Integrated energy management system (IEMS) is a must-have to ensure the operation of integrated energy systems (IES) and to provide foundation to advanced functions such as analysis, forecast and optimization. However, building an IEMS faces significant challenges, including the absence of unified data models across diversified disciplines, the dynamic and heterogeneous nature of IEMS, budget constraints for small-scale IESs, and the need for interdisciplinary cooperation. Previous research focused on proposing unified data model standards for each subfield of IES or particular IES projects. However, such approach inevitably struggles with the difficulties in covering vast and diverse topics encompassed by IES and the adoption in engineering practice. This research pivots away from attempting to create another data model standard but proposes a collaborative and software-aided method to foster community-driven data model and data integration. The method includes three key components: an IES data model framework, an IEMS data connector, and an operation strategy. The proposed method minimizes semantic ambiguity, translates human semantics to machine executions automatically, streamlines application interface connections, and fosters the development of a de-facto data model standard within the IES community. The method has been verified through a case study and theoretical criteria, offering a promising avenue for seamless data integration in IEMS.
Keywords data model, integrated energy system, data integration