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Systems","sub_category":"Energy System Modeling Frameworks","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"![](docs/assets/mesmo_logo.png)\n\n[![](https://zenodo.org/badge/201130660.svg)](https://zenodo.org/badge/latestdoi/201130660)\n[![](https://img.shields.io/github/release-date/mesmo-dev/mesmo?label=last%20release)](https://github.com/mesmo-dev/mesmo/releases)\n[![](https://img.shields.io/github/last-commit/mesmo-dev/mesmo?label=last%20commit%20%28develop%29)](https://github.com/mesmo-dev/mesmo/commits/develop)\n[![](https://img.shields.io/github/actions/workflow/status/mesmo-dev/mesmo/pythontests.yml?branch=develop)](https://github.com/mesmo-dev/mesmo/actions/workflows/pythontests.yml?query=branch%3Adevelop)\n\n\u003e Work in progress: The repository is under active development and interfaces may change without notice. Please use [GitHub issues](https://github.com/mesmo-dev/mesmo/issues) for raising problems, questions, comments and feedback.\n\n# What is MESMO?\n\nMESMO stand for \"Multi-Energy System Modeling and Optimization\" and is an open-source Python tool for the modeling, simulation and optimization of multi-scale electric and thermal distribution systems along with distributed energy resources (DERs), such as flexible building loads, electric vehicle (EV) chargers, distributed generators (DGs) and energy storage systems (ESS).\n\n## Features\n\nMESMO implements 1) non-linear models for simulation-based analysis and 2) convex models for optimization-based analysis of electric grids, thermal grids and DERs. Through high-level interfaces, MESMO enables modeling operation problems for both traditional scenario-based simulation as well as optimization-based decision support. An emphasis of MESMO is on the modeling of multi-energy systems, i.e. the coupling of multi-commodity and multi-scale energy systems.\n\n1. **Electric grid modeling**\n    - Simulation: Non-linear modeling of steady-state nodal voltage / branch flows / losses, for multi-phase / unbalanced AC networks.\n    - Optimization: Linear approximate modeling via global or local approximation, for multi-phase / unbalanced AC networks.\n2. **Thermal grid modeling**\n    - Simulation: Non-linear modeling of steady-state nodal pressure head / branch flow / pump losses, for radial district heating / cooling systems.\n    - Optimization: Linear approximate modeling via global or local approximation, for radial district heating / cooling systems.\n3. **Distributed energy resource (DER) modeling**\n    - Simulation \u0026 optimization: Time series models for non-dispatchable / fixed DERs.\n    - Optimization: Linear state-space models for dispatchable / flexible DERs.\n    - Currently implemented DER models: Conventional fixed loads, generic flexible loads, flexible thermal building loads, non-dispatchable generators, controllable electric / thermal generators, electric / thermal energy storage systems, combined heat-and-power plants.\n4. **Solution interfaces**\n    - Simulation: Solution of non-linear power flow problems for electric / thermal grids.\n    - Optimization: Solution of convex optimization problems for electric / thermal grids and DERs, through third-party numerical optimization solvers.\n    - Generic optimization problem interface: Supports defining custom constraints and objective terms to augment the built-in models. Enables retrieving duals / DLMPs for the study of decentralized / distributed control architectures for energy systems.\n    - High-level problem interfaces: Nominal operation problem for simulation-based studies; Optimal operation problem for optimization-based studies.\n\n## Documentation\n\nThe documentation is located at [mesmo-dev.github.io/mesmo](https://mesmo-dev.github.io/mesmo).\n\n## Installation\n\nMESMO has not yet been deployed to Python `pip` / `conda` package indexes, but can be installed in a local development environment as follows:\n\n1. Install `conda`-based Python distribution¹ such as [Anaconda](https://www.anaconda.com/distribution/) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html) or [Miniforge](https://github.com/conda-forge/miniforge).\n2. Clone or download the repository. Ensure that the `cobmo` submodule directory is loaded as well.\n3. In `conda`-enabled shell (e.g. Anaconda Prompt), run:\n    - `cd path_to_mesmo_repository`\n    - `conda create -n mesmo -c conda-forge python=3.10 contextily cvxpy numpy pandas scipy`\n    - `conda activate mesmo`\n    - `python development_setup.py`\n    - On Intel CPUs²: `conda install -c conda-forge \"libblas=*=*mkl\"`\n\nMESMO ships with [HiGHS](https://highs.dev/) as default optimization solver³, but also supports [Gurobi](http://www.gurobi.com/) and [any CVXPY-supported solvers](https://www.cvxpy.org/tutorial/advanced/index.html#choosing-a-solver).\n\nFor notes ¹/²/³ and alternative installation guide, see [docs/installation.md](docs/installation.md).\n\n## Contributing\n\nIf you are keen to contribute to this project, please see [docs/contributing.md](./docs/contributing.md).\n\n## Publications\n\nInformation on citing MESMO and a list of related publications is available at [docs/publications.md](docs/publications.md).\n\n## Acknowledgements\n\n- MESMO is developed in collaboration between [TUMCREATE](https://www.tum-create.edu.sg/), the [Institute for High Performance Computing, A*STAR](https://www.a-star.edu.sg/ihpc) and the [Chair of Renewable and Sustainable Energy Systems, TUM](https://www.ei.tum.de/en/ens/).\n- Sebastian Troitzsch implemented the initial version of MESMO and maintains this repository.\n- Sarmad Hanif and Kai Zhang developed the underlying electric grid modeling, fixed-point power flow solution and electric grid approximation methodologies.\n- Arif Ahmed implemented the implicit Z-bus power flow solution method \u0026 overhead line type definitions.\n- Mischa Grussmann developed the thermal grid modeling and approximation methodologies.\n- Verena Kleinschmidt implemented several multi-energy DER models, such as the heating plant and CHP plant models. \n- Sebastian Troitzsch and Tom Schelo implemented the optimization problem class.\n- This work was financially supported by the Singapore National Research Foundation under its Campus for Research Excellence And Technological Enterprise (CREATE) programme.\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/55085","html_url":"https://ost.ecosyste.ms/projects/55085"}