Summary of Reinforcement Learning Based Bidding Framework with High-dimensional Bids in Power Markets, by Jinyu Liu et al.
Reinforcement Learning Based Bidding Framework with High-dimensional Bids in Power Markets
by Jinyu Liu, Hongye Guo, Yun Li, Qinghu Tang, Fuquan Huang, Tunan Chen, Haiwang Zhong, Qixin Chen
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 A framework for Reinforcement Learning (RL)-based power market bidding is proposed to fully utilize High Dimensional Bids (HDBs) in the form of N price-power pairs. The approach employs Neural Network Supply Functions (NNSFs) to generate HDBs and embeds them into a Markov Decision Process (MDP) for compatibility with existing RL methods. Experiments on Energy Storage Systems (ESSs) in the PJM Real-Time (RT) power market show that the proposed method improves bidding flexibility, leading to increased profits for state-of-the-art RL bidding methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help computers decide how much electricity to buy and sell is being developed. Right now, computers use simple ways to make these decisions, but this can limit their ability to adapt to changing circumstances. The new method uses special kinds of computer networks to create a more complex system that can better handle uncertainties like the rise of renewable energy sources. This could lead to more profits for companies that manage energy supplies. |
Keywords
» Artificial intelligence » Neural network » Reinforcement learning
