urbannet-powerflow

DATA:

1. domain-based national: data/naerm/
2. distance-based national: data/urbannet/
3. regional: data/urbannet/
4. edge files to evaluate criticality criteria:
   1. data/v9/
   2. data/naerm-edge-files/

NOTE: some files (national_transmission_lines.csv, EIC_transmission_lines.csv) exceeds git limit hence not uploaded see this in google drive directory ornl/powerflow-dihen/

Code for distance-based network: path: distance-based-scripts/

1. ablation_model.py get results for ablation models e.g.: python3 ablation_model.py data/urbannet/ result_ablation_national national

Input parameters: a. data_path: data/urbannet/ b. outputfilename: result_ablation_national c. region: national/regional 1. national 2. TX 3. EIC

2. baseline_model.py: get results for baseline e.g.: python3 vary_k_urbannet.py ../data/urbannet/ result_baselines 50

Input parameters: a. data_path: ../data/urbannet/ b. outputfilename: result_baselines c. k: user defined int value, for paper we use 50

3. vary_k_urbannet.py: get results varying size k for national and regional network e.g.: python3 vary_k_urbannet.py ../data/urbannet/ vary_k national

Input parameters: a. data path: ../data/urbannet/ b. output file name: vary_k c. region: network name 1. national 2. TX 3. EIC 4. uncertainty_bar_urbannet.py: get results to compute error bar for every ablation model on distance-based national

e.g.: python3 ../data/urbannet/ uncertain_national national 50 1

Input parameters:

a. data_path: ../data/urbannet/ b. output file name: uncertain_national c. region: network_name 1. national 2. TX 3. EIC d. k: user defined int value for size k, for paper we use 50 e. case: int value between 1-5, where, 1-> DIHEN 2-> CB (U) 3-> FP (K) 4-> NER (Sibling-dist) 5-> Random_Gaussian

5. baseline_spread_k.py: results to obtain number of spread for baseline models vary size k

e.g., python3 ../data/urbannet/ spread_vary_k national

Input parameters:

a. data path: ../data/urbannet/ b. output file name: spread_vary_k c. region name: 1. national 2. TX 3. EIC

6. critical_rank_full_urbannet.py : rank all nodes in the network considering k= #nodes in the network

Code for domain-based network:

path: domain-based-scripts/

1. naerm_ablation_model.py : get ablation results
2. naerm_baseline_model.py : get baseline results
3. naerm_vary_k.py : get results varying size k
4. uncertainty_bar_naerm.py : get results to compute error bar for every ablation model e.g.: python3 ../data/naerm/ 500 1

Input parameters:

a. data_path: ../data/naerm/ b. k: 500 c. case: int value between 1-5, where, 1-> DIHEN 2-> CB (U) 3-> FP (K) 4-> NER (Sibling-dist) 5-> Random_Gaussian

5. critical_rank_full_naerm.py : rank all nodes in the network considering k= #nodes in the network

Code for plotting:

path: viz-scripts/