# Evaluating the Planning and Operational Resilience of Electrical Distribution Systems with Distributed Energy Resources using Complex Network Theory

arXiv: https://arxiv.org/abs/2208.11543 · HTML: https://mlsanity.com/paper/2208.11543/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 57 references that are in the map.
- First posted on arXiv 24 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [Matrix Completion for Low-Observability Voltage Estimation](https://mlsanity.com/paper/1801.09799/) (2018)
- [Risk-based Probabilistic Quantification of Power Distribution System Operational Resilience](https://mlsanity.com/paper/1912.03344/) (2019)
- [Resilient Identification of Distribution Network Topology](https://mlsanity.com/paper/2011.07981/) (2020)

## Built on by (most cited first, 5 of 5)

- [Identification of Surface Defects on Solar PV Panels and Wind Turbine Blades using Attention based Deep Learning Model](https://mlsanity.com/paper/2211.15374/) (2022)
- [Data-driven Insights for Informed Decision-Making: Applying LSTM Networks for Robust Electricity Forecasting in Libya](https://mlsanity.com/paper/2507.01034/) (2025)
- [Agentic-AI based Mathematical Framework for Commercialization of Energy Resilience in Electrical Distribution System Planning and Operation](https://mlsanity.com/paper/2508.04170/) (2025)
- [Game Theoretic Resilience Recommendation Framework for CyberPhysical Microgrids Using Hypergraph MetaLearning](https://mlsanity.com/paper/2509.00528/) (2025)
- [Fault Detection in Electrical Distribution System using Autoencoders](https://mlsanity.com/paper/2602.14939/) (2026)

## Abstract (arXiv)

Electrical Distribution Systems are extensively penetrated with Distributed Energy Resources (DERs) to cater the energy demands with the general perception that it enhances the system's resilience. However, integration of DERs may adversely affect the grid operation and affect the system resilience due to various factors like their intermittent availability, dynamics of weather conditions, non-linearity, complexity, number of malicious threats, and improved reliability requirements of consumers. This paper proposes a methodology to evaluate the planning and operational resilience of power distribution systems under extreme events and determines the withstand capability of the electrical network. The proposed framework is developed by effectively employing the complex network theory. Correlated networks for undesirable configurations are developed from the time series data of active power monitored at nodes of the electrical network. For these correlated networks, computed the network parameters such as clustering coefficient, assortative coefficient, average degree and power law exponent for the anticipation; and percolation threshold for the determination of the network withstand capability under extreme conditions. The proposed methodology is also suitable for identifying the hosting capacity of solar panels in the system while maintaining resilience under different unfavourable conditions and identifying the most critical nodes of the system that could drive the system into non-resilience. This framework is demonstrated on IEEE 123 node test feeder by generating active power time-series data for a variety of electrical conditions using simulation software, GridLAB-D. The percolation threshold resulted as an effective metric for the determination of the planning and operational resilience of the power distribution system.