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.

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.

No 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.

No abstract