Year: 2023
Type: preprint
Abstract: A spatio-temporal (ST) machine learning (ML) model for security-constrained unit commitment (SCUC) solution acceleration. The ML architecture with GNN and LSTM layers. Includes two models, one for node prediction to predict generator commitment status, and another for edge prediction, which predicts congested lines in the system. The predictions from the two models are then used to reduce the number of variables and constraints in a SCUC problem.NOTE: Codes are implemented in Python. ML model uses Keras, Tensorflow and Spektral (GNN) libraries. Optimization is implemented using Pyomo in python. A solver license (cplex/gurobi) is required for pyomo to run.
Source: arXiv (Cornell University)
Authors: Ramesh, Arun Venkatesh, Li, Xingpeng
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4379474648