An Artificial Intelligence framework for bidding optimization with uncertainty in multiple frequency reserve markets. (15th December 2020)

• Record Type: Journal Article
• Authors: Kempitiya, Thimal; Sierla, Seppo; De Silva, Daswin; Yli-Ojanperä, Matti; Alahakoon, Damminda; Vyatkin, Valeriy
• Is Part Of: Applied energy. Volume 280(2020)
• Publication Date: 2020-12-15
• Subjects: Frequency reserves -- Smart grid -- Artificial Intelligence -- Ancillary markets -- Bidding strategies -- Reschedulable loads -- Uncertainty metrics -- MC-Dropout -- Artificial neural networks -- Bayesian neural networks -- Epistemic uncertainty; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2020.115918
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: 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. 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. The results from this evaluation confirm the effectiveness of the proposed bidding strategies and the AI-based bidding optimization framework in terms of cumulative revenue generation, leading to an increased availability of frequency reserves. Highlights: A generalized model of frequency reserves markets with global significance. The formulation of three bidding strategies informed by this generalized model. A novel Artificial Intelligence based bidding strategy optimization framework. Empirical evaluation of real-world challenges of the Finnish frequency reserves.