考虑风电并网和电量分解的月度机组组合模型

合理的月度机组组合 (unit commitment，UC)可以在更长时间尺度上进一步拓展风电消纳空间，但大规模风电入网造成的较强不确定性会给机组组合带来很大挑战。另外，由于基于比例分配法编制的月度合同电量调度实施难度较大，文章提出一种结合改进月度合同电量分解方法与考虑源荷不确定性的月度火电机组组合模型。将风电历史数据应用于月度机组组合决策，采用基于数据驱动的分布鲁棒方法结合随机优化技术求解机组组合与电量分解方案。计算结果表明，该模型能有效减少弃风量，提高火电机组负荷率。

Monthly Combination Model of Thermal Power Units Considering Wind Power Absorption and Electricity Contract Decomposition

Reasonable monthly power unit commitment （UC） can further expand the space of wind power absorption in a longer time scale， but the strong uncertainty caused by large-scale wind power will bring great challenges to power unit commitment. In addition， because the implementation of monthly contract power dispatch based on proportional distribution method is difficult， this paper presents a monthly combined model of thermal power units which combines with improved monthly contract power decomposition method and the uncertainty of source and load. The historical data of wind power is applied to monthly unit commitment decision-making， and the data-driven distributed robust method combined with stochastic optimization technology is used to solve the unit commitment and power decomposition scheme. The calculation results show that the model can effectively reduce the amount of abandoned wind power and improve the load rate of thermal power units.