Summary of Optimizing Quantile-based Trading Strategies in Electricity Arbitrage, by Ciaran O’connor et al.
Optimizing Quantile-based Trading Strategies in Electricity Arbitrage
by Ciaran O’Connor, Joseph Collins, Steven Prestwich, Andrea Visentin
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an optimization framework for day-ahead and balancing market trading in electricity markets, focusing on the integration of renewable resources and energy storage devices. The authors employ quantile-based forecasts and three trading approaches with practical constraints to improve forecast assessment, increase trading frequency, and utilize flexible timestamp orders. The results show that simultaneous participation in both day-ahead and balancing markets can be profitable, especially with larger battery storage systems. The study also models four commercial battery storage systems and evaluates their economic viability through a scenario analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps solve the problem of matching real-time energy supply and demand while reducing waste from curtailments. It shows how using storage devices and clever trading strategies can make the grid more reliable, efficient, and profitable for participants. The authors find that trading in both day-ahead and balancing markets simultaneously can be a good way to make money, especially with bigger batteries. They also test four real-world battery storage systems and see when they would break even. |
Keywords
* Artificial intelligence * Optimization
