学习最佳功率流:环境设计很重要

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-08-13 DOI:10.1016/j.egyai.2024.100410
Thomas Wolgast, Astrid Nieße
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引用次数: 0

摘要

为了解决最优功率流(OPF)问题,强化学习(RL)成为一种很有前途的新方法。然而,RL-OPF 文献在将 OPF 问题作为 RL 环境的确切表述方面存在严重分歧。在这项工作中,我们收集并实施了文献中关于训练数据、观察空间、情节定义和奖励函数选择的各种环境设计决策。在实验分析中,我们展示了这些环境设计选项对 RL-OPF 训练性能的重大影响。此外,我们还就这些设计决策的选择提出了一些初步建议。创建的环境框架是完全开源的,可以作为 RL-OPF 领域未来研究的基准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Learning the optimal power flow: Environment design matters

To solve the optimal power flow (OPF) problem, reinforcement learning (RL) emerges as a promising new approach. However, the RL-OPF literature is strongly divided regarding the exact formulation of the OPF problem as an RL environment. In this work, we collect and implement diverse environment design decisions from the literature regarding training data, observation space, episode definition, and reward function choice. In an experimental analysis, we show the significant impact of these environment design options on RL-OPF training performance. Further, we derive some first recommendations regarding the choice of these design decisions. The created environment framework is fully open-source and can serve as a benchmark for future research in the RL-OPF field.

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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
期刊最新文献
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