发表机构
Electric Power Research Institute（电力研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过受控消融实验发现，在单周期经济调度中，空间相关场景生成带来的运行价值有限，而决策聚焦训练能带来更稳健的成本节省。
AI 中文摘要
可再生能源预测通常通过统计技能（如CRPS）进行评估，但电网运营商实际关注的是实现的调度成本。我们利用来自两个欧洲输电系统（CWE、DE-4TSO）的真实公共数据，诊断了在单周期报童型经济调度中驱动调度价值的因素。跨预测站点的空间一致性低于预设的1%实际显著性阈值：一项受控消融实验（保持各区域边际预测逐位相同，仅改变跨区域依赖，共10种配置、3个随机种子、配对自助置信区间）显示，一致性增益最多占调度成本的0.64%，在10种配置中有3种与零无显著差异，且仅在非现实的8倍预测误差压力测试下才达到。决策聚焦训练（该领域已确立的范式）提供了稳健的2.82%-5.19%的增益。参数化高斯copula近似在现实误差幅度下与经验copula匹配，但在极端压力下表现差于无依赖情况。单种子扫描显示，12%的能量分数增益使成本变化小于0.1%。结果刻画了此类单周期调度的特征；一个轻量级四周期扩展支持相同结论。对于此类调度，空间相关场景生成本身提供的运行价值有限；电网运营商和预测供应商应转而通过下游决策价值评估依赖模型，并优先考虑决策聚焦训练。
英文摘要
Renewable forecasts are evaluated by statistical skill (e.g., CRPS), but grid operators pay for realized dispatch cost. We diagnose what drives dispatch value in a single-period newsvendor-style economic dispatch using real public data from two European transmission systems (CWE, DE-4TSO). Spatial coherence across forecast sites falls below the pre-specified 1% practical-significance threshold: a controlled ablation holding per-zone marginal forecasts bit-identical and varying only cross-zone dependence (10 configurations, 3 seeds, paired-bootstrap confidence intervals) shows a coherence gain of at most 0.64% of dispatch cost, indistinguishable from zero in 3 of 10 configurations, reached only under an unrealistic 8-fold forecast-error stress test. Decision-focused training, an established paradigm in this venue, delivers a robust 2.82-5.19% gain. A parametric Gaussian-copula approximation matches the empirical copula at realistic error magnitudes but performs worse than no dependence under extreme stress. A single-seed sweep shows that a 12% energy-score gain changes cost by less than 0.1%. Results characterize this single-period dispatch class; a lightweight four-period extension supports the same conclusion. For this dispatch class, spatially-correlated scenario generation provides limited operational value on its own; grid operators and forecast vendors should instead evaluate dependence models by downstream decision value and prioritize decision-focused training.
CommentsThis work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. 10 pages, 3 figures, 2 tables
