Abstract According to the complementary characteristics of various power sources, this paper establishes a data-driven robust day-ahead unit commitment model for a hydro-thermal-wind-photovoltaic-nuclear power system that can be used by the independent system operaters (ISOs). A data-driven robust optimization method based on the robust kernel density estimation (RKDE) is employed to deal with the uncertainties of wind and photovoltaic (PV) power. That is, the distributional information of wind and PV power is extracted by RKDE from the big data, then it is incorporated into the data-driven uncertainty set, and finally a robust optimization model is formed. In view of the facts that the conventional water spillage methods fail to pay equal attention to the benefits of the basin and the individual hydro plants, eight kinds of strategies that can make the water spillages or hydropower curtailments distributed proportionally in each hydro plant are proposed. In addition, the operating model of nuclear power unit involved in peak load regulation is established to promote its operational flexibility. The numerical results and simulation on a modified New England 39-bus system verify the superiority and practicability of the proposed model.

中文翻译：

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用于水/热/风/光伏/核电系统的数据驱动的稳健日前机组承诺模型

摘要 根据各种电源的互补特性，建立了可供独立系统运营商（ISO）使用的水-热-风-光伏-核电系统的数据驱动的鲁棒日前机组承诺模型。 . 采用基于鲁棒核密度估计（RKDE）的数据驱动鲁棒优化方法来处理风能和光伏（PV）功率的不确定性。即RKDE从大数据中提取风电和光伏发电的分布信息，然后将其纳入数据驱动的不确定性集合，最终形成鲁棒优化模型。鉴于传统的溢水方式未能兼顾流域和单个水电站的效益，提出了八种策略，可以使每个水电站的溢水或限电按比例分配。此外，建立核电机组调峰运行模式，提升运行灵活性。对改进后的新英格兰 39 总线系统的数值结果和仿真验证了所提出模型的优越性和实用性。