flixopt 2.1.2

Vector based energy and material flow optimization framework in Python.

FlixOpt: Energy and Material Flow Optimization Framework

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🚀 Purpose

flixopt is a Python-based optimization framework designed to tackle energy and material flow problems using mixed-integer linear programming (MILP).

flixopt bridges the gap between high-level energy systems models like FINE used for design and (multi-period) investment decisions and low-level dispatch optimization tools used for operation decisions.

flixopt leverages the fast and efficient linopy for the mathematical modeling and xarray for data handling.

flixopt provides a user-friendly interface with options for advanced users.

It was originally developed by TU Dresden as part of the SMARTBIOGRID project, funded by the German Federal Ministry for Economic Affairs and Energy (FKZ: 03KB159B). Building on the Matlab-based flixOptMat framework (developed in the FAKS project), FlixOpt also incorporates concepts from oemof/solph.

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🌟 Key Features

• High-level Interface with low-level control

  • User-friendly interface for defining flow systems
  • Pre-defined components like CHP, Heat Pump, Cooling Tower, etc.
  • Fine-grained control for advanced configurations

• Investment Optimization

  • Combined dispatch and investment optimization
  • Size optimization and discrete investment decisions
  • Combined with On/Off variables and constraints

• Effects, not only Costs --> Multi-criteria Optimization

  • flixopt abstracts costs as so called 'Effects'. This allows to model costs, CO2-emissions, primary-energy-demand or area-demand at the same time.
  • Effects can interact with each other(e.g., specific CO2 costs)
  • Any of these Effects can be used as the optimization objective.
  • A Weigted Sum of Effects can be used as the optimization objective.
  • Every Effect can be constrained ($\epsilon$-constraint method).

• Calculation Modes

  • Full - Solve the model with highest accuracy and computational requirements.
  • Segmented - Speed up solving by using a rolling horizon.
  • Aggregated - Speed up solving by identifying typical periods using TSAM. Suitable for large models.

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📦 Installation

Install FlixOpt via pip. pip install flixopt With HiGHS included out of the box, flixopt is ready to use..

We recommend installing FlixOpt with all dependencies, which enables additional features like interactive network visualizations (pyvis) and time series aggregation (tsam). pip install "flixopt[full]"

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📚 Documentation

The documentation is available at https://flixopt.github.io/flixopt/latest/

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🛠️ Solver Integration

By default, FlixOpt uses the open-source solver HiGHS which is installed by default. However, it is compatible with additional solvers such as:

• Gurobi
• CBC
• GLPK
• CPLEX

For detailed licensing and installation instructions, refer to the respective solver documentation.

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📖 Citation

If you use FlixOpt in your research or project, please cite the following:

• Main Citation: DOI:10.18086/eurosun.2022.04.07
• Short Overview: DOI:10.13140/RG.2.2.14948.24969