Energy Management Method of Hybrid AC/DC Microgrid Using Artificial Neural Network
Kyung-Min Kang, Bong-Yeon Choi, Hoon Lee, Chang-Gyun An, Tae-Gyu Kim, Yoon-Seong Lee, Mina Kim, Junsin Yi, Chung-Yuen Won
Abstract
This paper proposes an artificial neural network (ANN)-based energy management system (EMS) for controlling power in AC–DC hybrid distribution networks. The proposed ANN-based EMS selects an optimal operating mode by collecting data such as the power provided by distributed generation (DG), the load demand, and state of charge (SOC). For training the ANN, profile data on the charging and discharging amount of ESS for various distribution network power situations were prepared, and the ANN was trained with an error rate within 10%. The proposed EMS controls each power converter in the optimal operation mode through the already trained ANN in the grid-connected mode. For the experimental verification of the proposed EMS, a small-scale hybrid AD/DC microgrid was fabricated, and simulations and experiments were performed for each operation mode.