用大模型让电网运维自动分析储能系统运行数据。
Large Language Model Assisted Operational Monitoring for Battery Energy Storage System Integrated Power Distribution Networks
- 大模型将自然语言问题转为有效SQL查询，对接储能电网数据
- 可识别电压反复越限、无功超调及有功跟踪偏差
- 适合电力系统运维人员快速诊断储能异常
电池储能系统（BESS）在配电网中广泛用于电压调节和需求响应，显著增加了电网运营者面临的遥测数据量与复杂性。本文提出一种AI增强的监控框架，通过大型语言模型（LLM）接口连接结构化遥测数据库，实现对集成BESS的配电网分析。运维人员以自然语言提问，系统基于预定义的数据库模式和核准的关键绩效指标（KPI）视图，将其转化为有效SQL查询。检索到的测量数据包括节点电压、储能状态（SoC）、有功功率与无功功率，并依据电压限制、储能运行规则及需求响应追踪标准进行工程约束评估。该框架在包含储能的馈线硬件在环协同仿真数据上验证，运行场景涵盖基于无功的电压控制与价格驱动的需求响应。案例研究显示，该框架能生成有效数据库查询，识别重复电压越限，检测无功功率超调，并评估有功功率跟踪性能。结果表明，基于大模型的监控可实现电网结构化遥测与自动化工程评估的无缝衔接，支持储能系统运行分析。
原文摘要 · Abstract (English)
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.
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