The global ambitions of a carbon-neutral society necessitate a stable and robust smart grid that capitalises on frequency reserves of renewable energy . Revenue generationmotivates the availability of these resources for managing such deviations . Three bidding strategies are proposed, based on this market model, tocapitalise on price peaks in multi-stage markets . The third research contribution is an Artificial Intelligence(AI) based bidding optimization framework that implements these threestrategies, with novel uncertainty metrics that supplement data-driven priceprediction . The results from this evaluation confirm the effectiveness of the proposed bidding strategies and theAI-based bidding optimize framework in terms of cumulative revenuegeneration, leading to an increased availability of frequency reserves, according to the authors. The results were evaluated empirically using a case study of multiple frequency reserves markets in Finland. The Results from thisevaluation confirm the outcome of the framework is to be based on the results of the proposal and the AI-based bid optimization framework, including the results from a case case study in which the framework was evaluated using an example study of a case-study of the

Author(s) : Thimal Kempitiyaa, Seppo Sierla, Daswin De Silvaa, Matti Yli-Ojanpera, Damminda Alahakoona, Valeriy Vyatkin

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Keywords : framework - based - bidding - results - study -

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