Part 2 of this four-part series discusses the complex tasks energy utility companies face as they shift to holistic grid asset management to manage through the energy transition. The first post of this series addressed the challenges of the energy transition with holistic grid asset management. In this part, we discuss the integrated asset management platform and data exchange that unite business disciplines in different domains in one network.
The asset management ecosystem
The asset management network is complex. No single system can manage all the required information views to enable end-to-end optimization. The following figure demonstrates how a platform approach can integrate data flows.
Asset data is the basis 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 their respective primary data responsibility. The EAM system is the center for maintenance planning and execution via work orders. The maintenance, repair and overhaul (MRO) system provides necessary spare parts to carry out work and maintains an optimum stock level with a balance of stock out risk and part holding costs.
The health, safety and environment (HSE) system manages work permits for safe work execution and tracks and investigates incidents. The process safety management (PSM) system controls hazardous operations through safety practices, uses bow-tie analysis to define and monitor risk barriers, and manages safety and environmental critical elements (SECE) to prevent primary containment loss. Monitoring energy efficiency and greenhouse gas or fugitive emissions can directly contribute to environmental, social and governance (ESG) reporting, helping to manage and reduce the carbon footprint.
Asset performance management (APM) strategy defines the balance between proactive and reactive maintenance tasks. Asset criticality defines whether a preventive or predictive task is justified in terms of cost and risk. The process of defining the optimum maintenance strategy is called reliability-centered maintenance. The mechanical integrity of hazardous process assets, such as vessels, reactors or pipelines, requires a deeper approach to define the optimum risk-based inspection intervals. For process safety devices, a safety instrumented system approach determines the test frequency and safety integrity level for alarm functions.
Asset data 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 defines asset health indexes to rank the asset conditions based on degradation models, failures, overdue preventive work and any other relevant parameters that reflect the health of the assets. Predict functionality builds predictive models to predict imminent failures and calculate assets’ remaining useful life. These models often incorporate machine learning and AI algorithms to detect the onset of degradation mechanisms in an early stage.
In the asset performance management and optimization (APMO) domain, the team collects and prioritizes asset needs resulting from asset strategies based on asset criticality. They optimize maintenance and replacement planning against the constraints of available budget and resource capacity. This method is useful for regulated industries such as energy transmission and distribution, as it allows companies to remain within the assigned budget for an arbitrage period of several years. The asset replacement requirements enter the asset investment planning (AIP) process, combining with new asset requests and expansion or upgrade projects. Market drivers, regulatory requirements, sustainability goals and resource constraints define the project portfolio and priorities for execution. The project portfolio management function manages the project management aspects of new build and replacement projects to stay within budget and on time. Product lifecycle management covers the stage-gated engineering process to optimize the design of the assets against the lowest total cost of ownership within the boundaries of all other stakeholders.
An industry-standard data model
A uniform data model is necessary to get a full view of combined systems with information flowing across the ecosystem. Technical, financial, geographical, operational and transactional data attributes are all parts of a data structure. In the utilities industry, the common information model offers a useful framework to integrate and orchestrate the ecosystem to generate optimum business value.
The integration of diverse asset management disciplines in one provides a full 360° view of assets. This integration allows companies to target the full range of business objectives and track performance across the lifecycle and against each stakeholder goal.
Read more about IBM Data Model for Energy and Utilities
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