8796faca63
- 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
86 lines
4.9 KiB
Markdown
86 lines
4.9 KiB
Markdown
# VPP AI Platform · 虚拟电厂多时空协同智能运营平台
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AI-assisted operations platform for a virtual power plant (VPP) in Hubei, China:
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five LLM agents propose market bids, resource dispatch, and load-control plans;
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a deterministic safety chain (rule check → simulation → envelope/human approval →
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execution permit) governs everything before any external effect. **The LLM never
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computes numbers and never touches the second-level control loop.**
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**Status: M1 and M2 implemented.** Design docs 00–13 are complete;
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implementation follows [ROADMAP.md](ROADMAP.md). Present today: domain schemas
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(`packages/domain`), the TS↔Python contract pipeline (`contracts/`,
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`skills-py/vpp_contracts`), ledger/snapshot/time-series/relational/ingestion
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services (`packages/services`), the Python skill service — load/PV/price
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forecasts with calibrated quantiles, bid-optimization MILP, report generator
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(`skills-py/vpp_skills`) — and the L2 eval harness with a committed baseline
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(`packages/evals`). The eval dataset is **synthetic** (no historical Hubei data
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yet); baselines on it measure the harness, not the KPI. M3 (runtime, agents,
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safety chain) is next.
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## Development
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Requires Node 24 and Python 3.11 (`skills-py/.python-version`; the generated
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pydantic models use `StrEnum` and PEP 604 unions).
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```sh
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npm ci
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npm run check # typecheck, re-export contracts, run TS tests
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npm run eval -w @vpp/evals -- --check # L2 harness vs baseline (needs the skill service below)
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cd skills-py
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uv venv --python 3.11 .venv && uv pip install -r requirements.txt # or python3.11 -m venv
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source .venv/bin/activate
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bash scripts/generate_models.sh # regenerate pydantic models (committed, never hand-edited)
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python -m pytest -q
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python -m uvicorn vpp_skills.app:app --port 8000 # skill service for the eval harness
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```
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CI (`.github/workflows/ci.yml`) runs both sides and fails if `contracts/` or
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`skills-py/vpp_contracts` are not regenerated after a schema change.
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## Orientation
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| You are… | Start with |
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| A coding agent about to implement | [CLAUDE.md](CLAUDE.md), then docs/00, 01, 09, 11 |
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| New to the project | [docs/00-overview.md](docs/00-overview.md) → [docs/07-scenario-walkthrough.md](docs/07-scenario-walkthrough.md) (the end-to-end reference scenario) |
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| Reviewing the business case | [proposal.md](proposal.md) (申报材料, source of requirements) |
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| Looking for a settled decision | [docs/adr/](docs/adr/) |
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| Wondering what's still undecided | [docs/open-questions.md](docs/open-questions.md) |
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## Document map (docs are in Chinese; implementation-facing files in English)
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| Doc | Content |
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| [00-overview](docs/00-overview.md) | System context, two-plane architecture, business objects |
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| [01-principles](docs/01-principles.md) | 9 principles + 8 hard invariants (binding for all code) |
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| [02-cognitive-plane](docs/02-cognitive-plane.md) | Five agents, Runtime, memory, Case Desk |
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| [03-safety-chain](docs/03-safety-chain.md) | Proposal state machine, envelopes, permits, staleness |
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| [04-control-plane](docs/04-control-plane.md) | Execution engine, edge autonomy, time/space cascades |
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| [05-skills-and-data](docs/05-skills-and-data.md) | Skill contracts, five-store data layer, policy packs |
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| [06-integration](docs/06-integration.md) | External system boundaries and degraded channels |
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| [07-scenario-walkthrough](docs/07-scenario-walkthrough.md) | Day-ahead spot bidding, D-1 → D → D+1 |
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| [08-implementation](docs/08-implementation.md) | Stack, LLM abstraction, deployment, milestones |
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| [09-runtime-implementation](docs/09-runtime-implementation.md) | Runtime on Mastra: workflows, suspend/resume, lineage |
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| [10-federation](docs/10-federation.md) | Cross-province boundary: signed artifacts only |
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| [11-contracts](docs/11-contracts.md) | Ports, canonical objects, TS↔Python contract pipeline |
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| [12-evaluation](docs/12-evaluation.md) | Four-layer evals, change gates, KPI definitions |
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| [13-risks-failure-modes](docs/13-risks-failure-modes.md) | FMEA, top-5 risks, kill-switch hierarchy |
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Supporting: [brainstorming.md](brainstorming.md) is an independent peer review
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whose findings were integrated (see docs/01 invariants note);
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[GLOSSARY.md](GLOSSARY.md) maps Chinese domain terms to canonical code names.
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## Target repository layout (from docs/09 §7)
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```
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packages/
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├── domain/ # zod schemas — single source of truth for all business objects
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├── runtime/ # Mastra instance, workflows, agents, tool registry, triggers
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├── services/ # deterministic services: policy engine, envelopes, ledger, authority
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├── adapters/ # anti-corruption layers: trading platform, dispatch, metering
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├── evals/ # eval harness, datasets, judges (docs/12 §5)
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└── skills-py/ # Python skill services (forecasting, MILP optimization, simulation)
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contracts/ # generated JSON Schema + golden fixtures (cross-language contract)
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```
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