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A ranked, confidence-tiered list of Massachusetts commercial and industrial sites where a 250 kW / 522 kWh battery captures a meaningful share of the monthly billing demand.
MarketingPython · JavaScriptGitHub @PrashanthvaraOnlineOwner unconfirmed
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Ranks Massachusetts C&I buildings by how much monthly billed demand a 250 kW / 522 kWh battery could absorb, from public assessor data and the filed National Grid tariff.
Taken from the repository README
Loading the full README. The saved excerpt is shown below.
Ranks commercial and industrial buildings in Worcester, Fall River and Lowell
by how much of their monthly billed electrical demand a 250 kW / 522 kWh
battery could actually absorb, and what that saves at the filed tariff rate.
All three are National Grid (Massachusetts Electric) territory, which the
G-2/G-3 tariff requires. data/town_utilities.csv is the checkable record.
4,179 parcels screened, 1,450 with enough demand charge to be worth a call.
It scores sites before anyone picks up the phone. The industry sequence is
talk, then ask for a utility bill, then decide. This inverts that.Read the full README ↗https://shave.pjayav.workers.dev
Ranks commercial and industrial buildings in Worcester, Fall River and Lowell by how much of their monthly billed electrical demand a 250 kW / 522 kWh battery could actually absorb, and what that saves at the filed tariff rate.
All three are National Grid (Massachusetts Electric) territory, which the
G-2/G-3 tariff requires. data/town_utilities.csv is the checkable record.
4,179 parcels screened, 1,450 with enough demand charge to be worth a call.
It scores sites before anyone picks up the phone. The industry sequence is talk, then ask for a utility bill, then decide. This inverts that.
This tool has never seen a meter. It is a structured prior over three public assessor fields plus a modelled load-shape library. Its value is that the prior is non-obvious and cheap, not that it is a measurement.
Use it to order a call list. Do not use it to underwrite a project.
Two numbers from National Grid's Massachusetts tariff:
| Rate | Eligibility | Distribution demand charge |
|---|---|---|
| G-2 | max demand under 200 kW | $15.06 / kW |
| G-3 | 12-month average at or above 200 kW | $10.48 / kW |
G-2 costs 44% more per kW than G-3. A 180 kW site on G-2 pays more in demand charges than a 250 kW site on G-3.
Then stack the energy budget. The battery holds 522 kWh nameplate, about 413 kWh usable, which is roughly two hours at full power. A hospital's monthly peak is a six-hour plateau and the battery runs flat before it ends. A machine shop's peak is a twenty-minute inrush at shift start and the battery barely notices.
So the target is a mid-size site with a spiky weekday load, not the biggest building on the block. That falls out of the tariff and the physics rather than being asserted.
From the filed tariff, M.D.P.U. No. 1591:
The Demand for each month shall be the greater of: a) The greatest fifteen-minute peak occurring during the Peak hours period within such a month as measured in kilowatts, or b) 90% of the greatest fifteen-minute peak occurring during the Peak hours period, of such month as measured in kilovolt-amperes.
Peak hours are 8:00 a.m. to 9:00 p.m., Monday to Friday, excluding nine observed holidays. A three-in-the-morning spike is free, so every calculation here runs inside that window and nowhere else.
src/shave/
assumptions.py every constant, with source and provenance. The method
page renders from this file, so published values cannot
drift from computed ones.
billing_window.py the tariff calendar. Nine holidays, observed-day shifts,
vectorised interval mask.
scorer.py root-find for the shave threshold, monthly-max loop,
rate class, recharge feasibility, annual saving.
archetype.py one interface over two load-shape sources: measured
(NREL ComStock) and modelled (industrial synthesis).
s3://oedi-data-lakeThe code here is MIT licensed. The data is not mine to license: each source above keeps its own terms, and the assessor extracts and the ComStock cache are downloaded at build time rather than redistributed from this repository.
uv sync
uv run pytest
uv run python scripts/build_site.py # every covered town, about 60 s
NB="$HOME/.nvm/versions/node/v22.18.0/bin"
"$NB/npx" wrangler dev # serve it locally on :8787
"$NB/npx" wrangler deploy # publish
It writes, under public/data:
index.json the towns, their counts, and the default
towns/<slug>/ranked.json one enriched export per town, in its own map frame
method.json one method payload over all towns
addresses.json one address index over all towns
The page loads index.json first, then one town at a time. Towns are never
merged into a single ranked list, for the same reason the two source lists are
not: each town ranks within itself and draws its own map frame.
data/raw/ holds the MassGIS L3 extract and is gitignored, so a fresh clone
must download it before the pipeline will run. data/interim/comstock/ is the
cached ComStock profile per archetype; delete it and
scripts/warm_comstock_cache.py refetches from S3, about six minutes.
node, npm and npx are nvm shell functions here, so export PATH does not
reach them, because a shell function takes precedence over a PATH lookup. Call the
binaries by absolute path, as above.
The full test suite takes about 75 seconds: several integration tests re-run
the whole 4,179-parcel pipeline end to end rather than working from fixtures.
Each town is scored once per session and shared (tests/conftest.py).