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.

翻译：智能电网数字化进程持续推进，正促使分布式能源系统中自动化信息交换的规模不断扩大。当现有模型无法对所需交换信息提供最优描述时，这一趋势推动了新型信息与数据模型的开发。为避免这些新设计模型中的缺陷可能导致潜在运行中断（例如在灵活性服务提供方面），必须在模型部署前的开发过程中进行评估。当前各领域的实践方法存在差异：在智能电网应用之外，信息模型通常通过基于质量特征的显式评审进行评估；而在智能电网领域，评估则侧重于数据模型及隐式的系统级一致性与互操作性测试。然而，现有方法均未能在开发过程中将显式与隐式评估相结合，同时覆盖信息模型与数据模型。这种局限性阻碍了早期缺陷检测，并增加了模型修正的潜在成本。为弥补这一空白，我们基于设计科学研究提出一种三阶段评估方法。该方法整合了显式与隐式评估路径，可同时应用于信息模型与数据模型，并能适应不同设计阶段。我们还引入了一套质量特征体系以支持显式模型评估。总体而言，本研究成果有助于提升智能电网信息交换的可靠性与互操作性。