Electricity Systems: Difference between revisions
(add citation in picture caption) |
(add subscript) |
||
Line 1: | Line 1: | ||
− | [[File:ElectricitySystems.png|thumb|A schematic of selected opportunities to reduce greenhouse emissions from electricity systems using machine learning. From "Tackling Climate Change with Machine Learning." |
+ | [[File:ElectricitySystems.png|thumb|A schematic of selected opportunities to reduce greenhouse emissions from electricity systems using machine learning. From "Tackling Climate Change with Machine Learning."]] |
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. |
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 |
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). |
* 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 |
+ | * Reduce emissions from existing CO<sub>2</sub>-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. |
* Implement these changes across all countries and contexts, as electricity systems are everywhere. |
||
Revision as of 03:26, 28 August 2020
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
- Chapter 7: "Energy Systems" in the IPCC Fifth Assessment Report (2014)[1]: An overview of "issues related to the mitigation of greenhouse gas emissions (GHG) from the energy supply sector."
- "Energy Primer: A Handbook for Energy Market Basics" by the U.S. Federal Energy Regulatory Commission (2020)[2]: A primer on wholesale electricity, natural gas, and oil/petroleum markets, as well as energy-related financial markets, in the United States.
Textbooks
- "Electric Power Systems: A Conceptual Introduction" (2006)[3]: A textbook "intended to bridge the gap between formal engineering texts and more popularly accessible descriptions of electric power technology."
- "Power Generation, Operation, and Control" (2013)[4]: A canonical reference on the engineering and economics of electric power systems.
- "Fundamentals of Power System Economics" (2018)[5]: An introduction to electricity markets.
Other
- Greening the Grid toolkit: A collection of readings, trainings, and other resources on power grids, renewable energy, energy storage, and electric vehicles.
- "Nuclear fusion" in Nature Physics: A collection of articles on the state of nuclear fusion research.
Online courses and course materials
- "Electric Power Systems" on Coursera: A course covering the "standards and policies of the electric utility industry," including "basic vocabulary used in the business" and an introduction of "the electric power system, from generation of the electricity all the way to the wall plug."
- "Computational Methods for the Smart Grid": "[A]n introduction to recent advances in computational methods applied to sustainable energy and the smart grid... provid[ing] students with a broad background in state-of-the-art computational methods that repeatedly arise in these domains, such as machine learning, optimization, and control."
Community
Major conferences
- IEEE Power & Energy Society General Meeting: One of the IEEE Power & Energy Society's flagship annual conferences, held in North America.
- Power Systems Computation Conference: A biennial conference held in Europe, focused on computational power system methods.
- PowerTech: The IEEE Power & Energy Society's anchor conference in Europe, held biennially.
- Also see additional conferences by IEEE and the IEEE Power & Energy Society
Major journals
- IEEE Transactions on Power Systems: Covers the "requirements, planning, analysis, reliability, operation, and economics of electric generating, transmission, and distribution systems for general industrial, commercial, public, and domestic consumption."
- IEEE Transactions on Smart Grid: "[A] cross disciplinary and internationally archival journal aimed at disseminating results of research on smart grid that relates to, arises from, or deliberately influences energy generation, transmission, distribution and delivery."
Major societies and organizations
- IEEE Power & Energy Society: "[T]he world's largest forum for sharing the latest in technological developments in the electric power industry, for developing standards that guide the development and construction of equipment and systems, and for educating members of the industry and the general public."
Libraries and tools
- PowerTAC: A power system simulation environment.
- Energy Policy Simulator: A tool to simulate the greenhouse gas emissions effects of various climate and energy policies.
- Optimal Power Flow (OPF) Sampler Package: A Julia package to generate power grid data samples via optimal power flow methods.
Data
Electricity system data
- Public Utility Data Liberation (PUDL) Project: A project integrating a variety of United States federal electricity data sources.
- United States ISO/RTO data: Electricity market data (e.g., prices, supply, and demand) are available online for a number of US independent system operator/regional transmission organization regions, namely CAISO, ERCOT, ISO-NE, MISO, NYISO, PJM, and SPP.
- European electricity market data from electricityMap: Links to (and parsers for) European power system data.
Renewables forecasting contest data
- SubseasonalRodeo: "A benchmark dataset for training and evaluating subseasonal forecasting systems—systems predicting temperature or precipitation 2-6 weeks in advance—in the western contiguous United States."
- American Meteorological Society 2013-2014 Solar Energy Prediction Contest: Contest data for producing daily forecasts of solar energy.
Demand data
- Rural Electricity Demand in India (REDI) Dataset: "The dataset contains detailed information on electricity demand in rural India. The dataset covers 10,000 households and 2,000 rural enterprises across 200 villages in Bihar, Uttar Pradesh, Odisha, and Rajasthan."
- Pecan Street: Disaggregated energy and water data.
GHG emissions data
- Copernicus global methane data: Dataset on global methane emissions from 2002 onwards from the European Space Agency.
- United States Environmental Protection Agency's Air Markets Program data: Datasets from the US EPA's emissions trading programs. For instance, the Continuous Emissions Monitoring System dataset (also available via the EPA's FTP site) provides hourly emissions and generation for many fossil fuel generators in the United States.
- See also: Satellite imagery datasets
Other
- Accelerated materials science datasets: Datasets that may be useful for research on solar fuels, next-generation battery conducting fluids, or other accelerated materials science applications in the electricity sector.
- Satellite imagery datasets: Datasets that may be useful for applications such as power plant emissions detection, power grid mapping, solar panel mapping, etc.
- Project Sunroof by Google: Detailed estimates of rooftop solar potential based on sunlight and roof space.
Selected problems
Under construction
References
- ↑ Bruckner T., I.A. Bashmakov, Y. Mulugetta, H. Chum, A. de la Vega Navarro, J. Edmonds, A. Faaij, B. Fungtammasan, A. Garg, E. Hertwich, D. Honnery, D. Infield, M. Kainuma, S. Khennas, S. Kim, H.B. Nimir, K. Riahi, N. Strachan, R. Wiser, and X. Zhang, 2014: Energy Systems. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Available at https://www.ipcc.ch/report/ar5/wg3/energy-systems/
- ↑ Federal Energy Regulatory Commission. "Energy Primer: A Handbook of Energy Market Basics." Federal Energy Regulatory Commission: Washington, DC, USA (2020). Available at https://www.ferc.gov/sites/default/files/2020-06/energy-primer-2020.pdf
- ↑ Von Meier, Alexandra. "Electric power systems." A Conceptual Introduction (2006).
- ↑ Wood, A. J., Wollenberg, B. F., & Sheblé, G. B. (2013). Power generation, operation, and control. John Wiley & Sons.
- ↑ Kirschen, D. S., & Strbac, G. (2018). Fundamentals of power system economics. John Wiley & Sons.