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A Semantic Risk-Aware Optimization Framework for Virtual Power Plant Dispatch Using Large Language Models
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DOI：10.3390/en19122820.png)
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摘要
En 中文
传统虚拟电厂（VPP）调度主要基于数值时间序列预测，可能对极端市场事件和以非结构化文本表达的预警信号反应缓慢。本文提出了一种检索增强生成虚拟电厂（RAG-VPP）框架，将ISO风格的市场通知、紧急警报、天气预警和监管更新整合到风险感知调度优化中。该框架包括语义感知引擎、混合数值预测引擎和人在回路调度网关。非结构化市场文本被转换为有界语义不确定性指标，并通过语义条件场景生成、语义暴露惩罚、动态语义备用边际和语义需求响应预激活约束嵌入到随机MIQP调度中。该框架通过为期7天的ERCOT风格受控压力测试进行评估，测试中使用了合成的ISO类EEA1/EEA2警报和5分钟市场分辨率。结果表明，RAG-VPP实现了28.58万美元的总利润，比确定性基准提高了32%。它还提高了85.2万美元的CVaR，在受控压力测试场景中显示了四小时的语义领先优势，实现了0.92的语义一致性，并保持了零备用边际违规小时。这些结果表明，基于语言信息的调度在极端事件条件下提高VPP韧性方面具有潜力。
Keyword:
virtual power plant (VPP)
semantic risk intelligence
retrieval-augmented generation (RAG)
demand response (DR)
stochastic optimization
期刊
IF:
3.2
论文数:
1.6W
被引数:
14.2W
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