Electricity Systems: Difference between revisions

From Climate Change AI Wiki
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ML can contribute on all fronts by informing the research, deployment, and operation of electricity system technologies. Such contributions include accelerating the development of clean energy technologies, improving forecasts of demand and clean energy, improving electricity system optimization and management, and enhancing system monitoring. These contributions require a variety of ML paradigms and techniques, as well as close collaborations with the electricity industry and other experts to integrate insights from operations research, electrical engineering, physics, chemistry, the social sciences, and other fields.
 
ML can contribute on all fronts by informing the research, deployment, and operation of electricity system technologies. Such contributions include accelerating the development of clean energy technologies, improving forecasts of demand and clean energy, improving electricity system optimization and management, and enhancing system monitoring. These contributions require a variety of ML paradigms and techniques, as well as close collaborations with the electricity industry and other experts to integrate insights from operations research, electrical engineering, physics, chemistry, the social sciences, and other fields.
   
== Readings ==
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== Readings and online courses ==
   
 
=== Primers on electricity systems ===
 
=== Primers on electricity systems ===
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* Wood, A.J. et al., [https://www.wiley.com/en-ca/Power+Generation%2C+Operation%2C+and+Control%2C+3rd+Edition-p-9780471790556 Power Generation, Operation, and Control.] (2013)
 
* Wood, A.J. et al., [https://www.wiley.com/en-ca/Power+Generation%2C+Operation%2C+and+Control%2C+3rd+Edition-p-9780471790556 Power Generation, Operation, and Control.] (2013)
 
* Kirschen,D D.S., and Strbac, G. [https://www.academia.edu/8171173/Fundamentals_of_power_system_economics Fundamentals of Power System Economics, Volume 1] (2004).
 
* Kirschen,D D.S., and Strbac, G. [https://www.academia.edu/8171173/Fundamentals_of_power_system_economics Fundamentals of Power System Economics, Volume 1] (2004).
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=== Online courses ===
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* [https://www.coursera.org/learn/electric-power-systems Coursera Electric Power Systems online course]
   
 
=== Primers on specific sub-topics ===
 
=== Primers on specific sub-topics ===
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== Community ==
 
== Community ==
   
=== Journals and conferences ===
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=== Major conferences ===
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* [https://pes-gm.org/2020/ IEEE Power & Energy Society General Meeting]
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* [https://pscc2020.pt/ Power Systems Computation Conference]
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* [https://attend.ieee.org/powertech-2019/ IEEE Power & Energy Society’s PowerTech]
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* Also see additional conferences by [https://www.ieee.org/conferences/index.html IEEE] and the [https://www.ieee-pes.org/meetings-and-conferences IEEE Power & Energy Society]
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=== Major journals ===
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* [https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=59 IEEE Transactions on Power Systems]
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* [https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165411 IEEE Transactions on Smart Grid]
   
 
=== Societies and organizations ===
 
=== Societies and organizations ===
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* [https://www.ieee.org/ Institute of Electrical and Electronics Engineers (IEEE)], particularly the [https://www.ieee-pes.org/meetings-and-conferences IEEE Power & Energy Society]
   
 
=== Past and upcoming events ===
 
=== Past and upcoming events ===
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* [https://us.energypolicy.solutions/docs/ Energy Policy Simulator] from [https://energyinnovation.org/ Energy Innovation LLC]
 
* [https://us.energypolicy.solutions/docs/ Energy Policy Simulator] from [https://energyinnovation.org/ Energy Innovation LLC]
 
* [https://github.com/invenia/OPFSampler.jl/ Optimal Power Flow (OPF) Sampler Package]
 
* [https://github.com/invenia/OPFSampler.jl/ Optimal Power Flow (OPF) Sampler Package]
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*[https://greeningthegrid.org/toolkits Greening the Grid toolkit]
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*[https://powertac.org/ PowerTAC testing platform]
   
 
== Data ==
 
== Data ==
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* [https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/IHBANG SubseasonalRodeo]
 
* [https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/IHBANG SubseasonalRodeo]
 
* [https://www.kaggle.com/c/ams-2014-solar-energy-prediction-contest American Meteorological Society 2013-2014 Solar Energy Prediction Contest]
 
* [https://www.kaggle.com/c/ams-2014-solar-energy-prediction-contest American Meteorological Society 2013-2014 Solar Energy Prediction Contest]
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*[https://www.google.com/get/sunroof/data-explorer/ Google Project Sunroof] (detailed estimates of solar potential based on sunlight and roof space) [TODO not sure if this belongs here]
   
 
=== Demand data ===
 
=== Demand data ===
   
 
* [https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/1ZNLUY Rural Electricity Demand in India (REDI) Dataset]
 
* [https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/1ZNLUY Rural Electricity Demand in India (REDI) Dataset]
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=== GHG emissions data ===
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* [https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-methane?tab=overview Global methane data]
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* US Environmental Protection Agency's Continuous Emissions Monitoring data ([https://ampd.epa.gov/ampd/ tool] or [ftp://newftp.epa.gov/DMDnLoad/emissions/ FTP site])
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* [https://github.com/tmrowco/electricitymap-contrib#data-sources ElectricityMap] data sources
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=== Accelerated science for materials ===
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* [https://materialsproject.org/ The Materials Project]
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* [http://www2.fiz-karlsruhe.de/icsd_home.html Inorganic Crystal Structure Database]
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* [https://www.cas.org/products/scifinder SciFinder] (paid)
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* [https://archive.ics.uci.edu/ml/datasets/ UCI Machine Learning Repository datasets, e.g. “Concrete Compressive Strength”]
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=== Other ===
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* Also see listings on satellite imagery [TODO]
   
 
== Selected problems ==
 
== Selected problems ==

Revision as of 16:45, 27 August 2020

A schematic of selected opportunities to reduce greenhouse emissions from electricity systems using machine learning. From "Tackling Climate Change with Machine Learning."[1]

AI has been called the new electricity, given its potential to transform entire industries. Interestingly, electricity itself is one of the industries that AI is poised to transform. Many electricity systems are awash in data, and the industry has begun to envision next-generation systems (smart grids) driven by AI and ML.

Electricity systems are responsible for about a quarter of human-caused greenhouse gas emissions each year. Moreover, as buildings, transportation, and other sectors seek to replace GHG-emitting fuels, demand for low-carbon electricity will grow. To reduce emissions from electricity systems, society must

  • Rapidly transition to low-carbon electricity sources (such as solar, wind, hydro, and nuclear) and phase out carbon-emitting sources (such as coal, natural gas, and other fossil fuels).
  • Reduce emissions from existing CO2-emitting power plants, since the transition to low-carbon power will not happen overnight.
  • Implement these changes across all countries and contexts, as electricity systems are everywhere.

ML can contribute on all fronts by informing the research, deployment, and operation of electricity system technologies. Such contributions include accelerating the development of clean energy technologies, improving forecasts of demand and clean energy, improving electricity system optimization and management, and enhancing system monitoring. These contributions require a variety of ML paradigms and techniques, as well as close collaborations with the electricity industry and other experts to integrate insights from operations research, electrical engineering, physics, chemistry, the social sciences, and other fields.

Readings and online courses

Primers on electricity systems

Textbooks on electricity systems

Online courses

Primers on specific sub-topics

Nuclear fusion

Community

Major conferences

Major journals

Societies and organizations

Past and upcoming events

Libraries and tools

Data

General electricity market data

Supply data

Demand data

GHG emissions data

Accelerated science for materials

Other

  • Also see listings on satellite imagery [TODO]

Selected problems

References

  1. "Tackling Climate Change with Machine Learning". Cite journal requires |journal= (help)