Distributed power resources (DPRs) offer a transformative opportunity to improve the efficiency, sustainability, and reliability of modern power infrastructures through their integration. This work presents a novel method based on a mix of renewable energy sources, energy storage technologies, and conventional generators for the optimization of DPR operations under dynamic market settings. Maximizing economic gains is the major objective while preserving system resilience and stability. To handle the complexity of DPR interactions, we offer a strong, hierarchical control architecture encompassing main, secondary, and tertiary levels. System performance is improved using advanced control strategies together with real-time market-responsive changes and predictive algorithms. The efficacy of the proposed methodology is validated through a detailed simulation of a small island grid using mixed-integer linear programming (MILP) and particle swarm optimization (PSO), which demonstrates significant operational improvements. Results indicate cost reductions of approximately 54.7%, which were achieved by effectively prioritizing renewable sources and optimizing energy storage usage. This research contributes both theoretically and practically to accelerating the transition toward sustainable, resilient, and economically viable power systems.

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.

Accurate multi-node energy market forecasting is critical for secure and economic grid operation under increasing penetration of renewable energy and electric vehicles. This paper proposes a physics-aware spatiotemporal forecasting framework that integrates Graph Convolutional Networks (GCNs) for modeling network-level spatial dependencies with a self-attention mechanism for capturing long-range temporal correlations. Unlike existing GCN + RNN or attention-based forecasting approaches, physical feasibility is enforced during learning through structured penalty terms reflecting power balance, generation limits, EV state-of-charge dynamics, and AC load flow constraints, rather than via post-processing optimization. The model is evaluated on a synthetic IEEE 24-bus benchmark with realistic load scaling, renewable variability, and EV charging profiles. Results show a mean squared error of 1.84 MW² and a 7–10% reduction in forecasting error relative to baseline ARIMA and LSTM models, while maintaining constraint violation rates below 5%. Multi-step forecasting experiments demonstrate stable error growth under high volatility conditions. The proposed framework establishes a bridge between purely data-driven forecasting and physically consistent grid-aware prediction, offering a scalable foundation for operationally feasible energy market forecasting.