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

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  • Kempitiya, Thimal
  • Sierla, Seppo
  • De Silva, Daswin
  • Yli-Ojanperä, Matti
  • Alahakoon, Damminda
  • Vyatkin, Valeriy

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.

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  • Kempitiya, Thimal & Sierla, Seppo & De Silva, Daswin & Yli-Ojanperä, Matti & Alahakoon, Damminda & Vyatkin, Valeriy, 2020. "An Artificial Intelligence framework for bidding optimization with uncertainty in multiple frequency reserve markets," Applied Energy, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:appene:v:280:y:2020:i:c:s0306261920313775
    DOI: 10.1016/j.apenergy.2020.115918
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    References listed on IDEAS

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    Cited by:

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    5. Adeel Luqman & Qingyu Zhang & Shalini Talwar & Meena Bhatia & Amandeep Dhir, 2024. "Artificial intelligence and corporate carbon neutrality: A qualitative exploration," Business Strategy and the Environment, Wiley Blackwell, vol. 33(5), pages 3986-4003, July.
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    7. Harri Aaltonen & Seppo Sierla & Ville Kyrki & Mahdi Pourakbari-Kasmaei & Valeriy Vyatkin, 2022. "Bidding a Battery on Electricity Markets and Minimizing Battery Aging Costs: A Reinforcement Learning Approach," Energies, MDPI, vol. 15(14), pages 1-19, July.
    8. Einolander, Johannes & Lahdelma, Risto, 2022. "Explicit demand response potential in electric vehicle charging networks: Event-based simulation based on the multivariate copula procedure," Energy, Elsevier, vol. 256(C).
    9. Rakshith Subramanya & Matti Yli-Ojanperä & Seppo Sierla & Taneli Hölttä & Jori Valtakari & Valeriy Vyatkin, 2021. "A Virtual Power Plant Solution for Aggregating Photovoltaic Systems and Other Distributed Energy Resources for Northern European Primary Frequency Reserves," Energies, MDPI, vol. 14(5), pages 1-23, February.
    10. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).

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