Optimising quantile-based trading strategies in electricity arbitrage

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2025-01-27 DOI:10.1016/j.egyai.2025.100476
Ciaran O’Connor , Joseph Collins , Steven Prestwich , Andrea Visentin
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Abstract

Efficiently integrating renewable resources into electricity markets is vital for addressing the challenges of matching real-time supply and demand while mitigating revenue losses caused by curtailments. To address this challenge effectively, the incorporation of storage devices can enhance the reliability and efficiency of the grid, improving market liquidity and reducing price volatility. In short-term electricity markets, participants face numerous options, each presenting unique challenges and opportunities, with trading strategies fundamental towards maximising profits. This study explores the optimisation of day-ahead and balancing market trading in the Irish electricity market from 2019 to 2022, leveraging quantile-based forecasts. Employing three trading approaches with practical constraints, our research evaluates trading strategies, increases trading frequency, and employs flexible timestamp orders. Our findings underscore the profit potential of simultaneous participation in both day-ahead and balancing markets, especially with larger battery storage systems; despite increased costs and narrower profit margins associated with higher-volume trading, with the implementation of dynamic dual-market strategies playing a significant role in maximising profits and addressing market challenges. Finally, we evaluate the economic viability of four commercial battery storage systems through scenario analysis, showing that larger batteries achieve shorter returns on investment.

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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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