aiqbench analysis
This research demonstrates a practical application where Large Language Models serve as natural language interfaces for querying complex telemetry databases within battery energy storage systems (BESS). By translating operator questions into validated SQL queries, the framework automates engineering assessments of voltage regulation and demand response without replacing traditional control logic. The study validates that LLM-assisted monitoring effectively identifies operational anomalies like reactive power overshoots while maintaining safety constraints in grid-integrated environments.
Large Language Model Assisted Operational Monitoring for Battery Energy Storage System Integrated Power Distribution Networks
arXiv:2608.15396v1 Announce Type: cross Abstract: 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.
Abstract: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.
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Systems and Control (eess.SY)
arXiv:2608.15396 [cs.AI]
(or arXiv:2608.15396v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.15396 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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