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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.

A flyback inverter using voltage sensorless maximum power point tracking (MPPT) for photovoltaic (PV) AC modules is presented. PV AC modules for a power rating from 150 W to 300 W are generally required for their small size and low price because of the installation on the back side of PV modules. In the conventional MPPT technique for PV AC modules, sensors for detecting PV voltage and PV current are required to calculate the PV output power. However, system size and cost increase when the voltage sensor and current sensor are used because of the addition of the auxiliary circuit for the sensors. The proposed method uses only the current sensor to track the MPP point. Therefore, the proposed control method overcomes drawbacks of the conventional control method. Theoretical analysis, simulation, and experiment are performed to verify the proposed control method.

This paper proposes an energy management system (EMS) of direct current (DC) microgrid. In order to implement the proposed EMS, the control and operation method of EMS is presented in this work. While most of the studies have individually examined the grid-connected mode used in building and the stand-alone operation mode applicable to the island, the proposed EMS allows it to be used in both grid-connected mode and stand-alone mode with 10 modes. In order to determine each mode in EMS, not only the amount of generated power, load power, and the state of charge (SOC) of the battery, but also the rated power of the energy storage system (ESS) converter that performs charging and discharging operations is additionally considered. Thus, various uncertainties that may occur in the actual DC microgrid environment can be improved. A laboratory-scale DC microgrid is fabricated to conduct experimental validation of proposed EMS. Experiments of DC microgrid with proposed EMS were performed for each mode, and the experiment waveforms of each power conversion device are included in detail.

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