This thesis aspires to model and control, the flow of power in a DC microgrid. Specifically, the energy sources are a photovoltaic system and the utility grid, a lead acid battery based energy storage system and twenty PEV charging stations as the loads. Theoretical principles of large scale state space modeling are applied to model the considerable number of power electronic converters needed for controlling voltage and current thresholds. The energy storage system is developed using principles of neural networks to facilitate a stable and uncomplicated model of the lead acid battery. Power flow control is structured as a hierarchical problem with multiple interactions between individual components of the microgrid. The implementation is done using fuzzy logic with scheduling the maximum use of available solar energy and compensating demand or excess power with the energy storage system, and minimizing utility grid use, while providing multiple speeds of charging the PEVs.
展开▼
