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的低压配电网最优潮流本地控制策略.采用1-范数的Wasserstein距离不确定集描述分布式能源出力不确定性,构建三相四线制低压配电网鲁棒随机优化模型,目标为控制成本和网络损耗最小化,并考虑最坏情况下的预期调整.通过卷积神经网络训练,获得分布式能源的本地控制策略,无需通信基础设施.仿真结果证实了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, which brings challenges of high proportion of distributed energy and asymmetric loads, such as voltage overstep and three-phase imbalance to distribution network. To cope with these problems, this paper proposes a local control strategy for optimal power flow (OPF) in low voltage distribution network based on robust stochastic optimization (RSO), which uses a 1-norm Wasserstein distance uncertainty set to describe the output uncertainty of distributed energy resource (DER), and builds a robust stochastic optimization model for three-phase four-wire low-voltage distribution network. This model aims to minimize control costs and network losses, while considering the expected adjustments under worst-case scenarios. Local control strategies for distributed energy are obtained without communication infrastructure by convolutional neural networks training. The simulation results verify the effectiveness and economy of the proposed control strategy.