EDAgent: A ReAct-Based Agentic Framework for Economic Dispatch

Authors: Group #6 — Lukun He, Yutao Li, Dong Zheng | ECE 285, UC San Diego

EDAgent achieves 97% end-to-end success with 0.42% average cost error vs CVXPY optimum across 30 test cases spanning IEEE 14-bus to 300-bus systems — compared to 57% success and 6.73% error for the LLM-only baseline.

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What is EDAgent?

Economic Dispatch (ED) is a core power systems optimization task: allocate generation output across units to meet demand at minimum cost, subject to generator capacity constraints.

The problem with LLMs alone: LLMs hallucinate numerical results, violate physical constraints (power balance, generator limits), and cannot reliably solve quadratic programs — especially for large systems with 50+ generators.

EDAgent's solution: Use the LLM as an orchestrator in a ReAct loop that calls a provably correct CVXPY solver, rather than predicting dispatch values directly.

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Architecture

User (natural language query)
          │
          ▼
┌─────────────────────────────────────────────────────┐
│                  ReAct Agent Loop                   │
│                                                     │
│   Thought → Action → Observation → Thought → ...   │
│                                                     │
│  ┌──────────┐  ┌────────┐  ┌────────┐  ┌───────┐  │
│  │load_case │  │ cvxpy  │  │ memory │  │  viz  │  │
│  └──────────┘  └────────┘  └────────┘  └───────┘  │
└─────────────────────────────────────────────────────┘
          │
          ▼
  Optimal dispatch + cost (guaranteed feasible)

Tool Suite

Tool	Description
load_case	Loads IEEE standard bus data via pandapower
cvxpy	Solves quadratic ED optimization (OSQP backend)
memory	TF-IDF retrieval with importance weighting + time decay
visualization	Dispatch bar chart + load distribution pie chart

Memory System

• Working memory with priority-based retention (importance score 0–1)
• Exponential time decay for stale entries
• TF-IDF retrieval for context lookup across multi-turn queries

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Project Structure

ED-agent/
│
├── README.md
├── .env                            # API key, model ID, base URL, timeout
├── start.sh                        # One-command startup script
│
├── ── Core Agent ──
├── app.py                          # FastAPI server (port 5001, OpenAI-compatible)
├── EDAgent.py                      # ReAct loop: Thought / Action / Observation
├── EDAgentLLM.py                   # LLM client wrapper (OpenAI SDK)
├── ToolExecutor.py                 # Tool registry and dispatcher
│
├── ── Tools ──
├── tool_cvxpy.py                   # CVXPY quadratic solver tool
├── load_case.py                    # IEEE case loader (14/30/57/118/200/300-bus)
├── memory_tool.py                  # Working memory: TF-IDF + time decay
├── visualize.py                    # Matplotlib dispatch visualization
│
├── ── Baseline (LLM-only, no tools) ──
├── BaselineAgent.py                # Single-shot chain-of-thought agent
├── baseline_benchmark.py           # Baseline benchmark runner
│
├── ── Evaluation ──
├── benchmark.py                    # EDAgent benchmark (30 test cases)
├── benchmark_reference.py          # CVXPY ground-truth reference solver
├── comparison_report.py            # Side-by-side comparison report generator
│
├── ── Results ──
├── benchmark_results.json          # EDAgent results (30 cases)
├── baseline_benchmark_results.json # Baseline results (30 cases)
├── comparison_report_output.txt    # Full generated comparison report
│
├── ── Docs ──
├── docs/
│   ├── proposal_ece285.pdf         # Project proposal
│   ├── EDAgent_Final_Presentation.pptx
│   ├── EDAgent_Overview.pptx
│   └── PRESENTATION_SUMMARY.md
│
├── ── Runtime Artifacts ──
├── memory/                         # Agent memory store
├── memory_storage/                 # Memory index files
├── output_charts/                  # Generated visualization PNGs
└── test.ipynb                      # Development notebook

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Quick Start

1. Install dependencies

pip install fastapi uvicorn openai python-dotenv cvxpy pandapower scikit-learn matplotlib

2. Configure .env

LLM_API_KEY=your_api_key
LLM_MODEL_ID=your_model_id        # e.g. gpt-4o, qwen-plus, claude-3-5-sonnet
LLM_BASE_URL=https://your_endpoint/v1
LLM_TIMEOUT=120

3. Start the server

bash start.sh
# or:
python app.py   # FastAPI on port 5001

The server exposes an OpenAI-compatible API at http://localhost:5001/v1/chat/completions.

4. Connect via OpenWebUI (recommended UI)

Start OpenWebUI on port 3000, then add a custom model:

• API Base URL: http://localhost:5001/v1
• Model ID: ed-agent-react

5. Example queries

Run Economic Dispatch for IEEE 14-bus system. Total load 259 MW.
Run Economic Dispatch for IEEE 57-bus system with Generator 1 offline.
Run Economic Dispatch for IEEE 118-bus system. Increase total load by 10%.
Run Economic Dispatch for IEEE 300-bus system. Total load 23525 MW.

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Supported IEEE Test Systems

System	Buses	Generators	Load Scale
IEEE14	14	5	~180–260 MW
IEEE30	30	6	~90–300 MW
IEEE57	57	7	~900–1600 MW
IEEE118	118	54	~3000–4000 MW
IEEE200	200	29	~900–1200 MW
IEEE300	300	69	~20000 MW

Test scenarios (5 per system):

• baseline — standard dispatch at nominal load
• unit_outage × 2 — one generator taken offline
• load_lo — reduced total load
• load_hi — increased total load

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Benchmark Results

30 test cases (6 IEEE systems × 5 scenarios), evaluated against CVXPY ground truth.

Overall

Metric	EDAgent (ReAct)	Baseline (LLM-only)
Total Test Cases	30	30
End-to-End Success Rate	29/30 (97%)	17/30 (57%)
Avg Cost Error vs CVXPY	0.42%	6.73%
Max Cost Error	6.33%	95.35%
Power Balance Satisfied	Guaranteed by solver	2/30
Generator Limits Respected	Guaranteed by solver	20/30

Per-System Breakdown

System	EDAgent Success	EDAgent Avg Err	Baseline Success	Baseline Avg Err
IEEE14	5/5	1.92%	5/5	0.00%
IEEE30	2/5	0.00%	4/5	0.00%
IEEE57	5/5	0.00%	4/5	23.84%
IEEE118	4/5	0.00%	2/5	1.40%
IEEE200	4/5	0.00%	2/5	0.00%
IEEE300	3/5	0.00%	0/5	N/A

IEEE 300-bus (69 generators): EDAgent completes 3/5 cases with zero cost error. The LLM-only baseline produces no valid output — demonstrating a decisive scalability advantage.

Failure Modes

Failure Mode	EDAgent	Baseline
Parse error (no cost extracted)	6	8
Timeout (max steps reached)	1	—

Key Findings

1. Correctness: EDAgent delegates to CVXPY, achieving near-optimal results. The LLM baseline estimates values directly, leading to large errors (up to 95%) on larger systems.

2. Feasibility: EDAgent guarantees power balance and generator limits via the solver. The baseline violates physical constraints in 93% of cases.

3. Scalability: EDAgent works on 69-generator systems. The baseline completely fails — the number of generators exceeds LLM numerical reasoning capacity.

4. Architecture advantage: The ReAct Thought→Action→Observation loop provides structured, tool-grounded reasoning that eliminates hallucination in the critical optimization step.

5. Trade-off: The baseline is faster (single API call vs multi-step loop) but sacrifices accuracy and constraint satisfaction.

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Cost Function

Each generator i has quadratic cost:

Cost_i(Pg_i) = a_i · Pg_i² + b_i · Pg_i + c_i

Objective: Minimize ∑ᵢ Cost_i(Pg_i)

Subject to:

• Power balance: ∑ᵢ Pg_i = P_demand
• Generator limits: Pmin_i ≤ Pg_i ≤ Pmax_i for all active units i

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Running the Evaluation

# Run full EDAgent benchmark (requires server running on port 5001)
python benchmark.py

# Run LLM-only baseline benchmark
python baseline_benchmark.py

# Generate side-by-side comparison report → comparison_report_output.txt
python comparison_report.py

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References

• Yao et al., "ReAct: Synergizing Reasoning and Acting in Language Models" — ICLR 2023
• Mohammadi et al., "Large Language Models for Economic Dispatch in Smart Grids" — arXiv:2505.21931
• Wood & Wollenberg, "Power Generation, Operation, and Control" — Wiley, 2013