The thesis investigates the optimization of energy management in residential nanogrids, focusing on integrating renewable energy sources, electric vehicles, and battery storage systems to minimize electricity costs and ensure nanogrid sustainability. It begins with the planning stage of a residential nanogrid, emphasizing the capacity evaluation of photovoltaic systems combined with battery energy storage systems (BESS), considering factors of size, operation, and charge/discharge efficiency.
Secondly, a MATLAB genetic algorithm is utilized to develop BESS scheduling strategies tailored to various residential demand profiles and operational constraints to minimize electricity costs. The operational constraints include the charging demand of electric vehicles to ensure daily commutes on weekdays and outdoor activities on weekends. Through extensive simulation-based analysis, the study offers valuable insights into sustainable nanogrid design and operation, achieving minimal time-of-use energy costs.

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