VPP AI Platform · 虚拟电厂多时空协同智能运营平台

AI-assisted operations platform for a virtual power plant (VPP) in Hubei, China: five LLM agents propose market bids, resource dispatch, and load-control plans; a deterministic safety chain (rule check → simulation → envelope/human approval → execution permit) governs everything before any external effect. The LLM never computes numbers and never touches the second-level control loop.

Status: M1 and M2 implemented. Design docs 00–13 are complete; implementation follows ROADMAP.md. Present today: domain schemas (packages/domain), the TS↔Python contract pipeline (contracts/, skills-py/vpp_contracts), ledger/snapshot/time-series/relational/ingestion services (packages/services), the Python skill service — load/PV/price forecasts with calibrated quantiles, bid-optimization MILP, report generator (skills-py/vpp_skills) — and the L2 eval harness with a committed baseline (packages/evals). The eval dataset is synthetic (no historical Hubei data yet); baselines on it measure the harness, not the KPI. M3 (runtime, agents, safety chain) is next.

Development

Requires Node 24 and Python 3.11 (skills-py/.python-version; the generated pydantic models use StrEnum and PEP 604 unions).

npm ci
npm run check            # typecheck, re-export contracts, run TS tests
npm run eval -w @vpp/evals -- --check   # L2 harness vs baseline (needs the skill service below)

cd skills-py
uv venv --python 3.11 .venv && uv pip install -r requirements.txt   # or python3.11 -m venv
source .venv/bin/activate
bash scripts/generate_models.sh   # regenerate pydantic models (committed, never hand-edited)
python -m pytest -q
python -m uvicorn vpp_skills.app:app --port 8000   # skill service for the eval harness

CI (.github/workflows/ci.yml) runs both sides and fails if contracts/ or skills-py/vpp_contracts are not regenerated after a schema change.

Orientation

You are…	Start with
A coding agent about to implement	CLAUDE.md, then docs/00, 01, 09, 11
New to the project	docs/00-overview.md → docs/07-scenario-walkthrough.md (the end-to-end reference scenario)
Reviewing the business case	proposal.md (申报材料, source of requirements)
Looking for a settled decision	docs/adr/
Wondering what's still undecided	docs/open-questions.md

Document map (docs are in Chinese; implementation-facing files in English)

Doc	Content
00-overview	System context, two-plane architecture, business objects
01-principles	9 principles + 8 hard invariants (binding for all code)
02-cognitive-plane	Five agents, Runtime, memory, Case Desk
03-safety-chain	Proposal state machine, envelopes, permits, staleness
04-control-plane	Execution engine, edge autonomy, time/space cascades
05-skills-and-data	Skill contracts, five-store data layer, policy packs
06-integration	External system boundaries and degraded channels
07-scenario-walkthrough	Day-ahead spot bidding, D-1 → D → D+1
08-implementation	Stack, LLM abstraction, deployment, milestones
09-runtime-implementation	Runtime on Mastra: workflows, suspend/resume, lineage
10-federation	Cross-province boundary: signed artifacts only
11-contracts	Ports, canonical objects, TS↔Python contract pipeline
12-evaluation	Four-layer evals, change gates, KPI definitions
13-risks-failure-modes	FMEA, top-5 risks, kill-switch hierarchy

Supporting: brainstorming.md is an independent peer review whose findings were integrated (see docs/01 invariants note); GLOSSARY.md maps Chinese domain terms to canonical code names.

Target repository layout (from docs/09 §7)

packages/
├── domain/       # zod schemas — single source of truth for all business objects
├── runtime/      # Mastra instance, workflows, agents, tool registry, triggers
├── services/     # deterministic services: policy engine, envelopes, ledger, authority
├── adapters/     # anti-corruption layers: trading platform, dispatch, metering
├── evals/        # eval harness, datasets, judges (docs/12 §5)
└── skills-py/    # Python skill services (forecasting, MILP optimization, simulation)
contracts/        # generated JSON Schema + golden fixtures (cross-language contract)