Market-oriented Two-stage Joint Optimization Strategy for Virtual Power Plants Considering Renewable Forecasting Errors

Virtual Power Plants (VPPs) have gained prominence as an effective approach to enhance renewable energy utilization and optimize power market mechanisms in recent years. To address the inherent uncertainties of renewable energy generation and improve the economic performance of VPPs in electricity markets, this paper proposes a two-stage economic dispatch model that explicitly incorporates renewable energy output forecasting errors. The day-ahead stage employs a data-driven chance-constrained approach to handle renewable uncertainties, ensuring reliable day-ahead scheduling. The real-time stage compensates for deviations between actual and scheduled generation outputs. The proposed coordinated two-stage optimization achieves optimal day-ahead bidding strategies that enhance VPP profitability and reduce real-time power deviations, as validated through case studies.