Work

Graph Convolutional-Optimization Framework for Carbon-Conscious Grid Management

Year: 2025

Type: article

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 CO2) 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.

Source: Sustainability

DOI: 10.3390/su17177940

Authors: J. N. Otshwe, Bin Li, Ngouokoua J. Chabrol, Bing Qi, Loris M. Tshish

Institutions: North China Electric Power University, University of Kinshasa

Language: English

Topic: Electric Power System Optimization

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $2,896