A Comprehensive Study of various Multi-Area Hybrid Power Systems for Generation Control

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Year : 2025 | Volume : 03 | 02 | Page :
    By

    Bibhu Prasad Ganthia,

  • Praveen B M,

  1. Research Scholar, College of Engineering and Technology, Srinivas University, Mangaluru, Karnataka, India
  2. Professor, Research and Innovation Council, College of Engineering and Technology, Srinivas University, Mangaluru, Karnataka, India

Abstract

This paper is a detailed examination of multi-area hybrid power systems in the control of the generation taking into consideration the two area up to five area connected networks. As renewable energy sources are more and more integrated, and modern grids become more and more complex, the stability of the system itself and the frequency regulation have risen to a major issue. The study highlights the significance of Automatic Generation Control (AGC) and Load Frequency Control (LFC) in the process of integrated functionality of the various energy sources which include thermal, hydro, gas, solar, wind and energy storage facilities (ESS). Comparison of evaluations of a 2-area, 3 area, 4 area and 5 area models show the gradual increase in complexity of the system, the tie-line interaction, and communication requirements. The paper further demonstrates how traditional PID based controllers have been replaced with intelligent and decentralized AGC mechanisms, which combine optimization and artificial intelligence-based methods to improve dynamic performance. Findings suggest that, despite the fact that five-area hybrid systems are highly efficient and resilient, they need strong control frameworks and complex monitoring systems to deal with the nonlinearities and the uncertainties that are caused by renewable penetration. The results offer compelling information on how to design and optimize the smart, adaptive, and sustainable future power grids in a multi-area hybrid structure.

Keywords: Frequency control, multi-Area hybrid power system, Renewable energy integration, Intelligent control, Load frequency stability, Optimization techniques.

How to cite this article:
Bibhu Prasad Ganthia, Praveen B M. A Comprehensive Study of various Multi-Area Hybrid Power Systems for Generation Control. International Journal of Electrical Power and Machine Systems. 2025; 03(02):-.
How to cite this URL:
Bibhu Prasad Ganthia, Praveen B M. A Comprehensive Study of various Multi-Area Hybrid Power Systems for Generation Control. International Journal of Electrical Power and Machine Systems. 2025; 03(02):-. Available from: https://journals.stmjournals.com/ijepms/article=2025/view=234684


References

  1. Sriramula S, Reddy BR, Kalavathi MS. Fractional Order ANN Controller for LFC of EVS integrated deregulated power system. International Journal of Advanced Research in Engineering and Technology (IJARET). 2020;11(10).
  2. Debnath MK, Agrawal R, Tripathy SR, Choudhury S. Artificial neural network tuned PID controller for LFC investigation including distributed generation. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields. 2020 Sep;33(5):e2740.
  3. Tran AT, Minh BL, Huynh VV, Tran PT, Amaefule EN, Phan VD, Nguyen TM. Load frequency regulator in interconnected power system using second-order sliding mode control combined with state estimator. Energies. 2021 Feb 7;14(4):863.
  4. Prasad S, Ansari MR. Frequency regulation using neural network observer based controller in power system. Control Engineering Practice. 2020 Sep 1;102:104571.
  5. Elmelegi A, Mohamed EA, Aly M, Ahmed EM, Mohamed AA, Elbaksawi O. Optimized tilt fractional order cooperative controllers for preserving frequency stability in renewable energy- based power systems. IEEE Access. 2021 Jan 8;9:8261-77.
  6. Qian J, Lv X. Load Frequency Control of Renewable Energy Power Systems Based on Adaptive Global Fast Terminal Sliding Mode Control. Applied Sciences. 2025 Jun 22;15(13):7030.
  7. Wen Y, Yang W, Wang R, Xu W, Ye X, Li T. Review and prospect of toward 100% renewable energy power systems. Proceedings of the CSEE. 2020 Mar;40(6):1843-56.
  8. Gao H, Xin H, Huang L, Xu T, Ju P, Qin X, Huang W. Characteristic analysis and quantification of common mode frequency in power systems with high penetration of renewable resources. Proceedings of the CSEE. 2021 Jan;41(3):890-900.
  9. Khokhar B, Parmar KS. Utilizing diverse mix of energy storage for LFC performance enhancement of a microgrid: A novel MPC approach. Applied Energy. 2023 Mar 1;333:120639.
  10. Ranjitha K, Sivakumar P, Monica M. Load frequency control based on an improved Chimp optimization algorithm using adaptive weight strategy. COMPEL-The international journal for computation and mathematics in electrical and electronic engineering. 2022 Mar 15;41(5):1618- 48.
  11. Zhang Z, Dou C, Yue D, Zhang B. Predictive voltage hierarchical controller design for islanded microgrids under limited communication. IEEE Transactions on Circuits and Systems I: Regular Papers. 2021 Oct 12;69(2):933-45.
  12. Satish R, PavanKumar G, Murali V. Design of PI controllers by using bacterial foraging strategy to control frequency for distributed generation. In2011 International Conference on Emerging Trends in Electrical and Computer Technology 2011 Mar 23 (pp. 98-104). IEEE.
  13. Ray PK, Bartwal A, Puhan PS. Load frequency control in interconnected microgrids using Hybrid PSO–GWO based PI–PD controller. International Journal of System Assurance Engineering and Management. 2024 Aug;15(8):4124-42.
  14. Wu D, Guo F, Yao Z, Zhu D, Zhang Z, Li L, Du X, Zhang J. Enhancing reliability and performance of load frequency control in aging multi-area power systems under cyber-attacks. Applied Sciences. 2024 Sep 25;14(19):8631.
  15. Gopi P, Alluraiah NC, Kumar PH, Bajaj M, Blazek V, Prokop L. Improving load frequency controller tuning with rat swarm optimization and porpoising feature detection for enhanced power system stability. Scientific Reports. 2024 Jul 2;14(1):15209.
  16. Sabahi K, Jamil M, Shokri-Kalandaragh Y, Tavan M, Arya Y. Deep deterministic policy gradient reinforcement learning based adaptive PID load frequency control of an AC micro-grid. IEEE Canadian Journal of Electrical and Computer Engineering. 2024 Mar 1;47(1):15-21.
  17. Alhejji A. L 1 adaptive load frequency control of single-area electrical power system. In2017 4th International Conference on Control, Decision and Information Technologies (CoDIT) 2017 Apr 5 (pp. 0595-0598). IEEE.
  18. Yakout AH, Dashtdar M, AboRas KM, Ghadi YY, Elzawawy A, Yousef A, Kotb H. Neural network-based adaptive PID controller design for over-frequency control in microgrid using honey badger algorithm. IEEE Access. 2024 Feb 19;12:27989-8005.
  19. Van Huynh V, Tran PT, Dong CS, Hoang BD, Kaynak O. Sliding surface design for sliding mode load frequency control of multiarea multisource power system. IEEE Transactions on Industrial Informatics. 2024 Feb 27;20(5):7797-809.
  20. Huynh VV, Minh BL, Amaefule EN, Tran AT, Tran PT, Phan VD, Pham VT, Nguyen TM. Load frequency control for multi-area power plants with integrated wind resources. Applied Sciences. 2021 Mar 29;11(7):3051.
  21. Tuan DH, Pidanic J, Van Huynh V, Duy VH, Nhan NH. Sliding mode without reaching phase design for automatic load frequency control of multi-time delays power system. IEEE Access. 2024 Aug 9.
  22. Lv X, Sun Y, Cao S, Dinavahi V. Event‐triggered load frequency control for multi‐area power systems based on Markov model: a global sliding mode control approach. IET Generation, Transmission & Distribution. 2020 Nov;14(21):4878-87.
  23. Petrík T, Gravalos I, Uhlíř I, Libra M, Poulek V. Parametric damping of microgrid frequency fluctuations at synchronous machines with using Lyapunov theory for exciter regulation. International Journal of Energy Research. 2023;2023(1):5569059.
  24. Shen X, Zhang Y, Li J, Zhao Y, Tang J, Qian B, Lin X. Novel efficient deep reinforcement learning-based load frequency control for isolated microgrid. AIP Advances. 2025 Feb 1;15(2):025026.
  25. Bu, X.H., Zhang, Y., Zeng, Y.M. & Hou, Z.S. (2025). Event-triggered data-driven distributed LFC using controller-dynamic-linearization method. IEEE Trans. Signal Inf. Process. Netw., 11, 85–96.
  26. Huo Z, Xu C. Multi-event collaborative triggering mechanism based distributed robust fault- tolerant load frequency control for offshore wind power systems. Ocean Engineering. 2025 Jul 30;333:121424.
  27. Hu S, Luo Y, Xie X, Zhang H. H∞ optimal load frequency control of power system: A novel model-free approach. IEEE Transactions on Circuits and Systems II: Express Briefs. 2024 Nov 11.
  28. Havrlík M, Libra M, Poulek V, Kouřím P. Analysis of output signal distortion of galvanic isolation circuits for monitoring the mains voltage waveform. Sensors. 2022 Oct 13;22(20):7769.
  29. El-Hameed MA, Saeed M, Kabbani A, Abd El-Hay E. Efficient load frequency controller for a power system comprising renewable resources based on deep reinforcement learning. Scientific Reports. 2025 May 26;15(1):18379.
  30. 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. Scientific Reports. 2025 Jan 30;15(1):3759.
  31. Samal P, Nayak N, Satapathy A, Bhuyan SK. Load frequency control in renewable based micro grid with deep neural network based controller. Results in Engineering. 2025 Mar 1;25:103554.
  32. Fan GD, Liu SL, Pei J. Load frequency coordinated optimization control of hybrid power generation system consisted of PV, thermal power and energy storage. Proc. CSU-EPSA. 2020 Aug;32(12):134-43.
  33. Refaai MR, Dhanesh L, Ganthia BP, Mohanty M, Subbiah R, Anbese EM. Design and implementation of a floating PV model to analyse the power generation. International Journal of Photoenergy. 2022;2022(1):3891881.
  34. Pahadasingh S, Jena C, Panigrahi CK, Ganthia BP. JAYA algorithm-optimized load frequency control of a four-area interconnected power system tuning using PID controller. Engineering, Technology & Applied Science Research. 2022 Jun 6;12(3):8646-51.
  35. Mishra S, Ganthia BP, Sridharan A, Rajakumar P, Padmapriya D, Kaliappan S. Optimization of load forecasting in smartgrid using artificial neural network based NFTOOL and NNTOOL. InJournal of Physics: Conference Series 2022 (Vol. 2161, No. 1, p. 012068). IOP Publishing.
  36. Pritam A, Sahu S, Rout SD, Ganthia S, Ganthia BP. Automatic generation control study in two area reheat thermal power system. InIOP Conference Series: Materials Science and Engineering 2017 Aug 1 (Vol. 225, No. 1, p. 012223). IOP Publishing.
  37. Xie H, Wang Y, Gao Z, Ganthia BP, Truong CV. Research on frequency parameter detection of frequency shifted track circuit based on nonlinear algorithm. Nonlinear Engineering. 2021 Jan 1;10(1):592-9.
  38. Priyadarshani S, Subhashini KR, Satapathy JK. Pathfinder algorithm optimized fractional order tilt-integral-derivative (FOTID) controller for automatic generation control of multi-source power system. Microsystem Technologies. 2021 Jan;27(1):23-35.
  39. Kumari S, Shankar G. Maiden application of cascade tilt‐integral–tilt‐derivative controller for performance analysis of load frequency control of interconnected multi‐source power system. IET Generation, Transmission & Distribution. 2019 Dec;13(23):5326-38.
  40. Oshnoei A, Khezri R, Muyeen SM, Oshnoei S, Blaabjerg F. Automatic generation control incorporating electric vehicles. Electric Power Components and Systems. 2019 May 9;47(8):720- 32.
Ahead of Print Subscription Review Article
Volume 03
02
Received 07/10/2025
Accepted 08/10/2025
Published 23/12/2025
Publication Time 77 Days
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