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MPC Based Energy Management System for Hosting Capacity of PVs and Customer Load with EV in Stand-Alone Microgrids

Author

Listed:
  • Kyung-Sang Ryu

    (Electric Power System Research Team, Jeju Global Research Center (JGRC), Korea Institute of Energy Research (KIER), Jeju-si 63357, Korea)

  • Dae-Jin Kim

    (Electric Power System Research Team, Jeju Global Research Center (JGRC), Korea Institute of Energy Research (KIER), Jeju-si 63357, Korea)

  • Heesang Ko

    (Electric Power System Research Team, Jeju Global Research Center (JGRC), Korea Institute of Energy Research (KIER), Jeju-si 63357, Korea)

  • Chang-Jin Boo

    (Department of Electrical Engineering, Jeju International University, Jeju-si 63309, Korea)

  • Jongrae Kim

    (School of Mechanical Engineering, University of Leeds, Leeds LS29JT, UK)

  • Young-Gyu Jin

    (Department of Electrical Engineering, Jeju National University, Jeju-si 63243, Korea)

  • Ho-Chan Kim

    (Department of Electrical Engineering, Jeju National University, Jeju-si 63243, Korea)

Abstract

This paper presents the improvements of the hosting capacity of photovoltaics (PVs) and electric vehicles (EVs) in a stand-alone microgrid (MG) with an energy storage system (ESS) by consider-ing a model predictive control (MPC) based energy management system. The system is configured as an MG, including PVs, an ESS, a diesel generator (DG), and several loads with EVs. The DG is controlled to operate at rated power and the MPC algorithm is used in a stand-alone MG, which supplies the energy demanded for several loads with EVs. The hosting capacity of the load in-cluding the EV and PVs can be expanded through the ESS to the terminal node of the microgrid. In this case, the PVs and the load can be connected in excess of the capacity of the diesel genera-tor, and each bus in the feeder complies with the voltage range required by the grid. The effec-tiveness of the proposed algorithm to resolve the hosting capacity is demonstrated by numerical simulations.

Suggested Citation

  • Kyung-Sang Ryu & Dae-Jin Kim & Heesang Ko & Chang-Jin Boo & Jongrae Kim & Young-Gyu Jin & Ho-Chan Kim, 2021. "MPC Based Energy Management System for Hosting Capacity of PVs and Customer Load with EV in Stand-Alone Microgrids," Energies, MDPI, vol. 14(13), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:4041-:d:588405
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    References listed on IDEAS

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    1. Park, Minsun & Barrett, Mark & Gallo Cassarino, Tiziano, 2019. "Assessment of future renewable energy scenarios in South Korea based on costs, emissions and weather-driven hourly simulation," Renewable Energy, Elsevier, vol. 143(C), pages 1388-1396.
    2. Kyung-Sang Ryu & Dae-Jin Kim & Yang-Hyun Nam & Heesang Ko & Byungki Kim & Ho-Chan Kim, 2019. "An Innovative Operation Strategy of ESS for Capacity Expansion of Renewable Energy and Customer Load with Electric Vehicle Chargers in Low Voltage Distribution Systems," Energies, MDPI, vol. 12(24), pages 1-22, December.
    3. Ahmed Belila & Mohamed Benbouzid & El-Madjid Berkouk & Yassine Amirat, 2018. "On Energy Management Control of a PV-Diesel-ESS Based Microgrid in a Stand-Alone Context," Energies, MDPI, vol. 11(8), pages 1-23, August.
    4. Byungki Kim & Yang-Hyun Nam & Heesang Ko & Chul-Ho Park & Ho-Chan Kim & Kyung-Sang Ryu & Dae-Jin Kim, 2019. "Novel Voltage Control Method of the Primary Feeder by the Energy Storage System and Step Voltage Regulator," Energies, MDPI, vol. 12(17), pages 1-18, August.
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    7. L. Alvarado-Barrios & A. Rodríguez del Nozal & A. Tapia & J. L. Martínez-Ramos & D. G. Reina, 2019. "An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes," Energies, MDPI, vol. 12(11), pages 1-23, June.
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    Cited by:

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    3. Nor Liza Tumeran & Siti Hajar Yusoff & Teddy Surya Gunawan & Mohd Shahrin Abu Hanifah & Suriza Ahmad Zabidi & Bernardi Pranggono & Muhammad Sharir Fathullah Mohd Yunus & Siti Nadiah Mohd Sapihie & Asm, 2023. "Model Predictive Control Based Energy Management System Literature Assessment for RES Integration," Energies, MDPI, vol. 16(8), pages 1-27, April.
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    5. Maciej Ławryńczuk & Piotr M. Marusak & Patryk Chaber & Dawid Seredyński, 2022. "Initialisation of Optimisation Solvers for Nonlinear Model Predictive Control: Classical vs. Hybrid Methods," Energies, MDPI, vol. 15(7), pages 1-21, March.

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