深度强化学习用于在线重新配置主动分销网络
IEEE transactions on neural networks and learning systems
|October 23, 2025
概括
本研究介绍了一种在线-离线的深度强化学习 (DRL) 框架,用于主动配电网络重新配置 (ADNR),以管理可再生能源的波动. 这种新的方法通过提高DRL在实时操作中的性能来提高电网稳定性和可再生能源的整合.
科学领域:
- 电气工程 电气工程
- 电力系统 电力系统
- 人工智能的人工智能
背景情况:
- 在主动配电网络 (ADN) 中可再生能源 (RE) 的高透率引入了不确定性和可变性,影响了电网稳定性和效率.
- 传统的深度强化学习 (DRL) 方法用于DNA重新配置 (ADNR) 通常依赖于历史数据,导致潜在的不匹配和未见的场景的挑战.
研究的目的:
- 开发一个先进的在线-离线DRL框架,以实现有效的在线DNAR.
- 为了减轻RE不确定性和DNA的变化所带来的挑战.
- 改进DRL算法对ADNR的一般化和实时性能.
主要方法:
- 制定了DNAR作为一个国家驱动的马尔科夫决策过程 (MDP),结合了DNA的操作特征.
- 提出了一个国家驱动的近距离政策优化 (SD-PPO) 算法,以增强DRL泛化.
- 引入了针对个性化的在线培训的优化行动近接政策优化 (OA-PPO) 算法.
主要成果:
- 拟议的在线-离线DRL框架有效地减少IEEE ADN系统中的功耗损失.
- 通过改进的ADNR战略,可以实现更好的可再生能源适应.
- 与传统的DRL和ADNR算法相比,表现出优越的计算性能.
结论:
- 新的在线-离线DRL框架为管理ADN中的RE波动提供了一个强大的解决方案.
- SD-PPO 和 OA-PPO 算法显著提高了实时 ADNR 的 DRL 性能.
- 该方法提供了一种计算高效和有效的方法来增强ADN的操作和控制.
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