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Optimized cascaded controller for frequency stabilization of marine microgrid system

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  • Daraz, Amil

Abstract

The trend towards reducing greenhouse gas emissions by dipping the use of traditional sources of energy in marine power grids, as well as the rapid expansion of renewable energy sources (RESs), were the driving forces behind the incorporation of RESs in maritime microgrid systems and enquiry of the ensuing prevalent control mechanism. The frequency stability of a Marine Microgrid System (MMGS) is a critical aspect that directly affects its reliable and efficient operation. This study describes a method for frequency stabilization in independent maritime microgrids consists of diverse renewable energy resources including wind turbine generators, sea wave energy/tidal power generation, solar generation, bio-diesel generator and energy storage systems. This paper aims to develop a new optimal cascaded order proportional integral based proportional derivative for marine load frequency control. Due to the fact that the performance of the controller is highly dependent on the parameters of the controller, optimizing these coefficients can have a significant impact on the output performance of the LFC control. In light of this, this paper presents the sewing training-based optimization (STBO), a new human-based metaheuristic algorithm to optimize the coefficient of the suggested controller. The essential stimulation of STBO is the teaching procedure of sewing to beginner tailors. By utilizing a population of candidate solutions, the algorithm iteratively refines the control parameters to find the optimal set that minimizes frequency deviations and enhances system stability. The responses of the cascaded PI-PD controller are linked to those of the PID and PI controllers in order to demonstrate its superiority. To evaluate the efficiency of STBO, it is compared to well-established recent techniques such as fitness dependent optimization, grey wolf optimization and Jellyfish search optimization. From the results, it is observed that our proposed approach improved the settling time by 25.35%, 45.89%, and 29.35%, reduced peak overshoot by 78.34%, 67.71%, and 78.23%, and similarly reduced undershoot by 81.56%, 56.22% and 76.56% as compared to FDO, GWO and JSO techniques respectively. Finally, the sensitivity analysis is performed under ±50% load variation and ± 40% power system parameters to prove the robustness of the proposed controller.

Suggested Citation

  • Daraz, Amil, 2023. "Optimized cascaded controller for frequency stabilization of marine microgrid system," Applied Energy, Elsevier, vol. 350(C).
    • Handle: RePEc:eee:appene:v:350:y:2023:i:c:s0306261923011388
    DOI: 10.1016/j.apenergy.2023.121774
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    References listed on IDEAS

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    1. Amil Daraz & Suheel Abdullah Malik & Ihsan Ul Haq & Khan Bahadar Khan & Ghulam Fareed Laghari & Farhan Zafar, 2020. "Modified PID controller for automatic generation control of multi-source interconnected power system using fitness dependent optimizer algorithm," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-31, November.
    2. Latif, Abdul & Hussain, S. M. Suhail & Das, Dulal Chandra & Ustun, Taha Selim, 2021. "Double stage controller optimization for load frequency stabilization in hybrid wind-ocean wave energy based maritime microgrid system," Applied Energy, Elsevier, vol. 282(PA).
    3. Tayyab Ali & Suheel Abdullah Malik & Ibrahim A. Hameed & Amil Daraz & Hana Mujlid & Ahmad Taher Azar, 2022. "Load Frequency Control and Automatic Voltage Regulation in a Multi-Area Interconnected Power System Using Nature-Inspired Computation-Based Control Methodology," Sustainability, MDPI, vol. 14(19), pages 1-30, September.
    4. Wen, Shuli & Lan, Hai & Hong, Ying-Yi & Yu, David C. & Zhang, Lijun & Cheng, Peng, 2016. "Allocation of ESS by interval optimization method considering impact of ship swinging on hybrid PV/diesel ship power system," Applied Energy, Elsevier, vol. 175(C), pages 158-167.
    5. Haseltalab, Ali & Negenborn, Rudy R., 2019. "Model predictive maneuvering control and energy management for all-electric autonomous ships," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    6. Hassan Haes Alhelou & Mohamad-Esmail Hamedani-Golshan & Reza Zamani & Ehsan Heydarian-Forushani & Pierluigi Siano, 2018. "Challenges and Opportunities of Load Frequency Control in Conventional, Modern and Future Smart Power Systems: A Comprehensive Review," Energies, MDPI, vol. 11(10), pages 1-35, September.
    7. Shang-Guan, Xingchen & He, Yong & Zhang, Chuanke & Jiang, Lin & Spencer, Joseph William & Wu, Min, 2020. "Sampled-data based discrete and fast load frequency control for power systems with wind power," Applied Energy, Elsevier, vol. 259(C).
    8. Geertsma, R.D. & Negenborn, R.R. & Visser, K. & Hopman, J.J., 2017. "Design and control of hybrid power and propulsion systems for smart ships: A review of developments," Applied Energy, Elsevier, vol. 194(C), pages 30-54.
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    1. Peddakapu, K. & Mohamed, M.R. & Srinivasarao, P. & Licari, J., 2024. "Optimized controllers for stabilizing the frequency changes in hybrid wind-photovoltaic-wave energy-based maritime microgrid systems," Applied Energy, Elsevier, vol. 361(C).
    2. Yin, Linfei & Zheng, Da, 2024. "Decomposition prediction fractional-order PID reinforcement learning for short-term smart generation control of integrated energy systems," Applied Energy, Elsevier, vol. 355(C).

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