Summary of Distributional Reinforcement Learning-based Energy Arbitrage Strategies in Imbalance Settlement Mechanism, by Seyed Soroush Karimi Madahi et al.
Distributional Reinforcement Learning-based Energy Arbitrage Strategies in Imbalance Settlement Mechanism
by Seyed Soroush Karimi Madahi, Bert Claessens, Chris Develder
First submitted to arxiv on: 23 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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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 proposed battery control framework uses distributional reinforcement learning to perform energy arbitrage in the imbalance settlement mechanism. By taking a risk-sensitive perspective, the framework optimizes a weighted sum of arbitrage profit and risk measure while constraining daily battery cycles. Two state-of-the-art RL methods are compared: deep Q learning and soft actor-critic. Results show that distributional soft actor-critic outperforms other methods, and a fully risk-averse agent learns to hedge against unknown imbalance prices by charging/discharging the battery only when certain about the price. This framework can help balance responsible parties (BRPs) adjust their risk preferences and make informed decisions in energy arbitrage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a new way for companies to buy and sell extra electricity from renewable sources. They used special computer learning tools to figure out how to make smart choices about when to store or use this extra energy. The goal is to make sure these companies don’t take too many risks with their decisions. The scientists tested their approach using real data from 2022 and found that it works better than some other methods they tried. |
Keywords
* Artificial intelligence * Reinforcement learning
