With the commitment to climate, globally many countries started reducing brownfield energy production and strongly opting towards green energy resources. However, the optimal allocation of distributed energy resources (DERs) in electrical distribution systems still pertains as a challenging issue to attain the maximum benefits. It happens due to the systems complex behaviour and inappropriate integration of DERs that adversely affects the distribution grid. In this work, we propose a methodology for the optimal allocation of DERs with vulnerable node identification in active electrical distribution networks. A failure or extreme event at the vulnerable node would interrupt the power flow in the distribution network. Also, the power variation in these vulnerable nodes would significantly affect the operation of other linked nodes. Thus, these nodes are found suitable for the optimal placement of DERs. We demonstrate the proposed data-driven approach on a standard IEEE-123 bus test feeder. Initially, we partitioned the distribution system into optimal microgrids using graph theory and graph neural network (GNN) architecture. Further, using Granger causality analysis, we identified vulnerable nodes in the partitioned microgrid; suitable for DERs integration. The placement of DERs on the vulnerable nodes enhanced network reliability and resilience. Improvement in resilience is validated by computing the percolation threshold for the microgrid networks. The results show a 20.45% improvement in the resilience of the system due to the optimal allocation of DERs.

翻译：随着对气候承诺的推进，全球多国开始减少存量能源生产，并大力转向绿色能源资源。然而，分布式能源资源在配电系统中的最优配置仍是一个具有挑战性的问题，制约着效益最大化。这源于系统的复杂行为以及分布式能源的不恰当整合对配电网产生的不利影响。本文提出了一种在主动配电网络中通过脆弱节点识别实现分布式能源最优配置的方法。脆弱节点发生故障或极端事件将中断配电网的潮流，同时这些节点的功率波动会显著影响其他关联节点的运行。因此，这些节点被证明适用于分布式能源的最优布点。我们在标准IEEE-123节点测试馈线上验证了所提出的数据驱动方法。首先，利用图论与图神经网络架构将配电系统划分为最优微电网；继而通过格兰杰因果分析识别划分后微电网中的脆弱节点，以确定适合分布式能源接入的位置。将分布式能源布设于脆弱节点显著提升了网络可靠性与弹性，并通过计算微电网网络的逾渗阈值验证了弹性的改善。结果表明，由于分布式能源的最优配置，系统弹性提升了20.45%。