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Graph Convolutional-Optimization Framework for Carbon-Conscious Grid Management

Author

Listed:
  • 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)

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.

Suggested Citation

  • J. N. Otshwe & Bin Li & Ngouokoua J. Chabrol & Bing Qi & Loris M. Tshish, 2025. "Graph Convolutional-Optimization Framework for Carbon-Conscious Grid Management," Sustainability, MDPI, vol. 17(17), pages 1-25, September.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:17:p:7940-:d:1741498
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