基于自适应扩散高斯核密度风电预测误差估计的 风火联合优化调度研究

随着风电大规模并网，风电出力不确定性增加了电力系统调度的难度。针对风荷不确定性对电力系统调度的影响，采用迭代自组织数据分析算法对风电功率预测值及对应风电功率预测误差进行分段。然后采用自适应扩散高斯核密度估计拟合分段后各风电功率区间段内的预测误差。在此基础上，提出一种整体考虑风电及负荷预测误差得到净负荷预测误差、并将净负荷预测误差计入正负旋转备用容量概率约束的优化调度模型。采用机会约束规划将概率约束转换为等价确定性约束进行求解。在IEEE39节点系统进行三种代表性场景的算例仿真，结果表明引入迭代自组织数据分析算法和自适应扩散高斯核密度估计后，备用成本降低6.71%，含碳排放的环境成本降低20.4%，总发电成本降低2.98%。最后分析了置信水平对备用容量和总发电成本的影响。

Wind-fire joint optimal dispatching based on adaptive diffusion Gaussian kernel density wind farm output power forecast error estimation

With the large-scale integration of wind power into the grid, the uncertainty of output increases the difficulty of power system dispatch. In this paper, an iterative self-organizing data analysis algorithm is used to segment the wind power prediction value and the corresponding wind power prediction error, and then the prediction error in each wind power interval segment after the adaptive diffusion Gaussian kernel density estimation is used. Then, an optimal scheduling model of the net load prediction error positive and negative rotational reserve capacity probability constraint considering the wind power and load prediction error as a whole is proposed. The probability constraint is converted into an equivalent deterministic constraint by using the opportunity constraint plan. The numerical analysis of three scenarios through the IEEE39 node system shows that after the introduction of the iterative self-organizing data analysis algorithm and adaptive diffusion Gaussian kernel density estimation, the backup cost is reduced by 6.71%, the environmental cost of carbon emissions is reduced by 20.4%, and the total power generation cost is reduced by 2.98%. Finally, the impact of the confidence level on standby capacity and total power generation cost is analyzed. This work is supported by the National Natural Science Foundation of China (No. 62062068 and No. 61761049).