Electricity Systems: Difference between revisions

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[[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."<ref name=":0">{{Cite journal|last=Rolnick|first=David|last2=Donti|first2=Priya L.|last3=Kaack|first3=Lynn H.|last4=Kochanski|first4=Kelly|last5=Lacoste|first5=Alexandre|last6=Sankaran|first6=Kris|last7=Ross|first7=Andrew Slavin|last8=Milojevic-Dupont|first8=Nikola|last9=Jaques|first9=Natasha|last10=Waldman-Brown|first10=Anna|last11=Luccioni|first11=Alexandra|date=2019-11-05|title=Tackling Climate Change with Machine Learning|url=http://arxiv.org/abs/1906.05433|journal=arXiv:1906.05433 [cs, stat]}}</ref>]]
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''This page is about the intersection of electricity systems and machine learning (ML) in the context of climate change mitigation. For an overview of electricity systems as a whole, please see the [https://en.wikipedia.org/wiki/Electric_power_system Wikipedia page] on this topic.''[[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."<ref name=":0">{{Cite journal|last=Rolnick|first=David|last2=Donti|first2=Priya L.|last3=Kaack|first3=Lynn H.|last4=Kochanski|first4=Kelly|last5=Lacoste|first5=Alexandre|last6=Sankaran|first6=Kris|last7=Ross|first7=Andrew Slavin|last8=Milojevic-Dupont|first8=Nikola|last9=Jaques|first9=Natasha|last10=Waldman-Brown|first10=Anna|last11=Luccioni|first11=Alexandra|date=2019-11-05|title=Tackling Climate Change with Machine Learning|url=http://arxiv.org/abs/1906.05433|journal=arXiv:1906.05433 [cs, stat]}}</ref>]]The energy supply sector contributes about 35% of human-caused greenhouse gas emissions,<ref>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.</ref> within which decarbonizing electricity supply plays an important role. In addition, many climate change strategies in sectors such as [[Buildings and Cities|buildings]], [[transportation]], and [[industry]] rely on low-carbon electricity. To reduce greenhouse gas emissions from electricity systems, it will be necessary to both transition quickly to low-carbon electricity sources (e.g., solar, wind, and nuclear) and to reduce emissions from existing electricity system operations in the meantime.
AI has been called the new electricity, given its potential to transform entire industries.<ref>{{Cite web|title=Andrew Ng: Artificial Intelligence is the New Electricity - YouTube|url=https://www.youtube.com/watch?v=21EiKfQYZXc|website=www.youtube.com}}</ref> 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.<ref>{{Cite journal|last=Ramchurn|first=Sarvapali D.|last2=Vytelingum|first2=Perukrishnen|last3=Rogers|first3=Alex|last4=Jennings|first4=Nicholas R.|date=2012-04|title=Putting the 'smarts' into the smart grid|url=http://dx.doi.org/10.1145/2133806.2133825|journal=Communications of the ACM|volume=55|issue=4|pages=86–97|doi=10.1145/2133806.2133825|issn=0001-0782}}</ref><ref>{{Citation|title=Machine Learning Techniques for Supporting Renewable Energy Generation and Integration: A Survey|url=http://dx.doi.org/10.1007/978-3-319-13290-7_7|publisher=Springer International Publishing}}</ref><ref>{{Cite web|title=How artificial intelligence will affect the future of energy and climate|url=https://www.brookings.edu/research/how-artificial-intelligence-will-affect-the-future-of-energy-and-climate/|website=Brookings|date=2019-01-10|language=en-US}}</ref>
 
   
 
AI and machine learning are often discussed in the electricity sector in the context of smart grids,<ref>{{Cite journal|last=Ramchurn|first=Sarvapali D.|last2=Vytelingum|first2=Perukrishnen|last3=Rogers|first3=Alex|last4=Jennings|first4=Nicholas R.|date=2012|title=Putting the 'smarts' into the smart grid|url=http://dx.doi.org/10.1145/2133806.2133825|journal=Communications of the ACM|volume=55|issue=4|pages=86–97|doi=10.1145/2133806.2133825|issn=0001-0782|via=}}</ref><ref>{{Citation|title=Machine Learning Techniques for Supporting Renewable Energy Generation and Integration: A Survey|url=http://dx.doi.org/10.1007/978-3-319-13290-7_7|publisher=Springer International Publishing}}</ref><ref>{{Cite web|title=How artificial intelligence will affect the future of energy and climate|url=https://www.brookings.edu/research/how-artificial-intelligence-will-affect-the-future-of-energy-and-climate/|website=Brookings|date=2019-01-10|language=en-US}}</ref> which broadly refer to the concept of "intelligent" electric grids managed automatically in a data-driven manner. In particular, ML has been used to forecast electricity supply and demand, to improve power system optimization, and to improve system efficiency through applications such as predictive maintenance. In addition, ML has also been used to accelerate scientific discovery of clean energy technologies, and to gather electricity infrastructure data that may be useful for system planners and policymakers.<ref name=":0" />
Electricity systems are responsible for about a quarter of human-caused greenhouse gas emissions each year.<ref>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.</ref> 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 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.
 
   
  +
== Machine Learning Application Areas ==
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.<ref name=":0" /> 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.
 
 
== 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 ===
   
  +
*'''[[Electricity Supply Forecasting|Supply]], [[Energy Demand Forecasting|demand]] and [[Energy Price Forecasting|price]] forecasting''': The supply and demand of power must both be forecast ahead of time to inform electricity planning and scheduling. In more volatile energy systems, also forecasting of prices becomes relevant to utilize flexibility effectively. ML can help make these forecasts more accurate, improve temporal and spatial resolution, and quantify uncertainty.
*[[Electricity Supply and Demand Forecasting|Electricity supply and demand forecasting]]
 
  +
*'''Improving [[Power System Optimization|power system optimization]]''': Scheduling algorithms on the power grid have trouble handling large quantities of solar, wind, and other time-varying electricity sources. ML can help improve electricity scheduling algorithms, control storage and flexible demand, and design real-time electricity prices that reduce CO<sub>2</sub> emissions.
* Improving power system optimization
 
  +
*'''Improving [[Power System Planning|system planning]]''': Algorithms for planning new low-carbon energy infrastructure are often large and slow. ML can help speed up or provide proxies for these algorithms.
* Accelerated materials science for clean energy technologies
 
  +
*'''Informing [[Maximum Power Point Tracking|maximum power point tracking]]''': Maximum power point tracking refers to a variety of techniques that aim to maximize the power output of weather-dependent renewable energy generators, such as solar panels and wind turbines. ML can help model attributes of renewable energy systems or actively control these systems (e.g., by modulating wind turbine rotation speed) in order to improve power output.
* Optimizing variable generators
 
  +
*'''[[Accelerated Science|Accelerated science]] for clean energy technologies''': Designing new materials is important for many applications, including energy storage via batteries or solar/chemical fuels. ML can help suggest promising materials to try, thereby speeding up the materials discovery process.
* System planning for clean energy technologies
 
  +
*'''Informing [[Nuclear Fusion|nuclear fusion]] research''': Nuclear fusion has the potential to produce safe, carbon-free electricity, but such reactors continue to consume more energy than they produce. While basic science and engineering are still needed, ML can help inform nuclear fusion research in a variety of ways, e.g., by suggesting parameters for physical experiments or modeling the behavior of plasma inside reactors.
* Predictive maintenance and fault detection
 
* Accelerating nuclear fusion science
 
   
 
=== Reducing current-system impacts ===
 
=== Reducing current-system impacts ===
   
  +
*[[Methane Leak Detection|'''Methane leak detection''']]: In addition to the unavoidable climate impacts of burning fossil fuels, natural gas extraction sites, pipelines, and compressor stations leak methane, a powerful greenhouse gas. ML can help detect and prevent these leaks.
* Methane leak detection
 
  +
*[[Power Grid Emissions Modeling|'''Power grid emissions modeling''']]: Reducing the emissions associated with electricity use requires understanding what the emissions on the electric grid actually are at any given moment. ML can help estimate and forecast emissions, and potentially model the uncertainty in these estimates.
* Modeling emissions
 
   
=== Additional areas ===
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=== General-purpose applications ===
   
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*[[Energy Infrastructure Mapping|'''Energy infrastructure mapping''']]: There are many cases in which decision-relevant information about energy infrastructure -- such as the locations and sizes of solar panels, or the location of power transmission and distribution infrastructure -- is not readily available. ML can help map some of this energy infrastructure using satellite imagery.
* Data collection via remote sensing
 
  +
*[[Predictive Maintenance|'''Predictive maintenance and fault detection''']]: Quickly detecting power system faults can help reduce power system waste or improve the utilization of low-carbon energy resources. ML can help detect faults in real time from sensor data, or even forecast them ahead of time to enable preemptive maintenance.
* Predictive maintenance
 
  +
*[[Power System State Estimation|'''State estimation''']]: Many power distribution systems have few sensors, but are increasingly necessary to monitor due to the increase in rooftop solar power. ML can provide algorithms for understanding the state of distribution systems in "low-observability" scenarios where traditional state estimation algorithms may not suffice.
  +
*'''[[Greenhouse Gas Emissions Detection|Greenhouse gas emissions mapping]]''': While some electricity system operators release publicly-available data on the emissions associated with fossil fuel generators, this data is not available in many cases. ML can help map greenhouse gas emissions using a combination of remote sensing and on-the-ground data.
  +
*'''[[Non-Intrusive Load Monitoring|Non-Intrusive Load Monitoring (NILM)]]''': ML can be used to disaggregate the net-load measurements into their individual components. It finds application in disaggregating native load and PV generation measurements from the net-load measurements obtained at the feeder-lever or at the residential-level. Another aspect of energy disaggregation is non intrusive load monitoring, wherein the load measurement is disaggregated into different components representing the consumption of different household electric appliances.
   
== Background readings ==
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== Background Readings ==
   
 
=== Primers ===
 
=== Primers ===
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*'''Greening the Grid toolkit''': A collection of readings, trainings, and other resources on power grids, renewable energy, energy storage, and electric vehicles. Available [https://greeningthegrid.org/toolkits here].
 
*'''Greening the Grid toolkit''': A collection of readings, trainings, and other resources on power grids, renewable energy, energy storage, and electric vehicles. Available [https://greeningthegrid.org/toolkits here].
* '''"Nuclear fusion" in Nature Physics''': A collection of articles on the state of nuclear fusion research. Available [https://www.nature.com/collections/bccqhmkbyw/ here].
 
   
== Online courses and course materials ==
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== 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 [http://www.cs.cmu.edu/~zkolter/course/15-884/ here].
 
*'''"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 [http://www.cs.cmu.edu/~zkolter/course/15-884/ 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 [https://www.coursera.org/learn/electric-power-systems 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 [https://www.coursera.org/learn/electric-power-systems here].
   
  +
== Conferences, Journals, and Professional Organizations ==
== Community ==
 
   
 
=== Major conferences ===
 
=== Major conferences ===
   
  +
  +
  +
*'''ACM e-Energy''': "The International Conference on Future Energy Systems (ACM e-Energy) aims to be the premier venue for researchers working in the broad areas of computing and communication for smart energy systems (including the smart grid), and in energy-efficient computing and communication systems". Website [https://energy.acm.org/eenergy-conference/ here].
 
*'''IEEE Power & Energy Society General Meeting''': One of the [https://www.ieee-pes.org/meetings-and-conferences IEEE Power & Energy Society's] flagship annual conferences, held in North America. Website [https://pes-gm.org/ here].
 
*'''IEEE Power & Energy Society General Meeting''': One of the [https://www.ieee-pes.org/meetings-and-conferences IEEE Power & Energy Society's] flagship annual conferences, held in North America. Website [https://pes-gm.org/ here].
 
*'''Power Systems Computation Conference''': A biennial conference held in Europe, focused on computational power system methods. Website [https://pscc-central.epfl.ch/ here].
 
*'''Power Systems Computation Conference''': A biennial conference held in Europe, focused on computational power system methods. Website [https://pscc-central.epfl.ch/ here].
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*'''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 [https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=59 here].
 
*'''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 [https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=59 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 [https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165411 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 [https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165411 here].
  +
*'''IEEE Transactions on Sustainable Energy:''' This journal is aimed at disseminating results of research on the design, implementation, grid-integration, and control of sustainable energy technologies and systems that can be integrated into the power transmission and/or distribution grid. Journal website [https://www.ieee-pes.org/ieee-transactions-on-sustainable-energy here].
   
=== Major societies and organizations ===
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=== Major professional 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 [https://www.ieee-pes.org/ here].
 
*'''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 [https://www.ieee-pes.org/ here].
  +
*'''ACM Special Interest Group on Energy Systems and Informatics''': "ACM SIGEnergy is a professional forum for scientists, engineers, educators, and professionals for discussing energy systems and energy informatics. It brings together an inter-disciplinary group of computer scientists with diverse backgrounds [...] to discuss and address key challenges in future energy systems, and their impact on society." Website [https://energy.acm.org/ here].
   
== Libraries and tools ==
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== Libraries and Tools ==
 
 
*'''PowerTAC''': A power system simulation environment, available [https://powertac.org/ here].
 
*'''PowerTAC''': A power system simulation environment, available [https://powertac.org/ here].
 
*'''Energy Policy Simulator''': A tool to simulate the greenhouse gas emissions effects of various climate and energy policies, available [https://us.energypolicy.solutions/docs/ here].
 
*'''Energy Policy Simulator''': A tool to simulate the greenhouse gas emissions effects of various climate and energy policies, available [https://us.energypolicy.solutions/docs/ here].
 
*'''Optimal Power Flow (OPF) Sampler Package''': A Julia package to generate power grid data samples via optimal power flow methods, available [https://github.com/invenia/OPFSampler.jl/ here].
 
*'''Optimal Power Flow (OPF) Sampler Package''': A Julia package to generate power grid data samples via optimal power flow methods, available [https://github.com/invenia/OPFSampler.jl/ here].
  +
*'''Pyiso''': A Python client library for data from power grid balancing authorities in the United States, Canada, and Europe. Documentation [https://pyiso.readthedocs.io/en/latest here].
  +
*'''List of Resources by ACM SIG Energy'''': A list of models, libraries, software and datasets curated by the [https://energy.acm.org/ ACM Special Interest Group on Energy Systems and Informatics], available [https://energy.acm.org/resources/ here].
  +
* '''OPFLEarn.jl''': A [https://github.com/NREL/OPFLearn.jl Julia package] for creating datasets for machine learning approaches to solving AC optimal power flow (AC OPF).
   
 
== Data ==
 
== Data ==
  +
''See the subpages listed [[Electricity Systems#Machine Learning Application Areas|above]] for application-specific datasets.''
   
=== Electricity system data ===
+
=== United States ===
   
 
*'''Public Utility Data Liberation (PUDL) Project''': A project integrating a variety of United States federal electricity data sources, available [https://github.com/catalyst-cooperative/pudl here] via GitHub.
 
*'''Public Utility Data Liberation (PUDL) Project''': A project integrating a variety of United States federal electricity data sources, available [https://github.com/catalyst-cooperative/pudl 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 [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].
+
*'''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 [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]. (These data can also be accessed via the [https://pyiso.readthedocs.io/en/latest pyiso Python library].)
*'''European electricity market data from electricityMap''': Links to (and parsers for) European power system data, available [https://github.com/tmrowco/electricitymap-contrib#data-sources here] via GitHub.
+
*'''Pecan Street:''' Disaggregated energy and water data, available [https://www.pecanstreet.org/dataport/about/ here] (requires login)
 
*'''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://newftp.epa.gov/DMDnLoad/emissions/ FTP site]) provides hourly emissions and generation for many fossil fuel generators in the United States. Available [https://ampd.epa.gov/ampd/ here].
  +
* '''Utility Transition Hub™ Data''': Datasets on US-based utility finances, investments, operations, emissions and the utilities' alignment and commitments for the 1.5°C goals. Available [https://utilitytransitionhub.rmi.org/data-download/ here].
   
 
=== Europe ===
=== Renewables forecasting contest data ===
 
   
  +
*'''European electricity market data from electricityMap''': Links to (and parsers for) European power system data, available [https://github.com/tmrowco/electricitymap-contrib#data-sources here] via GitHub.
*'''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 [https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/IHBANG here].
 
  +
*'''European electricity market data via Pyiso''': The [https://pyiso.readthedocs.io/en/latest pyiso Python library] provides data from European power grid control areas.
*'''American Meteorological Society 2013-2014 Solar Energy Prediction Contest''': Contest data for producing daily forecasts of solar energy, available [https://www.kaggle.com/c/ams-2014-solar-energy-prediction-contest here].
 
   
=== Demand data ===
+
=== India ===
   
 
*'''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 [https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/1ZNLUY 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 [https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/1ZNLUY here].
   
  +
=== Other ===
*'''Pecan Street:''' Disaggregated energy and water data, available [https://www.pecanstreet.org/dataport/about/ here] (requires login)
 
   
 
*'''Project Sunroof by Google''': Detailed estimates of rooftop solar potential based on sunlight and roof space, available [https://www.google.com/get/sunroof/data-explorer/ here].
=== GHG emissions data ===
 
  +
*'''EDP Open Data''': EDP, a multinational utility company, publishes open data on solar and wind power assets (including clean datasets put together for previous competitions). Available [https://opendata.edp.com/pages/homepage/ here].
  +
*'''Ausgrid Data'''': Ausgrid, a DSO in NSW, Australia, [https://www.ausgrid.com.au/Industry/Our-Research/Data-to-share provides several datasets], among them 300 households, 225 substations and data about outages.
  +
*'''Transmission H-frame Dataset 1.0'''': The [https://www.kaggle.com/competitions/electric-transmission-imagery/overview Dataset] contains 12,158 drone collected images collected and shard by EPRI.
   
  +
== Relevant Groups and Organizations ==
*'''Copernicus global methane data''': Dataset on global methane emissions from 2002 onwards from the European Space Agency, available [https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-methane?tab=overview 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://newftp.epa.gov/DMDnLoad/emissions/ FTP site]) provides hourly emissions and generation for many fossil fuel generators in the United States. Available [https://ampd.epa.gov/ampd/ here].
 
*See also: '''[[Remote Sensing Datasets|Satellite imagery datasets]]'''
 
   
  +
* '''Electric Power Research Institute (EPRI)''': A non-profit organization that conducts research, development, and demonstration projects focused on electricity generation and delivery, and with over 1,000 member organizations around the world. Website [https://www.epri.com/ here].
=== Other ===
 
  +
*'''The Global Power System Transformation Consortium (G-PST)''': An international consortium of the leading electricity system operators "to identify common, cutting-edge research questions that can inform large- scale national research and development investments." Website [https://globalpst.org/ here] and inaugural research agenda [https://globalpst.org/resources/ here].
 
* '''[[Accelerated Materials Science 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.
 
* '''[[Remote Sensing Datasets|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 [https://www.google.com/get/sunroof/data-explorer/ here].
 
   
 
== References ==
 
== References ==

Latest revision as of 20:15, 20 September 2022

This page is about the intersection of electricity systems and machine learning (ML) in the context of climate change mitigation. For an overview of electricity systems as a whole, please see the Wikipedia page on this topic.

AI and machine learning are often discussed in the electricity sector in the context of smart grids,[3][4][5] which broadly refer to the concept of "intelligent" electric grids managed automatically in a data-driven manner. In particular, ML has been used to forecast electricity supply and demand, to improve power system optimization, and to improve system efficiency through applications such as predictive maintenance. In addition, ML has also been used to accelerate scientific discovery of clean energy technologies, and to gather electricity infrastructure data that may be useful for system planners and policymakers.[1]

A schematic of selected opportunities to reduce greenhouse emissions from electricity systems using machine learning. From "Tackling Climate Change with Machine Learning."[1]
The energy supply sector contributes about 35% of human-caused greenhouse gas emissions,[2] within which decarbonizing electricity supply plays an important role. In addition, many climate change strategies in sectors such as buildings, transportation, and industry rely on low-carbon electricity. To reduce greenhouse gas emissions from electricity systems, it will be necessary to both transition quickly to low-carbon electricity sources (e.g., solar, wind, and nuclear) and to reduce emissions from existing electricity system operations in the meantime.

Machine Learning Application AreasEdit

Enabling low-carbon electricityEdit

  • Supply, demand and price forecasting: The supply and demand of power must both be forecast ahead of time to inform electricity planning and scheduling. In more volatile energy systems, also forecasting of prices becomes relevant to utilize flexibility effectively. ML can help make these forecasts more accurate, improve temporal and spatial resolution, and quantify uncertainty.
  • Improving power system optimization: Scheduling algorithms on the power grid have trouble handling large quantities of solar, wind, and other time-varying electricity sources. ML can help improve electricity scheduling algorithms, control storage and flexible demand, and design real-time electricity prices that reduce CO2 emissions.
  • Improving system planning: Algorithms for planning new low-carbon energy infrastructure are often large and slow. ML can help speed up or provide proxies for these algorithms.
  • Informing maximum power point tracking: Maximum power point tracking refers to a variety of techniques that aim to maximize the power output of weather-dependent renewable energy generators, such as solar panels and wind turbines. ML can help model attributes of renewable energy systems or actively control these systems (e.g., by modulating wind turbine rotation speed) in order to improve power output.
  • Accelerated science for clean energy technologies: Designing new materials is important for many applications, including energy storage via batteries or solar/chemical fuels. ML can help suggest promising materials to try, thereby speeding up the materials discovery process.
  • Informing nuclear fusion research: Nuclear fusion has the potential to produce safe, carbon-free electricity, but such reactors continue to consume more energy than they produce. While basic science and engineering are still needed, ML can help inform nuclear fusion research in a variety of ways, e.g., by suggesting parameters for physical experiments or modeling the behavior of plasma inside reactors.

Reducing current-system impactsEdit

  • Methane leak detection: In addition to the unavoidable climate impacts of burning fossil fuels, natural gas extraction sites, pipelines, and compressor stations leak methane, a powerful greenhouse gas. ML can help detect and prevent these leaks.
  • Power grid emissions modeling: Reducing the emissions associated with electricity use requires understanding what the emissions on the electric grid actually are at any given moment. ML can help estimate and forecast emissions, and potentially model the uncertainty in these estimates.

General-purpose applicationsEdit

  • Energy infrastructure mapping: There are many cases in which decision-relevant information about energy infrastructure -- such as the locations and sizes of solar panels, or the location of power transmission and distribution infrastructure -- is not readily available. ML can help map some of this energy infrastructure using satellite imagery.
  • Predictive maintenance and fault detection: Quickly detecting power system faults can help reduce power system waste or improve the utilization of low-carbon energy resources. ML can help detect faults in real time from sensor data, or even forecast them ahead of time to enable preemptive maintenance.
  • State estimation: Many power distribution systems have few sensors, but are increasingly necessary to monitor due to the increase in rooftop solar power. ML can provide algorithms for understanding the state of distribution systems in "low-observability" scenarios where traditional state estimation algorithms may not suffice.
  • Greenhouse gas emissions mapping: While some electricity system operators release publicly-available data on the emissions associated with fossil fuel generators, this data is not available in many cases. ML can help map greenhouse gas emissions using a combination of remote sensing and on-the-ground data.
  • Non-Intrusive Load Monitoring (NILM): ML can be used to disaggregate the net-load measurements into their individual components. It finds application in disaggregating native load and PV generation measurements from the net-load measurements obtained at the feeder-lever or at the residential-level. Another aspect of energy disaggregation is non intrusive load monitoring, wherein the load measurement is disaggregated into different components representing the consumption of different household electric appliances.

Background ReadingsEdit

PrimersEdit

  • Chapter 7: "Energy Systems" in the IPCC Fifth Assessment Report (2014)[6]: 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)[7]: A primer on wholesale electricity, natural gas, and oil/petroleum markets, as well as energy-related financial markets, in the United States. Available here.

TextbooksEdit

  • "Electric Power Systems: A Conceptual Introduction" (2006)[8]: 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)[9]: A canonical reference on the engineering and economics of electric power systems.
  • "Fundamentals of Power System Economics" (2018)[10]: An introduction to electricity markets.

OtherEdit

  • Greening the Grid toolkit: A collection of readings, trainings, and other resources on power grids, renewable energy, energy storage, and electric vehicles. Available here.

Online Courses and Course MaterialsEdit

  • "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.

Conferences, Journals, and Professional OrganizationsEdit

Major conferencesEdit

  • ACM e-Energy: "The International Conference on Future Energy Systems (ACM e-Energy) aims to be the premier venue for researchers working in the broad areas of computing and communication for smart energy systems (including the smart grid), and in energy-efficient computing and communication systems". Website 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

Major journalsEdit

  • 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.
  • IEEE Transactions on Sustainable Energy: This journal is aimed at disseminating results of research on the design, implementation, grid-integration, and control of sustainable energy technologies and systems that can be integrated into the power transmission and/or distribution grid. Journal website here.

Major professional organizationsEdit

  • 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.
  • ACM Special Interest Group on Energy Systems and Informatics: "ACM SIGEnergy is a professional forum for scientists, engineers, educators, and professionals for discussing energy systems and energy informatics. It brings together an inter-disciplinary group of computer scientists with diverse backgrounds [...] to discuss and address key challenges in future energy systems, and their impact on society." Website here.

Libraries and ToolsEdit

  • 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.
  • Pyiso: A Python client library for data from power grid balancing authorities in the United States, Canada, and Europe. Documentation here.
  • List of Resources by ACM SIG Energy': A list of models, libraries, software and datasets curated by the ACM Special Interest Group on Energy Systems and Informatics, available here.
  • OPFLEarn.jl: A Julia package for creating datasets for machine learning approaches to solving AC optimal power flow (AC OPF).

DataEdit

See the subpages listed above for application-specific datasets.

United StatesEdit

  • 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. (These data can also be accessed via the pyiso Python library.)
  • Pecan Street: Disaggregated energy and water data, available here (requires login)
  • 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.
  • Utility Transition Hub™ Data: Datasets on US-based utility finances, investments, operations, emissions and the utilities' alignment and commitments for the 1.5°C goals. Available here.

EuropeEdit

  • European electricity market data from electricityMap: Links to (and parsers for) European power system data, available here via GitHub.
  • European electricity market data via Pyiso: The pyiso Python library provides data from European power grid control areas.

IndiaEdit

  • 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.

OtherEdit

  • Project Sunroof by Google: Detailed estimates of rooftop solar potential based on sunlight and roof space, available here.
  • EDP Open Data: EDP, a multinational utility company, publishes open data on solar and wind power assets (including clean datasets put together for previous competitions). Available here.
  • Ausgrid Data': Ausgrid, a DSO in NSW, Australia, provides several datasets, among them 300 households, 225 substations and data about outages.
  • Transmission H-frame Dataset 1.0': The Dataset contains 12,158 drone collected images collected and shard by EPRI.

Relevant Groups and OrganizationsEdit

  • Electric Power Research Institute (EPRI): A non-profit organization that conducts research, development, and demonstration projects focused on electricity generation and delivery, and with over 1,000 member organizations around the world. Website here.
  • The Global Power System Transformation Consortium (G-PST): An international consortium of the leading electricity system operators "to identify common, cutting-edge research questions that can inform large- scale national research and development investments." Website here and inaugural research agenda here.

ReferencesEdit

  1. 1.0 1.1 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].
  2. 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.
  3. Ramchurn, Sarvapali D.; Vytelingum, Perukrishnen; Rogers, Alex; Jennings, Nicholas R. (2012). "Putting the 'smarts' into the smart grid". Communications of the ACM. 55 (4): 86–97. doi:10.1145/2133806.2133825. ISSN 0001-0782.
  4. Machine Learning Techniques for Supporting Renewable Energy Generation and Integration: A Survey, Springer International Publishing
  5. "How artificial intelligence will affect the future of energy and climate". Brookings. 2019-01-10.
  6. 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.
  7. Federal Energy Regulatory Commission. "Energy Primer: A Handbook of Energy Market Basics." Federal Energy Regulatory Commission: Washington, DC, USA (2020).
  8. Von Meier, Alexandra. "Electric power systems." A Conceptual Introduction (2006).
  9. Wood, A. J., Wollenberg, B. F., & Sheblé, G. B. (2013). Power generation, operation, and control. John Wiley & Sons.
  10. Kirschen, D. S., & Strbac, G. (2018). Fundamentals of power system economics. John Wiley & Sons.