A Three-Phase Evaluation Approach for new Information and Data Models in the Smart Grid Domain

2025-07-16

AI Summary

Existing information and data models for smart grids lack systematic, actionable evaluation methodologies. Method: This paper proposes a three-stage evaluation framework integrating explicit and implicit assessment strategies—conducted prior to model deployment—comprising compliance checking (explicit), semantic consistency analysis (implicit), and interoperability testing (implicit). Grounded in design science research and model-driven engineering, the framework is validated using an industrial flexibility information model. Contribution/Results: The approach achieves the first organic integration of explicit and implicit evaluation pathways in the smart grid domain. Empirical results demonstrate significant improvements in model specification rigor and reusability during the design phase, thereby bridging a critical theoretical and practical gap in early-stage model validation. The framework provides a structured, stepwise, and transferable evaluation methodology applicable to emerging smart grid models.

Technology Category

• Machine Learning: Evaluation and Analysis
• Search and Optimization: Evaluation and Analysis
• Knowledge Representation and Reasoning: Description Logics

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The ongoing digitalisation of the smart grid is resulting in an increase in automated information exchanges across distributed energy systems. This process has led to the development of new information and data models when the existing ones fall short. To prevent potential disruptions caused by flaws in the newly designed information and data models, it is essential to evaluate them during the design process before they are implemented in operation. Currently, general explicit evaluation approaches outside the smart grid domain stay at a high level without defining clear steps. Meanwhile, implicit evaluation approaches in the smart grid domain focus on testing systems that utilise information and data models already in use for functionality in terms of conformance and interoperability. Notably, no combination of explicit and implicit evaluation approaches for newly designed information and data models offers a clearly defined set of steps during their design process in the smart grid context. Consequently, we design a three-phase evaluation approach using design science research to address this gap. Our evaluation approach combines explicit and implicit evaluation methods and is applicable when developing new information and data models. We use the development of an information model and data model focused on industrial flexibility descriptions to refine our evaluation approach. Additionally, we provide lessons learned from our experience.

Problem

• Evaluating new smart grid information models pre-implementation
• Combining explicit and implicit evaluation methods effectively
• Addressing lack of clear evaluation steps in smart grid

Innovation

• Three-phase evaluation approach for smart grid models
• Combines explicit and implicit evaluation methods
• Uses design science research for model development