This paper presents a dual-strategy approach to optimizing charging and discharging schedules for school bus fleets, using the limited charging infrastructure effectively. We aim to ensure that each bus is fully charged for daily operations and aids in grid stability during peak demand. The first strategy utilizes linear programming to schedule overnight charging at available station sockets and strategic discharging during peak periods, efficiently coordinating limited resources. The second strategy employs metaheuristic techniques for continuous optimization, focusing on precise power requirements and offering greater flexibility than the linear model.

With Measurements of grid voltage and current are essential for the optimal operation of the grid protection and control (P&C) systems. Grid parameters vary through time during the faults and especially in the converter interfaced resources (CIRs) rich power grid, and thus accurate estimation is critical to avoid the mis-operation of the P&C systems. In this paper, a moving horizon estimation (MHE) as an observer is devised and applied to estimate the grid line parameters and grid voltages for protection enhancement. Due to the proprietary and confidentiality of CIRs, the proposed approach uses the black-box model to represent their dynamics. Leveraging the easily accessible measurements of output current from the black-box model of CIR and voltage at the point of common coupling, the proposed method estimates the grid impedance and grid voltage during normal and faulty operating conditions. The performance shows that the optimization-based observer was able to closely observe the accurate states and parameters, which can be utilized by the P&C systems.

With the advancement in power electronics technology and grid standards, traditional converters are being supplemented with the new IEEE 1547-2018 standard based grid support functions (GSFs) to support power system voltage and frequency. Inverter dynamics in power systems vary with different modes of operation, thus new modeling methods for proper system planning, operation, and dispatch are required. This work presents a data-driven approach for partitioned dynamic modeling of inverters to speed up simulation time and reduce computational complexity while ensuring acceptable accuracy. The proposed method was tested for a smart inverter with voltage support (Volt-VAr function) on a two-bus system considering dynamic residential loads, and the results showed a four-time speedup in simulation time compared to the use of the detailed model with acceptable levels of accuracy.