Graph-X: Graph-Structured Deep Learning for Price Forecasting and Risk-Aware Virtual Power Plant Market Participation

The increasing integration of distributed energy resources, renewable generation, and flexible loads has made Virtual Power Plant (VPP) market participation highly exposed to price volatility and operational uncertainty. This paper proposes Graph-X, a unified graph-structured deep learning and stochastic optimization framework for day-ahead electricity price forecasting and risk-aware VPP bidding. Unlike conventional temporal forecasting models, Graph-X captures structural market-clearing behavior by converting raw bids into continuous differentiable curves represented on a discrete price–quantity graph. The proposed architecture combines sparse graph convolutions, recurrent temporal learning, dilated temporal convolutions, and cyclical calendar encodings to model spatial, temporal, and operational market dependencies. Forecasts are further integrated with a stochastic bidding model governed by a coherent spectral risk measure to align prediction accuracy with financial performance. The framework is validated using historical hourly data from the ISO New England day-ahead electricity market. Results show that Graph-X achieves an MAE of 1.81 $/MWh and an R2 score of 0.944, outperforming GNN, LSTM, Transformer, and ARIMA baselines. In VPP bidding, Graph-X delivers an average daily profit of 52.3 k$, improving profitability by 18.1% (equivalent to $2.92 annually) over the GNN baseline, with an average inference time of 12.5 ms per forecast.