考核电量最小化导向的风电场功率上报优化策略

Wind Farm Power Reporting Optimization Strategy Based on Minimization of Assessment Power

风电功率预测误差引起电力系统运行经济性降低和风电场考核费用增加,因此提出了一种考核电量最小化导向的风电场功率上报优化策略。首先,利用分布函数差异化导向的改进K-means算法,建立不同气象模式下具备明显差异的预测误差概率分布模型。其次,利用支持向量机算法实现气象模式匹配,进而利用考虑时间相关性的拉丁超立方抽样进行日前功率曲线场景集生成。最后,建立以考核电量期望值最小为目标的日前功率上报优化模型,并采用粒子群算法求解。以华中地区某风电场历史数据为例,验证了所提方法相较于预测值直接上报方式,可有效提高风电场运行经济收益。

The wind power forecast error leads to the reduction of the operating economy of the power system and the increase of the wind farm assessment cost.An optimization strategy for wind farm power reporting based on minimization of assessment power is proposed.Firstly,the improved K-means algorithm based on the differentiation of distribution function is used to establish the prob⁃ability distribution model of forecast error with obvious differences under different meteorological modes.Secondly,the support vector machine is used to realize the meteorological mode recognition,and then the Latin hypercube sampling method considering the time correlation is used to generate the day-ahead power curve scenario set.Finally,an optimization model of the day-ahead power report⁃ing with the goal of minimizing the expected value of the assessment power is established,and the particle swarm algorithm is used to solve it.Taking the historical data of a wind farm in central China as an example,it is verified that the proposed method can effectively improve the operational economic benefit of wind farm compared with the strategy of directly reporting the forecast wind power.