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🔋 ECOGrid - Energy Community Optimization & Grid Analysis

ECOGrid simulates Energy Communities using two complementary approaches:

  1. Agent-Based Modeling (ABM) via MESA to explore individual decisions and scenario discovery.
  2. MonteCarlo Simulation for large-scale stochastic data generation and reproducible experimentation.

🎯 What Does This Project Do?

ECOGrid helps answer critical research questions:

  • 💡 Which incentives work best to increase adoption?
  • 👥 What types of people are most likely to join?
  • 💰 How do trust and income affect decision-making?
  • 📊 What policies maximize community participation?
  • 🎲 How can stochastic simulations support scenario analysis?

🧩 Key Features

  • ⚡ Agent-Based Modeling with MESA.
  • 🎲 MonteCarlo Data Generation for robust synthetic datasets.
  • 🗺️ Scenario Discovery using PRIM.
  • 📈 3 Policy Scenarios: No Incentive (NI), Services Incentive (SI), Economic Incentive (EI).
  • 🔧 Reproducible Data Pipelines: MonteCarlo output and validation notebooks.
  • 🗂️ Visual Reports: Heatmaps, PRIM trajectory plots, and demographic tables.

🔗 Documentation Index (The ECOGrid Launchpad)

Topic Focus File Link
🚀 Getting Started Installation, setup, and first run commands 🎓 GETTING_STARTED.md
🏗️ Architecture Design principles (SOLID/DRY) and system structure 🏗️ ARCHITECTURE.md
📦 ABM Data Generation Guide to generating Agent-Based Model datasets 📊 DATA_GENERATION_ABM.md
📦 MonteCarlo Pipeline Guide to generating and validating MonteCarlo datasets 🎲 DATA_GENERATION_MONTECARLO.md
⚙️ Scripts & Experiments Overview of all executable scripts in the project 🚀 SCRIPTS.md
📊 Reports & Visualization Detailed descriptions of all generated reports (Heatmaps, PRIM Trajectory, Tables) 🗺️ VISUALIZATION_SCRIPTS.md
⚙️ Tutorials Step-by-step guides for specific usage scenarios 📖 TUTORIAL.md
🧪 API Reference Function and class documentation 🔍 API_REFERENCE.md

📁 Project Structure (High Level)


ECOGrid/
├── src/                        # 🐍 Core Python code (Simulation, Analysis, Incentives)
├── tests/                      # ✅ Unit and integration tests
├── data/                       # 💾 Input/output storage (raw, processed, MonteCarlo results)
├── config/                     # ⚙️ YAML configuration files (base, scenarios, MonteCarlo)
├── docs/                       # 📚 Documentation files (see table above)
├── notebooks/                  # 📓 Jupyter analysis and validation notebooks
└── README.md                   # 📖 This file

🛠️ Built With

  • MESA - Agent-based modeling framework
  • Python 3.9+ - Programming language
  • NumPy & Pandas - Data processing
  • Matplotlib & Seaborn - Chart generation
  • PyYAML - Configuration management
  • Pytest - Testing framework

📄 License & Contact

This research project is licensed under CC BY-NC-ND 4.0.

Authors: G. Antonio Pierro
Contact: antonio.pierro@gmail.com