23fdf49ff2
- packages/domain: AwardNotice, DISPATCH_PLAN proposal payload, ExecutionReport, MeteringRecord, potential-assessment and dispatch-optimization contracts, ReviewFinding (+ typed writebacks), SemanticMemoryEntry, EnvelopeChangeRequest, InsightCard — exported with fixtures on both sides. - skills-py: potential-assessment (certified × rolling fulfilment, evidence days) and dispatch-optimization (per-interval LP on HiGHS, shortfall reported) skills + routes + tests. - packages/services: dispatch rules in the policy pack (over-allocation, award anchor, lineage integrity for allocations); PowerBalanceSimulator; SimulationGateway (permit-only, idempotent, seeded execute → ExecutionReports); envelope deviation-streak suspension + apply(); ReviewService (attribution, reliability EWMA writeback, semantic memory, envelope recommendations as change requests); dispatch assembler; skill client methods. - packages/runtime: resource agent; award-decomposition, review and envelope-review workflows; lifecycle selects simulator/gateway by proposal type; trigger hooks for awards, execution reports, metering; decide() resumes either lifecycle or envelope-review runs; insight cards API. - Tests: docs/07 D-1 16:00 and D+1 end to end; reliability score 0.9 → 0.880 and the next assessment de-rates capacity; envelope suspension on a seeded 3-day streak; WIDEN request applied only by a human. 184 TS + 80 Python. - docs/open-questions: B10 (reliability/potential parameters). README and CLAUDE.md status → M4 done, M5 next. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UoYoGYzHkFyv3ALenkRPhA
74 lines
3.0 KiB
Python
74 lines
3.0 KiB
Python
"""Adjustable-potential assessment skill v1 (docs/05 §2.2 可调潜力辨识).
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For each resource: adjustable capacity = certified capacity × rolling
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fulfillment rate (delivered / planned over the evidence window), with
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confidence from the profile's own confidence, the reliability score and the
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amount of evidence. A resource with no execution history keeps its certified
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figure at the profile's confidence — the "画像时效性" risk (docs/13 §0) is
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made visible through `evidence_days` rather than hidden.
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The de-rating exponent and evidence saturation are placeholders: OPEN-QUESTION
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B10 (可靠性/潜力辨识口径). The target accuracy (≥ 90%) is judged by the eval
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harness on real execution feedback (docs/12 L2).
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"""
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from __future__ import annotations
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from decimal import Decimal
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import numpy as np
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from vpp_contracts.potential_assessment_request import PotentialAssessmentRequest
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from vpp_contracts.potential_assessment_result import PotentialAssessmentResult
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from . import SKILL_VERSIONS
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from .numeric import INTERVALS, SCALE_MW, curve, quantize
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SKILL = "potential-assessment"
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EVIDENCE_WINDOW_DAYS = 14 # OPEN-QUESTION B10
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EVIDENCE_SATURATION_DAYS = 7 # OPEN-QUESTION B10: confidence saturates after this many days
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def assess_potential(req: PotentialAssessmentRequest) -> PotentialAssessmentResult:
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assessments = []
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total = np.zeros(INTERVALS)
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for r in req.resources:
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profile = r.profile
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certified = float(Decimal(profile.certified_adjustable_mw))
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recent = sorted(r.fulfillment, key=lambda f: f.market_date)[-EVIDENCE_WINDOW_DAYS:]
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planned = sum(Decimal(f.planned_mwh) for f in recent)
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delivered = sum(Decimal(f.delivered_mwh) for f in recent)
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if planned > 0:
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rate = min(Decimal(1), delivered / planned)
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evidence_days = len(recent)
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else:
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rate = None
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evidence_days = 0
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derate = float(rate) if rate is not None else 1.0
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adjustable = np.full(INTERVALS, certified * derate)
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evidence_factor = min(1.0, evidence_days / EVIDENCE_SATURATION_DAYS)
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base_conf = float(Decimal(profile.confidence))
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reliability = float(Decimal(profile.reliability_score))
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confidence = base_conf * (0.5 + 0.5 * evidence_factor) * (0.5 + 0.5 * reliability)
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total += adjustable
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assessments.append(
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{
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"resource_id": profile.resource_id,
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"adjustable_mw": curve(adjustable, req.market_date, SCALE_MW),
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"confidence": quantize(confidence, 3),
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"fulfillment_rate": quantize(rate, 4) if rate is not None else None,
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"evidence_days": evidence_days,
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}
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)
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return PotentialAssessmentResult.model_validate(
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{
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"market_date": req.market_date,
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"assessments": assessments,
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"total_adjustable_mw": curve(total, req.market_date, SCALE_MW),
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"skill_version": SKILL_VERSIONS[SKILL],
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}
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)
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