High penetration of renewable energy sources (RES) introduces significant uncertainty and intermittency into microgrid operations, posing challenges to economic and reliable scheduling. To address this, this paper proposes an end-to-end decision-focused framework that jointly optimizes probabilistic forecasting and robust operation for microgrids. A multilayer encoder-decoder (MED) probabilistic forecasting model is integrated with a two-stage robust optimization (TSRO) model involving direct load control (DLC) through a differentiable decision pathway, enabling gradient-based feedback from operational outcomes to improve forecasting performance. Unlike conventional sequential approaches, the proposed method aligns forecasting accuracy with operational objectives by directly minimizing decision regret via a surrogate smart predict-then-optimize (SPO) loss function. This integration ensures that probabilistic forecasts are optimized for downstream decisions, enhancing both economic efficiency and robustness. Case studies on modified IEEE 33-bus and 69-bus systems demonstrate that the proposed framework achieves superior forecasting accuracy and operational performance, reducing total and net operation costs by up to 18% compared with conventional forecasting and optimization combinations. The results verify the effectiveness and scalability of the end-to-end decision-focused approach for resilient and cost-efficient microgrid management under uncertainty.

翻译：可再生能源的高渗透率给微电网运行引入了显著的不确定性与间歇性，对经济可靠的调度提出了挑战。为此，本文提出了一种端到端的决策聚焦框架，联合优化微电网的概率预测与鲁棒运行。通过可微分的决策路径，将多层编码器-解码器概率预测模型与包含直接负荷控制的两阶段鲁棒优化模型相集成，实现了从运行结果到预测模型的梯度反馈，从而提升预测性能。与传统串行方法不同，所提方法通过代理智能预测-优化损失函数直接最小化决策遗憾，使预测精度与运行目标保持一致。这种集成确保了概率预测针对下游决策进行优化，从而同时提高了经济效率与鲁棒性。在改进的IEEE 33节点和69节点系统上的案例研究表明，所提框架实现了更优的预测精度与运行性能，与传统预测-优化组合方法相比，总运行成本与净运行成本最高可降低18%。结果验证了端到端决策聚焦方法在不确定性下实现弹性且经济高效的微电网管理的有效性与可扩展性。