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    "info": {
        "author": "Matthias Hotz",
        "author_email": "matthias.hotz@tum.de",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Scientific/Engineering"
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        "description": "# Welcome to *hynet*\n\n*hynet* is a package for the computation of the optimal power flow (OPF) in hybrid AC/DC power systems, i.e., the cost- or loss-minimizing allocation of generation resources and the corresponding system state to serve a given load while satisfying the system's technical boundary conditions. *hynet* supports power systems that comprise an arbitrary interconnection of AC grids and radial DC grids, i.e., point-to-point and radial multi-terminal HVDC systems. With respect to OPF methods, it supports the solution of the nonconvex quadratically constrained quadratic program (QCQP) as well as its semidefinite relaxation (SDR) and second-order cone relaxation (SOCR). For more information, please refer to *hynet*'s documentation ([HTML](https://hynet.readthedocs.io)/[PDF](https://readthedocs.org/projects/hynet/downloads/pdf/latest/)) for a description the software and [this paper](https://arxiv.org/abs/1811.10496) for the mathematical background. *hynet* uses [SQLite](https://www.sqlite.org)-based SQL databases to store grid infrastructure and scenario information. A library with several grid databases is provided [here](https://gitlab.com/tum-msv/hynet-databases).\n\n\n## Installation\n\n*hynet* was developed for Python 3.5 and higher and requires [NumPy](http://www.numpy.org/), [SciPy](https://www.scipy.org/), [pandas](https://pandas.pydata.org/), [SQLAlchemy](https://www.sqlalchemy.org/), [Matplotlib](https://matplotlib.org/), [tqdm](https://github.com/tqdm/tqdm), [h5py](https://www.h5py.org/) as well as at least one of the supported [solvers](#solvers). For a convenient installation, the Python distribution [Anaconda](http://www.anaconda.com/download/) (or the stripped-down [Miniconda](https://conda.io/miniconda.html)) may be used, where the included package manager [Conda](https://conda.io) supports a straightforward installation of the supported solvers.\n\n\nTo install *hynet* using Python's package management system, run\n\n```sh\npip install hynet\n```\n\nThe installation of *hynet* and the installed [solvers](#solvers) can be tested with\n\n```sh\npython -m hynet test\n```\n\nTo install *hynet* from its sources, get the latest source code by cloning the *hynet* repository with [Git](https://git-scm.com/) via\n\n```sh\ngit clone https://gitlab.com/tum-msv/hynet.git\n```\n\nand initiate the installation with\n\n```sh\npython setup.py install\n```\n\n\n### Solvers\n\nIn the following, the supported solvers are listed. **Currently, the utilization of the following solvers is recommended: IPOPT for the QCQP, MOSEK for the SDR, and MOSEK or CPLEX for the SOCR.** Regarding the latter, it was found empirically that CPLEX is more robust while MOSEK is computationally more efficient. Please note that **even if only QCQPs are solved, it is recommended to install MOSEK or CPLEX**, as they enable the efficient computation of an initial point for QCQP solvers.\n\n\n#### IPOPT\n\n[IPOPT](https://projects.coin-or.org/Ipopt) is an open-source software package for large-scale nonlinear optimization and [CYIPOPT](https://github.com/matthias-k/cyipopt) is a Python wrapper for IPOPT. With [Conda](https://conda.io), both can be installed as follows.\n\n* Linux and MAC OS X:\n\n```sh\nconda install -c conda-forge cyipopt\n```\n\n* Windows:\n\n```sh\nconda install -c pycalphad cyipopt\n```\n\n#### MOSEK\n\n[MOSEK](http://www.mosek.com) is an interior-point optimizer for large-scale conic optimization problems. It is commercial, but offers a [free academic license](https://www.mosek.com/products/academic-licenses/). With [Conda](https://conda.io), MOSEK can be installed with\n\n```sh\nconda install -c mosek mosek\n```\n\n*hynet*'s SDR solver interface for MOSEK supports a [chordal conversion](http://epubs.siam.org/doi/abs/10.1137/S1052623400366218) of the semidefinite program to enable the computation of the SDR for medium- and large-scale systems with a viable computational effort. To utilize the chordal SDR, [CHOMPACK](http://github.com/cvxopt/chompack), a library for chordal matrix computations, and [CVXOPT](http://cvxopt.org), a Python package for convex optimization, are required. With Python's package management system, both can be installed with\n\n```sh\npip install chompack cvxopt\n```\n\n\n#### IBM ILOG CPLEX\n\n[CPLEX](http://www.ibm.com/analytics/cplex-optimizer) is a high-performance mathematical programming solver for linear, mixed integer, quadratic, and quadratically constrained programming problems. It is commercial, but offers a [free academic license](http://www.ibm.com/products/ilog-cplex-optimization-studio) through the [IBM Academic Initiative](https://ibm.biz/academic). For the installation, please refer to the instructions provided with CPLEX as well as the section \"Setting up the Python API of CPLEX\" of the CPLEX documentation.\n\n\n#### PICOS\n\n*hynet* supports the solution of the SDR and SOCR with [PICOS](http://picos.zib.de/index.html). However, the additional modeling layer causes a performance drawback. [PICOS](http://picos.zib.de/index.html) is an open-source Python-based modeling language for linear and conic optimization problems. It supports several solvers, including the open-source solver [CVXOPT](http://cvxopt.org). With Python's package management system, PICOS and CVXOPT can be installed with\n\n```sh\npip install picos cvxopt\n```\n\n\n#### PYOMO\n\n*hynet* supports the solution of the QCQP with [Pyomo](http://www.pyomo.org/). However, the additional modeling layer causes a performance drawback. Furthermore, the import of Pyomo is demanding and **slows down the import** of *hynet* significantly, thus the installation is only recommended if Pyomo is actually utilized. [Pyomo](http://www.pyomo.org/) is an open-source optimization modeling language and includes support for the solver [IPOPT](https://projects.coin-or.org/Ipopt). With [Conda](https://conda.io), both can be installed with\n\n```sh\nconda install -c conda-forge pyomo libgfortran\nconda install -c cachemeorg ipopt_bin\n```\n\n\n## Usage\n\nOpen a terminal, navigate to the directory that contains the [grid databases](https://gitlab.com/tum-msv/hynet-databases), and start a Python shell, either the standard shell (``python``) or a more convenient one like [IPython](https://ipython.org) or [ptpython](https://github.com/jonathanslenders/ptpython). At the Python command prompt, import *hynet* via\n\n```python\nimport hynet as ht\n```\n\nTo access the data of the system in the file ``pjm_hybrid.db``, connect to this database using\n\n```python\ndatabase = ht.connect('pjm_hybrid.db')\n```\n\nThe optimal power flow for the default scenario of this system can then be calculated with\n\n```python\nresult = ht.calc_opf(database)\n```\n\nThe object ``result`` contains all result data. For example, to print a summary, print details of the solution, and access the determined bus voltages, type\n\n```python\nprint(result)\nprint(result.details)\nresult.bus['v']\n```\n\nBy default, *hynet* selects the most appropriate QCQP solver among those installed. To specify the type of solver explicitly, set the ``solver_type`` as illustrated below.\n\n```python\nht.calc_opf(database, solver_type=ht.SolverType.QCQP)\nht.calc_opf(database, solver_type=ht.SolverType.SDR)\nht.calc_opf(database, solver_type=ht.SolverType.SOCR)\n```\n\nIn case that the scenario shall be modified prior to the OPF calculation, it can be loaded explicitly via\n\n```python\nscenario = ht.load_scenario(database)\n```\n\nFor example, to set the load at bus 2 to 100MW and 50Mvar, use\n\n```python\nscenario.bus.at[2, 'load'] = 100 + 50j\n```\n\nThe optimal power flow for this modified scenario can be calculated with\n\n```python\nht.calc_opf(scenario)\n```\n\nFor more information and usage examples, please refer to the tutorials in [USAGE.md](https://gitlab.com/tum-msv/hynet/blob/master/USAGE.md), *hynet*'s documentation ([HTML](https://hynet.readthedocs.io)/[PDF](https://readthedocs.org/projects/hynet/downloads/pdf/latest/)), and [this paper](https://arxiv.org/abs/1811.10496).\n\n\n## Contributing\n\nContributions to *hynet* are very welcome.  Please refer to [CONTRIBUTING.md](https://gitlab.com/tum-msv/hynet/blob/master/CONTRIBUTING.md) for more information. In case that *hynet* is useful to you, we would appreciate if you star this project.\n\n\n## Credits\n\nThis software was developed at the [Professur f\u00fcr Methoden der Signalverarbeitung](http://www.msv.ei.tum.de/), [Technische Universit\u00e4t M\u00fcnchen](https://www.tum.de/) (TUM). The principal developer and project maintainer is Matthias Hotz (@matthias_hotz), who would like to recognize the highly appreciated support of the following contributors:\n\n- Vincent Bode (TUM): Database management, network graph export\n- Michael Mitterer (TUM): Distributed computation, MATPOWER import, database management\n- Christian Wahl (TUM): Capability region visualizer, CI configuration\n- Yangyang He (TUM): CVXPY and PICOS solver interface\n- Julia Sistermanns (TUM): Feature- and structure-preserving network reduction\n\n\n## Citation\n\nIn case that *hynet* is used in the preparation of a scientific publication, we would appreciate the citation of the following work:\n\n> M. Hotz and W. Utschick, \"*hynet:* An optimal power flow framework for hybrid AC/DC power systems,\" arXiv:1811.10496, Nov. 2018. \\[Online\\]. Available: [http://arxiv.org/abs/1811.10496](http://arxiv.org/abs/1811.10496)\n\nThe corresponding BibTeX entry is provided below.\n\n```\n@article{Hotz2018,\n    Author = {Matthias Hotz and Wolfgang Utschick},\n    Journal = {arXiv:1811.10496},\n    Month = {Nov.},\n    Title = {\\textit{{hynet}:} {A}n Optimal Power Flow Framework for Hybrid {AC}/{DC} Power Systems},\n    Url = {http://arxiv.org/abs/1811.10496v1},\n    Year = {2018}}\n```\n\nFurthermore, in case that the feature- and structure-preserving network reduction functionality in *hynet* is utilized, we would appreciate the citation of the following work:\n\n> J. Sistermanns, M. Hotz, D. Hewes, R. Witzmann, and W. Utschick, \"[Feature- and Structure-Preserving Network Reduction for Large-Scale Transmission Grids](http://arxiv.org/abs/1903.11590),\" 13th IEEE PES PowerTech Conf., Milano, Italy, Jun. 2019.\n\n\n## License\n\n[BSD 3-clause license](https://gitlab.com/tum-msv/hynet/blob/master/LICENSE)",
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