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
68 lines
1.7 KiB
Python
68 lines
1.7 KiB
Python
# generated by datamodel-codegen:
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# filename: dispatch_optimization_result.json
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from __future__ import annotations
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from enum import StrEnum
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from typing import Literal
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from pydantic import BaseModel, ConfigDict, Field, RootModel, conint, constr
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class Value(RootModel[constr(pattern=r'^-?\d+(\.\d+)?$')]):
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root: constr(pattern=r'^-?\d+(\.\d+)?$')
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class TotalTargetMw(BaseModel):
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model_config = ConfigDict(
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extra='forbid',
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)
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interval_minutes: Literal[15]
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date: constr(pattern=r'^\d{4}-\d{2}-\d{2}$')
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values: list[Value] = Field(..., max_length=96, min_length=96)
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class TargetMw(BaseModel):
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model_config = ConfigDict(
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extra='forbid',
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)
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interval_minutes: Literal[15]
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date: constr(pattern=r'^\d{4}-\d{2}-\d{2}$')
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values: list[Value] = Field(..., max_length=96, min_length=96)
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class Allocation(BaseModel):
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model_config = ConfigDict(
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extra='forbid',
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)
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unit_id: constr(min_length=1)
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target_mw: TargetMw
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class Status(StrEnum):
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OPTIMAL = 'OPTIMAL'
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INFEASIBLE = 'INFEASIBLE'
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ERROR = 'ERROR'
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class Solver(BaseModel):
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model_config = ConfigDict(
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extra='forbid',
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)
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name: constr(min_length=1)
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version: constr(min_length=1)
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status: Status
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wall_time_ms: conint(ge=0, le=9007199254740991)
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class DispatchOptimizationResult(BaseModel):
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model_config = ConfigDict(
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extra='forbid',
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)
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market_date: constr(pattern=r'^\d{4}-\d{2}-\d{2}$')
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total_target_mw: TotalTargetMw
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allocations: list[Allocation] = Field(..., min_length=1)
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shortfall_mwh: constr(pattern=r'^-?\d+(\.\d+)?$')
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solver: Solver
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skill_version: constr(pattern=r'^\d+\.\d+\.\d+$')
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