Replication package for "What Aggregate Metrics Hide in Electric Vehicle Charging Control: A Distributional Evaluation on a Two-Network Digital Twin"

This is the replication package for a study of what aggregate reporting conceals in electric vehicle (EV) charging control. Evaluations of EV charging control are dominated by aggregate metrics: total energy delivered, aggregate demand completion, mean curtailment and overload counts. Drivers, however, experience individual sessions, and a controller operating under a binding capacity constraint must decide whose charge to reduce. This package contains the per-session evidence behind a distributional evaluation of six controllers on a predictive digital twin driven by real charging demand from two independent networks. CONTENTS Data. Per-session logs covering 129,864 rows, being 21,644 held-out sessions evaluated under each of six controllers (an uncontrolled baseline, PID, a forecast-driven proportional throttle, and PPO, SAC and TD3 reinforcement learning agents). Session identifiers are stable across controllers, so all controller comparisons are paired. Hourly logs, a per-controller aggregate summary, and the 1,702-hour forecast evaluation window are also included. Code. Five standalone Python scripts reproducing every number in the paper: aggregate and distributional metrics with 10,000-resample bootstrap confidence intervals, Kendall rank agreement between aggregate and distributional measures, Wilcoxon signed-rank tests on paired service fractions, Lorenz curves and Gini coefficients, request-size quartile analysis, and a variance decomposition of per-session service with cluster-bootstrap confidence intervals. The scripts require only numpy, pandas and matplotlib. Notebook. The end-to-end pipeline that produced the reported run: data loading, quantile LSTM forecaster training, split-conformal calibration, and all six controllers. Figures. The four figures from the paper at 300 dpi. Literature audit. A coding sheet of 20 EV charging control papers with a codebook, recording for each paper whether it reports an aggregate metric, a per-session distribution, an explicit fairness index, a worst-case or tail outcome, and whether its formulation guarantees each vehicle's energy as a hard constraint. Nine included papers all report aggregate metrics; five report per-session distributions; three report a fairness index, all three from the scheduling-theory literature rather than from machine-learning or model-predictive control work. KNOWN ISSUES KNOWN_ISSUES.txt documents defects openly, including a curtailment double-counting bug confined to the curtailment counter, a development bootstrap setting left in the notebook (the published intervals come from the analysis script, not the notebook), and PID gains selected at the edge of the tuning grid. SOURCE DATA The raw third-party inputs are not redistributed. The included files are derived simulation outputs. Rebuilding from source requires obtaining the UK charging transaction datasets and ACN-Data from their original providers under those providers' terms.