NEMO (National Electricity Market Optimiser)

The National Electricity Market Optimiser (NEMO) is a chronological dispatch model for testing and optimising different portfolios of conventional and renewable electricity generation technologies.

chronologicaldispatchconventionalrenewableelectricity

Contributor(s)

Initial contribute: 2019-10-20

Authorship

:  
Centre for Energy and Environmental Markets, University of New South Wales
:  
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Detailed Description

English {{currentDetailLanguage}} English

Quoted from: https://nemo.ozlabs.org/ 

The National Electricity Market Optimiser (NEMO) is a chronological dispatch model for testing and optimising different portfolios of conventional and renewable electricity generation technologies. It was first developed by Dr Ben Elliston in 2011 at the Centre for Energy and Environmental Markets, University of New South Wales. NEMO continues to be actively developed and improved with a growing number of users.

NEMO is free software and is licensed under the GPL version 3 license. It requires no proprietary software to run, making it particularly accessible to the governments of developing countries, academic researchers and students. The model is available for others to inspect and, importantly, to validate results. Academic journals should not accept papers containing results from "black box" models.

Installation

The easiest way to install NEMO is with the Python pip utility which will install other packages to satisfy dependencies:

$ pip install nemopt

The package is called nemopt as nemo was already claimed in the Python Package Index.

System requirements

NEMO should run on any operating system where Python 3 is available (eg, Windows, Mac OS X, Linux). NEMO utilises some add-on packages: DEAPGooeyMatplotlibNumpyPandas and Pint.

For simple simulations or scripted sensitivity analyses, a laptop or desktop PC will be adequate. However, for optimisations, a cluster of compute nodes is desirable. The model is highly scalable and you can devote as many CPU cores to the model as you wish. For multiprocessing across CPU cores and hosts, the SCOOP (Scalable COncurrent Operations in Python) package is required. This can be optionally installed using PIP.

Documentation

Documentation will be progressively added to a user's guide in the form of an IPython notebook.

Auto-generated library documentation exists for the nemo module. This is useful when building new tools that use the simulation framework.

The model is described in an Energy Policy paper titled Least cost 100% renewable electricity scenarios in the Australian National Electricity Market by Elliston, MacGill and Diesendorf (2013). NEMO no longer uses genetic algorithms, but has adopted the better performing CMA-ES algorithm. However, the approach of searching for least cost solutions is the same.

Source code 

The NEMO source code (written in Python) is easy to extend and modify. The source code is distributed under the GNU General Public License. Code snapshots are available as a ZIP archive or from Github.

Enhancements and bug fixes are very welcome. Please report bugs in the issue tracker. Authors retain copyright over their work.

Mailing list

The nemo-devel mailing list is where users and developers can correspond.

Useful references

Australian cost data are taken from the Australian Energy Technology Assessments (2012, 2013) and the Australian Power Generation Technology Report (2015). More recent costs are now available from the AEMO Integrated System Plan and will be incorporated into NEMO soon. Costs for other countries may also be added in time.

Renewable energy trace data covering the Australian National Electricity Market territory are taken from the AEMO 100% Renewables Study. An accompanying report describes the method of generating the traces.

Acknowledgements

Early development of NEMO was financially supported by the Australian Renewable Energy Agency (ARENA).

模型元数据

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Ben Elliston (2019). NEMO (National Electricity Market Optimiser), Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/b1a5ccf2-973a-4a51-ad02-2cb391f2f461
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Contributor(s)

Initial contribute : 2019-10-20

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Authorship

:  
Centre for Energy and Environmental Markets, University of New South Wales
:  
View
Is authorship not correct? Feed back

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