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Improving Microgrid Frequency Regulation Based on the Virtual Inertia Concept while Considering Communication System Delay

Author

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  • Gholam Ali Alizadeh

    (Department of Electrical and Computer Engineering, Faculty of Ghazi Tabatabai, Urmia Branch, Technical and Vocational University (TUV), Urmia 5716933959, Iran)

  • Tohid Rahimi

    (Electrical and Computer Engineering Faculty, University of Tabriz, Tabriz 57734, Iran)

  • Mohsen Hasan Babayi Nozadian

    (Electrical and Computer Engineering Faculty, University of Tabriz, Tabriz 57734, Iran)

  • Sanjeevikumar Padmanaban

    (Department of Energy Technology, Aalborg University, Esbjerg 6700, DK-9220 Aalborg, Denmark)

  • Zbigniew Leonowicz

    (Faculty of Electrical Engineering, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50370 Wroclaw, Poland)

Abstract

Frequency stability is an important issue for the operation of islanded microgrids. Since the upstream grid does not support the islanded microgrids, the power control and frequency regulation encounter serious problems. By increasing the penetration of the renewable energy sources in microgrids, optimizing the parameters of the load frequency controller plays a great role in frequency stability, which is currently being investigated by researchers. The status of loads and generation sources are received by the control center of a microgrid via a communication system and the control center can regulate the output power of renewable energy sources and/or power storage devices. An inherent delay in the communication system or other parts like sensors sampling rates may lead microgrids to have unstable operation states. Reducing the delay in the communication system, as one of the main delay origins, can play an important role in improving fluctuation mitigation, which on the other hand increases the cost of communication system operation. In addition, application of ultra-capacitor banks, as a virtual inertial tool, can be considered as an effective solution to damp frequency oscillations. However, when the ultra-capacitor size is increased, the virtual inertia also increases, which in turn increases the costs. Therefore, it is essential to use a suitable optimization algorithm to determine the optimum parameters. In this paper, the communication system delay and ultra-capacitor size along with the parameters of the secondary controller are obtained by using a Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm as well as by considering the costs. To cover frequency oscillations and the cost of microgrid operation, two fitness functions are defined. The frequency oscillations of the case study are investigated considering the stochastic behavior of the load and the output of the renewable energy sources.

Suggested Citation

  • Gholam Ali Alizadeh & Tohid Rahimi & Mohsen Hasan Babayi Nozadian & Sanjeevikumar Padmanaban & Zbigniew Leonowicz, 2019. "Improving Microgrid Frequency Regulation Based on the Virtual Inertia Concept while Considering Communication System Delay," Energies, MDPI, vol. 12(10), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:2016-:d:234473
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    References listed on IDEAS

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    Cited by:

    1. Pierluigi Siano & Miadreza Shafie-khah, 2020. "Special Issue on Advanced Approaches, Business Models, and Novel Techniques for Management and Control of Smart Grids," Energies, MDPI, vol. 13(11), pages 1-3, May.
    2. Anshuman Satapathy & Niranjan Nayak & Tanmoy Parida, 2022. "Real-Time Power Quality Enhancement in a Hybrid Micro-Grid Using Nonlinear Autoregressive Neural Network," Energies, MDPI, vol. 15(23), pages 1-35, November.
    3. Giulio Ferro & Michela Robba & Roberto Sacile, 2020. "A Model Predictive Control Strategy for Distribution Grids: Voltage and Frequency Regulation for Islanded Mode Operation," Energies, MDPI, vol. 13(10), pages 1-27, May.
    4. Ziba Rostami & Sajad Najafi Ravadanegh & Navid Taghizadegan Kalantari & Josep M. Guerrero & Juan C. Vasquez, 2020. "Dynamic Modeling of Multiple Microgrid Clusters Using Regional Demand Response Programs," Energies, MDPI, vol. 13(16), pages 1-19, August.
    5. Roni Irnawan & Ahmad Ataka Awwalur Rizqi & Muhammad Yasirroni & Lesnanto Multa Putranto & Husni Rois Ali & Eka Firmansyah & Sarjiya, 2023. "Model-Free Approach to DC Microgrid Optimal Operation under System Uncertainty Based on Reinforcement Learning," Energies, MDPI, vol. 16(14), pages 1-20, July.

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