How the calculators actually work

Every locality default you see in the calculators comes from a public, named, dated dataset. We don't make up "typical household" numbers; we pull from the best public AU source we can find for each state and tell you which one was used.

Every result you see is tagged with a calibration tier (the small italicised line under each calculator's output). The tier tells you how solid the locality data behind that result is.

The ten-stage pipeline:

1. Eligibility gate. Gas HWS, heat pump HWS, no HWS, non-homeowner, or no tank access — any of these returns a friendly rejection card instead of a calculation.
2. Annual HWS energy need = household_size × 50 L × ΔT × specific_heat / heater_efficiency × 365. ΔT comes from your postcode's climate zone.
3. Available solar surplus = daily_export × 365 × 0.85 (seasonal overlap factor — solar peaks summer, HWS demand peaks winter, the factor captures the mismatch).
4. Tank absorption ceiling = tank_litres × 45°C ×specific_heat × 365. A small tank limits how much surplus you can store.
5. Divertible = min of the three. The smallest number is the binding constraint.
6. Battery interaction. If you have a battery, it consumes surplus before the diverter can.
7. Capture factor = 0.75. Real-world inefficiency — you don't capture every theoretical kWh.
8. Grid boost = HWS need − actual diverted. What you still pay for from the grid.
9. Cost before/after, FiT income lost, net savings.
10. Capex ($1,300 default), payback, lifetime savings, suitability score, warnings.

The exact Python source-of-truth lives in our open repo at excessenergy-validation/src/calculator.py. The TypeScript implementation that runs in production is parity-tested against it on every change: 8 hand-curated reference cases + 500 randomised scenarios must match within ±1 kWh on integers and ±0.05 yr on payback. CI rejects any drift.

First-principles arithmetic: annual_export × FiT vs annual_export × retail. The headline gap is what most AU solar households are effectively giving the grid for free every year at 2c FiT vs 33c retail. No calibration involved — just multiplication.

Deliberately conservative. We model solar self-consumption arbitrage only: the battery charges from your solar surplus (energy you'd otherwise export at FiT) and discharges later when you'd otherwise be importing at retail. Per-kWh value = retail − FiT. Net savings = cycled × value, after round-trip efficiency (0.9).

What we don't model (and why):

• Time-of-use arbitrage on non-solar days. Varies by retailer plan, and modelling it honestly requires per-plan assumptions we can't make for you.
• Backup power valuation. Subjective and household-specific. If backup matters to you, factor it in separately.
• VPP enrolment revenue. Programme-specific and changes constantly.
• Battery degradation. Modest impact (~5% over 10 years for LFP). We'll add it when the model gets more sophisticated.

Net effect: our battery savings number is a lower bound. A real installation may do better via the deferred items. We'd rather understate than overstate.

The 2025 federal Cheaper Home Batteries rebate is applied at $372/kWh of usable capacity (≈30% subsidy), capped at 50 kWh per household, per the ARENA-published guidance. Update annually as the STC value drifts.

The honest list of known weaknesses:

• Single-occupant households are under-predicted by our HWS model by 20–33%, because the formula doesn't capture fixed standing tank losses that apply regardless of household size. If you live alone, treat the result as a lower bound.
• Apartment dwellers are usually wrong-targeted. Most apartments have central building HWS the diverter can't reach. The eligibility gate catches this, but you may see a result that overstates feasibility if you have an unusual setup.
• Real-world performance depends on weather, real solar generation, real export patterns, real HWS usage. We model the central case; your year will differ.
• The "ground truth" is itself a model. Our HWS estimate is validated against Ausgrid LGA aggregates, which are themselves model-derived. We are not validating against real diverter installations, because that data doesn't exist publicly. The empirical claim has bounds.

The calculation engine is open source: Python validation, TS production, and the parity test suite that keeps them in sync. If you want to audit our maths line-by-line, the code is at github.com/levitatingletteropener/excessenergy. Bug reports and methodology PRs welcome.