NOTE The RTC-Tools repository has been migrated from GitLab to here; see Migration from GitLab.
Project Overview
RTC-Tools is an open-source Python package designed to model, simulate, and optimize networks or portfolios of assets, such as reservoirs, pumps, renewables, and batteries. It is part of LF Energy.
Project Description
RTC-Tools provides a modular and extensible framework for operational optimization and control of complex systems across multiple domains, with a primary focus on water management and energy systems. Originally initiated at Deltares in 2015, RTC-Tools is deployed for water and power trading applications globally, with active implementations in North and South America, Europe, Asia, and Australia.
Application Domains
- Water Management: Model-predictive control of canals, polders, reservoirs, hydropower scheduling, and pumped storage systems, including turbines, pumps, weirs, and other hydraulic structures.
- Energy Systems: Battery Energy Storage System (BESS) optimization, power trading, heat network design, and multi-energy system planning.
Multi-Domain Modeling Libraries
RTC-Tools offers extensible libraries for building complex system models using model components. Implement custom model components, linear or nonlinear, using the Modelica systems modeling language or directly using the Python API.
The following modeling libraries are available (this list is non-exhaustive):
- RTC-Tools Channel Flow: Hydraulic channel flow and water level dynamics
- RTC-Tools Hydraulic Structures: Hydraulic assets such as weirs, pumps, and other control structures
- RTC-Tools Simulation: Reservoir and system simulation workflows
- Mesido: Heat network design and multi-energy system optimization
- BESS Trading Examples: Battery energy storage trading formulations for NEM (Australia), ERCOT (Texas), and EU-style markets
Core Capabilities
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Simulation: Simulate a given model to analyze system behavior over time.
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Optimization: Define optimization goals, constraints, and decision variables to specify optimization models for a given problem. RTC-Tools is solver-agnostic and supports both open-source solvers (CBC, HiGHS, Ipopt) and commercial solvers (Gurobi, CPLEX, Knitro) for solving several types of optimization problems:
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Linear and non-linear: RTC-Tools supports both linear and non-linear optimization problems.
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Continuous and discrete: RTC-Tools can handle both continuous and discrete decision variables. This makes it suitable for optimizing systems with a mix of continuous controls (such as pump speeds or gate positions) and discrete decisions (such as on/off states of equipment).
-
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Multi-Objective Optimization: When multiple, and perhaps conflicting, objectives need to be considered (e.g., minimize operational costs while minimizing deviations of water levels from a given range), RTC-Tools offers two approaches:
- Weighting method: Assigns weights to each objective and optimizes them simultaneously.
- Lexicographic goal programming: Optimizes different objectives sequentially according to a user-defined priority ordering.
-
Optimization Under Uncertainty: RTC-Tools supports multi-stage stochastic optimization that uses ensemble forecasts to compute solutions that are robust under uncertainty. Features include control tree generation and aggregation. Optional risk constraints such as Conditional Value at Risk (CVaR) can be implemented depending on the user's specific formulation.
Integration
To streamline integration with user interfaces and data management systems (such as Delft-FEWS), RTC-Tools supports CSV and XML file formats for reading/writing timeseries and other model parameters. Support for other formats can be implemented using Python mixins.
RTC-Tools uses CasADi as a symbolic framework for algorithmic differentiation, as well as for interfacing with numerical optimization solvers.
Install
pip install rtc-tools
Documentation
Documentation and examples are available at:
Contributing
We welcome contributions to RTC-Tools! Please see our Contributing Guidelines for details on how to get started, coding standards, and our development process.
The project is governed according to our Technical Charter and Governance Model.
Migration from GitLab
The RTC-Tools repository has been migrated from GitLab (https://gitlab.com/rtc-tools/rtc-tools) to here. To change the Git remote URL, run
git remote set-url origin https://github.com/rtc-tools/rtc-tools.git
and
git remote set-url --push origin https://github.com/rtc-tools/rtc-tools.git.
License
RTC-Tools is licensed under the GNU Lesser General Public License v3.0, and can be used free of charge.
Support
Community Support
For questions, issues, and discussions, please use:
- GitHub Issues for bug reports and feature requests
- GitHub Discussions for questions and community discussions
Commercial Support
For applications in water management and hydropower, Deltares offers commercial support.
For applications in power trading and Battery Energy Storage Systems (BESS), PortfolioEnergy offers commercial support.
Governance & Roadmap
RTC-Tools development follows an open governance model as defined in our Technical Charter and Governance documents.
To learn more about the project roadmap:
- Review the roadmap discussion
- Check project milestones
Ecosystem & Collaboration
RTC-Tools is part of the LF Energy ecosystem and actively welcomes collaboration from researchers, practitioners, and organizations working on optimization—whether in general or in specialized fields—and on applied domains such as water and energy system planning and control. We encourage contributions that extend the framework’s capabilities—such as new modeling libraries, integrations, and advanced extensions—to broaden its reach and impact.
Acknowledgment
If you use RTC-Tools in your work, please acknowledge it in any resulting publications. You can do this by citing the RTC-Tools software and providing a link to our GitHub repository.
Metadata
Release files for rtc-tools 2.8.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| rtc_tools-2.8.1.tar.gz | 2.5 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| rtc_tools-2.8.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.6 MB
Release files / rtc_tools-2.8.1.tar.gz
| Download URL | rtc_tools-2.8.1.tar.gz |
|---|---|
| Size | 2.5 MB |
| Tags | Source |
|
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Transparency logRelease files / rtc_tools-2.8.1-py3-none-any.whl
| Download URL | rtc_tools-2.8.1-py3-none-any.whl |
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| Size | 181.4 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 22, 2026.
Transparency log