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

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.

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“…It employs a dual-phase technique to ascertain the operational state and regulate the replenishment and release of the energy storage system. The microgrid integrated diverse converters and effectively functioned and governed with the suggested SNN-based EMA, as evidenced by empirical findings on a laboratory-scale setup [29]. Scientists have sought to enhance the effectiveness of photovoltaic systems (PVS) by forecasting weather conditions that influence PV module performance.…”