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

Tae-Gyu Kim, Hoon Lee, Chang-Gyun An, Junsin Yi and Chung-Yuen Won ()
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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

Energies, 2023, vol. 16, issue 4, 1-23

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.

Keywords: energy management strategy; distributed generation; interlinking converter; artificial neural network; hybrid AC/DC microgrid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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