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== Problem areas ==
== Problem areas ==
Applications through which machine learning can help reduce greenhouse gas emissions in electricity systems are listed below. (For demand-side applications, see the pages on [[transportation]], [[Buildings and Cities|buildings and cities]], and [[industry]].)
=== Enabling low-carbon electricity ===
=== Enabling low-carbon electricity ===
Revision as of 17:11, 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.
Applications through which machine learning can help reduce greenhouse gas emissions in electricity systems are listed below. (For demand-side applications, see the pages on transportation, buildings and cities, and industry.)
Enabling low-carbon electricity
- Electricity supply and demand forecasting
- Improving power system optimization
- Accelerated materials science for clean energy technologies
- Optimizing variable generators
- System planning for clean energy technologies
- Predictive maintenance and fault detection
- Accelerating nuclear fusion science
Reducing current-system impacts
- Methane leak detection
- Modeling emissions
- Data collection via remote sensing
- Predictive maintenance
- Chapter 7: "Energy Systems" in the IPCC Fifth Assessment Report (2014): An overview of "issues related to the mitigation of greenhouse gas emissions (GHG) from the energy supply sector." Available here.
- "Energy Primer: A Handbook for Energy Market Basics" by the U.S. Federal Energy Regulatory Commission (2020): A primer on wholesale electricity, natural gas, and oil/petroleum markets, as well as energy-related financial markets, in the United States. Available here.
- "Electric Power Systems: A Conceptual Introduction" (2006): 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): A canonical reference on the engineering and economics of electric power systems.
- "Fundamentals of Power System Economics" (2018): An introduction to electricity markets.
- Greening the Grid toolkit: A collection of readings, trainings, and other resources on power grids, renewable energy, energy storage, and electric vehicles. Available here.
- "Nuclear fusion" in Nature Physics: A collection of articles on the state of nuclear fusion research. Available here.
Online courses and course materials
- "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." Lecture slides, videos, and assignments available here.
- "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." Enroll here.
- IEEE Power & Energy Society General Meeting: One of the IEEE Power & Energy Society's flagship annual conferences, held in North America. Website here.
- Power Systems Computation Conference: A biennial conference held in Europe, focused on computational power system methods. Website here.
- PowerTech: The IEEE Power & Energy Society's anchor conference in Europe, held biennially. Website for the 2019 iteration here.
- Also see additional conferences by IEEE and the IEEE Power & Energy Society
- 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." Journal website here.
- 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." Journal website here.
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." Website here.
Libraries and tools
- PowerTAC: A power system simulation environment, available here.
- Energy Policy Simulator: A tool to simulate the greenhouse gas emissions effects of various climate and energy policies, available here.
- Optimal Power Flow (OPF) Sampler Package: A Julia package to generate power grid data samples via optimal power flow methods, available here.
Electricity system data
- Public Utility Data Liberation (PUDL) Project: A project integrating a variety of United States federal electricity data sources, available here via GitHub.
- 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, available here via GitHub.
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." Available here.
- American Meteorological Society 2013-2014 Solar Energy Prediction Contest: Contest data for producing daily forecasts of solar energy, available here.
- 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." Available here.
- Pecan Street: Disaggregated energy and water data, available here (requires login)
GHG emissions data
- Copernicus global methane data: Dataset on global methane emissions from 2002 onwards from the European Space Agency, available here.
- 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. Available here.
- See also: Satellite imagery datasets
- 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, available here.
- Rolnick, David; Donti, Priya L.; Kaack, Lynn H.; Kochanski, Kelly; Lacoste, Alexandre; Sankaran, Kris; Ross, Andrew Slavin; Milojevic-Dupont, Nikola; Jaques, Natasha; Waldman-Brown, Anna; Luccioni, Alexandra (2019-11-05). "Tackling Climate Change with Machine Learning". arXiv:1906.05433 [cs, stat].
- "Andrew Ng: Artificial Intelligence is the New Electricity - YouTube". www.youtube.com.
- Ramchurn, Sarvapali D.; Vytelingum, Perukrishnen; Rogers, Alex; Jennings, Nicholas R. (2012-04). "Putting the 'smarts' into the smart grid". Communications of the ACM. 55 (4): 86–97. doi:10.1145/2133806.2133825. ISSN 0001-0782. Check date values in:
- Machine Learning Techniques for Supporting Renewable Energy Generation and Integration: A Survey, Springer International Publishing
- "How artificial intelligence will affect the future of energy and climate". Brookings. 2019-01-10.
- IPCC. Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [O. Edenhofer, R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlomer, C. von Stechow, T. Zwickel, J.C. Minx, (eds.)]. 2014.
- 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.
- Federal Energy Regulatory Commission. "Energy Primer: A Handbook of Energy Market Basics." Federal Energy Regulatory Commission: Washington, DC, USA (2020).
- 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.