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Coordination between Demand Response Programming and Learning-Based FOPID Controller for Alleviation of Frequency Excursion of Hybrid Microgrid

Author

Listed:
  • Masoud Babaei

    (Departmant of Electrical Engineering, Tarbiat Modares University, Tehran 1193653471, Iran)

  • Ahmadreza Abazari

    (Departmant of Electrical Engineering, University of Tehran, Tehran 1193653471, Iran)

  • S. M. Muyeen

    (School of Electrical Engineering Computing and Mathematical Sciences, Curtin University, Perth, WA 6845, Australia)

Abstract

In recent years, residential rate consumptions have increased due to modern appliances which require a high level of electricity demands. Although mentioned appliances can improve the quality of consumers’ lives to a certain extent, they suffer from various shortcomings including raising the electricity bill as well as serious technical issues such as lack of balance between electricity generation and load disturbances. This imbalance can generally lead to the frequency excursion which is a significant concern, especially for low-inertia microgrids with unpredictable parameters. This research proposes an intelligent combination of two approaches in order to alleviate challenges related to the frequency control mechanism. Firstly, a learning-based fractional-order proportional-integral-derivative (FOPID) controller is trained by recurrent adaptive neuro-fuzzy inference (RANFIS) in the generation side during various operational conditions and climatic changes. In the following, a decentralized demand response (DR) programming in the load side is introduced to minimize consumption rate through controllable appliances and energy storage systems (ESSs). Furthermore, parameters uncertainties and time delay, which are generally known as two main concerns of isolated microgrids, are regarded in the frequency plan of a low-inertia microgrid including renewable energy sources (RESs), and energy storage systems (ESSs). Simulation results are illustrated in three different case studies in order to compare the performance of the proposed two methods during various operational conditions. It is obvious that the frequency deviation of microgrid can be improved by taking advantage of intelligent combination of both DR program and modern control mechanism.

Suggested Citation

  • Masoud Babaei & Ahmadreza Abazari & S. M. Muyeen, 2020. "Coordination between Demand Response Programming and Learning-Based FOPID Controller for Alleviation of Frequency Excursion of Hybrid Microgrid," Energies, MDPI, vol. 13(2), pages 1-23, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:442-:d:309574
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    References listed on IDEAS

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    1. Xiaofeng Dong & Xiaoshun Zhang & Tong Jiang, 2018. "Adaptive Consensus Algorithm for Distributed Heat-Electricity Energy Management of an Islanded Microgrid," Energies, MDPI, vol. 11(9), pages 1-17, August.
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    3. Xiao Qi & Yan Bai & Huanhuan Luo & Yiqing Zhang & Guiping Zhou & Zhonghua Wei, 2018. "Fully-distributed Load Frequency Control Strategy in an Islanded Microgrid Considering Plug-In Electric Vehicles," Energies, MDPI, vol. 11(6), pages 1-18, June.
    4. Guo-Qiang Zeng & Xiao-Qing Xie & Min-Rong Chen, 2017. "An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations," Energies, MDPI, vol. 10(11), pages 1-23, November.
    5. Huan Wang & Guoqiang Zeng & Yuxing Dai & Daqiang Bi & Jingliao Sun & Xiaoqing Xie, 2017. "Design of a Fractional Order Frequency PID Controller for an Islanded Microgrid: A Multi-Objective Extremal Optimization Method," Energies, MDPI, vol. 10(10), pages 1-18, October.
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    Cited by:

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    2. Faisal R. Badal & Zannatun Nayem & Subrata K. Sarker & Dristi Datta & Shahriar Rahman Fahim & S. M. Muyeen & Md. Rafiqul Islam Sheikh & Sajal K. Das, 2021. "A Novel Intrusion Mitigation Unit for Interconnected Power Systems in Frequency Regulation to Enhance Cybersecurity," Energies, MDPI, vol. 14(5), pages 1-18, March.
    3. Ning Wang & Shuai Zheng & Weiqiang Gao, 2022. "Microgrid Harmonic Mitigation Strategy Based on the Optimal Allocation of Active Power and Harmonic Mitigation Capacities of Multi-Functional Grid-Connected Inverters," Energies, MDPI, vol. 15(17), pages 1-20, August.

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