Part 2 of this four-part series dives into the intricate tasks energy utility companies must tackle as they transition to holistic grid asset management in order to navigate through the energy transition. The previous post in this series focused on the challenges of the energy transition with holistic grid asset management. In this installment, we delve into the integrated asset management platform and data exchange that bring together various business disciplines in different domains within a single network.
The asset management ecosystem
The asset management network is a complex web. No single system can handle all the necessary information views to facilitate end-to-end optimization. The following diagram illustrates how a platform approach can integrate data flows.
Asset data serves as the foundation for the network. Enterprise asset management (EAM) systems, geographic information systems, and enterprise resource planning systems share technical, geographic, and financial asset data, each with its respective primary data responsibility. The EAM system acts as the hub for maintenance planning and execution through work orders. The maintenance, repair, and overhaul (MRO) system supplies essential spare parts for carrying out work and maintains an optimal stock level while balancing stock out risk and part holding costs.
The health, safety, and environment (HSE) system oversees work permits for safe work execution, as well as tracks and investigates incidents. The process safety management (PSM) system manages hazardous operations through safety practices, utilizes bow-tie analysis to define and monitor risk barriers, and handles safety and environmental critical elements (SECE) to prevent primary containment loss. Monitoring energy efficiency, greenhouse gas emissions, and fugitive emissions can directly contribute to environmental, social, and governance (ESG) reporting, aiding in managing and reducing the carbon footprint.
The asset performance management (APM) strategy determines the balance between proactive and reactive maintenance tasks. Asset criticality helps decide whether a preventive or predictive task is warranted in terms of cost and risk. The process of defining the optimal maintenance strategy is known as reliability-centered maintenance. Hazardous process assets’ mechanical integrity, such as vessels, reactors, or pipelines, necessitates a thorough approach to define the best risk-based inspection intervals. For process safety devices, a safety instrumented system approach establishes the test frequency and safety integrity level for alarm functions.
APM collects real-time process data. Asset health monitoring and predictive maintenance functions receive data via distributed control systems or supervisory control and data acquisition systems (SCADA). Asset health monitoring establishes asset health indexes to rank asset conditions based on degradation models, failures, overdue preventive work, and other relevant parameters reflecting asset health. Predictive functionality constructs predictive models to anticipate imminent failures and calculate assets’ remaining useful life. These models often incorporate machine learning and AI algorithms to detect early-stage degradation mechanisms.
In the asset performance management and optimization (APMO) domain, the team prioritizes asset needs arising from asset strategies based on asset criticality. They optimize maintenance and replacement planning within the constraints of available budget and resource capacity. This approach proves valuable for regulated industries like energy transmission and distribution, enabling companies to stay within the allocated budget for a specified period. Asset replacement requirements feed into the asset investment planning (AIP) process, merging with new asset requests and expansion or upgrade projects. Market drivers, regulatory demands, sustainability objectives, and resource limitations define the project portfolio and priorities for execution. The project portfolio management function oversees the project management aspects of new build and replacement projects to adhere to budget and timelines. Product lifecycle management encompasses the stage-gated engineering process to optimize asset design for the lowest total cost of ownership while considering all stakeholders.
An industry-standard data model
A standardized data model is essential to gain a comprehensive view of interconnected systems with data flowing throughout the ecosystem. Technical, financial, geographical, operational, and transactional data attributes constitute a data structure. In the utilities industry, the common information model provides a valuable framework for integrating and orchestrating the ecosystem to generate optimal business value.
The integration of diverse asset management disciplines offers a complete 360° view of assets, enabling companies to address a wide range of business objectives and monitor performance across the lifecycle and against each stakeholder’s goals.
Read more about IBM Data Model for Energy and Utilities
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