Researchers from La Trobe University Report Details of New Studies and Findings in the Area of Artificial Intelligence (An Artificial Intelligence Framework for Bidding Optimization With Uncertainty In Multiple Frequency Reserve Markets)

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Information Technology Daily
2021 JAN 27 (NewsRx) -- By a News Reporter-Staff News Editor at Information Technology Daily -- Investigators publish new report on Artificial Intelligence. According to news originating from Bundoora, Australia, by NewsRx correspondents, research stated, “The global ambitions of a carbon-neutral society necessitate a stable and robust smart grid that capitalizes on frequency reserves of renewable energy. Frequency reserves are resources that adjust power production or consumption in real time to react to a power grid frequency deviation.”
Financial support for this research came from La Trobe University Net Zero Project and Business Finland.
Our news journalists obtained a quote from the research from La Trobe University, “Revenue generation motivates the availability of these resources for managing such deviations. However, limited research has been conducted on data-driven decisions and optimal bidding strategies for trading such capacities in multiple frequency reserves markets. We address this limitation by making the following research contributions. Firstly, a generalized model is designed based on an extensive study of critical characteristics of global frequency reserves markets. Secondly, three bidding strategies are proposed, based on this market model, to capitalize on price peaks in multi-stage markets. Two strategies are proposed for non-reschedulable loads, in which case the bidding strategy aims to select the market with the highest anticipated price, and the third bidding strategy focuses on rescheduling loads to hours on which highest reserve market prices are anticipated. The third research contribution is an Artificial Intelligence (AI) based bidding optimization framework that implements these three strategies, with novel uncertainty metrics that supplement data-driven price prediction. Finally, the framework is evaluated empirically using a case study of multiple frequency reserves markets in Finland.”

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