基于数据潮流模型的高比例光伏配电网三相不平衡优化

随着分布式光伏的大规模接入，配电网固有的三相不平衡问题日益严重，给系统的电能质量、经济运行等带来不利影响。此外，高比例光伏的接入使得配电网的物理结构和运行方式更加复杂多变，导致当前依赖精确拓扑结构和线路参数的三相不平衡优化方法难以应用。因此，提出一种基于数据潮流模型的高比例光伏配电网三相不平衡优化方法。首先，采用基于双阶段注意力机制的循环神经网络方法建立数据潮流模型，拟合三相潮流约束中变量之间的函数关系。同时，提出图特征嵌入的方法将部分已知的拓扑信息嵌入到数据潮流模型中以提高拟合精度。其次，以训练后的数据潮流模型为基础重建配电网三相不平衡优化模型。最后，通过条件梯度下降方法求解该模型，以修改的IEEE33节点配电网络为例，验证了所提方法的有效性。

Three-phase unbalanced optimization of a distribution network with a high proportion of distributed photovoltaic energy based on a data-driven power flow model

With the increasing penetration of distributed photovoltaic energy, the inherent three-phase unbalanced problem of a distribution network is becoming more serious. This brings adverse effects on power quality and economic operation of the system. In addition, a high proportion of photovoltaic leads to a more complex physical structure and operation mode of the distribution network, resulting in it being difficult to apply the current three-phase unbalanced optimization method that relies on precise topology and line parameters. Therefore, this paper proposes a three-phase unbalanced optimization method of a distribution network with a high proportion of photovoltaic based on a data-driven power flow model. First, a dual-stage attention-based recurrent neural network is used to establish the data-driven power flow model, and the functional relationship between the variables in the three-phase power flow constraint is fitted. At the same time, a graph feature embedding method is proposed to embed the partially known topology information into the model to improve the fitting accuracy. Secondly, the three-phase unbalanced optimization model is reconstructed based on a trained data-driven power flow model. Finally, the conditional gradient descent method is used to analyze the model, and a modified IEEE 33-node distribution network is taken as an example to verify the effectiveness of the proposed method.