使用机器学习方法预测虚拟发电厂的发电和电力市场的能源套利经济学
Tirunagaru V Sarathkumar1, Arup Kumar Goswami2, Baseem Khan3
1School of Engineering, Malla Reddy University, Hyderabad, India. sarathkumartirunagaru@gmail.com.
Scientific reports
|January 30, 2025
概括
虚拟发电厂 (VPP) 将可再生能源纳入智能电网. 这项研究使用了一种新的AOLSTM方法和能量储存,以最大限度地提高VPP在前一天市场的收入.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 机器学习应用 机器学习应用
背景情况:
- 间歇性可再生能源的日益集成需要先进的电网管理解决方案.
- 虚拟发电厂 (VPP) 对于将可变发电纳入智能电网至关重要.
- 改进的预测和收入优化对于电网稳定性,灵活性和经济可行性至关重要.
研究的目的:
- 提出一项创新的双重战略,以最大限度地提高VPP在前一天电力市场的收入.
- 引入一种新的机器学习技术,用于预测VPP的产生.
- 整合能源储存和优化方法,以优化收入估计.
主要方法:
- 预测VPP发电单元 (太阳能光伏,风能,热电) 使用一个新的Adam Optimizer长短期记忆 (AOLSTM) 机器学习技术.
- 通过整合储能系统和蒙特卡洛优化来估计收入边界.
- 对AOLSTM与传统预测方法 (持久性,梯度提升,随机森林) 的比较分析.
主要成果:
- 与传统方法相比,拟议的AOLSTM方法在预测VPP产生的过程中显示出最小的误差指标.
- 综合方法将VPP,储能和AOLSTM协同使用,以提高收入.
- 这种双重战略有效地最大限度地提高了前一天电力市场的收入.
结论:
- 新的AOLSTM方法为VPP发电机组的预测准确性提供了显著的改进.
- 将储能与先进预测相结合,提供了一种可靠的方法来优化VPP收入.
- 这项研究通过先进的VPP管理,为更稳定,更灵活,更有利可图的智能电网运营做出了贡献.
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