flixopt

FlixOpt: Progressive Flow System Optimization

Flexible  •  Low-entry  •  Investment  •  X-sector  •  OPTimization

Model more than costs · Easy to prototype · Based on dispatch · Sector coupling · Mathematical optimization

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FlixOpt is a Python framework for progressive flow system optimization - from district heating networks to industrial production lines, from renewable energy portfolios to supply chain logistics.

Build simple models quickly, then incrementally add investment decision, multi-period planning, stochastic scenarios, and custom constraints without refactoring.

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🚀 Quick Start

pip install flixopt

That’s it! FlixOpt comes with the HiGHS solver included. You’re ready to optimize.

The basic workflow:

import flixopt as fx

# 1. Define your system structure
flow_system = fx.FlowSystem(timesteps)
flow_system.add_elements(buses, components, effects)

# 2. Optimize
flow_system.optimize(fx.solvers.HighsSolver())

# 3. Analyze results
flow_system.solution        # Raw xarray Dataset
flow_system.stats           # Convenient analysis accessor

Get started with real examples:

• 📚 Full Documentation
• 💡 Examples Gallery - Complete working examples from simple to complex
• 🔧 API Reference

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🌟 Why FlixOpt?

Progressive Enhancement - Your Model Grows With You

Start simple:

# Basic single-period model
flow_system = fx.FlowSystem(timesteps)
boiler = fx.linear_converters.Boiler("Boiler", eta=0.9, ...)

Add complexity incrementally:

• Investment decisions → Add InvestParameters to components
• Multi-period planning → Add periods dimension to FlowSystem
• Uncertainty modeling → Add scenarios dimension with probabilities
• Custom constraints → Extend with native linopy syntax

No refactoring required. Your component definitions stay the same - periods, scenarios, and features are added as dimensions and parameters.

→ Learn more about multi-period and stochastic modeling

For Everyone

• Beginners: High-level components that “just work”
• Experts: Full access to modify models with linopy
• Researchers: Quick prototyping with customization options
• Engineers: Reliable, tested components without black boxes
• Students: Clear, Pythonic interfaces for learning optimization

Key Features

Multi-criteria optimization: Model costs, emissions, resource use - any custom metric. Optimize single objectives or use weighted combinations and ε-constraints. → Effects documentation

Performance at any scale: Choose optimization modes without changing your model - full optimization, rolling horizon, or clustering (using TSAM). → Scaling notebooks

Built for reproducibility: Self-contained NetCDF result files with complete model information. Load results months later - everything is preserved. → Results documentation

Flexible data operations: Transform FlowSystems with xarray-style operations (sel(), resample()) for multi-stage optimization.

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🎯 What is FlixOpt?

A General-Purpose Flow Optimization Framework

FlixOpt models any system involving flows and conversions:

• Energy systems: District heating/cooling, microgrids, renewable portfolios, sector coupling
• Material flows: Supply chains, production lines, chemical processes
• Integrated systems: Water-energy nexus, industrial symbiosis

While energy systems are our primary focus, the same foundation applies universally. This enables coupling different system types within integrated models.

Modern Foundations

Built on linopy and xarray, FlixOpt delivers performance and transparency. Full access to variables, constraints, and model structure. Extend anything with native linopy syntax.

Our Position

We bridge the gap between high-level strategic models (like FINE) and low-level dispatch tools - similar to PyPSA. FlixOpt is the sweet spot for detailed operational planning and long-term investment analysis in the same framework.

Academic Roots

Originally developed at TU Dresden for the SMARTBIOGRID project (funded by the German Federal Ministry for Economic Affairs and Energy, FKZ: 03KB159B). FlixOpt evolved from the MATLAB-based flixOptMat framework while incorporating best practices from oemof/solph.

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🛣️ Roadmap

FlixOpt aims to be the most accessible, flexible, and universal Python framework for energy and material flow optimization. We believe optimization modeling should be approachable for beginners yet powerful for experts, minimizing context switching between different planning horizons.

Current focus:

• Enhanced component library (sector coupling, hydrogen, thermal networks)
• Examples showcasing multi-period and stochastic modeling
• Advanced result analysis and visualization

Future vision:

• Modeling to generate alternatives (MGA) for robust decision-making
• Advanced stochastic optimization (two-stage, CVaR)
• Community ecosystem of user-contributed components

→ Full roadmap and vision

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🛠️ Installation

Basic Installation

pip install flixopt

Includes the HiGHS solver - you’re ready to optimize immediately.

Full Installation

For additional features (interactive network visualization, time series aggregation):

pip install "flixopt[full]"

Solver Support

FlixOpt supports many solvers via linopy: HiGHS (included), Gurobi, CPLEX, CBC, GLPK, and more.

→ Installation guide

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🤝 Contributing

FlixOpt thrives on community input. Whether you’re fixing bugs, adding components, improving docs, or sharing use cases - we welcome your contributions.

→ Contribution guide

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📖 Citation

If FlixOpt supports your research or project, please cite:

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

To pinpoint which version you used in your work, please reference one of these doi’s here:

• 

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📄 License

MIT License - See LICENSE for details.