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.