Solar PPA microgrid
Outputs below are real: the notebook runs against the
energypack's benchmark model, which CFDL validates against an independent reference. To run it yourself, see the Python SDK guide.
A two-minute agent-driven walkthrough: an AI agent executes this notebook cell by cell, every output computing live in the take. Captions carry the narration.
A solar-plus-storage microgrid with a PPA revenue contract, degradation, O&M escalation, ITC/PTC tax attributes and MACRS depreciation, financed with sculpted debt.
This notebook uses one of the benchmark models, which CFDL validates against an independent reference to the penny.
# On Colab, install the SDK and fetch the models this notebook reads.
# Inside a checkout both are already present and this cell does nothing.
import subprocess, sys
from pathlib import Path
REPO = "https://github.com/cfdl-dev/cfdl"
def repo_root() -> Path:
"""The checkout holding benchmarks/ and packs/, cloning it if need be.
Searching a bounded set of ancestors means a plain `python` run outside a
checkout fails with an explanation rather than walking to the filesystem
root. On a hosted runtime there is no checkout to find, so fetch one.
"""
here = Path.cwd().resolve()
for candidate in (here, *here.parents):
if (candidate / "Cargo.toml").exists() and (candidate / "packs").is_dir():
return candidate
if "google.colab" not in sys.modules:
raise RuntimeError(
f"No CFDL checkout found above {here}. This notebook reads a model "
f"from benchmarks/ and pack definitions from packs/, so run it "
f"inside a clone of {REPO}."
)
subprocess.run([sys.executable, "-m", "pip", "install", "-q", "cfdl-sdk[viz]"], check=True)
# Packs and benchmark models track the engine, so take the checkout at the
# tag matching the wheel pip just resolved. `main` runs ahead of the last
# release and its packs may use metric ops the released engine rejects.
import importlib
from importlib.metadata import PackageNotFoundError, version
importlib.invalidate_caches()
try:
tag = f"v{version('cfdl-sdk')}"
except PackageNotFoundError:
tag = None
clone = ["git", "clone", "--depth", "1", "-q", REPO]
target = Path("/content/cfdl")
if not target.exists():
pinned = tag is not None and not subprocess.run(
clone + ["--branch", tag, str(target)]
).returncode
if not pinned:
# A dev or pre-release wheel has no matching tag; main is the best
# available, and the notebook may fail if the two have diverged.
print(f"warning: no {tag} tag for this SDK build; falling back to main.")
subprocess.run(clone + [str(target)], check=True)
return target
ROOT = repo_root()
PACKS = ROOT / "packs"
import cfdl_sdkCompile
Compile the model directory to IR.
model_dir = ROOT / "benchmarks/energy/solar_ppa_microgrid"
model = cfdl_sdk.compile(model_dir, packs_dir=PACKS)
print("streams:", len(model.ir["streams"]))streams: 9
Run
Run with the benchmark's configuration and apply the energy pack's domain metrics.
results = model.run(
config=str(model_dir / "run.json"),
pack="energy",
)
print("status:", results.status, "| warnings:", len(results.warnings))status: ok | warnings: 0
Cash flows
The engine returns per-period signed cash flows; cashflows() gives a wide DataFrame indexed by period.
cf = results.cashflows()
print('shape:', cf.shape)
cf.head()shape: (300, 18)
account.asset.microgrid.balance domain.energy.cfads \
period
2026-01 1.596537e+06 30166.666667
2026-02 1.593057e+06 30166.666667
2026-03 1.589559e+06 30166.666667
2026-04 1.586044e+06 30166.666667
2026-05 1.582512e+06 30166.666667
domain.energy.debt_service_periodic domain.energy.dscr_periodic domain.energy.ebitda \
period
2026-01 11462.896936 2.631679 30166.666667
2026-02 11462.896936 2.631679 30166.666667
2026-03 11462.896936 2.631679 30166.666667
2026-04 11462.896936 2.631679 30166.666667
2026-05 11462.896936 2.631679 30166.666667
domain.energy.opex domain.energy.revenue entity.asset.microgrid.net_cash_flow \
period
2026-01 5833.333333 36000.0 -2.381296e+06
2026-02 5833.333333 36000.0 1.870377e+04
2026-03 5833.333333 36000.0 1.870377e+04
2026-04 5833.333333 36000.0 1.870377e+04
2026-05 5833.333333 36000.0 1.870377e+04
model.net_cash_flow stream.energy.capacity.revenue stream.energy.capex.outlay \
period
2026-01 -2.381296e+06 5000.0 -2400000.0
2026-02 1.870377e+04 5000.0 0.0
2026-03 1.870377e+04 5000.0 0.0
2026-04 1.870377e+04 5000.0 0.0
2026-05 1.870377e+04 5000.0 0.0
stream.energy.debt.interest stream.energy.debt.principal stream.energy.debt.proceeds \
period
2026-01 -8000.000000 -3462.896936 0.0
2026-02 -7982.685515 -3480.211420 0.0
2026-03 -7965.284458 -3497.612477 0.0
2026-04 -7947.796396 -3515.100540 0.0
2026-05 -7930.220893 -3532.676043 0.0
stream.energy.itc.credit stream.energy.om.expense stream.energy.ppa.revenue \
period
2026-01 0.0 -5833.333333 29750.0
2026-02 0.0 -5833.333333 29750.0
2026-03 0.0 -5833.333333 29750.0
2026-04 0.0 -5833.333333 29750.0
2026-05 0.0 -5833.333333 29750.0
stream.energy.storage.margin
period
2026-01 1250.0
2026-02 1250.0
2026-03 1250.0
2026-04 1250.0
2026-05 1250.0
# Requires the [viz] extra (pip install cfdl-sdk[viz]).
results.plot.cumulative()<Axes: xlabel='period', ylabel='cumulative amount'>
Metrics
Core metrics (NPV/IRR/MOIC/...) plus the pack's domain metrics, with their source labeled.
results.metrics_frame() metric value currency source
0 domain.energy.debt_service 2.751095e+06 USD domain:energy
1 domain.energy.dscr 3.708691e+00 domain:energy
2 domain.energy.ebitda 1.020296e+07 USD domain:energy
3 domain.energy.opex 2.391043e+06 USD domain:energy
4 domain.energy.revenue 1.259400e+07 USD domain:energy
5 domain.energy.tax_benefits 7.200000e+05 USD domain:energy
6 entity.asset.microgrid.total 5.771866e+06 USD core
7 model.irr 1.468450e-01 core
8 model.moic 3.423834e+00 core
9 model.npv 1.220669e+06 USD core
10 model.payback_periods 8.500000e+01 core
11 model.payback_years 7.166667e+00 core
12 model.total 5.771866e+06 USD core
13 model.wal_years 1.299224e+01 core
14 run.annual_discount_rate 8.000000e-02 core
15 run.periods_per_year 1.200000e+01 core
16 stream.energy.capacity.revenue.total 1.500000e+06 USD core
17 stream.energy.capex.outlay.total -2.400000e+06 USD core
18 stream.energy.debt.interest.total -1.151095e+06 USD core
19 stream.energy.debt.principal.total -1.600000e+06 USD core
20 stream.energy.debt.proceeds.total 0.000000e+00 USD core
21 stream.energy.itc.credit.total 7.200000e+05 USD core
22 stream.energy.om.expense.total -2.391043e+06 USD core
23 stream.energy.ppa.revenue.total 1.071900e+07 USD core
24 stream.energy.storage.margin.total 3.750000e+05 USD core
What-if
Re-run at a higher discount rate and compare NPV.
base = results.metrics()["model.npv"]
stressed = model.run(config={"deterministic": {"annual_discount_rate": 0.10}}, pack="energy")
print(f"NPV @ base: {base:,.0f}")
print(f"NPV @ 10%: {stressed.metrics()['model.npv']:,.0f}")NPV @ base: 1,220,669
NPV @ 10%: 737,280
Extended analysis — verify, decompose, cover
The same discipline an agent uses: don't trust a series, interrogate it.
# Verify the degradation convention: annual PPA revenue should grow at a constant
# escalation-net-of-degradation rate. pandas makes the check one line.
ppa_year = cf["stream.energy.ppa.revenue"].groupby(cf.index.year).sum()
ppa_year.pct_change().dropna().round(4).unique()array([0.0149])
A constant ~1.49% — exactly (1 + 2% escalation) x (1 - 0.5% degradation) - 1.
The engine's convention, recovered from the output.
# Revenue decomposition: contracted PPA vs storage arbitrage vs capacity payments.
rev = cf[["stream.energy.ppa.revenue", "stream.energy.storage.margin", "stream.energy.capacity.revenue"]]
rev.groupby(cf.index.year).sum().rename(columns=lambda c: c.split(".")[2]).plot.area(title="Revenue stack by year")<Axes: title={'center': 'Revenue stack by year'}, xlabel='period'>
# Coverage: CFADS against debt service, annually, over the debt's life.
annual = cf[["domain.energy.cfads", "domain.energy.debt_service_periodic"]].groupby(cf.index.year).sum()
live = annual[annual["domain.energy.debt_service_periodic"] > 0]
(live["domain.energy.cfads"] / live["domain.energy.debt_service_periodic"]).plot(title="Annual DSCR (CFADS / debt service)")<Axes: title={'center': 'Annual DSCR (CFADS / debt service)'}, xlabel='period'>
# Equity payback, from the cumulative net line and the engine's own metric.
cum = cf["model.net_cash_flow"].cumsum()
print("first cumulative-positive month:", cum[cum > 0].index.min(),
"| model.payback_years:", results.metrics()["model.payback_years"])
cum.plot(title="Cumulative net cash flow")first cumulative-positive month: 2033-02 | model.payback_years: 7.166667
<Axes: title={'center': 'Cumulative net cash flow'}, xlabel='period'>
