Managing distributed energy resources and plug-in electric vehicles simultaneously presents significant challenges in unbalanced microgrids, where voltage and loading differ across the three phases. Traditional scheduling approaches typically model loads as a single lumped value, overlooking these operational differences and producing unrealistic optimization results.
Researchers have developed an advanced scheduling framework that treats each phase independently while accounting for time-varying voltage-dependent load characteristics. This per-phase modeling approach better represents actual microgrid conditions, where residential, commercial, and industrial loads are unevenly distributed across phases.
The study employed an enhanced grasshopper optimization algorithm—a nature-inspired computational method—to minimize both generation costs and system losses while scheduling PEVs and DERs. By allowing the algorithm to optimize resource deployment phase-by-phase, the framework captures realistic voltage variations and loading imbalances that standard approaches miss.
Results demonstrated that per-phase analysis yields substantially different and more accurate scheduling decisions compared to lumped-load methods. The integration of PEVs proved particularly valuable, offering significant cost reductions and improved system efficiency when properly coordinated with distributed resources.
This research has practical implications for utilities operating unbalanced microgrids, particularly in urban and rural settings where load distribution across phases is inherently uneven. The framework enables operators to exploit PEV flexibility—such as controlled charging timing and vehicle-to-grid capabilities—to balance phases and reduce peak demand charges.
As electric vehicle adoption accelerates and distributed generation expands, microgrids must accommodate these new resources without compromising reliability or efficiency. Phase-aware scheduling optimization represents a key advancement in managing complex, modern distribution networks. Implementation of such methods could unlock substantial economic and operational benefits for utility operators and microgrid developers.
