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Hybrid AC/DC Microgrid Energy Management Strategy Based on Two-Step ANN

Author

Listed:
  • Tae-Gyu Kim

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea)

  • Hoon Lee

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea)

  • Chang-Gyun An

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea)

  • Junsin Yi

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea)

  • Chung-Yuen Won

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea)

Abstract

In grid-connected operations, a microgrid can solve the problem of surplus power through regeneration; however, in the case of standalone operations, the only method to solve the surplus power problem is charging the energy storage system (ESS). However, because there is a limit to the capacity that can be charged in an ESS, a separate energy management strategy (EMS) is required for stable microgrid operation. This paper proposes an EMS for a hybrid AC/DC microgrid based on an artificial neural network (ANN). The ANN is composed of a two-step process that operates the microgrid by outputting the operation mode and charging and discharging the ESS. The microgrid consists of an interlinking converter to link with the AC distributed system, a photovoltaic converter, a wind turbine converter, and an ESS. The control method of each converter was determined according to the mode selection of the ANN. The proposed ANN-based EMS was verified using a laboratory-scale hybrid AC/DC microgrid. The experimental results reveal that the microgrid operation performed stably through control of individual converters via mode selection and reference to ESS power, which is the result of ANN integration.

Suggested Citation

  • Tae-Gyu Kim & Hoon Lee & Chang-Gyun An & Junsin Yi & Chung-Yuen Won, 2023. "Hybrid AC/DC Microgrid Energy Management Strategy Based on Two-Step ANN," Energies, MDPI, vol. 16(4), pages 1-23, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1787-:d:1065133
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    References listed on IDEAS

    as
    1. Mostafa Kermani & Domenico Luca Carnì & Sara Rotondo & Aurelio Paolillo & Francesco Manzo & Luigi Martirano, 2020. "A Nearly Zero-Energy Microgrid Testbed Laboratory: Centralized Control Strategy Based on SCADA System," Energies, MDPI, vol. 13(8), pages 1-15, April.
    2. Sadaqat Ali & Zhixue Zheng & Michel Aillerie & Jean-Paul Sawicki & Marie-Cécile Péra & Daniel Hissel, 2021. "A Review of DC Microgrid Energy Management Systems Dedicated to Residential Applications," Energies, MDPI, vol. 14(14), pages 1-26, July.
    3. Maysam Abbasi & Ehsan Abbasi & Li Li & Ricardo P. Aguilera & Dylan Lu & Fei Wang, 2023. "Review on the Microgrid Concept, Structures, Components, Communication Systems, and Control Methods," Energies, MDPI, vol. 16(1), pages 1-36, January.
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