大型语言模型辅助的电池储能系统集成配电网运行监测

中文摘要 AI 辅助

电池储能系统（BESS）正越来越多地应用于配电网，用于电压调节和需求响应，这增加了电网运营人员可用的运行遥测数据的数量和复杂性。本文提出一种人工智能驱动的监测框架，该框架将大型语言模型（LLM）接口与结构化遥测数据库相连，用于BESS集成配电系统分析。运营人员以自然语言提交问题，系统利用预定义的数据库模式信息和经批准的关键绩效指标（KPI）视图，将问题转换为经过验证的SQL查询。检索到的测量数据（包括母线电压、荷电状态、有功功率和无功功率）会对照电压限值、BESS运行和需求响应跟踪等工程约束进行评估。该框架通过配备BESS的配电馈线的硬件在环协同仿真数据进行验证，该馈线在基于无功功率的电压控制和价格驱动的需求响应下运行。案例研究表明，该框架能生成有效的数据库查询，识别重复的电压越限，检测无功功率过冲，并评估有功功率跟踪性能。结果显示，LLM辅助监测可将结构化电网遥测数据与BESS运行分析的自动化工程评估相连。

英文摘要

Battery energy storage systems (BESS) are increasingly used in distribution networks for voltage regulation and demand response, which increases the volume and complexity of operational telemetry available to grid operators. This paper presents an AI-enabled monitoring framework that connects a large language model (LLM) interface with a structured telemetry database for BESS-integrated distribution system analysis. Operator questions are submitted in natural language and translated into validated SQL queries using predefined database schema information and approved KPI views. Retrieved measurements, including bus voltages, state of charge, active power, and reactive power, are evaluated against engineering constraints for voltage limits, BESS operation, and demand response tracking. The framework is validated using hardware-in-the-loop co-simulation data from a BESS-equipped distribution feeder operating under reactive power-based voltage control and price-driven demand response. Case studies show that the framework generates valid database queries, identifies repeated voltage violations, detects reactive power overshoot, and evaluates active-power tracking performance. The results show that LLM-assisted monitoring can connect structured grid telemetry with automated engineering assessment for BESS operation analysis.

发表机构

• West Virginia University（西弗吉尼亚大学）

机构由 AI 辅助整理，请以论文原文为准。