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 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.
In this paper, energy storage system(ESS) operation algorithm for economics considering battery degradation properties is proposed. Hourly electricity charge according to real-time pricing (RTP) and battery properties are analyzed for high profit through ESS operation. Especially, hourly electricity charge of Illinois State is analyzed. Characteristics of battery accounts for a high percentage of ESS configuration. Thus, degradation properties of the battery for the price considering the loss of battery usage is considered. The algorithm is proposed for the highest profit through analysis of the battery degradation properties and electricity charge, and the proposed algorithm is verified by mathematical analysis.
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