Electricity Systems

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.

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 and online courses

Primers on electricity systems

• IPCC chapter on energy systems
• Energy Primer by US Federal Energy Regulatory Commission

Textbooks on electricity systems

• Von Meier, A. Electric Power Systems: A Conceptual Introduction. (2006)
• Wood, A.J. et al., Power Generation, Operation, and Control. (2013)
• Kirschen,D D.S., and Strbac, G. Fundamentals of Power System Economics, Volume 1 (2004).

Online courses

• Coursera Electric Power Systems online course

Primers on specific sub-topics

Nuclear fusion

• Nuclear fusion collection in Nature Physics

Community

Major conferences

• IEEE Power & Energy Society General Meeting
• Power Systems Computation Conference
• IEEE Power & Energy Society’s PowerTech
• Also see additional conferences by IEEE and the IEEE Power & Energy Society

Major journals

• IEEE Transactions on Power Systems
• IEEE Transactions on Smart Grid

Societies and organizations

• Institute of Electrical and Electronics Engineers (IEEE), particularly the IEEE Power & Energy Society

Libraries and tools

• Energy Policy Simulator from Energy Innovation LLC
• Optimal Power Flow (OPF) Sampler Package
• Greening the Grid toolkit
• PowerTAC testing platform

Data

General electricity market data

• Public Utility Data Liberation (PUDL) Project
• US independent system operators/regional transmission organizations (ISOs/RTOs): CAISO, ERCOT, ISO-NE, MISO, NYISO, PJM, and SPP

Supply data

• Global Energy Forecasting Competition
• SubseasonalRodeo
• American Meteorological Society 2013-2014 Solar Energy Prediction Contest
• Google Project Sunroof (detailed estimates of solar potential based on sunlight and roof space) [TODO not sure if this belongs here]

Demand data

• Rural Electricity Demand in India (REDI) Dataset

GHG emissions data

• Global methane data
• US Environmental Protection Agency's Continuous Emissions Monitoring data (tool or FTP site)
• ElectricityMap data sources

Accelerated science for materials

• The Materials Project
• Inorganic Crystal Structure Database
• SciFinder (paid)
• UCI Machine Learning Repository datasets, e.g. “Concrete Compressive Strength”

Other

• Also see listings on satellite imagery [TODO]