Field telemetry dataset and analysis code for decentralized PV–battery resilience in Myanmar

# Decentralized Solar PV–Battery Resilience in Myanmar (Field Telemetry) This repository contains the **dataset, Python scripts, and figure generators**supporting the manuscript: **“Decentralized solar PV systems for energy resilience: lessons from Myanmar’s post-2021 political turmoil”** The study uses **5-minute, one-year telemetry data** from a commercial-scalePV–battery system in Myanmar to quantify energy resilience, avoided emissions,economic performance, and seasonal variability under prolonged grid instability. --- ## 1. Dataset ### File- `data/Combine_Data.xlsx` ### DescriptionTime-stamped inverter and meter telemetry exported from a SOLARMAN-type platform. ### Key Columns- `Updated Time`- `Production Power (W)`- `Consumption Power (W)`- `Charging Power (W)`- `Discharging Power (W)`- `Purchasing Power (W)`- `Feed-in Power (W)` Sampling interval is **5 minutes** (modal timestep detected automatically). --- ## 2. Software Requirements - Python ≥ 3.9- Required packages: - numpy - pandas - matplotlib- Optional (for statistical tests): - scipy All scripts run on standard Python environments (tested on Windows/Linux). --- ## 3. Scripts Overview ### 3.1 Avoided CO₂ emissions**`avoided_co2_by_source_embedded.py`**- Computes avoided CO₂ from self-consumed PV- Separates grid-present vs grid-absent (diesel-displaced) hours- Outputs annual and monthly CSV summaries --- ### 3.2 Economic performance and payback**`make_table_economics.py`**- Computes LCOE, annual savings, and payback period- Uses Myanmar tariff blocks and diesel benchmarks- Exports CSV, Excel, and LaTeX tables for publication --- ### 3.3 Seasonal PV performance**`daily_pv_energy_by_season.py`**- Daily PV energy distributions for Dry vs Wet seasons- Produces publication-quality histograms **`daily_pv_energy_by_season_stats.py`**- Bootstrap 95% confidence intervals- Effect sizes (Cliff’s delta)- Optional Welch, Mann–Whitney, and KS tests --- ### 3.4 Compact statistical summary with 95% CIs**`stat_analysis_compact.py`**- Daily PV share (Dry vs Wet)- Peak-hour coverage by PV+Battery- Battery round-trip efficiency- Outputs journal-ready figures (PDF/PNG) --- ### 3.5 Grid reliability context (Monte Carlo)**`reliability_distributions.py`**- Monte Carlo availability modeling (MTTF/MTTR)- Used for contextual comparison of pre- and post-2021 grid conditions- Does not affect PV–battery telemetry calculations --- ### 3.6 Daily power flow visualization**`daily_power_flows_best_low.py`**- High-PV vs low-PV day comparison- Renewable Energy–style figures (vector PDF + 600 dpi raster) --- ## 4. How to Run From the repository root: ```bashpython scripts/avoided_co2_by_source_embedded.pypython scripts/make_table_economics.pypython scripts/daily_pv_energy_by_season_stats.pypython scripts/stat_analysis_compact.py