Abstract
Disruptive events, such as the winter storm of 2021 that left 40 million people in the U.S. without power, have revealed the potential danger of societal dependence on centralized energy sources. Localized energy grids (called microgrids (MGs)) can help add energy reliability and independence by using distributed generators (DGs) with photovoltaic (PV) energy sources and energy storage systems (ESSs). Such MGs can independently energize critical energy demand nodes (DNs) when isolated from the primary grid with renewable energy. The optimal sizes and assignments of PVDG/ESS units to the DNs during outages are crucial to increasing energy reliability. However, finding an optimal configuration−energy management strategy is difficult due to the investment costs, complexity of assignments, potential capacities, and uncertainties in the PV system output. In this research, we developed a simulation framework, augmented by genetic algorithms (GAs), to optimize costs and fulfill energy demands by selecting the appropriate MG configuration and ESS management strategy for an islanded MG for emergency power during an extended disruption. The simulation model was based on historical data, referencing Knoxville, TN, models, and changing the output and load conditions due to the time of day and weather for PVDG/ESS MGs to help quantify some stochastic attributes. The solutions were evaluated under given investment budgets with minimal costs and maximal average hourly energy demands met. Solutions also provide an appropriate energy management strategy and prioritization of specific DNs during load shedding.