TY - JOUR
T1 - Graph Neural Networks for Voltage Stability Margins with Topology Flexibilities
AU - Guddanti, Kishan Prudhvi
AU - Weng, Yang
AU - Marot, Antoine
AU - Donnot, Benjamin
AU - Panciatici, Patrick
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2023
Y1 - 2023
N2 - High penetration of distributed energy resources (DERs) changes the flows in power grids causing thermal congestions which are managed by real-time corrective topology switching. It is crucial to consider voltage stability margin (VSM) as a constraint when modifying grid topology. However, it is nontrivial to exhaustively search using AC power flow (ACPF) for all control actions with desired VSM. Sensitivity methods are used to solve this issue of 'power flow-free VSM estimation' to screen candidate control actions. However, due to the volatile nature of DERs, sensitivity methods do not perform well near nonlinear operating regions which is overcome by solving ACPF. Here, we propose a new VSM estimation method that performs well at nonlinear operating regions without solving ACPF. We achieve this by formulating the learning of graph neural networks like the matrix-free power flow algorithms. We empirically demonstrate how this similarity bypasses the inaccuracy issues and performs well on unseen operating conditions and topologies without further re-training. The effectiveness is demonstrated on a power network with realistic load and generation profiles, various generation mix, and large control actions. The benefits are showcased in terms of speed, reliability to identify insecure controls, and adaptability to unseen scenarios and grid topologies.
AB - High penetration of distributed energy resources (DERs) changes the flows in power grids causing thermal congestions which are managed by real-time corrective topology switching. It is crucial to consider voltage stability margin (VSM) as a constraint when modifying grid topology. However, it is nontrivial to exhaustively search using AC power flow (ACPF) for all control actions with desired VSM. Sensitivity methods are used to solve this issue of 'power flow-free VSM estimation' to screen candidate control actions. However, due to the volatile nature of DERs, sensitivity methods do not perform well near nonlinear operating regions which is overcome by solving ACPF. Here, we propose a new VSM estimation method that performs well at nonlinear operating regions without solving ACPF. We achieve this by formulating the learning of graph neural networks like the matrix-free power flow algorithms. We empirically demonstrate how this similarity bypasses the inaccuracy issues and performs well on unseen operating conditions and topologies without further re-training. The effectiveness is demonstrated on a power network with realistic load and generation profiles, various generation mix, and large control actions. The benefits are showcased in terms of speed, reliability to identify insecure controls, and adaptability to unseen scenarios and grid topologies.
KW - Voltage collapse
KW - graph convolution neural networks
KW - machine learning
KW - topology switching
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U2 - 10.1109/OAJPE.2022.3223962
DO - 10.1109/OAJPE.2022.3223962
M3 - Article
AN - SCOPUS:85144016183
SN - 2332-7707
VL - 10
SP - 73
EP - 85
JO - IEEE Open Access Journal of Power and Energy
JF - IEEE Open Access Journal of Power and Energy
ER -