학술논문

Graph Convolutional Networks for probabilistic power system operational planning
Document Type
Conference
Source
2023 IEEE Belgrade PowerTech PowerTech, 2023 IEEE. :1-6 Jun, 2023
Subject
Power, Energy and Industry Applications
Costs
Computational modeling
Neural networks
Probabilistic logic
Planning
Power system reliability
Convolutional neural networks
probabilistic operational planning
power system reliability
contingency analysis
machine learning
graph neural networks
Language
Abstract
Probabilistic operational planning of power systems usually requires computationally intensive and time consuming simulations. The method presented in this paper provides a time efficient alternative to predict the socio-economic cost of system operational strategies using graph convolutional networks. It is intended for fast screening of operational strategies for the purpose of operational planning. It can also be used as a proxy for operational planning that can be used in long term development studies. The performance of the model is demonstrated on a network inspired by the Nordic power system.