随着“双碳”目标的推进和新型电力系统的构建，电力系统的复杂度和不确定性剧增，配电网面临高比例的分布式能源和不对称负载带来的电压越限和三相不平衡等挑战。为应对这些问题，提出基于鲁棒随机优化（RSO）的低压配电网最优潮流（OPF）本地控制策略。策略采用1-范数的Wasserstein距离不确定集描述分布式能源（DER）出力不确定性，构建三相四线制低压配电网鲁棒随机优化模型，旨在最小化控制成本、网络损耗，并考虑最坏情况下的预期调整。通过卷积神经网络（CNN）训练，获得分布式能源的本地控制策略，无需通信基础设施。仿真结果显示，所提控制策略的有效性和经济性。

With the promotion of the "dual carbon" goal and the construction of new power systems, the complexity and uncertainty of power systems have increased dramatically, and the distribution network is faced with the challenges of high proportion of distributed energy and asymmetric loads, such as voltage overstep and three-phase imbalance. To cope with these problems, put forward based on robust stochastic optimization (RSO) of low voltage distribution network optimal power flow (OPF) local control strategy. The strategy uses a 1-norm Wasserstein distance uncertainty set to describe distributed energy resource (DER) output uncertainty, the construction of a three-phase four-wire low-voltage distribution network robust stochastic optimization model, designed to minimize control costs network losses and take into account the worst-case expected adjustments. Convolutional neural networks (CNN) training to obtain local control strategies for distributed energy without communication infrastructure. The simulation results show that the proposed control strategy is effective and economical.