AI 中文总结
该研究提出与电源类型无关的灵活性备用容量框架，通过非参数密度估计和CVaR指标推导备用，在NYISO数据集上验证其备用量更低且避免尾部风险，可集成到现有电力模型中。
AI 中文摘要
不断增长的可变可再生能源（VRES）份额增加了预测的不确定性和变异性，要求灵活性备用适应不断变化的运行条件，并反映预测偏差的可能性和严重程度。传统的固定规则或高斯方法错误地表示了不对称、重尾误差，导致采购效率低下或风险估计乐观。本文提出了一种与电源类型无关的框架，该框架从历史偏差或概率预测中构建负荷、风电和光伏的条件误差分布，将它们组合成条件净负荷误差分布，并使用基于覆盖率和风险的标准推导向上和向下备用。非参数密度估计无需参数假设即可捕获经验误差行为，而基于条件风险价值（CVaR）的指标量化了未覆盖偏差的预期值。基于纽约独立系统运营商（NYISO）的合成数据集进行的实验表明，该框架以比静态基准更低的备用量满足目标覆盖率，且避免了基于高斯的方法的尾部风险扭曲。由于备用直接由资源不确定性分布构建，该框架透明且可解释，作为确定性备用约束无缝集成到现有生产成本模型中，避免了基于场景的随机优化。它在电力研究协会（EPRI）的DynADOR工具中实现，用于运行备用调度。
英文摘要
Growing shares of variable renewable energy sources (VRES) increase forecast uncertainty and variability, requiring flexibility reserves that adapt to changing operating conditions and reflect the likelihood and severity of forecast deviations. Conventional fixed-rule or Gaussian approaches misrepresent asymmetric, heavy-tailed errors, leading to inefficient procurement or optimistic risk estimates. This paper presents a source-agnostic framework that constructs conditional error distributions for load, wind, and solar from historical deviations or probabilistic forecasts, combines them into a conditional net-load error distribution, and derives upward and downward reserves using coverage- and risk-based criteria. Nonparametric density estimation captures empirical error behavior without a parametric assumption, while a Conditional Value-at-Risk (CVaR)-based metric quantifies expected uncovered deviations. Experiments on a New York Independent System Operator (NYISO)-based synthetic dataset show that the framework meets target coverage with lower reserve volumes than static benchmarks and avoids the tail-risk distortion of Gaussian-based methods. Because reserves are built directly from the resource uncertainty distributions, the framework is transparent and interpretable, and it integrates seamlessly into existing production-cost models as deterministic reserve constraints, avoiding scenario-based stochastic optimization. It is implemented in the Electric Power Research Institute's (EPRI) DynADOR tool for operational reserve scheduling.
