PowerCIM: A Standardized Data Environment for Decision-making in Complex Power Grids

These trends place additional requirements on the quality of grid modelling and integration solutions, as data often originate from heterogeneous sources and diverse information systems.

Data fragmentation and practical obstacles in use of standardized exchange mechanisms hinder analysis, planning, and operational decision-making.

To overcome these challenges, a data environment platform was developed that leverages the international Common Information Model (CIM) standard. Its core is CIM repository that supports management of CIM data from various sources through multiple branches/repositories. The application enables merging and reconstruction of different repositories (sources) and versions for analytical or operational purposes and serving this data via REST API. The platform harmonizes data from Supervisory Control and Data

Acquisition/Energy Management System (SCADA/EMS), Geographic Information System (GIS), and asset management systems, enabling consistent use across analyses, simulations, real-time operation, and medium- to long-term planning.

Data is persisted in a relational database as objects of various granularity. To make retrieval fast, data is indexed and accessed from RAM. Only data changes are stored using bitemporal versioning (transaction and valid time), enabling traceability and efficient reconstruction of historical and planned grid states. An additional advantage of the bitemporal approach is the ability to perform detailed historical data reviews, which is particularly relevant for reporting, regulatory compliance, and cybersecurity requirements (the repository is append-only). This ensures that data remain available for different types of analysis without losing contextual information about their validity.

The paper presents the platform architecture, data model, and mechanisms for versioning and retrieval, with relevant application areas and use-cases. In this context, the data environment platform can be seen as a building block for future energy data spaces, enabling interoperable, standardized, and secure data exchange to support reliable decision-making across the energy sector.