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Smart Grid Seminar

Monday, July 1, 2019
12:00pm to 1:00pm
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Annenberg 213
Recent Advances in Autonomous Energy Grid
Changhong Zhao, PhD, Lead Researcher, National Renewable Energy Laboratory,

NREL's Autonomous Energy Grid project aims to develop and validate a scalable and responsive information and control architecture that promises substantial enhancement of grid security, efficiency, and resiliency. The proposed architecture can self-optimize in real time and in a distributed fashion to ensure economically efficient performance while protecting the system against variations of renewable generation and restoring the system from outages. In this talk, I will give an overview of the project, followed by two sample designs as part of the proposed architecture.

Our first design is a hierarchical distributed algorithm to solve an optimal power flow problem that dispatches distributed energy resources for voltage regulation with minimum cost. The proposed algorithm features unprecedented scalability to large multiphase distribution networks by exploiting the radial structure of network power flow to derive a hierarchical, distributed implementation of primal-dual algorithm. Numerical simulation on a 4,521-node network demonstrates more than 10-fold acceleration achieved by the proposed design compared to the centrally coordinated primal-dual algorithm.

Our second design is a fully decentralized leaky integral controller for frequency restoration. We study steady-state asymptotic optimality, nominal stability, input-to-state stability, noise rejection, transient performance, and robustness properties of this controller in closed loop with a nonlinear power system model. We demonstrate that the leaky integral controller can strike an acceptable trade-off between performance and robustness as well as between asymptotic disturbance rejection and transient convergence rate by tuning its DC gain and time constant. We compare our findings to conventional decentralized integral control and distributed averaging-based integral control in theory and simulations.

For more information, please contact Yu Su by email at [email protected].