Power System Optimization: Difference between revisions
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''This page is about the applications of machine learning (ML) in the context of power system simulation and optimization. For an overview of power system optimization more generally, please see the [https://en.wikipedia.org/wiki/Power_system_simulation Wikipedia page] on this topic.'' |
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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. |
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. |
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== Background Readings == |
== Background Readings == |
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== Conferences, Journals, and Professional Organizations == |
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== Community == |
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== Libraries and Tools == |
== Libraries and Tools == |
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== Future Directions == |
== Future Directions == |
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== Relevant Groups and Organizations == |
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== References == |
== References == |
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Latest revision as of 14:16, 26 August 2021
🌎 This article is a stub, and is currently under construction. You can help by adding to it!
This page is about the applications of machine learning (ML) in the context of power system simulation and optimization. For an overview of power system optimization more generally, please see the Wikipedia page on this topic.
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.