Feedback Control Approaches for Restoration of Power Grids from Blackouts

Miller, Joseph M.; Villegas Pico, Hugo N.; Dobson, Ian; Bernstein, Andrey; Cui, Bai

doi:10.1016/j.epsr.2022.108414

Feedback Control Approaches for Restoration of Power Grids from Blackouts

Journal Article · 12 July 2022 · Electric Power Systems Research

DOI:https://doi.org/10.1016/j.epsr.2022.108414· OSTI ID:1890154

Miller, Joseph M.; Villegas Pico, Hugo N.; Dobson, Ian; Bernstein, Andrey;

The automated restoration of power systems with variable energy resources is a timely problem to tackle. Automated restoration advice can support operators in deciding on strategic actions to restore power grids from a blackout with a mix of conventional and renewable generation resources. To this end, this paper frames the restoration process of power grids with solar resources as a nonlinear dynamic model with algebraic constraints in discrete time which is steered by feedback control loops. We discuss two feedback-control strategies based on greedy and reinforcement learning algorithms, and contrast their performance with restoration plans generated by a mixed-integer linear program. We found that the reinforcement learning algorithm infers restoration actions faster than the greedy one. However, the tuning process of the reinforcement learning parameters is slower than for the greedy one.

Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE National Renewable Energy Laboratory (NREL), Laboratory Directed Research and Development (LDRD) Program

DOE Contract Number:: AC36-08GO28308

OSTI ID:: 1890154

Report Number(s):: NREL/JA-5D00-84159; MainId:84932; UUID:353c0c54-0aed-482b-8541-f7fd84f40593; MainAdminID:67697

Journal Information:: Electric Power Systems Research, Journal Name: Electric Power Systems Research Vol. 211

Country of Publication:: United States

Language:: English

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