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Aalto University Researchers Add New Study Findings to Research in Energy (A Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage)

  • Sep 24, 2021 1:12 pm GMT
  • 214 views
Source: 
Energy Daily News

2021 SEP 23 (NewsRx) -- By a News Reporter-Staff News Editor at Energy Daily News -- Fresh data on energy are presented in a new report. According to news reporting from Espoo, Finland, by NewsRx journalists, research stated, “Battery storages are an essential element of the emerging smart grid. Compared to other distributed intelligent energy resources, batteries have the advantage of being able to rapidly react to events such as renewable generation fluctuations or grid disturbances.”

Financial supporters for this research include Business Finland.

The news reporters obtained a quote from the research from Aalto University: “There is a lack of research on ways to profitably exploit this ability. Any solution needs to consider rapid electrical phenomena as well as the much slower dynamics of relevant electricity markets. Reinforcement learning is a branch of artificial intelligence that has shown promise in optimizing complex problems involving uncertainty. This article applies reinforcement learning to the problem of trading batteries. The problem involves two timescales, both of which are important for profitability. Firstly, trading the battery capacity must occur on the timescale of the chosen electricity markets. Secondly, the real-time operation of the battery must ensure that no financial penalties are incurred from failing to meet the technical specification. The trading-related decisions must be done under uncertainties, such as unknown future market prices and unpredictable power grid disturbances.”

According to the news editors, the research concluded: “In this article, a simulation model of a battery system is proposed as the environment to train a reinforcement learning agent to make such decisions. The system is demonstrated with an application of the battery to Finnish primary frequency reserve markets.”

For more information on this research see: A Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage. Energies, 2021,14(5587):5587. (Energies - http://www.mdpi.com/journal/energies). The publisher for Energies is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/en14175587.

Our news journalists report that more information may be obtained by contacting Harri Aaltonen, Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland. Additional authors for this research include Seppo Sierla, Rakshith Subramanya, Valeriy Vyatkin.

 

(Our reports deliver fact-based news of research and discoveries from around the world.)

 

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