Reliable Aggregated Capacity Estimation for DPV-Integrated VPP via Dynamic Spatiotemporal Forecasting and Risk-Averse Stochastic Programming

The deterministic evaluation framework for traditional virtual power plants (VPP) overlooks the capacity squeeze caused by day-ahead forecasting errors in real-time settlement, resulting in a significant overestimation of the adjustable capacity committed to external parties; simultaneously, conventional distributed PV forecasting methods struggle to capture the dynamic spatiotemporal coupling characteristics arising from meteorological time-varying factors. To address these issues, this paper proposes a comprehensive evaluation method for VPP adjustable capacity that integrates front-end dynamic spatiotemporal forecasting with back-end risk-averse scheduling. Firstly, a two-step dynamic sub-region partitioning strategy based on a sliding time window is proposed. By combining the Spacetimeformer self-attention model with K-means error mining, high-precision scenario-based available power boundaries are generated. Secondly, conditional value at risk (CVaR) is introduced to construct a two-stage risk-averse stochastic programming (RA-SP) model that accounts for expected physical default penalties. Finally, a parallel control evaluation system is established based on the physical baseline anchoring without response of internal flexible resources and real-time post-event verification. Simulations using real operational data demonstrate that the proposed dynamic spatiotemporal forecasting architecture significantly improves regional power forecasting accuracy; compared with traditional deterministic models, the RA-SP model effectively eliminates artificially inflated committed capacity exceeding physical limits (reducing the assessed tracking root-mean-square error by 32.42%), and by proactively setting aside a physical risk-resistance margin, the cumulative physical default electricity consumption throughout the day was reduced by 99.99%. This not only significantly enhanced the reliability of physical delivery but also achieved a flexible and reliable settlement rate with high confidence.