Electricity Systems: Difference between revisions

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== Readings ==
 
== Readings ==
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=== Primers on electricity systems ===
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* [https://www.ipcc.ch/report/ar5/wg3/energy-systems/ IPCC chapter on energy systems]
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* [https://www.ferc.gov/sites/default/files/2020-06/energy-primer-2020.pdf Energy Primer by US Federal Energy Regulatory Commission]
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=== Textbooks on electricity systems ===
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* Von Meier, A. [https://www.personal.psu.edu/sab51/vls/vonmeier.pdf Electric Power Systems: A Conceptual Introduction.] (2006)
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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)
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* 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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=== Primers on specific sub-topics ===
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==== Nuclear fusion ====
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* [https://www.nature.com/collections/bccqhmkbyw/ Nuclear fusion collection in Nature Physics]
   
 
== Community ==
 
== Community ==
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== Libraries and tools ==
 
== Libraries and tools ==
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* [https://us.energypolicy.solutions/docs/ Energy Policy Simulator] from [https://energyinnovation.org/ Energy Innovation LLC]
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* [https://github.com/invenia/OPFSampler.jl/ Optimal Power Flow (OPF) Sampler Package]
   
 
== Data ==
 
== Data ==
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=== General electricity market data ===
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* [https://catalyst.coop/pudl/ Public Utility Data Liberation (PUDL) Project]
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* US independent system operators/regional transmission organizations (ISOs/RTOs): [http://www.caiso.com/TodaysOutlook/Pages/prices.aspx CAISO], [http://www.ercot.com/gridinfo ERCOT], [https://www.iso-ne.com/markets-operations ISO-NE], [https://www.misoenergy.org/markets-and-operations/ MISO], [https://www.nyiso.com/energy-market-operational-data NYISO], [https://dataminer2.pjm.com/list PJM], and [https://marketplace.spp.org/ SPP]
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=== Supply data ===
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* [http://www.drhongtao.com/gefcom Global Energy Forecasting Competition]
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* [https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/IHBANG SubseasonalRodeo]
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* [https://www.kaggle.com/c/ams-2014-solar-energy-prediction-contest American Meteorological Society 2013-2014 Solar Energy Prediction Contest]
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=== Demand data ===
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* [https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/1ZNLUY Rural Electricity Demand in India (REDI) Dataset]
   
 
== Selected problems ==
 
== Selected problems ==

Revision as of 15:29, 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

Primers on electricity systems

Textbooks on electricity systems

Primers on specific sub-topics

Nuclear fusion

Community

Journals and conferences

Societies and organizations

Past and upcoming events

Libraries and tools

Data

General electricity market data

Supply data

Demand data

Selected problems

References

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