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…nalytical-validation-framework-and-elasticity-sensitivity-analysis
Implements utils/post/diagnose_demand_flex.py: an analytical diagnostic that loads real HP building loads and TOU derivation data to sweep candidate elasticity values (-0.02 to -0.20), compute peak reduction % and rate-arbitrage savings per HP building, and recommend the elasticity that best matches the Arcturus 2.0 "no enabling technology" empirical target for each NY utility. Runs in ~15s per utility without requiring a CAIRO run. Made-with: Cursor
Implements utils/post/validate_demand_flex_shift.py: reproduces the constant-elasticity demand-response shift analytically using the same functions CAIRO uses, then verifies correctness via three checks: energy conservation (per-building kWh preserved), direction (peak kWh decreases, off-peak increases), and cross-check against CAIRO's saved demand_flex_elasticity_tracker.csv. All 7 NY utilities pass at ε=-0.10. Produces five diagnostic plots per utility: aggregate daily load profile (pre/post with shaded fill), net shift by hour bars, month×hour heatmap, per-building peak reduction distribution, and per-building daily profiles at representative consumption percentiles. Also writes CSV/parquet data outputs for report reuse. Made-with: Cursor
context/methods/tou_and_rates/demand_flex_elasticity_calibration.md: accessible writeup of the per-utility elasticity calibration methodology, Arcturus 2.0 empirical anchor, two savings mechanisms (rate arbitrage vs RR reduction), results table (ε=-0.10 or -0.12 per utility), and known limitations. context/plans/demand-flex_elasticity_calibration.md: living plan document for the broader demand-flex calibration and validation work. Made-with: Cursor
rate_design/hp_rates/ny/Justfile: add validate-demand-flex, validate-demand-flex-all, and diagnose-demand-flex recipes for running the shift validation and elasticity sweep diagnostic from the command line. context/README.md: index the new demand_flex_elasticity_calibration.md context document. Made-with: Cursor
5 tasks
context/methods/tou_and_rates/demand_flex_elasticity_calibration.md: - Add Validation section: all-utilities pass results at ε=-0.10 (energy conservation, direction, CAIRO tracker cross-check) - Add per-utility peak reduction table at ε=-0.10 - Document the five diagnostic plot types and what they show - Add invoke instructions for the new Justfile recipes context/plans/demand-flex_elasticity_calibration.md: - Update todos to reflect Phase 1 completion (diagnostic script, validation script, Justfile recipes, results review all done) - Add status table at the top of the plan body - Add pending items: scenario YAML update, optional Phase 2 sweep Made-with: Cursor
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Implements the analytical demand-flex validation and elasticity calibration framework for NY HP rate design, grounded in the Arcturus 2.0 meta-analysis.
What this PR adds
utils/post/diagnose_demand_flex.py— Elasticity sweep diagnostic. For each NY utility, loads real HP building loads and TOU derivation data, sweeps candidate elasticity values, and recommends the value whose peak reduction % most closely matches the Arcturus 2.0 "no enabling technology" empirical prediction. Runs in ~15s per utility without a CAIRO run.utils/post/validate_demand_flex_shift.py— Shift validation and diagnostic plots. Reproduces the constant-elasticity demand-response shift analytically using the same functions CAIRO uses, then verifies: (1) energy conservation, (2) correct direction (peak kWh down, off-peak up), (3) match against CAIRO's saveddemand_flex_elasticity_tracker.csv. All 7 NY utilities pass at ε=-0.10. Produces 5 diagnostic plots per utility (aggregate daily profile, net-shift-by-hour bars, month×hour heatmap, peak reduction distribution, per-building daily profiles) and CSV/parquet outputs for report reuse.context/methods/tou_and_rates/demand_flex_elasticity_calibration.md— Methodology writeup: Arcturus 2.0 anchor, two savings mechanisms (rate arbitrage vs RR reduction), per-utility results table (ε=-0.10 or -0.12), known limitations.rate_design/hp_rates/ny/Justfile— Three new recipes:validate-demand-flex,validate-demand-flex-all,diagnose-demand-flex.Reviewer focus
diagnose_demand_flex.pynormalizes by number of HP buildings (n_bldgs) not by weighted customer count — this is intentional and correct; see the savings decomposition in the context doc.Closes #377
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