| 引用本文: | 蔡田田,姚 浩,杨英杰,等.基于云-边协同的配电网快速供电恢复智能决策方法[J].电力系统保护与控制,2023,51(19):94-103.[点击复制] |
| CAI Tiantian,YAO Hao,YANG Yingjie,et al.Cloud-edge collaboration-based supply restoration intelligent decision-making method[J].Power System Protection and Control,2023,51(19):94-103[点击复制] |
|
| 摘要: |
| 分布式电源高渗透率接入对配电网故障自愈能力提出了更高的要求。基于模型的供电恢复方法利用精准的网络参数构建优化模型，可以实现供电恢复策略的准确制定。但在配电网实际运行中，精准的配电网络参数往往难以获取，导致基于模型的供电恢复方法应用受限。云-边协同运行模式可作为配电网快速供电恢复的一种实现方案。提出一种基于云-边协同的配电网快速供电恢复智能决策方法。首先，在云端基于图卷积神经网络建立配电网快速供电恢复智能决策模型，包括网络重构模块和潮流模拟模块。当故障发生后，云端利用网络重构模块，快速制定网络重构策略，经过破圈法/避圈法验校后下发至配电网边缘侧的边缘计算装置。边缘侧根据云端的网络重构策略利用潮流模拟模块就地制定负荷恢复策略，实现系统的快速供电恢复。最后，依托改进的IEEE33节点配电网算例对所提模型进行分析，验证了所提方法可有效提升配电网的供电恢复能力。 |
| 关键词: 配电网 云-边协同 供电恢复 分布式电源 图卷积神经网络 |
| DOI：10.19783/j.cnki.pspc.221918 |
| 投稿时间：2022-12-09修订日期：2023-02-18 |
| 基金项目:国家重点研发计划项目资助(2020YFB0906000，2020YFB0906002) |
|
| Cloud-edge collaboration-based supply restoration intelligent decision-making method |
| CAI Tiantian1,YAO Hao1,YANG Yingjie1,ZHANG Ziqi2,JI Haoran2,LI Peng2 |
| (1. Digital Grid Research Institute, China Southern Power Grid, Guangzhou 510700, China; 2. Key Laboratory of
Smart Grid of Ministry of Education (Tianjin University), Tianjin 300072, China) |
| Abstract: |
| The high-penetration integration of distributed generators (DGs) makes higher demands on the self-healing ability of a distribution network. The model-based supply restoration methods build the optimization model with accurate network parameters, which can realize the accurate formulation of restoration strategies. However, the accurate network parameters are often difficult to acquire in practical operation, which may limit the application of the model-based methods. The cloud-edge collaboration control mode can be used as an implementation scheme for fast supply restoration. A fast supply restoration intelligent decision-making method for distribution network based on cloud-edge collaboration is proposed. First, an intelligent decision-making model is established based on a graph convolutional neural network (GCN) on the cloud, containing network reconstruction and power flow simulation modules. When a failure occurs, the network reconstruction module is used to customize the reconstruction strategy on the cloud. After correction by loop-breaking/loop-avoiding method, the reconstruction strategy will be sent to the edge calculation device of distribution network edge side. With the power flow simulation module, the supply recovery strategy can be determined rapidly at the edge side to realize a fast supply restoration. Finally, the proposed strategy is analyzed using the modified IEEE 33-node system. The results show that the proposed method can effectively improve the supply restoration ability of a distribution network. |
| Key words: distribution network cloud-edge collaboration supply restoration distributed generators (DGs) graph convolutional neural networks (GCN) |
