Recently, Electrical Distribution Systems are extensively penetrated with the Distributed Energy Resources (DERs) to cater the energy demands with general perception that it enhances the system resiliency. However, it may be adverse for the grid operation due to various factors like its intermittent availability, dynamics in weather condition, introduction of nonlinearity, complexity etc. This needs a detailed understanding of system resiliency that our method proposes here. We introduce a methodology using complex network theory to identify the resiliency of distribution system when incorporated with Solar PV generation under various undesirable configurations. Complex correlated networks for different conditions were obtained and various network parameters were computed for identifying the resiliency of those networks. The proposed methodology identifies the hosting capacity of solar panels in the system while maintaining the resiliency under different unwanted conditions hence helps to obtain an optimal allocation topology for solar panels in the system. The proposed method also identifies the critical nodes that are highly sensitive to the changes and could drive the system into non-resiliency. This framework was demonstrated on IEEE-123 Test Feeder system with time-series data generated using GridLAB-D and variety of analysis were performed using complex network and machine learning models.

翻译：近年来，配电系统中广泛渗透了分布式能源资源（DERs），普遍认为这能提升系统弹性以满足能源需求。然而，由于DERs的间歇可用性、天气条件动态变化、非线性引入以及复杂性等因素，可能对电网运行产生不利影响。这需要对系统弹性进行深入理解，本文提出一种方法。我们引入了一种基于复杂网络理论的方法，用于识别在不同不利配置下集成太阳能光伏发电的配电系统弹性。通过获取不同条件下的复杂相关网络，并计算多种网络参数来识别这些网络的弹性。该方法能够在不同不利条件下维持系统弹性的同时，识别系统中太阳能电池板的承载容量，从而有助于获得系统内太阳能电池板的最优分配拓扑。此外，所提方法还能识别对变化高度敏感、可能将系统推向非弹性状态的关键节点。该框架在IEEE-123测试馈线系统上进行了验证，使用了GridLAB-D生成的时序数据，并利用复杂网络和机器学习模型进行了多类分析。