Data and Code for Service-Envelope Analysis of Inverter-Based Air-Conditioner Fleets Providing Concurrent Frequency-Watt and Volt-Var Support
Description
This dataset contains the processed data, scripts, model files, tests, and generated result files used to reproduce the service-envelope analysis of inverter-based air-conditioner fleets providing simultaneous Frequency-Watt and Volt-Var grid support. The archive includes Python workflows for service-envelope boundary sweeps, duration sensitivity, thermal-stress cases, probabilistic heterogeneous-fleet analysis, empirically anchored recovery modeling, public-data anchoring, fleet-size sensitivity, placement sensitivity, planning-criteria sensitivity, feeder-control sensitivity, boundary validation, and selected multi-layer validation. It also includes processed calibration data derived from NIST NZERTF and ResStock/EnergyPlus sources, IEEE 13-node OpenDSS example files, selected CSV/JSON results, generated figures, and focused regression tests. The dataset does not include manuscript submission files, LaTeX source files, cover letters, declarations, or raw large public datasets. Large public datasets and restricted-access datasets are documented in the README with access notes. The README also provides installation instructions, reproduction commands, expected outputs, and platform notes for OpenDSS, ANDES, and related validation tools.
Files
Steps to reproduce
1. Download and extract service_envelope_reproducibility_mendeley_data.zip. 2. Create a Python environment and install the base dependencies: pip install -r requirements.txt 3. Optional validation layers require additional dependencies: pip install -r requirements-validation.txt 4. From the extracted archive root, run the focused test suite: python -m pytest tests 5. Regenerate the main service-envelope boundary results: python run_service_envelope_sweep.py --output-dir experiments/service_envelope_boundary_planning 6. Regenerate duration, thermal, probabilistic, empirical-recovery, and public-data anchoring studies: python run_service_envelope_duration.py --output-dir experiments/service_envelope_duration python run_service_envelope_duration_thermal_stress.py --output-dir experiments/service_envelope_duration_thermal_stress python run_service_envelope_duration_probabilistic.py --output-dir experiments/service_envelope_duration_probabilistic python run_service_envelope_duration_empirical_recovery.py --output-dir experiments/service_envelope_duration_empirical_recovery python run_service_envelope_public_data_anchor.py --output-dir experiments/service_envelope_public_data_anchor 7. Regenerate sensitivity studies: python run_service_envelope_fleet_size_sensitivity.py --output-dir experiments/service_envelope_fleet_size_sensitivity python run_service_envelope_placement_sensitivity.py --output-dir experiments/service_envelope_placement_sensitivity python run_service_envelope_planning_criteria_sensitivity.py --output-dir experiments/service_envelope_planning_criteria_sensitivity python run_service_envelope_control_sensitivity.py --output-dir experiments/service_envelope_control_sensitivity 8. Regenerate selected validation results: python validate_service_envelope_boundary.py --output-dir experiments/service_envelope_boundary_validation python validate_service_envelope_multilayer.py --output-dir experiments/service_envelope_multilayer_fresh_solve 9. Outputs are written under the experiments/ directory. The archive also includes selected pre-generated CSV, JSON, and figure outputs under results/experiments/ for comparison.
Institutions
- Skolkovo Institute of Science and TechnologyMoscow Oblast, Skolkovo