Decision-Focused Learning-Based Optimization for Renewable Imbalance Settlement and Flexible Resource Dispatch

High renewable penetration makes imbalance settlement inseparable from the physical decisions governing reserve procurement and flexibility activation. This paper develops a decision-focused learning-based optimization framework that trains renewable-deviation and flexible-resource deliverability representations through downstream dispatch, reliability, and settlement consequences. The mathematical contribution is a settlement-aware learning objective that couples learned uncertainty, resource-time credible-capacity certification, network-constrained multi-stage dispatch, and counterfactual marginal-contribution allocation while retaining an exact revenue-adequacy identity. The 33-node Zhangjiakou-type regional case uses 15 min intervals and comprises five resource classes: independent storage, data-center flexibility, industrial adjustable load, commercial demand response, and electric-vehicle aggregation. Relative to a fixed-ratio reserve rule, the proposed method lowers the regional balancing cost from 950 to 618 thousand USD (34.9%), achieves 97.8% renewable accommodation, limits the shortage probability to 0.7%, and attains a settlement-fairness index of 0.92. The framework solves a 500-asset instance in 118 s. External validation uses 4027 half-hour observations from the 2025 Elexon/BMRS market, including measured wind and solar output, day-ahead forecasts, load, imbalance prices, and procured-reserve prices. On the 1487-interval December test set, the proposed model reduces the replay cost from 2953.3 to 2598.2 thousand GBP (12.0%), decreases the shortage-interval frequency from 4.64% to 1.28%, and reaches 99.74% renewable accommodation. Comparisons with forecast-then-optimize, Wasserstein distributionally robust optimization, off-policy reinforcement learning, and graph-based behavioral cloning establish that the improvement comes from jointly learning which uncertainty matters for dispatch and which flexible capacity is deliverable.