Data-driven robust day-ahead unit commitment model for hydro/thermal/wind/photovoltaic/nuclear power systems

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.