Summary of Time-varying Constraint-aware Reinforcement Learning For Energy Storage Control, by Jaeik Jeong et al.
Time-Varying Constraint-Aware Reinforcement Learning for Energy Storage Control
by Jaeik Jeong, Tai-Yeon Ku, Wan-Ki Park
First submitted to arxiv on: 17 May 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 In this paper, researchers propose a novel approach to optimizing the control of energy storage devices using reinforcement learning. The authors highlight the importance of determining the optimal charging and discharging levels for each time period to ensure a stable and sustainable power supply. They argue that traditional optimization methods are limited by their inability to adapt to dynamic environments, whereas reinforcement learning can effectively handle complex scenarios. However, the continuous nature of energy storage levels poses challenges for discrete reinforcement learning, which led the authors to develop a novel continuous reinforcement learning approach that takes into account time-varying feasible charge-discharge ranges based on state of charge (SoC) variability. The proposed method incorporates an additional objective function to learn the feasible action range for each time period, promoting the utilization of energy storage and preventing suboptimal states such as continuous full charging or discharging. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists develop a new way to control energy storage devices using reinforcement learning. They want to find the best way to charge and discharge energy storage devices like batteries to help fight climate change by providing a stable power supply. The authors say that traditional methods are not good enough because they can’t adapt to changing situations. They propose a new approach called continuous reinforcement learning, which considers how much energy is available at any given time. This method helps prevent energy storage devices from getting stuck in bad states like always being fully charged or discharged. |
Keywords
» Artificial intelligence » Objective function » Optimization » Reinforcement learning
