虚拟发电厂的AI增强的多时间尺度优化策略:推进损失预测和动态需求响应集成
Guojun Xu1, Guangjie Yang1, Jie Bao1
1State Grid Handan Power Supply Company, Handan, China.
PloS one
|January 23, 2026
概括
本研究介绍了一个AI增强的框架,通过整合负载预测,调度和需求响应来优化虚拟发电厂 (VPP). 这种新的方法提高了VPP的弹性,并降低了可再生能源电网的运营成本.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 能源系统 能源系统
背景情况:
- 由于不确定性和间歇性,可再生能源 (RES) 的整合在电网稳定性方面带来了挑战.
- 现有的虚拟发电厂 (VPP) 的人工智能 (AI) 解决方案往往缺乏关键组件的整体集成,如负载预测,系统调度和需求响应.
- 这种碎片化限制了对现代电网固有的深度不确定性的有效管理.
研究的目的:
- 开发一种新的AI增强的多时间尺度优化策略,用于协同的VPP运行.
- 创建一个综合框架,解决现有的VPP优化方法中松散合组件的局限性.
- 在面对可再生能源整合挑战时,提高VPP运营的弹性和适应性.
主要方法:
- 利用注意力增强的双向长短期记忆 (BiLSTM) 模型进行高准确度的时空负载预测,结合空间意识输入.
- 实施了模型预测控制 (MPC) 战略,以实现强大的前一天和当天调度,利用增强的负载预测.
- 集成了一个动态需求响应 (DDR) 机制,与实时MPC输出直接合,以调动响应式负载灵活性.
主要成果:
- 与传统模型相比,拟议的综合战略在预测准确度方面取得了显著的改进.
- 模拟显示,使用新框架的VPP的运营成本下降.
- 人工智能增强的方法导致了更具弹性和适应性的VPP运营范式.
结论:
- 先进的人工智能技术的协同整合,包括BiLSTM和MPC与DDR,有效地解决了VPP优化挑战.
- 开发的框架为管理电网不确定性提供了更强大,更积极的方法.
- 这项研究为VPP运营建立了新的范式,提高了电网稳定性和经济效率.
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