adminandClaude Opus 5
017b798fe6
Two controllers writing one actuator is the failure this system exists to
avoid. "Exactly one writer" was true, but only as a convention held up by
careful reading - which does not survive an EV charger and a heat pump wanting
the same battery.
Strategies now return claims and arbiter.py resolves them:
highest-priority `set` wins (none at all means 0 W), then every `limit` whose
priority is >= that set's applies, most restrictive first; contradictory
limits command 0 W and are flagged as the bug they are.
The second clause is the whole point. "Money outranks maintenance" used to be a
hand-written exception inside a Jinja template; it is now a consequence of the
priorities - the charge-only limit binds the loop but cannot bind a
higher-priority peak claim.
Also: maintenance shaping moved out of control.py, which is a controller again
and not a policy engine; the loop now tracks the arbiter's actual output rather
than its own last wish, so it does not jump when it regains control; and every
decision explains itself ("loop -> 0 W, limited by maintenance(charge-only)")
in the UI and the log.
19 new assertions in test_arbiter.py, each one a precedence question someone
will eventually ask in the field. Deployed to the reference site as 0.2.0 and
holding grid within a few watts of zero.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016NckgXecasQb2eSsPYNSW6
105 lines
4.2 KiB
Python
105 lines
4.2 KiB
Python
"""Runnable check for the control law. `python3 test_control.py`
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No framework, no fixtures - it needs to run on a tech's laptop and in CI with
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nothing installed. Every assert here corresponds to a rule that exists because
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its absence caused an observed failure on real hardware.
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If you change control.py, run this. If it fails, the inverter would have done
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something you did not intend.
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"""
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import sys
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from app.control import Tuning, compute, maintenance_charge_floor, peak_at_risk
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T = Tuning()
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fails = []
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def check(name, cond):
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if cond:
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print(f" ok {name}")
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else:
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print(f" FAIL {name}")
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fails.append(name)
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print("control law")
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# Deadband: inside meter noise, hold exactly - do not drift.
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d = compute(prev_w=900, grid_w=10, actual_w=900, tuning=T)
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check("deadband holds the command", d.target_w == 900 and d.reason == "deadband")
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d = compute(prev_w=900, grid_w=20, actual_w=900, tuning=T)
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check("outside deadband it acts", d.target_w != 900)
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# Proportional: 0 + 0.6*500 = 300
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d = compute(prev_w=0, grid_w=500, actual_w=0, tuning=T)
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check("proportional step (gain 0.6)", d.target_w == 300)
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# Sign: exporting (negative grid) must CHARGE (negative target).
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d = compute(prev_w=0, grid_w=-500, actual_w=0, tuning=T)
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check("export drives charging", d.target_w == -300)
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# Clamp
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d = compute(prev_w=1900, grid_w=1000, actual_w=1900, tuning=Tuning(max_w=2000, slew_w=5000))
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check("clamped to max_w", d.target_w == 2000)
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# Slew: from 0 with a huge error, no more than slew_w in one cycle.
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d = compute(prev_w=0, grid_w=5000, actual_w=0, tuning=Tuning(max_w=5000, slew_w=1000))
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check("slew limits one cycle", d.target_w == 1000)
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# Saturation needs DURATION: one diverging cycle must NOT freeze.
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t = Tuning(saturation_w=500, saturation_cycles=3)
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d1 = compute(prev_w=2000, grid_w=500, actual_w=1000, tuning=t, sat_count=0)
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check("one saturated cycle does not freeze", not d1.frozen and d1.sat_count == 1)
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d2 = compute(prev_w=2000, grid_w=500, actual_w=1000, tuning=t, sat_count=d1.sat_count)
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d3 = compute(prev_w=2000, grid_w=500, actual_w=1000, tuning=t, sat_count=d2.sat_count)
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check("three consecutive saturated cycles freeze", d3.frozen)
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check("freeze forbids raising magnitude", d3.target_w <= 2000)
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# ...and one good cycle clears the counter immediately.
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d4 = compute(prev_w=2000, grid_w=500, actual_w=1990, tuning=t, sat_count=3)
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check("counter resets when tracking resumes", d4.sat_count == 0 and not d4.frozen)
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# Freeze must still allow the magnitude to FALL (that is the escape route).
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d = compute(prev_w=2000, grid_w=-800, actual_w=1000, tuning=t, sat_count=3)
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check("freeze still allows magnitude to fall", d.target_w < 2000)
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# Maintenance shaping (charge-only, cheap-window floor) is no longer this
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# function's business - it is expressed as limit claims. See test_arbiter.py.
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# Quantisation
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d = compute(prev_w=0, grid_w=7, actual_w=0, tuning=Tuning(deadband_w=1, step_w=10))
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check("quantised to step_w", d.target_w % 10 == 0)
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print("capacity tariff")
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check("no forecast means no cap", maintenance_charge_floor(2500, None, 3500) == 2500)
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check("headroom caps the charge", maintenance_charge_floor(2500, 2000, 3500) == 1500)
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check("no headroom means no charge", maintenance_charge_floor(2500, 4000, 3500) == 0)
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check("peak risk detected", peak_at_risk(4000, 3500) is True)
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check("peak risk off without forecast", peak_at_risk(None, 3500) is False)
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print("behaviour: 2 kW load step converges")
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# Closed-loop sim. The plant is modelled as first-order-ish: it moves most of
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# the way to the command each cycle (measured: 94 % by 3.3 s against a ~5 s
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# cycle). House load steps by 2000 W at t=0.
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prev, actual, sat, load = 0.0, 0.0, 0, 2000.0
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cycles = 0
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for i in range(12):
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grid = load - actual # what the meter sees
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d = compute(prev, grid, actual, T, sat)
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prev, sat = d.target_w, d.sat_count
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actual = actual + 0.94 * (prev - actual) # plant follows
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cycles += 1
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if abs(load - actual) < T.deadband_w:
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break
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check(f"converges within deadband in {cycles} cycles (<=6)", cycles <= 6)
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check("no overshoot past the load", actual <= load + T.deadband_w)
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print()
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if fails:
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print(f"{len(fails)} FAILED: {', '.join(fails)}")
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sys.exit(1)
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print("all checks passed")
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