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A novel adaptive intelligent MPC scheme for frequency stabilization of a microgrid considering SoC control of EVs

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  • Khokhar, Bhuvnesh
  • Parmar, K. P. Singh

Abstract

In the recent years, the vehicle-to-grid (V2G) technology has been successfully implemented to stabilize the frequency deviations in a microgrid (MG) whereby charging/discharging of battery of electric vehicles (EVs) is utilized depending upon their state-of-charge (SoC). Compared to the conventional battery energy storage technology, lower degradation tendency and lesser cost of an EV battery are obvious reasons for its utilization as an alternate. This paper proposes a novel adaptive intelligent model predictive control (AIMPC) scheme for frequency stabilization of an MG considering the SoC control of the battery of the EVs. The MPC scheme operates by predicting the future behavior of a plant whereby an explicit discrete-time state-space model of the plant is utilized. Since optimal performance of the MPC depends upon the tuning parameter (τw) present in its cost function, an intelligent optimization algorithm is implemented to dynamically optimize the parameter τw and simultaneously the proposed control scheme is made adaptive. Effect of the SoC control on the frequency deviation response (FDR) of the MG is demonstrated. Further, competence of the proposed control scheme is established over the adaptive fuzzy MPC and PID controller considering diverse loading conditions in the MG. Simulation results clearly establish that the FDRs of the MG are improved with the implementation of the proposed control scheme. Lastly, sensitivity of the proposed scheme is corroborated considering parametric uncertainties in the MG.

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  • Khokhar, Bhuvnesh & Parmar, K. P. Singh, 2022. "A novel adaptive intelligent MPC scheme for frequency stabilization of a microgrid considering SoC control of EVs," Applied Energy, Elsevier, vol. 309(C).
    • Handle: RePEc:eee:appene:v:309:y:2022:i:c:s0306261921016524
    DOI: 10.1016/j.apenergy.2021.118423
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    References listed on IDEAS

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    Cited by:

    1. Trinadh Pamulapati & Muhammed Cavus & Ishioma Odigwe & Adib Allahham & Sara Walker & Damian Giaouris, 2022. "A Review of Microgrid Energy Management Strategies from the Energy Trilemma Perspective," Energies, MDPI, vol. 16(1), pages 1-34, December.
    2. Adlan Pradana & Mejbaul Haque & Mithulanathan Nadarajah, 2023. "Control Strategies of Electric Vehicles Participating in Ancillary Services: A Comprehensive Review," Energies, MDPI, vol. 16(4), pages 1-36, February.
    3. Yin, WanJun & Qin, Xuan, 2022. "Cooperative optimization strategy for large-scale electric vehicle charging and discharging," Energy, Elsevier, vol. 258(C).
    4. Oshnoei, Soroush & Aghamohammadi, Mohammad Reza & Oshnoei, Siavash & Sahoo, Subham & Fathollahi, Arman & Khooban, Mohammad Hasan, 2023. "A novel virtual inertia control strategy for frequency regulation of islanded microgrid using two-layer multiple model predictive control," Applied Energy, Elsevier, vol. 343(C).
    5. Khokhar, Bhuvnesh & Parmar, K.P. Singh, 2023. "Utilizing diverse mix of energy storage for LFC performance enhancement of a microgrid: A novel MPC approach," Applied Energy, Elsevier, vol. 333(C).

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