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Systems","sub_category":"Energy Markets","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# National Electricity Market Optimiser (NEMO)\n\n![Build status\nbadge](https://github.com/bje-/NEMO/actions/workflows/buildtest.yml/badge.svg)\n[![Coverage\nStatus](https://coveralls.io/repos/github/bje-/NEMO/badge.svg?branch=master)](https://coveralls.io/github/bje-/NEMO?branch=master)\n[![CodeFactor](https://www.codefactor.io/repository/github/bje-/nemo/badge)](https://www.codefactor.io/repository/github/bje-/nemo)\n[![Bandit](https://img.shields.io/badge/security-bandit-yellow.svg)](https://github.com/PyCQA/bandit)\n\nNEMO is a chronological production-cost and capacity expansion model\nfor testing and optimising different portfolios of renewable and\nfossil electricity generation technologies. It has been developed and\nimproved over the past decade and has a growing number of users.\n\n![NEMO dispatch results](http://nemo.ozlabs.org/theworks.png)\n\nIt requires no proprietary software to run, making it particularly\naccessible to the governments of developing countries, academic\nresearchers and students. The model is available for others to inspect\nand, importantly, to validate the results.\n\n## Installation\n\n```bash\npip install nemopt\n```\n\n## Features\n\nFor a set of given (or default) generation or demand traces, users can:\n\n  1. Specify \u0026 simulate a custom resource mix, or;\n  2. \"Evolve\" a resource mix using pre-configured scenarios, or\n     configure their own scenario\n\n### Evolution strategy\n\nThe benefit of an evolutionary approach is that while NEMO is\nsearching for the least-cost solution, NEMO can also explore\n\"near-optimal\" resource mixes.\n\nNEMO no longer uses genetic algorithms, but has adopted the better\nperforming [CMA-ES](https://en.wikipedia.org/wiki/CMA-ES) method.\n\n### Resource models\n\nNEMO has models for the following resources: wind (including\noffshore), photovoltaics, concentrating solar power (CSP), hydropower,\npumped storage hydro, biomass, black coal, open cycle gas turbines\n(OCGTs), combined cycle gas turbines (CCGTs), diesel generators, coal\nwith carbon capture and storage (CCS), CCGT with CCS, geothermal,\ndemand response, batteries, electrolysers, hydrogen fuelled gas\nturbines, and more.\n\n## Documentation\n\nDocumentation is progressively being added to a [User's\nGuide](https://nbviewer.org/urls/nemo.ozlabs.org/guide.ipynb?flush_cache=1)\nin the form of a Jupyter notebook.\n\n[API documentation](http://nemo.ozlabs.org/pdoc/index.html) exists for\nthe `nemo` module. This is useful when building new tools that use the\nsimulation framework.\n\nThe model is described in an Energy Policy paper titled [Least cost\n100% renewable electricity scenarios in the Australian National\nElectricity\nMarket](http://ceem.unsw.edu.au/sites/default/files/documents/LeastCostElectricityScenariosInPress2013.pdf)\nby Elliston, MacGill and Diesendorf (2013).\n\n## System requirements\n\nNEMO should run on any operating system where Python 3 is available\n(eg, Windows, Mac OS X, Linux). It utilises some add-on packages:\n\n- [DEAP](https://deap.readthedocs.io/en/master/),\n- [Gooey](https://pypi.org/project/Gooey/),\n- [Matplotlib](http://matplotlib.org/),\n  [Numpy](http://www.numpy.org/), [Pandas](http://pandas.pydata.org/)\n  and\n- [Pint](https://pint.readthedocs.io).\n\n### Scaling up\n\nFor simple simulations or scripted sensitivity analyses, a laptop or\ndesktop PC will be adequate. However, for optimising larger systems, a\ncluster of compute nodes is desirable. The model is scalable and you\ncan devote as many locally available CPU cores to the model as you\nwish.\n\n\u003e #### Note\n\u003e\n\u003e Due to a lack of active development, support for\n\u003e [SCOOP](https://pypi.python.org/pypi/scoop) has been removed. It\n\u003e will be soon replaced with something like [Ray](https://ray.io/).\n\n## Citation\n\nIf you use NEMO, please cite the following paper:\n\n\u003e Ben Elliston, Mark Diesendorf, Iain MacGill, [Simulations of\n\u003e scenarios with 100% renewable electricity in the Australian National\n\u003e Electricity\n\u003e Market](https://www.sciencedirect.com/science/article/pii/S0301421512002169?via=ihub#s0010),\n\u003e Energy Policy, Volume 45, 2012, Pages 606-613, ISSN 0301-4215,\n\u003e \u003chttps://doi.org/10.1016/j.enpol.2012.03.011\u003e\n\n## Community\n\nThe [nemo-devel](https://lists.ozlabs.org/listinfo/nemo-devel) mailing\nlist is where users and developers can correspond.\n\n## Contributing\n\nEnhancements and bug fixes are very welcome. Please report bugs in the\n[issue tracker](https://github.com/bje-/NEMO/issues). Authors retain\ncopyright over their work.\n\n## License\n\nNEMO was first developed by [Dr Ben\nElliston](https://www.ceem.unsw.edu.au/staff/ben-elliston) in 2011 at\nthe [Collaboration for Energy and Environmental Markets, UNSW\nSydney](https://www.ceem.unsw.edu.au/).\n\nNEMO is free software and the source code is licensed under the [GPL version 3 license](COPYING).\n\n## Useful references\n\nAustralian cost data are taken from the [Australian Energy Technology\nAssessments](https://www.industry.gov.au/Office-of-the-Chief-Economist/Publications/Pages/Australian-energy-technology-assessments.aspx)\n(2012, 2013), the [Australian Power Generation Technology\nReport](http://www.co2crc.com.au/publication-category/reports/) (2015)\nand the CSIRO [GenCost\nreports](https://data.csiro.au/collections/collection/CIcsiro:44228)\n(2021, 2022, 2023). The GenCost reports provide the basis of the input\ncost assumptions for the AEMO [Integrated System\nPlan](https://aemo.com.au/en/energy-systems/major-publications/integrated-system-plan-isp).\nCosts for other countries may be added in time.\n\nRenewable energy trace data covering the Australian National\nElectricity Market territory are taken from the AEMO 100% Renewables\nStudy. An accompanying\n[report](http://content.webarchive.nla.gov.au/gov/wayback/20140211194248/http://www.climatechange.gov.au/sites/climatechange/files/files/reducing-carbon/APPENDIX3-ROAM-report-wind-solar-modelling.pdf)\ndescribes the method of generating the traces.\n\n## Acknowledgements\n\nEarly development of NEMO was financially supported by the [Australian\nRenewable Energy Agency](http://www.arena.gov.au/) (ARENA). 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