Unlocking data interoperability in the energy industry

Data interoperability is crucial, as it enables efficient communication and collaboration between diverse systems and stakeholders. It facilitates the integration of renewable energy sources, smart grids and energy management systems, leading to optimized operations, better decision-making and improved reliability of energy supply. more cost-efficient and faster in this video.

To succeed with data interoperability, the energy industry should prioritize standardized data formats, encourage collaboration among stakeholders, invest in interoperable technologies and establish robust data management systems. Promoting data sharing initiatives and maintaining data security are also crucial for success.

Data interoperability can be tested through interoperability assessments, compatibility testing and interoperability trials where systems exchange data to verify seamless communication. Additionally, conducting interoperability pilots and implementing standardized testing frameworks can help evaluate the effectiveness of data interoperability solutions.

Key use cases include integrating renewable energy sources into the grid, optimizing energy distribution and demand response, facilitating energy trading and market transactions, enabling predictive maintenance of infrastructure, and supporting regulatory compliance and reporting requirements. Learn how our customers are working with data interoperability here.

Common information models in the energy industry offer benefits such as improved data interoperability and integration across systems, streamlined decision-making, reduced duplication of efforts, and better alignment with industry standards and regulations.

Risks and challenges include compatibility issues between different systems, data security and privacy concerns, potential data inaccuracies or inconsistencies, complexity in integrating diverse data sources and the need for ongoing maintenance and updates to ensure continued interoperability.

To manage risks associated with data interoperability in the energy sector, employ robust data governance frameworks, conduct thorough risk assessments, ensure regulatory compliance, utilize encryption and anonymized data, establish clear data sharing agreements and regularly monitor and audit data exchange processes.

Key regulations and standards for data interoperability in the energy industry include GDPR (General Data Protection Regulation), ISO 27001 (Information Security Management), IEC 61850 (Communication Networks and Systems for Power Utility Automation), CIM (Common Information Model), and OpenADR (Open Automated Demand Response).

Asset information modelling in the energy industry involves creating and managing digital representations of physical assets throughout their lifecycle. It integrates various data types such as design, construction, and operational information to support asset management and decision-making processes.

Asset information modelling is crucial for the energy industry as it enhances decision-making processes, improves maintenance planning, optimizes asset performance and supports risk management. For instance, an effective asset management strategy could involve leveraging digital asset representations to predict maintenance needs, thereby minimizing downtime and optimizing asset performance. Conversely, a less effective strategy may involve reactive maintenance, addressing repairs only after equipment failures occur, leading to increased downtime and higher maintenance costs.

To succeed with asset information modelling, the energy industry should implement robust data management systems, foster collaboration among stakeholders, ensure comprehensive asset data integration, leverage predictive analytics for maintenance planning and invest in training personnel to effectively utilize technologies and processes for managing asset information.

DNV is developing a recommended practice for asset information modelling framework (DNV-RP-0670) launching at the end of 2024. Register your interest here. 

Asset information models can be tested through various methods, such as validation against real-world asset data, simulation of different operational scenarios, verification of model accuracy through field tests, and peer review by domain experts. Additionally, conducting usability testing with end-users can help identify any usability issues and ensure the effectiveness of the models.

Key use cases include predictive maintenance to anticipate equipment failures, optimizing asset performance through data-driven insights, facilitating regulatory compliance by maintaining accurate asset records, enabling informed decision-making for asset investments and upgrades, and supporting energy efficiency initiatives through better asset utilization and management.

Common asset information models in the energy industry offer enhanced data interoperability and integration across systems, streamlined decision-making processes, reduced duplication of efforts, better alignment with industry standards, improved regulatory compliance, and optimized asset performance through comprehensive and accurate asset data management.

Risks and challenges of asset information modelling range over data security and privacy concerns, potential inaccuracies or inconsistencies in asset data, complexity in integrating diverse data sources, compatibility issues with existing systems and the need for ongoing maintenance and updates to ensure relevance and accuracy. Additionally, organizational resistance to change can pose significant challenges.

Risk management involves implementing robust data governance frameworks, conducting thorough risk assessments, ensuring compliance with relevant regulations and standards, employing encryption and data anonymization techniques, establishing clear data sharing agreements and regularly monitoring and auditing asset information modelling processes. Additionally, fostering a culture of collaboration and communication among stakeholders can help mitigate risks effectively.

Key regulations and standards for asset information modelling include the ISO 55000 series (Asset Management), IEC 81346 (Industrial systems, installations, and equipment), PAS 1192 (Building Information Modelling), CIM (Common Information Model), ISO 15926 (Industrial automation systems), IEC 61970 (Energy Management), ISO/DIS 16739-1 (Industry Foundation Classes), CFIHOS (Capital Facilities Information), DEXPI (Process Industry Data Exchange), and ISO/IEC 21838 (Product Life Cycle Support). They guide asset management practices, data interoperability and information modelling in the energy sector. 

The ongoing development of ISO/WD 23726-3 (Automation systems and integration — Ontology based interoperability) deserves special mention. Centered on the Industrial Data Ontology (IDO), it enables automated reasoning over complex asset data, providing engineers with accessible language and relevant modelling patterns. IDO addresses industry demands for semantic interoperability, improving efficiency and accuracy in asset information management across the energy sector and beyond.