Unbalanced AC microgrids present unique operational challenges when integrating distributed energy resources and plug-in electric vehicles. Traditional scheduling approaches often oversimplify the problem by treating microgrid loads as lumped, single-phase equivalents, which fails to capture the distinct electrical conditions present across all three phases in real systems. This research addresses this gap by developing a sophisticated optimization framework that models each phase independently while accounting for time-varying, voltage-dependent load characteristics.
The study employs an enhanced grasshopper optimization algorithm to solve the scheduling problem, minimizing both generation costs and system losses simultaneously. By modeling loads on a per-phase basis rather than as aggregate lumped values, the framework produces more realistic dispatch schedules that reflect actual operating conditions. This granular approach proves especially valuable in residential and commercial microgrids where single-phase loads are common and load imbalances frequently occur.
The research demonstrates that integrating plug-in electric vehicles alongside conventional distributed energy resources delivers substantial economic benefits. PEVs function as flexible load resources that can be strategically charged during low-cost periods and discharged during peak-demand hours, effectively smoothing generation requirements and reducing overall operating expenses. When combined with DERs such as solar arrays, small wind turbines, and battery storage systems, the economic advantage grows significantly.
Results confirm that neglecting phase-specific variations leads to suboptimal scheduling decisions that increase costs and system losses. The per-phase modeling approach captures voltage variations across phases, which directly influences both load demand and distributed generator output, particularly for voltage-dependent loads common in modern distribution systems.
These findings have practical implications for microgrid operators seeking to maximize efficiency and cost-effectiveness while maintaining reliable three-phase system balance. The optimization framework can guide real-time operational decisions for charging PEVs, dispatching DERs, and managing demand-side flexibility to achieve optimal economic and technical performance.
