Doctoral Defense

Advanced Networked Microgrid Analystics

Fei Feng 

May 2, 2024
1:00 PM
Light Engineering, Room 250
Advisor:  Peng Zhang

Networked microgrids provide a paradigm for large-scale distributed energy resources (DERs) integrations. However, the low inertia, various controls and high uncertainties induced by DERs bring unprecedented challenges for traditional power system analytics.

Thus, this work aims to address two challenges: 1) complex and unattainable operation states due to low inertia and various controls of DERs. To track the steady state of DER-controlled microgrids, a series of microgrid power flow algorithms including enhanced Newton-type and implicit Zbus power flows are first devised to incorporate the hierarchical controls of DER in individual microgrids. A distributed networked microgrids power flow is subsequently developed to integrate the power coordination of DERs among neighboring microgrids. To track the dynamic state of DER-controlled microgrid, a neuro-dynamic state estimation algorithm is devised by integrating the ODE-Net into Kalman filters. 2) increasing computational burdens due to the high uncertainties of DERs. A noise-free quantum power flow algorithm is first devised to reduce the computation complexity of traditional power flow through Harrow-Hassidim-Lloyd (HHL) algorithm. A noise-free microgrid state estimation is then developed for microgrid analytics through HHL. Next, in order to address the scalability problem, a set of noise-resilient quantum power flow and state estimation algorithms are respectively devised to track the steady states of power grids. Then, on top of state analysis, we devised a quantum surrogate lagrangian relaxation (QSLR) approach potential for the energy management of large-scale networked microgrids. Unit commitment is investigated by the QSLR method.