vpp-ai-platform/contracts/schema/dispatch_optimization_result.json
Thomas Bayes 23fdf49ff2
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M4: resource agent, envelopes live, review loop, insight cards
- 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
2026-09-02 19:31:12 -04:00

137 lines
3.0 KiB
JSON

{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"type": "object",
"properties": {
"market_date": {
"type": "string",
"pattern": "^\\d{4}-\\d{2}-\\d{2}$"
},
"total_target_mw": {
"type": "object",
"properties": {
"interval_minutes": {
"type": "number",
"const": 15
},
"date": {
"type": "string",
"pattern": "^\\d{4}-\\d{2}-\\d{2}$"
},
"values": {
"minItems": 96,
"maxItems": 96,
"type": "array",
"items": {
"type": "string",
"pattern": "^-?\\d+(\\.\\d+)?$"
}
}
},
"required": [
"interval_minutes",
"date",
"values"
],
"additionalProperties": false
},
"allocations": {
"minItems": 1,
"type": "array",
"items": {
"type": "object",
"properties": {
"unit_id": {
"type": "string",
"minLength": 1
},
"target_mw": {
"type": "object",
"properties": {
"interval_minutes": {
"type": "number",
"const": 15
},
"date": {
"type": "string",
"pattern": "^\\d{4}-\\d{2}-\\d{2}$"
},
"values": {
"minItems": 96,
"maxItems": 96,
"type": "array",
"items": {
"type": "string",
"pattern": "^-?\\d+(\\.\\d+)?$"
}
}
},
"required": [
"interval_minutes",
"date",
"values"
],
"additionalProperties": false
}
},
"required": [
"unit_id",
"target_mw"
],
"additionalProperties": false
}
},
"shortfall_mwh": {
"type": "string",
"pattern": "^-?\\d+(\\.\\d+)?$"
},
"solver": {
"type": "object",
"properties": {
"name": {
"type": "string",
"minLength": 1
},
"version": {
"type": "string",
"minLength": 1
},
"status": {
"type": "string",
"enum": [
"OPTIMAL",
"INFEASIBLE",
"ERROR"
]
},
"wall_time_ms": {
"type": "integer",
"minimum": 0,
"maximum": 9007199254740991
}
},
"required": [
"name",
"version",
"status",
"wall_time_ms"
],
"additionalProperties": false
},
"skill_version": {
"type": "string",
"pattern": "^\\d+\\.\\d+\\.\\d+$"
}
},
"required": [
"market_date",
"total_target_mw",
"allocations",
"shortfall_mwh",
"solver",
"skill_version"
],
"additionalProperties": false,
"$id": "https://vpp-ai-platform/contracts/dispatch_optimization_result.json",
"title": "DispatchOptimizationResult"
}