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

TL;DR

The paper addresses the need to evaluate information and data models for smart grids during their development to prevent operational disruptions. It proposes a three-phase design science method that combines explicit (conceptual) and implicit (conformance/interoperability) validation, applicable across development stages for both IM and DM. A consolidated set of 21 quality characteristics is developed by merging literature-based factors with observation-driven categories, including three new characteristics to address practical needs in information exchange. The artefact is demonstrated on industrial energy-flexibility models (EFDM) from the SynErgie project, with expert feedback indicating practicality and potential adoption, though limitations include qualitative evaluation and the need for broader validation. Overall, the method provides a structured, adaptable framework to improve the reliability and interoperability of smart grid information exchanges during model development.

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.

Figures (6)

• Figure 1: Simplified visualisation of the information exchange between two systems in the information layer.
• Figure 2: Structured search and analysis protocol for combined literature review.
• Figure 3: Adopted and applied process based on 2007_Peffers_DesignScienceResearchc.
• Figure 4: Three-Phase Evaluation Method for in their development process.
• Figure 5: Sequence diagram of the information exchange of the first use case assess flexibility.