Regression-Based Small Language Models for DER Trust Metric Extraction from Structured and Semi-Structured Data

Renewable energy sources like wind turbines and solar panels are integrated into modern power grids as Distributed Energy Resources (DERs). These DERs can operate independently or as part of microgrids. Interconnecting multiple microgrids creates Networked Microgrids (NMGs) that increase reliability, resilience, and independent power generation. However, the trustworthiness of individual DERs remains a critical challenge in NMGs, particularly when integrating previously deployed or geographically distributed units managed by entities with varying expertise. Assessing DER trustworthiness ensuring reliability and security is essential to prevent system-wide instability. Thisresearch addresses this challenge by proposing a lightweight trust metric generation system capable of processing structured and semi-structured DER data to produce key trust indicators. The system employs a Small Language Model (SLM) with approximately 16 million parameters for textual data understanding and metric extraction, followed by a regression head to output bounded trust scores. Designed for deployment in computationally constrained environments, the SLM requires only 64.6 MB of disk space and 200–250 MB of memory that is significantly lesser than larger models such as DeepSeek R1, Gemma-2, and Phi-3, which demand 3–12 GB. Experimental results demonstrate that the SLM achieves high correlation and low mean error across all trust metrics while outperforming larger models in efficiency. When integrated into a full neural network-based trust framework, the generated metrics enable accurate prediction of DER trustworthiness. These findings highlight the potential of lightweight SLMs for reliable and resource-efficient trust assessment in NMGs, supporting resilient and sustainable energy systems in smart cities.