含风电场的机组组合的两阶段随机规划模型

提出了计及风电的机组组合的两阶段随机优化模型。该模型采用场景树以模拟日前风电功率预测的误差，将火电机组启停以及启动后机组运行位置视为两阶段随机优化模型下的不同阶段决策。其中，第一阶段对应火电机组的启停，第二阶段对应启动后机组的调度，通过引入场景约束使不同运行场景下的两个阶段决策量之间建立了联系，满足了非预测条件。如此两个阶段的决策量相互牵制构成一个完整的数学优化模型。在求解过程中，应用场景缩减技术来权衡计算速度和精度之间的关系，通过算例验证了两阶段随机优化模型对此问题求解的有效性。

A Two-stage Stochastic Optimization Model of Unit Commitment Considering Wind Power

The paper proposes a two-stage stochastic optimization model of unit commitment considering wind power. In this model, wind power forecasting error is modeled as scenario trees using the monte carlo simulation method. Start-stop decisions for thermal units and output levels for units are recognized as different stage decisions in a stochastic programming framework. Start-stop decisions for thermal units will become first-stage decisions, whereas output levels for units will be second-stage decisions. Second-stage decisions in different scenarios are connected through scenario bundle constraint, and meet nonanticipativity condition. So the decision in those two stages supervise mutually to make a complete optimization model. The scenario reduction method is introduced for enhancing a tradeoff between calculation speed and accuracy. Case study shows that the method proposed is appropriate for unit commitment considering wind power integration.