reliability of feeders in benin city nigeria as a siting criteria of EVCS
# Benin City EV Charging Site Reliability Screening
Reproducible data pipeline supporting the study *"A Reliability-Based Screening Framework for Fast Electric Vehicle Charging Site Selection in the Benin City Distribution Network, Nigeria."*
This repository contains the raw source data, processing scripts, and all intermediate/final output files needed to independently reproduce every numerical result in the paper, from the 13 raw monthly BEDC files through to the final Reliability Index and MCDA/TOPSIS candidate site rankings.
## What this pipeline does
1. **Harmonizes** 13 months of BEDC Band A feeder daily supply-hours records (April 2025 to April 2026), which arrive in three different, inconsistent column schemas, into a single clean panel.
2. **Filters** to 23 confirmed Benin City feeders (BEDC's coverage extends into Delta, Ondo, and Ekiti States, so this filtering step matters).
3. **Computes** a composite, entropy-weighted Reliability Index per feeder, tied to NERC's Service-Based Tariff Band A regulatory thresholds (20-hour and 18-hour daily supply minimums), with a robustness check against equal weighting.
4. **Screens** candidate feeders for fast EV charging site suitability using a TOPSIS multi-criteria ranking, combining the Reliability Index with a commercial land-use proxy and a reliability trend measure, with a weight-sensitivity analysis.
## Repository structure
```
data/
raw/ 13 original monthly BEDC CSV files, unmodified
processed/ All intermediate and final output CSVs produced by the scripts
scripts/
merge_bedc.py Step 1: raw file harmonization and Benin City filtering
reliability_index.py Step 2: Reliability Index construction and robustness check
mcda_topsis.py Step 3: MCDA/TOPSIS candidate site screening and sensitivity analysis
```
## How to reproduce the results
Requirements: Python 3, `pandas`, `numpy`.
```bash
pip install pandas numpy
cd scripts
python3 merge_bedc.py # reads data/raw/, writes …