基于随机预测控制理论和功率波动相关性的风电集群优化调度

Optimal Dispatch of System Integrated Wind Farm Clusters Based on Stochastic Model Predictive Control Considering Temporal Correlation of Wind Power

为解决大规模风电并网的日内调度和实时控制问题,提出一种基于功率波动时间相关性和随机模型预测控制理论（fluctuation temporal correlation stochastic model predictive control,FTC-SMPC）的风电集群优化调度方法。基于单风场预测模型及误差模型,建立了表征风电集群功率动态波动时间相关性的预测误差的多元高斯概率密度函数（multivariate Gaussian probability density function,MGPDF）。通过逆变换抽样技术和场景缩减技术,从MGPDF随机生成的大量符合风电集群功率波动时间相关性的场景中选取风电功率预测典型场景。以典型场景集合为基础,建立调度周期内各场景弃风电量期望最小的日内调度模型。以风电集群日内调度曲线为参考,建立各风场功率缺额期望最小的风场实时控制策略。算例分析表明,相比于传统SMPC调度模型,文中所提调度模型能更准确地反映风电的波动特性并使调度曲线更加平滑,进而提高了对大规模风电的消纳能力。

To cope with the intraday dispatch and real time control with high penetration of wind power, a power system optimization dispatch method with wind farm cluster was proposed based on Fluctuation Temporal Correlation of wind power and Stochastic Model Predictive Control （FTC-SMPC）. Based on the forecasting model and forecasting error model for each wind farm, the multivariate Gaussian probability density function （MGPDF） was applied to characterize the temporal correlation of the wind farm cluster＇s dynamic fluctuation. The inverse transform sampling and scenario cutting method were used to select typical scenarios from a large number of scenarios generated by MGPDF corresponding to temporal correlation characteristics of wind farm cluster. With these typical scenarios, an intraday economic dispatch model was established by minimizing the expectation of wind power curtailment in each scenario. Then the intraday schedule of wind farm cluster was used as reference curve for real-time wind farm control strategy by minimizing the expectation of wind power shortage of each wind farm. Case study shows that the proposed method can more accurately characterize wind power fluctuation, smooth dispatching curve in comparison with traditional SMPC dispatch model, consequently improve the large-scale integration of wind power.