含深度学习代理模型的有源配电网电压无功控制进化算法

分布式可再生能源的大规模接入，加剧了有源配电网(Active Distribution Network, ADN)的三相不平衡，容易导致系统电压越限与线损增加。然而，由于当前配电网量测设备安装不全，部分节点负荷数据难以准确获取，因此传统基于全局观测的ADN电压控制方法难以满足实际控制需求。为解决上述问题，提出一种含深度学习代理模型的电压无功控制(Volt/Var control, VVC)进化算法。设计以高速公路神经网络为代理模型，精确拟合局部量测负荷信息、调压控制策略与系统性能指标之间的映射关系。将训练后的代理模型嵌入非支配排序遗传算法的迭代寻优过程中，对电压偏移率、三相不平衡度及线路损耗指标进行直接计算，实现数据驱动的配电网VVC策略快速求取。在改进的IEEE 123节点三相配电网算例上进行测试，验证了所提算法的性能优势及求解效率。

An evolutionary algorithm for Volt/Var control in an active distribution network with a deep learning surrogate mode

The integration of large-scale distributed renewable energy sources brings new challenges to the active distribution network (ADN), including the three-phase imbalance problem, unexpected voltage violations and increased line losses. However, due to the incomplete installation of measurement equipment in the current distribution network, it is difficult to accurately obtain the load data of some nodes. Therefore, the traditional ADN voltage control method based on global observation is difficult to meet the actual control requirements. To solve these problems, a fast Volt/Var control (VVC) evolutionary algorithm with a deep learning surrogate model is proposed. In the development of the algorithm, a highway neural network is first designed as the surrogate model to accurately fit the mapping relationship between limited measured load information, voltage regulation control strategy and system performance indices. Then, the trained surrogate model is embedded into the iterative optimization process of the non-dominated sorting genetic algorithm, and the voltage deviation rate, three-phase unbalance degree and line losses indicators are directly calculated, and the data-driven distribution network VVC strategy can be quickly obtained. A modified IEEE 123-node three-phase distribution network is employed to verify the advantages and efficiency of the proposed algorithm. This work is supported by the National Natural Science Foundation of China (No. 51977133).