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This content will become publicly available on October 1, 2023

Title: Improved primary frequency response through deep reinforcement learning
This paper explores the application of deep reinforcement learning (DRL) to create a coordinating mechanism between synchronous generators (SGs) and distributed energy resources (DERs) for improved primary frequency regulation. Renewable energy sources, such as wind and solar, may be used to aid in frequency regulation of the grid. Without proper coordination between the sources, however, the participation only results in a delay of SG governor response and frequency deviation. The proposed DRL application uses a deep deterministic policy gradient (DDPG) agent to create a generalized coordinating signal for DERs. The coordinating signal communicates the degree of distributed participation to the SG governor, resolving delayed governor response and reducing system rate of change of frequency (ROCOF). The validity of the coordinating signal is presented with a single-machine finite bus system. The use of DRL for signal creation is explored in an under-frequency event. While further exploration is needed for validation in large systems, the development of this concept shows promising results towards increased power grid stabilization.
Authors:
;
Editors:
Mostafa Sahraei-Ardakani; Mingxi Liu
Award ID(s):
2033910
Publication Date:
NSF-PAR ID:
10351473
Journal Name:
North American Power Symposium
ISSN:
2163-4939
Sponsoring Org:
National Science Foundation
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