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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):

Core Capabilities

  • Simulation: Simulate a given model to analyze system behavior over time.

  • 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:

    • Linear and non-linear: RTC-Tools supports both linear and non-linear optimization problems.

    • 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).

  • 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:

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:

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

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Source distribution (sdist)

Source distribution for rtc-tools 2.8.1
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rtc_tools-2.8.1.tar.gz 2.5 MB Details

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Table of built distributions (wheels) for rtc-tools 2.8.1
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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

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2.8.1 This release

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2.8.0

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2.7.3

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2.6.1

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