能源分享人工智能:通过先进的深度学习转变P2P能源交易
Nouf Atiahallah Alghanmi1, Hanadi Alkhudhayr2
1Faculty of Computing and Information Technology, Department of Information Technology, King Abdulaziz University, Rabigh, 21911, Saudi Arabia.
Heliyon
|September 19, 2024
概括
能源分享人工智能通过机器学习实现点对点 (P2P) 能源交易,将家庭与太阳能阵列,存储和电动汽车连接起来. 该系统优化了能源共享,降低了成本,并改善了消费者和前期消费者的可持续性.
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
- 可持续能源 可持续能源
背景情况:
- 同行对同行 (P2P) 能源交易正在成为能源需求管理的变革性方法.
- 传统的能源系统在整合分布式能源和优化能源流动方面面临着挑战.
研究的目的:
- 引入EnergyShare AI,这是一个新的P2P能源交换系统,利用机器学习.
- 通过太阳能电池阵列,能源存储系统 (ESS) 和电动汽车 (EV) 来证明系统在管理消费者和前期消费者之间的能源共享方面的能力.
主要方法:
- 在EnergyShareAI框架内开发和应用深度强化学习 (DRL) 算法.
- 优化双向能源传输,考虑到ESS和光伏 (PV) 系统的作用.
主要成果:
- 能源共享人工智能显著提高能源管理效率并降低运营成本.
- 该系统有助于大幅节省成本,并通过高效的P2P能源交换促进改善可持续性.
- 在不同家庭形状和发育阶段之间观察到能量转移的增加.
结论:
- 能源共享人工智能为P2P能源交易提供了与传统的线性整数编程模型的优越替代方案.
- 对于高效和成本效益的P2P能源交易,DRL,ESS和EV的整合至关重要.
- 成功的P2P能源交易有助于更可持续和经济可行的能源格局.
相关概念视频
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