Jump to content

Electricity Systems: Difference between revisions

Add descriptions for sub-problems
(Add links to new pages)
(Add descriptions for sub-problems)
=== Enabling low-carbon electricity ===
*[[Electricity Supply and Demand Forecasting|'''Supply and 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]]
*'''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.
*'''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.
*Informing [[Maximum Power Point Tracking|maximum power point tracking]]
*'''[[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.
*'''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.
*Informing [[Nuclear Fusion|nuclear fusion]] research
=== 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|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.
*[[Power Grid Emissions Modeling|Power grid emissions modeling]]
=== General-purpose applications ===
*[[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.
*[[Energy Infrastructure Mapping|Energy infrastructure mapping]]
*[[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.
*[[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]]
== Background Readings ==
Cookies help us deliver our services. By using our services, you agree to our use of cookies.