Supplementary material for the paper Enhancing Grid Resilience: The role of Weighted Centrality Measures in the Allocation of Energy Storage Systems

Description

This dataset contains the supplementary material for the paper "Enhancing Grid Resilience: The role of Weighted Centrality Measures in the Allocation of Energy Storage Systems" by Paim, F. J. et al. It provides the Python-based computational framework and data required to verify the results and reproduce the analysis. The repository includes: 1. Centrality measures and Vulnerability Index - PJM 5-Bus System: A Python notebook (Simulations_PJM5.py) containing the implementation of centrality measures, simulation of generator and line failures and calculation of Vulnerability Index for the PJM 5-bus system. 2. Centrality measures and Vulnerability Index - IEEE 118 Bus System: A Python notebook (Simulations_IEEE118.py) containing the implementation of centrality measures, simulation of generator and line failures and calculation of Vulnerability Index for the IEEE 118-bus test case. 3. Data and Libraries: The simulations utilize the pandapower library and network data based on MATPOWER formats (embedded or loaded within the scripts). 4. Visualizations: The code for generating analysis plots is integrated within the provided Python notebooks.

Steps to reproduce

1. Environment Setup: Ensure you have Python installed with the following libraries: pandapower, pandas, networkx, and matplotlib. 2. Data Loading: The scripts automatically load the test systems (case5.m and case118.m) using the pandapower.networks library (which corresponds to the MATPOWER .m files). 3. Execution: - Run the notebooks to compute the Weighted Centrality Measures and the Vulnerability Index. For the IEEE 118-bus system, the script is configured to filter and display the top 20 most critical cases. 4. Validation: Compare the outputs generated by the scripts with the values provided in the.csv files. The final graphs and surfaces for analysis are generated at the end of each notebook's execution. 5. Export tables (.csv files)

Institutions

• Universidade Federal do Espírito Santo

  Espírito Santo, Vitória

Categories

Funders

• Fundação de Amparo à Pesquisa do Espírito Santo

  Espírito Santo, Vitória

Licence