EOS Energy Optimization System: Efficient Energy Management

EOS (Energy Optimization System) is an open-source platform founded by Dr. Andreas Schmitz that simulates and optimizes energy distribution for residential and commercial setups. Built in Python, it supports PV generation, battery storage, load management, heat pumps, and EV integration, leveraging price forecasting and dynamic optimization to maximize efficiency and minimize costs.

Usage

1. Visit the official : https://github.com/Akkudoktor-EOS/EOS

2. Prerequisites: Python 3.11+ recommended (3.12 supported), Docker optional.

3. From source:

   git clone https://github.com/Akkudoktor-EOS/EOS.git
   cd EOS
   python -m venv .venv
   source .venv/bin/activate   # Windows: .venv\Scripts\Activate
   pip install -r requirements.txt
   pip install -e .
   python src/akkudoktoreos/server/eos.py
   

   access http://localhost:8503/docs View Swagger documentation

4. Docker:

   docker compose up

   Ports 8503/8504 exposed by default; adjust mappings as needed.

Features

• Multi-component Simulation: Classes for Battery, PVForecast, Load, Heatpump, and Strompreis cover core energy system elements.

• Forecasting & Optimization: Built-in algorithms predict load and electricity prices for cost-effective scheduling.

• Modular & Extensible: Easily add or customize modules to suit specific hardware or control strategies.

• Robust Documentation: Detailed ReadTheDocs guides and active GitHub discussions ensure quick issue resolution and community collaboration.

Use Cases

• Home Energy Management: Optimize solar self-consumption and battery usage to maximize self-sufficiency.

• Commercial Buildings: Shift heat pump and battery operations during peak tariffs to reduce operational costs.

• EV Charging Stations: Dynamically adjust charging schedules based on price signals and demand patterns.

• Research & Education: Serve as a testbed for academic projects in renewable energy modeling and optimization.