Addressing challenges of the energy transition with grid asset management

The energy transition is gearing up to full speed as renewable energy sources replace fossil-based systems of energy production. The grid itself must green to operate within the environmental, social and governance (ESG) objectives and become carbon neutral by 2050. This shift requires energy utility companies to plan their grid asset management holistically as they find a new balance between strategic objectives.

Sustainable asset performance has become one of the key drivers in decision-making for asset planning and grid modernization business processes. New emerging technology enables AI-powered digital twins to operate the smart grid. However, operators must balance intermittent renewable energy intake to produce a controlled, stable output.

A balanced transition between old and new systems

The demand to fulfill existing long-term contracts and an abundance of new demands for industrial electrification pose new challenges to grid management. Finding the right balance requires load forecasting and simulation to prevent net congestion. Economical optimization must factor in new market dynamics and ensure reliable operation.

Existing network assets are aging, and more intelligent asset management strategies must emerge to maintain and replace the grid within tightening budgets. Asset investment planning must find a balance between these systems while minimizing risk and carbon footprint.

To manage the grid of the future, utility companies must shift from traditional asset management to a holistic approach. This shift will broaden insights so these companies can take strategic, tactical steps to optimize operational network development and operation decisions.

Asset lifecycle management

Holistic grid asset management adopts a lifecycle view across the whole asset lifespan to obtain a safe, secure, reliable and affordable network. Utility companies must break down the internal departmental walls between the silos of grid planning, construction, operation, maintenance and replacement to allow end-to-end visibility. They must connect underlying technology systems to create a single pane of glass for all operations. A shared data model across operating systems serves as the basis for integration, simulation, prediction and optimization by using generative AI models to drive next-level business value.

The goal of asset management is to optimize capital expenditures (CapEx) and operating expenses (OpEx) in a seamless transition between the timescale of the planning horizons. The following figure demonstrates the complex planning and optimization objectives required for a holistic view of the asset management lifecycle:

A top-down strategic approach for whole-life planning of asset investment portfolio matching future ESG goals needs to connect with a bottom-up maintenance and replacement strategy for existing assets. Asset investment planning (AIP) results in project portfolio management and product lifecycle management to plan, prioritize and run asset expansion and replacement projects within the boundaries of available budget and resource capacity.

Real-time operational data provides an asset health view that drives condition-based maintenance and replacement planning. This is the domain of enterprise asset management (EAM) for maintenance execution and asset performance management (APM) for strategy optimization. Traditionally, a disconnect at the tactical level has separated these planning and optimization methodologies. At the same time, operational risk management requires respecting health, safety and environment (HSE) management and process safety management to manage potentially hazardous operations. The maintenance repair and overhaul (MRO) spare parts strategy must align with the asset strategy in terms of criticality and optimal stock value.

Acknowledgment of the complexity of planning with multidimensional objectives on different timescales is the starting point for adopting a holistic view of asset management.