基于场景分析的含风电系统机组组合的两阶段随机优化

A two-stage stochastic optimization of unit commitment considering wind power based on scenario analysis

风电场输出功率具有随机性、间歇性的特点,其大规模并网发电使电力系统的调度决策面临着新的挑战。在此背景下,以日前调度为研究对象,提出了一种解决含风电系统机组组合问题的两阶段随机规划方法。该方法将发电机组的启停决策及启停决策下机组的发电计划制定视为两个阶段的决策问题,第一阶段决策对应机组启停,旨在寻求给定条件下具有最小启停成本的机组组合方式,而第二阶段决策则用于评估随机条件下已制定机组启停计划所对应次日机组运行成本的期望值,方法通过对两个阶段决策问题的统筹考虑,最终确定出机组的最佳组合方式。采用场景树模拟日前风电场输出功率预测误差的时间分布特性,并利用场景缩减技术实现在较高计算精度下模型复杂度的降低。通过对某实际26节点11机系统的分析计算验证了所提出方法的有效性。

With the stochastic and intermittent 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-node 11-machine power system demonstrates the validity of the model.