew Power Gener. 2017;11:1101–9. doi:10.1049/iet-rpg.2016.0350.

[32] Varzaneh SG, Abedi M, Gharehpetian GB. A new simplified model for assessment of power variation of DFIG-based wind farm participating in frequency control system. Electr Power Syst Res. 2017;148:220–9. doi:10.1016/j.epsr.2017.03.033.

[33] Bhagat SK, Saikia LC, Babu NR. Application of artificial hummingbird algorithm in a renewable energy source integrated multi-area power system considering Fuzzy based tilt integral derivative controller. E-Prime-Advances Electr Eng Electron Energy. 2023;4:100153. doi:10.1016/j.prime.2023.100153.

[34] Siti MW, Mbungu NT, Tungadio DH, Banza BB, Ngoma L. Application of load frequency control method to a multi-microgrid with energy storage system. J Energy Storage. 2022;52:104629. doi:10.1016/j.est.2022.104629.

[35] Saadatinezhad H, Ramezani A, Alizadeh M, Hajimalek E. Fault tolerant load frequency sharing of a multi‐area power system using model predictive control. J Eng. 2022;2022:337–47. doi:10.1049/tje2.12117.

[36] Alahakoon S, Roy RB, Arachchillage SJ. Optimizing Load Frequency Control in Standalone Marine Microgrids Using Meta-Heuristic Techniques. Energies. 2023;16:4846. doi:10.3390/en16134846.

[37] Mishra DK, Mohanty A, Ray PK. An optimal frequency regulation in interconnected power system through differential evolution and firefly algorithm. Soft Comput. 2024;28:593–606. doi:10.1007/s00500-023-08314-6.

[38] Krishna PJ, Meena VP, Patnana N, Mathur A, Singh VP. Grey wolf optimizer-assisted R-method-based weighted minimization for automatic generation control in two-area interconnected power system. Int J Dyn Control. 2023;11:1779–96. doi:10.1007/s40435-022-01070-9.

[39] El-Bahay MH, Lotfy ME, El-Hameed MA. Effective participation of wind turbines in frequency control of a two-area power system using coot optimization. Prot Control Mod Power Syst. 2023;8:1–15. doi:10.1186/s41601-023-00289-8.

[40] Clark K, Miller NW, Sanchez-Gasca JJ. Modeling of GE wind turbine-generators for grid studies. vol. 4.5. 4.5. General Electric International, Inc. New York, NY, USA; 2010.

[41] Ghani F, Rosengarten G, Duke M, Carson JK. The numerical calculation of single-diode solar-cell modelling parameters. Renew Energy. 2014;72:105–12. doi:10.1016/j.renene.2014.06.035.

[42] Ali EE, El-Hameed MA, El-Fergany AA, El-Arini MM. Parameter extraction of photovoltaic generating units using multi-verse optimizer. Sustain Energy Technol Assessments. 2016;17:68–76. doi:10.1016/j.seta.2016.08.004.

[43] De Soto W, Klein SA, Beckman WA. Improvement and validation of a model for photovoltaic array performance. Sol Energy. 2006;80:78–88. doi:10.1016/j.solener.2005.06.010.

[44] El-Fergany A. Efficient Tool to Characterize Photovoltaic Generating Systems Using Mine Blast Algorithm. Electr Power Components Syst. 2015;43:890–901. doi:10.1080/15325008.2015.1014579.

[45] K. Arthishri, R. Balasubramanian, Parkavi Kathirvelu SPS and RA. Maximum Power Point Tracking of Photovoltaic Generation System using Artificial Neural Network with Improved Tracking Factor. J Appl Sci. 2014;14:1858–64. doi:10.3923/jas.2014.1858.1864.

[46] Kundur, P. S., Malik OP. Power system stability and control. USA: Mc-Graw Hill Education; 2022.

[47] Abdollahzadeh B, Khodadadi N, Barshandeh S, Trojovský P, Gharehchopogh FS, El-kenawy E-SM, Abualigah L, Mirjalili S. Puma optimizer (PO): A novel metaheuristic optimization algorithm and its application in machine learning. Cluster Comput. 2024:1–49. doi:10.1007/s10586-023-04221-5.

[48] Andic C, Ozturk A, Aydin E, Turkay B. A Novel Puma Optimizer Based TID Controller for Load Frequency Control. 2024 Glob. Energy Conf., 2024, p. 1–6. doi:10.1109/GEC61857.2024.10881981.

[49] Nguyen N, Pandit D, Quigley R, Mitra J. Frequency Response in the Presence of Renewable Generation: Challenges and Opportunities. IEEE Open Access J Power Energy. 2021;8:543–56. doi:10.1109/OAJPE.2021.3118393.

[50] Amiri MH, Mehrabi Hashjin N, Montazeri M, Mirjalili S, Khodadadi N. Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm. Sci Rep. 2024;14:5032. doi:10.1038/s41598-024-54910-3.

[51] Hussain J, Zou R, Wu Z, Pathak PK, Akhtar S. Design and Performance Analysis of Walrus Optimization Algorithm (WaOA)-Based Cascade Controller for Load Frequency Control of a Multi-Area Power System With Renewable Sources. Int J Numer Model Electron Networks, Devices Fields. 2025;38:e70046. doi:10.1002/jnm.70046.

[52] Huang Z, Chen Z, Zheng Y, Sun M, Sun Q. Optimal design of load frequency active disturbance rejection control via double-chains quantum genetic algorithm. Neural Comput Appl. 2021;33:3325–3345. doi:10.1007/s00521-020-05199-6.

[53] Commission IE. IEC TS 62786 Distributed Energy Resources Connection with the Grid. Int Electrotech Comm Geneva, Switz. 2023.

[54] Committee PSR. Ieee guide for the application of protective relays used for abnormal frequency load shedding and restoration. IEEE Std C. 2007;37:c1–43. doi:10.1109/IEEESTD.2007.4299516.