Editing Electricity Systems

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=== 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.
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*'''[[Electricity Supply Forecasting|Supply]] and [[Energy Demand Forecasting|demand]] forecasting''': The supply and demand of power must both be forecast ahead of time to inform electricity planning and scheduling. ML can help make these forecasts more accurate, improve temporal and spatial resolution, and quantify uncertainty.
 
*'''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|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 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.
 
*'''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.
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*[[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.
 
*[[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.
 
*'''[[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 ==
 
== Background Readings ==
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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 professional organizations ===
 
=== Major professional organizations ===
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== Libraries and Tools ==
 
== Libraries and Tools ==
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*'''PowerTAC''': A power system simulation environment, available [https://powertac.org/ here].
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*'''[https://powertac.org/ PowerTAC]''': A power system simulation environment.
*'''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].
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*'''[https://us.energypolicy.solutions/docs/ 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, available [https://github.com/invenia/OPFSampler.jl/ here].
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*'''[https://github.com/invenia/OPFSampler.jl/ Optimal Power Flow (OPF) Sampler Package]''': A Julia package to generate power grid data samples via optimal power flow methods.
*'''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].
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*'''[https://pyiso.readthedocs.io/en/latest Pyiso]''': A Python client library for data from power grid balancing authorities in the United States, Canada, and Europe.
*'''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 ==
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