High-Performance Optimal Power Flow Estimation for EV-Interfaced Microgrids With Standardized Grid Services

Demand for the grid state estimation with partial power network observation is growing rapidly with the increasing amount of distributed energy resources (DER) (such as electric vehicles (EVs)) with incomplete measured information connected to the grid. The grid services that can be provided by these DER, such as bidirectional EV chargers, have the potential to affect the resilience and efficiency of the grid. We propose a constrained optimization solver based on AC power flows to recover incomplete information from the grid [1]. We evaluate the performance of the proposed solver using the IEEE 9/30/57/118-bus systems and show that it excels the state-of-the-art approaches (pandapower and MATPOWER) in recovering the grid state from partial observations with up to six orders of magnitude smaller mean absolute error. The proposed solver has better scalability to deal with larger systems and with systems with more unknown measurements. Further reactive power constraints are added to the solver following the specifications in IEEE 1547 to explore the effects of adding grid services to the steady-state microgrid. We demonstrate and discuss novel case studies on these four IEEE bus systems. And the reactions of the IEEE bus systems when following the grid services' constraints are introduced. We illustrate a sensitivity analysis that reflects how the system's physical characteristics would impact the implementation of grid services. The proposed solver with grid services constraints can serve as a tool to help evaluate distributed grid services and fine-tune grid services to optimal results.