基于场景分析的含风电系统机组组合的两阶段随机优化

With the stochastic characteristics of wind power output, the large-scale wind power integration brings a significant challenge to the unit commitment of power system. Taking day-ahead dispatch as research object, this paper proposes a two-stage stochastic optimization model for solving the unit commitment considering wind power. In this model, start-stop decisions for coal fired units and output levels for coal fired units are recognized as different stage decisions in a stochastic programming framework. Start-stop decisions for coal fired units will be the first-stage decision, aiming to find the unit commitment mode with the minimum start-stop cost under the given conditions. Output levels for coal fired units will be the second-stage decisions, aiming to evaluate the expected value of unit operation cost under stochastic conditions. After the overall consideration of the two-stage decision problem, the best unit commitment is made. The temporal characteristic of the day-ahead wind power forecasting error is modeled as scenario trees. The scenario reduction method is introduced for enhancing a tradeoff between calculation speed and accuracy. Case study in a 26 nodes power system demonstrates validity of the model.