Interpretable SLM-Driven Trust Framework for Smart Cities: Managing Distributed Energy Resources in Networked Microgrids

Networked Microgrids (NMGs) have revolutionized the energy landscape by enhancing grid flexibility and decentralizing power generation, playing a pivotal role in the development of smart cities. Distributed Energy Resources (DERs) are a fundamental component of a typical NMG; hence, their trustworthiness is of utmost importance for the reliable and efficient operation of NMGs within smart city environments. However, the processing and analysis of unstructured data when performing trust assessments of these DERs is still not well explored. This research fills this gap by proposing a new trust framework that leverages the advanced capabilities of Neural Networks to assess the trustworthiness of DERs in NMGs. Furthermore, the proposed framework analyzes and converts the unstructured data from DERs into a structured format for generating trust scores for DERs. There are two primary components of this framework: (1) an SLM (Small Language Model)-based module for data analysis, (2) a neural network-based module for trust score calculation. These two components provide an end-to-end process for transforming an unstructured input into meaningful trust metrics. Several experiments were conducted to evaluate the performance of the proposed framework, and it turned out that the results produced by the proposed framework were highly precise, accurate and consistent. Furthermore, the proposed framework outperformed the existing frameworks in size and efficiency, making it a promising solution for trustworthy DER management in smart city microgrid ecosystems.