IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i9p3808-d1387552.html
   My bibliography  Save this article

A Comparative Study of Load Frequency Regulation for Multi-Area Interconnected Grids Using Integral Controller

Author

Listed:
  • Awadh Ba Wazir

    (Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Smart Grids Research Group, Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Ahmed Althobiti

    (Smart Grids Research Group, Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Department of Electrical and Computer Engineering, Faculty of Engineering, K. A. CARE Energy Research and Innovation Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Abdullah A. Alhussainy

    (Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Smart Grids Research Group, Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Sultan Alghamdi

    (Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Smart Grids Research Group, Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Mahendiran Vellingiri

    (Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Smart Grids Research Group, Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Thangam Palaniswamy

    (Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Smart Grids Research Group, Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Muhyaddin Rawa

    (Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Smart Grids Research Group, Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

The present paper provides an optimal design for load frequency control (LFC) in the interconnected power system. To obtain an adequate LFC response alongside shortening implementation time and minimizing costs, an integral (I) controller is used. A deep analysis of the I controller-based LFC is presented. At first, a two-area interconnected power system is used, and to enhance the LFC response, the I controller and frequency bias parameters are optimized using three novel optimization algorithms, which are the incomprehensible but intelligible-in-time logic algorithm (ILA), the coati optimization algorithm (COA), and the brown-bear optimization algorithm (BOA). Also, five well-known techniques, namely, particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), pattern search (PS), and nonlinear programming (NP), are used. A new objective function utilizing the integral of squared error (ISE), settling time, settling-max, and settling-min of the dynamic response is used to increase the efficacy of estimating the parameters. The presented results in this paper showed that the optimized I controller outperforms the classic I controller. After considering a load change in one area by 18.75%, the optimized I controller achieved the lowest ISE values. ISE values were: 0.00582, 0.00179, 0.00176, 0.00178, 0.00321, 0.00304, 0.00179, 0.00185, and 0.00181, for classic I, PSO-I, GA-I, SA-I, PS-I, NP-I, ILA-I, COA-I, and BOA-I. Then, the proposed method is applied to a nonlinear two-area system, demonstrating that the proposed strategies can deal with nonlinearity. As the purpose of the hybrid power system is to create a robust energy infrastructure that adheres to sustainability standards, the proposed algorithms are analyzed in a three-area multi-source power system comprising renewable energy sources (RESs) such as photovoltaic (PV) and wind turbine (WT), a battery energy storage system (BESS), and an electric vehicle (EV).

Suggested Citation

  • Awadh Ba Wazir & Ahmed Althobiti & Abdullah A. Alhussainy & Sultan Alghamdi & Mahendiran Vellingiri & Thangam Palaniswamy & Muhyaddin Rawa, 2024. "A Comparative Study of Load Frequency Regulation for Multi-Area Interconnected Grids Using Integral Controller," Sustainability, MDPI, vol. 16(9), pages 1-53, May.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:9:p:3808-:d:1387552
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/9/3808/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/9/3808/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohamed Mokhtar & Mostafa I. Marei & Mariam A. Sameh & Mahmoud A. Attia, 2022. "An Adaptive Load Frequency Control for Power Systems with Renewable Energy Sources," Energies, MDPI, vol. 15(2), pages 1-22, January.
    2. Soha Mansour & Ahmed O. Badr & Mahmoud A. Attia & Mariam A. Sameh & Hossam Kotb & Elmazeg Elgamli & Mokhtar Shouran, 2022. "Fuzzy Logic Controller Equilibrium Base to Enhance AGC System Performance with Renewable Energy Disturbances," Energies, MDPI, vol. 15(18), pages 1-18, September.
    3. Du, Wei & Li, Yanjun & Shi, Jianxin & Sun, Baozhi & Wang, Chunhui & Zhu, Baitong, 2023. "Applying an improved particle swarm optimization algorithm to ship energy saving," Energy, Elsevier, vol. 263(PE).
    4. Omar Makram Kamel & Ahmed A. Zaki Diab & Mohamed Metwally Mahmoud & Ameena Saad Al-Sumaiti & Hamdy M. Sultan, 2023. "Performance Enhancement of an Islanded Microgrid with the Support of Electrical Vehicle and STATCOM Systems," Energies, MDPI, vol. 16(4), pages 1-19, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zaid Hamid Abdulabbas Al-Tameemi & Tek Tjing Lie & Ramon Zamora & Frede Blaabjerg, 2024. "Enhanced Coordination in the PV–HESS Microgrids Cluster: Introducing a New Distributed Event Consensus Algorithm," Energies, MDPI, vol. 17(2), pages 1-21, January.
    2. Rasha Kassem & Mohamed Metwally Mahmoud & Nagwa F. Ibrahim & Abdulaziz Alkuhayli & Usama Khaled & Abderrahmane Beroual & Hedra Saleeb, 2024. "A Techno-Economic-Environmental Feasibility Study of Residential Solar Photovoltaic/Biomass Power Generation for Rural Electrification: A Real Case Study," Sustainability, MDPI, vol. 16(5), pages 1-24, February.
    3. Bashar Abbas Fadheel & Noor Izzri Abdul Wahab & Ali Jafer Mahdi & Manoharan Premkumar & Mohd Amran Bin Mohd Radzi & Azura Binti Che Soh & Veerapandiyan Veerasamy & Andrew Xavier Raj Irudayaraj, 2023. "A Hybrid Grey Wolf Assisted-Sparrow Search Algorithm for Frequency Control of RE Integrated System," Energies, MDPI, vol. 16(3), pages 1-28, January.
    4. Hussain A. Alhaiz & Ahmed S. Alsafran & Ali H. Almarhoon, 2023. "Single-Phase Microgrid Power Quality Enhancement Strategies: A Comprehensive Review," Energies, MDPI, vol. 16(14), pages 1-28, July.
    5. Nandakumar Sundararaju & Arangarajan Vinayagam & Veerapandiyan Veerasamy & Gunasekaran Subramaniam, 2022. "A Chaotic Search-Based Hybrid Optimization Technique for Automatic Load Frequency Control of a Renewable Energy Integrated Power System," Sustainability, MDPI, vol. 14(9), pages 1-27, May.
    6. Angelos Patsidis & Adam Dyśko & Campbell Booth & Anastasios Oulis Rousis & Polyxeni Kalliga & Dimitrios Tzelepis, 2023. "Digital Architecture for Monitoring and Operational Analytics of Multi-Vector Microgrids Utilizing Cloud Computing, Advanced Virtualization Techniques, and Data Analytics Methods," Energies, MDPI, vol. 16(16), pages 1-19, August.
    7. Jikai Sun & Mingrui Chen & Linghe Kong & Zhijian Hu & Veerapandiyan Veerasamy, 2023. "Regional Load Frequency Control of BP-PI Wind Power Generation Based on Particle Swarm Optimization," Energies, MDPI, vol. 16(4), pages 1-15, February.
    8. Elnaz Ghorbani & Tristan Fluechter & Laura Calvet & Majsa Ammouriova & Javier Panadero & Angel A. Juan, 2023. "Optimizing Energy Consumption in Smart Cities’ Mobility: Electric Vehicles, Algorithms, and Collaborative Economy," Energies, MDPI, vol. 16(3), pages 1-19, January.
    9. Othman A. M. Omar & Mostafa I. Marei & Mahmoud A. Attia, 2023. "Comparative Study of AVR Control Systems Considering a Novel Optimized PID-Based Model Reference Fractional Adaptive Controller," Energies, MDPI, vol. 16(2), pages 1-19, January.
    10. Othman A. M. Omar & Ahmed O. Badr & Ibrahim Mohamed Diaaeldin, 2023. "Novel Fractional Order and Stochastic Formulations for the Precise Prediction of Commercial Photovoltaic Curves," Mathematics, MDPI, vol. 11(21), pages 1-19, October.
    11. Ahmed O. Badr & Soha Mansour & Mariam A. Sameh & Mahmoud A. Attia, 2022. "Seamless Transition and Fault-Ride-Through by Using a Fuzzy EO PID Controller in AVR System," Energies, MDPI, vol. 15(22), pages 1-20, November.
    12. Shruti Singh & David Wenzhong Gao, 2023. "Comparison amongst Lagrange, Firefly, and ABC Algorithms for Low-Noise Economic Dispatch and Reactive Power Compensation in Islanded Microgrids," Energies, MDPI, vol. 16(13), pages 1-19, July.
    13. Xin Peng & Hui Chen & Cong Guan, 2023. "Energy Management Optimization of Fuel Cell Hybrid Ship Based on Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 16(3), pages 1-15, January.
    14. Zhong Guan & Hui Wang & Zhi Li & Xiaohu Luo & Xi Yang & Jugang Fang & Qiang Zhao, 2024. "Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm," Energies, MDPI, vol. 17(7), pages 1-20, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:9:p:3808-:d:1387552. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.