LLM-LTB: AI-Enabled Power System Large-scale Testbed

Update:

We are developing LLM for ANDES for transient stability modeling and simulation; LLM for AMS for scheduling modeling and simulation.

Conversational LangGraph agent that drives CURENT LTB AMS — DCOPF, RTED, ED, UC, ACOPF — through natural-language commands.

Built as a sibling to pv-curve-llm; same multi-agent architecture, swapped domain engine (AMS / cvxpy / ANDES instead of pandapower).

Overview

LLM-AMS lets you ask things like

"Load case ieee14_uced, trip generator PV_1, then solve with SCS"

…and have the agent translate that into the exact AMS API calls you'd otherwise write by hand from examples/ex2.ipynb:

sp = ams.load(ams.get_case('ieee14/ieee14_uced.xlsx'), setup=True, no_output=True)
sp.StaticGen.set(src='u', idx='PV_1', attr='v', value=0)
sp.RTED.update()
sp.RTED.run(solver='SCS')

The agent uses a LangGraph state machine with a classifier → router → six route-specific nodes (Q&A, Discovery, Case I/O, Configure, Modify, Solve), plus a planner for compound queries like "change load PQ_1 to 3.2 then solve".

Technology stack

Agent framework: LangGraph + LangChain
LLMs: Ollama (default, local) or OpenAI
Power simulator: LTB AMS (ltbams) + ANDES + cvxpy
Solvers: open-source CLARABEL, OSQP, SCS, HiGHS, SCIP, SCIPY (commercial GUROBI / MOSEK / CPLEX / COPT auto-detected if installed)

Phase 1 Routes (1-6)

Route	Node	Maps to AMS API	ex2 cell
1 – Q&A	`question_general`	concept Q&A: routines, constraints, cvxpy DCP	(educational)
2 – Discovery	`question_parameter`	`cvxpy.installed_solvers()`, `all_routines`, list loads / gens / lines, constraint ON/OFF	cell 64
3 – Case I/O	`case_io`	`ams.load(ams.get_case(...))`, case info	cells 6, 50, 59
4 – Configure	`configure`	active routine, solver, `routine.config.update(t=...)`, `routine.disable([...])`, `routine.enable([...])`	cells 69, 76, 81, 92
5 – Modify	`modify`	`PQ.alter('p0', ...)`, `StaticGen.set('u', ...)`, `Line.alter('u', ...)`, `Line.set('rate_a', ...)`	cells 19, 41, 52, 61
6 – Solve	`solve`	`routine.run(solver=...)` → `pg`, `plf`, `pd`, `obj` + bar-chart plots	cells 13, 23, 34, 45, 54, 66, 72, 78, 89, 96, 103

Routes 7-11 (multi-period temporal, ANDES co-sim, custom cvxpy formulations, exporting) are Phase 2 / 3.

Architecture

┌─────────┐
│  START  │
└────┬────┘
     │
     ▼
┌────────────┐
│ CLASSIFIER │  → one of six message_type labels
└────┬───────┘
     │
     ▼
┌────────────┐
│   ROUTER   │  → simple single-route OR planner for compound
└────┬───────┘
     │
     ├──► QUESTION_GENERAL ────► END | ADVANCE_STEP
     ├──► QUESTION_PARAMETER ──► END | ADVANCE_STEP
     ├──► CASE_IO ─────────────► END | ADVANCE_STEP | ERROR_HANDLER
     ├──► CONFIGURE ───────────► END | ADVANCE_STEP | ERROR_HANDLER
     ├──► MODIFY ──────────────► END | ADVANCE_STEP | ERROR_HANDLER
     ├──► SOLVE ───────────────► END | ADVANCE_STEP | ERROR_HANDLER
     └──► PLANNER ─────────────► STEP_CONTROLLER ──► (any of the 6) ──► ADVANCE_STEP ──► SUMMARY ──► END

┌────────────────┐
│ ERROR_HANDLER  │ ─────► ADVANCE_STEP | END
└────────────────┘

Core components

State (agent/state/app_state.py): TypedDict LangGraph state holding messages, current Inputs, last results, plan / step counter, error info.
Inputs (agent/schemas/inputs.py): pydantic model — case_path, routine, solver, config_t, disabled_constraints, load_overrides, gen_off, line_off, line_rate_overrides.
AMSContext (agent/ams_engine/engine.py): thin object that owns one ams.System and exposes idempotent methods matching ex2's API (load_case, alter_load_p0, set_gen_status, set_line_status, set_config_t, disable_constraints, solve, …). Held by the SessionManager; bound into node closures (same pattern as retriever in pv-curve-llm).
Workflow (agent/workflows/workflow.py): compiled LangGraph StateGraph wiring all 13 nodes.
Plotting (agent/ams_engine/plotting.py): bar charts for pg, plf, pd saved under generated/.

Installation

Prerequisites

Python ≥ 3.11 (tested on 3.12)
conda or any venv tool
Ollama for local LLM (optional if you use OpenAI)

Conda environment

conda create -n llm-ams python=3.12 -y
conda activate llm-ams
pip install -r requirements.txt

requirements.txt pulls in ltbams (which brings ANDES + cvxpy + kvxopt), LangChain stack, plus open-source solvers highspy + pyscipopt. cvxpy's bundled CLARABEL, OSQP, SCS, SCIPY are always available.

Ollama (default LLM)

ollama pull llama3.1:8b

Add a .env file (copy .env.example) if you want OpenAI:

OPENAI_API_KEY=sk-...
OPENAI_MODEL=gpt-4o-mini

Verify

python -c "import ams, cvxpy; print(ams.__version__, cvxpy.installed_solvers())"
# 1.3.0 ['CLARABEL', 'SCS', 'SCIP', 'SCIPY', 'HIGHS', 'OSQP']

Quick Start

conda activate llm-ams
cd LLM_AMS
python main.py

You will be asked three things in order (each has a default — just hit Enter):

Provider: openai or ollama (default ollama)
Routine: any AMS class name (RTED, DCOPF, ED, UC, …) (default RTED)
Solver: shown filtered by your routine's compatibility (default first in list)

The agent then loads the ex2 default case 5bus/pjm5bus_demo.xlsx and you're at the prompt.

Usage Examples

1. Replay ex2.ipynb section "Change Load"

Message: Change load PQ_1 to 3.2 and PQ_2 to 3.2
  → modify: load PQ_1 p0 → 3.2 pu, load PQ_2 p0 → 3.2 pu

Message: Solve
  → solve: RTED with CLARABEL, obj = 0.846329
            pg = [0.20, 1.64, 0.60, 5.96, 2.00]   ← exact match with ex2

2. Replay ex2 section "Trip a Generator"

Message: Trip generator PV_1 then solve
  → planner: 2 steps
  → modify:  tripped generator PV_1
  → solve:   pg = [-0.00, 0.50, 0.60, 2.97, 0.33]   ← matches ex2 cell 47

3. Replay ex2 section "Disable Constraints"

Message: Disable plflb and plfub
  → configure: disabled plflb, disabled plfub

Message: Solve
  → solve: line-flow limits ignored; plf swings to ex2 cell 74 values

4. Switch routine / solver / grid case

Message: Switch routine to DCOPF
Message: Use solver SCS
Message: Load case ieee14_uced
Message: Solve
  → DCOPF on IEEE-14 with SCS, plots saved under generated/

5. Discovery

Message: What routines are available?
  → list of 33 routines grouped by family

Message: Which solvers can I use for UC?
  → ['SCIP', 'SCIPY']   ← only MIP-capable installed solvers

6. Pure Q&A

Message: What is RTED and how is it different from DCOPF?
  → educational answer from the AMS concepts reference

Type quit (or q) to exit.

LangGraph Workflow

13 nodes wired in agent/workflows/workflow.py:

Category	Nodes
Routing	`classifier`, `router`
Planning	`planner`, `step_controller`, `advance_step`, `summary`
Actions (Routes 1-6)	`question_general`, `question_parameter`, `case_io`, `configure`, `modify`, `solve`
Recovery	`error_handler`

Simple-query path: START → classifier → router → <one route> → END.

Multi-step path (planner-triggered by "then", "and then", "compare", or change-then-solve patterns):

START → classifier → router → planner →
  step_controller → <route node> → advance_step →
    ... loop ...
  → summary → END

Recovery: any route node may attach error_info; the conditional edge routes to error_handler, which uses the LLM to explain the failure and resets retry state.

Configuration

Environment variables (`.env`)

# Default — local Ollama
OLLAMA_MODEL=llama3.1:8b
OLLAMA_BASE_URL=http://localhost:11434

# Optional — OpenAI
OPENAI_API_KEY=sk-...
OPENAI_MODEL=gpt-4o-mini

# Where to drop generated plots
AMS_OUTPUT_DIR=generated

Adding commercial solvers

# GUROBI (license required)
pip install gurobipy

# MOSEK (license required)
pip install mosek

Both are auto-detected via cvxpy.installed_solvers() and will appear in the solver-selection prompt for compatible routines (UC, ACOPF, large-scale DCOPF).

Adding cases

Drop any AMS-readable file (.xlsx, .json, .raw, MATPOWER .m) into AMS's cases/ folder, or pass an absolute path:

Message: Load case /Users/me/my_grid.xlsx

Custom banner

The startup banner is an ASCII-art string in agent/utils/display.py (constant _BANNER_LINES, ANSI-Shadow font). Edit those lines to change the wordmark.

Project Structure

LLM_AMS/
├── main.py                       # entry point
├── cli.py                        # terminal interface (provider → routine → solver prompts)
├── requirements.txt
├── .env.example
│
├── agent/
│   ├── core.py                   # LLM + AMSContext + graph factory
│   ├── session.py                # SessionManager (bootstraps default case, streams turns)
│   ├── prompts.py                # all system / user prompts
│   │
│   ├── workflows/
│   │   └── workflow.py           # compiled LangGraph
│   │
│   ├── nodes/                    # 13 nodes
│   │   ├── classify.py
│   │   ├── route.py
│   │   ├── planner.py
│   │   ├── step_controller.py
│   │   ├── advance_step.py
│   │   ├── summary.py
│   │   ├── error_handler.py
│   │   ├── question_general.py   # Route 1
│   │   ├── question_parameter.py # Route 2
│   │   ├── case_io.py            # Route 3
│   │   ├── configure.py          # Route 4
│   │   ├── modify.py             # Route 5
│   │   └── solve.py              # Route 6
│   │
│   ├── state/
│   │   └── app_state.py          # TypedDict
│   │
│   ├── schemas/                  # pydantic structured-output schemas
│   │   ├── inputs.py
│   │   ├── classifier.py
│   │   ├── parameter.py
│   │   ├── planner.py
│   │   └── response.py
│   │
│   ├── ams_engine/               # the AMS-side "engine" (mirrors pv_curve/)
│   │   ├── engine.py             # AMSContext: live ams.System wrapper
│   │   ├── routines.py           # routine ↔ solver compatibility
│   │   └── plotting.py           # pg / plf / pd bar charts
│   │
│   └── utils/
│       ├── common_utils.py
│       ├── context.py
│       └── display.py            # rich banner / parameter table / streaming
│
└── generated/                    # output plots (gitignored)

Roadmap (Phase 2 & 3)

Phase 1 ships Routes 1-6 (single-period DCOPF / RTED workflows from ex1-ex4). The following routes from the design table are deferred:

Phase	Route	Function
2	7 – Temporal	multi-period ED / UC schedule, time-series plots
2	8 – Scenario	snapshot + compare two solve results (matches pv-curve Route 8)
3	9 – ANDES	`ss.to_andes(...)` co-simulation (AMS's unique feature)
3	10 – Custom Formulation	runtime cvxpy extension (ex8)
3	11 – Export	dump case to xlsx / JSON / MATPOWER

License

This project follows the licenses of its dependencies:

LangChain / LangGraph: MIT
LTB AMS: GPL-3.0
cvxpy: Apache-2.0
ANDES: GPL-3.0

Acknowledgments

CURENT LTB — the testbed AMS is part of
pv-curve-llm — the architecture this project mirrors
LTB AMS — the scheduling simulator being driven
LangChain / LangGraph — agent framework
Ollama — local LLM runtime

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
LLM_AMS		LLM_AMS
.DS_Store		.DS_Store
LICENSE		LICENSE
README.md		README.md

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LLM-LTB: AI-Enabled Power System Large-scale Testbed

Update:

Table of Contents

Overview

Phase 1 Routes (1-6)

Architecture

Core components

Installation

Prerequisites

Conda environment

Ollama (default LLM)

Verify

Quick Start

Usage Examples

1. Replay ex2.ipynb section "Change Load"

2. Replay ex2 section "Trip a Generator"

3. Replay ex2 section "Disable Constraints"

4. Switch routine / solver / grid case

5. Discovery

6. Pure Q&A

LangGraph Workflow

Configuration

Environment variables (`.env`)

Adding commercial solvers

Adding cases

Custom banner

Project Structure

Roadmap (Phase 2 & 3)

License

Acknowledgments

About

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Repository files navigation

LLM-LTB: AI-Enabled Power System Large-scale Testbed

Update:

Table of Contents

Overview

Phase 1 Routes (1-6)

Architecture

Core components

Installation

Prerequisites

Conda environment

Ollama (default LLM)

Verify

Quick Start

Usage Examples

1. Replay ex2.ipynb section "Change Load"

2. Replay ex2 section "Trip a Generator"

3. Replay ex2 section "Disable Constraints"

4. Switch routine / solver / grid case

5. Discovery

6. Pure Q&A

LangGraph Workflow

Configuration

Environment variables (.env)

Adding commercial solvers

Adding cases

Custom banner

Project Structure

Roadmap (Phase 2 & 3)

License

Acknowledgments

About

Resources

Stars

Watchers

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Releases

Packages

Contributors

Languages

Environment variables (`.env`)