Graph Convolutional-Optimization Framework for Carbon-Conscious Grid Management

J. N. Otshwe (), Bin Li (), Ngouokoua J. Chabrol, Bing Qi and Loris M. Tshish
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J. N. Otshwe: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Bin Li: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Ngouokoua J. Chabrol: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Bing Qi: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Loris M. Tshish: Computer Science Department, University of Kinshasa, Kinshasa 999069, Democratic Republic of the Congo

Sustainability, 2025, vol. 17, issue 17, 1-25

Abstract: Amid the escalating climate crisis, integrating variable renewables into power systems demands innovative carbon-conscious grid management. This research presents a Graph Convolutional-Optimization Framework that synergizes Graph Convolutional Networks (GCNs) with hybrid optimization Interior-Point Method, Genetic Algorithms, and Particle Swarm Optimization to minimize emissions while ensuring grid stability under uncertainty. GCNs capture spatial–temporal grid dynamics, providing robust initial solutions that enhance convergence. Chance constraints, scenario reduction via k-medoids, and slack variables address stochasticity and stringent emission caps, overcoming infeasibility challenges. Validated on a 24-bus microgrid, the framework achieves superior performance, with PSO yielding minimal emissions (1.59 kg CO 2 ) and efficient computation. This scalable, topology-aware approach redefines sustainable grid operations, bridging machine learning and optimization for resilient, low-carbon energy systems aligned with global decarbonization goals.

Keywords: Graph Convolutional Networks; carbon-conscious grid management; hybrid optimization; renewable energy; stochastic systems; decarbonization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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