An Artificial Intelligence framework for bidding optimization with uncertainty in multiple frequency reserve markets

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2020-12-15
Major/Subject
Mcode
Degree programme
Language
en
Pages
14
Series
Applied Energy, Volume 280
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.
Description
Keywords
Ancillary markets, Artificial Intelligence, Artificial neural networks, Bayesian neural networks, Bidding strategies, Epistemic uncertainty, Frequency reserves, MC-Dropout, Reschedulable loads, Smart grid, Uncertainty metrics
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Citation
Kempitiya, T, Sierla, S, De Silva, D, Yli-Ojanperä, M, Alahakoon, D & Vyatkin, V 2020, ' An Artificial Intelligence framework for bidding optimization with uncertainty in multiple frequency reserve markets ', Applied Energy, vol. 280, 115918 . https://doi.org/10.1016/j.apenergy.2020.115918