Enhancing Smart Grid Information Exchanges: A Three-Phase Method for Evaluating Information and Data Models during their Development Process

TL;DR

This paper introduces a comprehensive three-phase evaluation method that combines explicit and implicit approaches to improve the development and reliability of information and data models in smart grids.

Contribution

It presents a novel, adaptable evaluation framework that integrates different assessment methods during model development to enhance early fault detection and interoperability.

Findings

The method improves early detection of model flaws.

It enhances interoperability of smart grid information exchange.

The approach is adaptable to various development stages.

Abstract

The ongoing process of smart grid digitalisation is increasing the volume of automated information exchange across distributed energy systems. This has driven the development of new information and data models when existing models fail to offer an optimal description of the requisite information due to be exchanged. To prevent potential operational disruption - i.e. in the provision of flexibility - caused by flaws in these newly designed models, it is essential to conduct evaluations during the development process before these models are deployed. Current practices differ across domains. Beyond smart grid applications, information models are evaluated through explicit reviews using quality characteristics. Within smart grid contexts, evaluation focuses on data models and implicit system-level conformance and interoperability testing. However, no existing approach combines these explicit and implicit evaluation methods for both information and data models during their development. This limits early fault detection and increases potential model correction costs. To address this gap, we propose a three-phase evaluation method based on design science research. Our method integrates explicit and implicit approaches, applies them to information and data models and is adaptable to various design stages. We also introduce a set of quality characteristics to support explicit model evaluation. Overall, our contribution enhances the reliability and interoperability of smart grid information exchange.