层次的深度强化学习用于自适应的经济调度
Mengshi Li1, Dongyan Yang1, Yuhan Xu1
1School of Electric Power Engineering, South China University of Technology, 510000, Guangzhou, China.
Heliyon
|August 8, 2024
概括
本研究介绍了一种层次的深度强化学习 (HDRL) 方法,以优化电力系统调度决策. HDRL有效地管理可再生能源的不确定性,提高决策速度和效率.
科学领域:
- 电力系统工程 电力系统工程
- 人工智能的人工智能
- 整合可再生能源的整合
背景情况:
- 由于波动性,对大规模间歇性可再生能源 (风能,光伏) 的电力系统不确定性建模具有挑战性.
- 深度强化学习 (DRL) 提供了适应性策略,但在这种系统中面临着稀缺的回报和高维度问题.
研究的目的:
- 开发一种高效的方法,用于在不确定性条件下的电力系统经济调度.
- 解决大规模,不确定的电力系统中标准DRL的局限性.
主要方法:
- 设计了一个分层的深度强化学习 (HDRL) 方案.
- 高强度回转率方法将问题分解为全球阶段 (RL代理) 和局部阶段 (启发式算法).
主要成果:
- 拟议的HDRL方案在解决电力系统经济调度问题方面表现出效率.
- HDRL成功地适应了系统的不确定性和波动性,提高了在线决策速度.
结论:
- HDRL是优化电力系统运营的有效策略,具有显著的可再生能源整合.
- 该方法提高了复杂,不确定的电力系统环境中的适应性和决策速度.
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