考虑负荷多无功用电场景的城市配电网无功优化配置

高比例电力电子设备与高比例分布式光伏的广泛接入以及城市电缆化率的提升,使配电网用户侧的无功特性变得复杂,导致负荷无功用电不确定性增加,不利于配电网安全运行。因此,为了更好地进行无功优化配置,文章采用不同负荷日功率因数变化曲线的组合场景及其概率来反映无功用电的不确定性,以运行成本的期望值最小为目标,建立多无功用电场景的期望值优化配置模型。首先,利用多重一维卷积自编码器(one-dimensional convolutional autoencoders,1D-CAEs)提取不同用户日功率因数数据的低维表征;随后,利用k-means方法进行场景缩减,获得典型日功率因数变化场景,并组合出多用户的场景集;最后,建立期望值无功优化模型,采用粒子群算法求解,确定出最优配置方案。依据上海市某配电网不同类型用户实际的无功用电信息,采用改进的IEEE 33节点系统进行仿真,以验证所提方法的有效性。

Reactive Power Optimization of Urban Distribution Network Considering Multiple Reactive Power Scenarios of Loads

The wide access of the high proportion of power electronic equipment and the high proportion of distributed photovoltaic power, and the improvement of the urban cabling rate make the reactive power characteristics on the user side of the distribution network complicate. The increased uncertainty of the load reactive power consumption is not conducive to the safe operation of distribution network. Therefore, to better optimize reactive power, the combined scenarios and their probabilities of daily power-factor variation curves of different loads are used to reflect the uncertainty of reactive power. Taking the minimum expected value of operation cost as the objective function, an optimal configuration model for the expected value of multiple reactive power scenarios is established. Firstly, multiple one-dimensional convolutional autoencoders (1D-CAEs) is used to extract the low-dimensional representation of the daily power factor data of different users. Then, the k-means method is used for scene reduction to obtain typical daily power-factor variation scenes, and multi-user scenario set is combined. Finally, the expected value reactive power optimization model is established, and the particle swarm algorithm is used to solve it to determine the optimal configuration scheme. According to the reactive power consumption scenarios of users in a distribution network in Shanghai, the modified IEEE 33-node system is taken as an example to verify the effectiveness of the proposed method.