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集成预测和深度强化学习用于基于价格的PV-BESS自编程:智利的公用事业规模证据
Juan Pérez1, Gustavo Lobos1, Milena Bonacic1
1Facultad de Ingeniería y Ciencias Aplicadas, Universidad de Los Andes, Santiago, Chile.
PloS one
|January 9, 2026
概括
本研究验证了使用现实数据对电池储能系统 (BESS) 和光伏 (PV) 电站控制的深度增强学习 (DRL). DRL代理商显著提高了利,并证明了适应性运营,证明了其实际可行性.
科学领域:
- 能源系统工程 能源系统工程
- 人工智能的人工智能
- 整合可再生能源的整合.
背景情况:
- 深度增强学习 (DRL) 显示出优化与光伏 (PV) 电厂协调的电池能量存储系统 (BESS) 的前景.
- 大多数现有研究依赖于模拟,缺乏与真实世界的操作数据的验证.
研究的目的:
- 通过使用实际运营数据,实证公用事业规模PV-BESS资产的综合预测和控制框架.
- 为了弥合模拟的DRL性能和能源存储优化中的实际应用之间的差距.
主要方法:
- 开发了一个集成框架,将一个序列对序列 (Seq2Seq) LSTM预测器与DRL代理 (PPO,SAC) 结合起来.
- 训练有素的DRL代理人在每个站点1000个概率场景中使用两年的运营数据 (2022-2023).
- 与甲骨文,预测然后优化,模型预测控制 (MPC) 和模拟政策对比14天的DRL政策.
主要成果:
- Seq2Seq预测器提高了价格预测准确度 (34.5%的RMSE减少与SARIMAX相比).
- DRL代理商的表现始终优于预测-然后-优化基线,平均14天的利接近5万美元.
- DRL政策表现出强大的,适应性的反周期性行为,没有过度的电池循环.
结论:
- 该研究为数据驱动的BESS控制在现实环境中使用DRL提供了经验验验证.
- 开发的框架为实际的PV-BESS优化提供了可重复的蓝图.
- 基于DRL的控制证明了储能系统的显著经济效益和实际可行性.
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