Multi-Dimensional Risk Assessment of Day-Ahead Power Supply Adequacy in Power Systems with High Shares of Renewable Energy

To address the limitation that traditional static capacity evaluation fails to comprehensively reflect the day-ahead power supply adequacy risks under high-penetration renewable energy integration, this paper proposes a multi-dimensional power supply adequacy evaluation method that considers source-load uncertainty, temporal matching, spatial network constraints, and dynamic flexibility. First, a similar-day matching library is constructed based on the operating-state proxy features and calendar characteristics of the target evaluation day, and source-load scenarios with intra-day temporal correlations are generated via conditional error sampling to characterize the variations in source-load forecasting errors under different operating conditions. Second, an evaluation index system for power supply adequacy is established from four dimensions: absolute adequacy, relative matching, spatial transmission, and dynamic response. Smoothed Min–Max normalization and constrained weight calibration are then applied to fuse the multi-dimensional risk information. Finally, the comprehensive day-ahead power supply adequacy risk evaluation results are generated, and the impacts of key modules on the risk identification results are analyzed through ablation experiments. Case studies based on the RTS-GMLC test system demonstrate a significant source-load temporal mismatch on the test day, with a comprehensive power supply adequacy risk index of 0.3052. Specifically, the risk contributions of peak load and network congestion are 0.1411 and 0.1190, respectively. In the ablation experiments, the risk identification consistency of the proposed full model reaches 83.5%, indicating that the developed method effectively improves the identification of day-ahead power supply adequacy risks.