A Semantic Risk-Aware Optimization Framework for Virtual Power Plant Dispatch Using Large Language Models

초록

Traditional Virtual Power Plant (VPP) dispatch is mainly based on numerical time-series forecasting, which may respond slowly to extreme market events and early-warning signals expressed in unstructured text. This paper proposes a Retrieval-Augmented Generation Virtual Power Plant (RAG-VPP) framework that integrates ISO-style market notices, emergency alerts, weather warnings, and regulatory updates into risk-aware dispatch optimization. The framework includes a semantic perception engine, a hybrid numerical forecasting engine, and a human-in-the-loop dispatch gateway. Unstructured market text is converted into a bounded semantic uncertainty metric and embedded into stochastic MIQP dispatch through semantic-conditioned scenario generation, a semantic exposure penalty, Dynamic Semantic Reserve Margin, and Semantic Demand Response Pre-Activation constraints. The framework is evaluated using a 7-day ERCOT-style controlled stress-test with synthetic ISO-like EEA1/EEA2 alerts and 5 min market resolution. The results show that RAG-VPP achieved a total profit of $285.8k,representinga32$85.2 k, showed a four-hour semantic lead in the controlled stress-test scenario, achieved 0.92 semantic alignment, and maintained zero reserve-margin violation hours. These results indicate the potential of linguistically informed dispatch for improving VPP resilience under extreme-event conditions.

키워드