Distributed Reinforcement Learning and Consensus Control of Energy Systems

Jennifer King; Paul Fleming; Emiliano Dall-Anese; Christopher Bay; Peter Graf

Distributed Reinforcement Learning and Consensus Control of Energy Systems

Jennifer King (Inventor), Paul Fleming (Inventor), Emiliano Dall-Anese (Inventor), Christopher Bay (Inventor), Peter Graf (Inventor)

Research output: Patent

Abstract

Disclosed herein are methods, systems, and devices for utilizing distributed reinforcement learning and consensus control to most effectively generate and utilize energy. In some embodiments, individual turbines within a wind farm may communicate to reach a consensus as to the desired yaw angle based on the wind conditions.

Original language	American English
Patent number	11,725,625 B2
Filing date	15/08/23
State	Published - 2023

NREL Publication Number

NREL/PT-5000-87269

Keywords

alternating direction method of multipliers (ADMM)
operating parameter
wind measurement

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Cite this

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title = "Distributed Reinforcement Learning and Consensus Control of Energy Systems",

abstract = "Disclosed herein are methods, systems, and devices for utilizing distributed reinforcement learning and consensus control to most effectively generate and utilize energy. In some embodiments, individual turbines within a wind farm may communicate to reach a consensus as to the desired yaw angle based on the wind conditions.",

keywords = "alternating direction method of multipliers (ADMM), operating parameter, wind measurement",

author = "Jennifer King and Paul Fleming and Emiliano Dall-Anese and Christopher Bay and Peter Graf",

year = "2023",

language = "American English",

type = "Patent",

note = "11,725,625 B2",

}

TY - PAT

T1 - Distributed Reinforcement Learning and Consensus Control of Energy Systems

AU - King, Jennifer

AU - Fleming, Paul

AU - Dall-Anese, Emiliano

AU - Bay, Christopher

AU - Graf, Peter

PY - 2023

Y1 - 2023

N2 - Disclosed herein are methods, systems, and devices for utilizing distributed reinforcement learning and consensus control to most effectively generate and utilize energy. In some embodiments, individual turbines within a wind farm may communicate to reach a consensus as to the desired yaw angle based on the wind conditions.

AB - Disclosed herein are methods, systems, and devices for utilizing distributed reinforcement learning and consensus control to most effectively generate and utilize energy. In some embodiments, individual turbines within a wind farm may communicate to reach a consensus as to the desired yaw angle based on the wind conditions.

KW - alternating direction method of multipliers (ADMM)

KW - operating parameter

KW - wind measurement

M3 - Patent

M1 - 11,725,625 B2

Y2 - 2023/08/15

ER -

Distributed Reinforcement Learning and Consensus Control of Energy Systems

Abstract

NREL Publication Number

Keywords

Access to Document

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Cite this