vpp-ai-platform/skills-py/vpp_contracts/bid_optimization_result.py

84 lines
2.3 KiB
Python
Raw Normal View History

M2: skill contracts, Python skill service, L2 eval harness with baseline - packages/domain: ForecastRequest, BidOptimizationRequest/Result, ReportRequest, SkillReport (+ golden and invalid fixtures, exported to contracts/ and regenerated as pydantic models). - skills-py/vpp_skills: FastAPI service with versioned registry; load/PV/ price forecasts (same-day-type EWM point forecast, conformal residual quantiles — coverage test as acceptance gate); bid-optimization MILP on HiGHS (binary block participation, hard ledger energy bounds, exact Decimal fit of the rounded curve inside the bounds, revenue distribution over quantile paths); report generator whose every figure is a {tool_call_id, path} reference, with a verifier. 48 tests incl. hypothesis property test that bids respect ledger constraints. - packages/services: LedgerService.dayAheadBounds (the P7 cascade band handed to the optimizer); Decimal resolved once for CJS/ESM interop. - packages/evals: L2 metrics (MAPE, nRMSE, coverage, direction accuracy, naive/hindsight revenue baselines), HTTP skill client, rolling-origin harness that pushes each bid through the real ledger, CLI with --check/--write-baseline; committed baseline on the SYNTHETIC dataset (no historical Hubei data yet — baselines measure the harness, not KPI). - CI: evals job boots the skill service and fails on baseline digest drift. - docs/open-questions: A6 (flexibility marginal cost = offer floor); A4/B6 wired as placeholders. README/CLAUDE.md status → M2 done, M3 next. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UoYoGYzHkFyv3ALenkRPhA
2026-09-02 06:29:08 -04:00
# generated by datamodel-codegen:
# filename: bid_optimization_result.json
from __future__ import annotations
from enum import StrEnum
from typing import Literal
from pydantic import BaseModel, ConfigDict, Field, RootModel, conint, constr
class Value(RootModel[constr(pattern=r'^-?\d+(\.\d+)?$')]):
root: constr(pattern=r'^-?\d+(\.\d+)?$')
class PricesYuanPerMwh(BaseModel):
model_config = ConfigDict(
extra='forbid',
)
interval_minutes: Literal[15]
date: constr(pattern=r'^\d{4}-\d{2}-\d{2}$')
values: list[Value] = Field(..., max_length=96, min_length=96)
class QuantitiesMwh(BaseModel):
model_config = ConfigDict(
extra='forbid',
)
interval_minutes: Literal[15]
date: constr(pattern=r'^\d{4}-\d{2}-\d{2}$')
values: list[Value] = Field(..., max_length=96, min_length=96)
class RevenueDistributionYuan(BaseModel):
model_config = ConfigDict(
extra='forbid',
)
p10: constr(pattern=r'^-?\d+(\.\d+)?$')
p50: constr(pattern=r'^-?\d+(\.\d+)?$')
p90: constr(pattern=r'^-?\d+(\.\d+)?$')
class PositionBounds(BaseModel):
model_config = ConfigDict(
extra='forbid',
)
ledger_version: conint(ge=0, le=9007199254740991)
daily_energy_min_mwh: constr(pattern=r'^-?\d+(\.\d+)?$')
daily_energy_max_mwh: constr(pattern=r'^-?\d+(\.\d+)?$')
class Status(StrEnum):
OPTIMAL = 'OPTIMAL'
INFEASIBLE = 'INFEASIBLE'
TIME_LIMIT = 'TIME_LIMIT'
ERROR = 'ERROR'
class Solver(BaseModel):
model_config = ConfigDict(
extra='forbid',
)
name: constr(min_length=1)
version: constr(min_length=1)
status: Status
objective_value: constr(pattern=r'^-?\d+(\.\d+)?$') | None
wall_time_ms: conint(ge=0, le=9007199254740991)
class BidOptimizationResult(BaseModel):
model_config = ConfigDict(
extra='forbid',
)
market_date: constr(pattern=r'^\d{4}-\d{2}-\d{2}$')
prices_yuan_per_mwh: PricesYuanPerMwh
quantities_mwh: QuantitiesMwh
daily_energy_mwh: constr(pattern=r'^-?\d+(\.\d+)?$')
expected_revenue_yuan: constr(pattern=r'^-?\d+(\.\d+)?$')
revenue_distribution_yuan: RevenueDistributionYuan
position_bounds: PositionBounds
solver: Solver
binding_constraints: list[str]
skill_version: constr(pattern=r'^\d+\.\d+\.\d+$')