DeepAI
Log In Sign Up

Autonomous Voltage Control for Grid Operation Using Deep Reinforcement Learning

04/24/2019
by   Ruisheng Diao, et al.
0

Modern power grids are experiencing grand challenges caused by the stochastic and dynamic nature of growing renewable energy and demand response. Traditional theoretical assumptions and operational rules may be violated, which are difficult to be adapted by existing control systems due to the lack of computational power and accurate grid models for use in real time, leading to growing concerns in the secure and economic operation of the power grid. Existing operational control actions are typically determined offline, which are less optimized. This paper presents a novel paradigm, Grid Mind, for autonomous grid operational controls using deep reinforcement learning. The proposed AI agent for voltage control can learn its control policy through interactions with massive offline simulations, and adapts its behavior to new changes including not only load/generation variations but also topological changes. A properly trained agent is tested on the IEEE 14-bus system with tens of thousands of scenarios, and promising performance is demonstrated in applying autonomous voltage controls for secure grid operation.

READ FULL TEXT

page 1

page 2

page 3

page 4

10/14/2019

Coordination of PV Smart Inverters Using Deep Reinforcement Learning for Grid Voltage Regulation

Increasing adoption of solar photovoltaic (PV) presents new challenges t...
11/26/2020

Exploring grid topology reconfiguration using a simple deep reinforcement learning approach

System operators are faced with increasingly volatile operating conditio...
03/09/2019

Adaptive Power System Emergency Control using Deep Reinforcement Learning

Power system emergency control is generally regarded as the last safety ...
03/01/2022

Approximating a deep reinforcement learning docking agent using linear model trees

Deep reinforcement learning has led to numerous notable results in robot...
01/09/2023

Reducing Datacenter Operational Carbon Emissions Effectively by Cooperating with the Grid

Facing growing concerns about power consumption and carbon emissions, cl...
10/03/2020

Attractor Selection in Nonlinear Energy Harvesting Using Deep Reinforcement Learning

Recent research efforts demonstrate that the intentional use of nonlinea...