Over the past decade, regulators have pushed utilities to justify capital plans in terms of risk. In California, the Public Utilities Commission (CPUC) formalized this approach through its Risk Assessment Mitigation Process (RAMP), embedding risk-based planning into rate cases for investor-owned utilities. Similar approaches had emerged earlier in Europe, and today risk-informed planning is widely expected.
It wasn’t that long ago that infrastructure investment decisions were based on historical spend or engineering judgment alone. Requiring utilities to articulate expected outcomes has brought greater structure and transparency to capital planning, and that marks a huge shift for asset managers.
But this pressure has also stretched risk models beyond their limits. Utilities are now expected to show, with increasing rigor, how investment decisions affect customer outcomes. In many cases, the models used to quantify that impact were not designed to carry that weight.
When utilities submit rate applications, their goal is to demonstrate that planned spending is justified: the capital projects proposed are necessary to deliver safe, reliable, and affordable service. Increasingly, that justification is framed in terms of risk reduction: replace these assets to reduce this amount of risk. Yet how that risk is measured and how it maps to outcomes regulators care about, is usually unclear.
What planners, regulators, and customers ultimately focus on are outages (and the costs of reducing them). But estimating how likely an asset is to fail, and what happens if it does, is easier said than done. Not all asset failures lead to an outage, and even identical assets installed in different parts of the network can result in a huge range of outcomes if they fail. In practice, limited data and tools often reduce analysis to risk scores and abstract measures as “close-enough” proxies to what utilities are really trying to understand.
These measures fall short in many ways:
Time variance: Over time, assets degrade, but the system also evolves. New loads, electrification, and climate change affect both failure probability and consequence. Modeling aging alone is no longer sufficient.
Contingencies and mitigations: Just because an asset fails doesn’t mean there’s an outage. Spares, redundancy, and network configuration all shape the impact of asset failures, and the path to restoring power varies depending on how the grid was designed.
System-level impacts: An asset failure impacts other assets too. A fault in a line may trigger a switch, load may be shed or redistributed, and increased load elsewhere raises the likelihood of additional failures.
While not an exhaustive list, it shows how asset management demands a lot more than a risk matrix. Power flow, topology, redundancy, operational flexibility, and reliability forecasts are all essential inputs for determining asset investment needs. A narrow scope of risk analysis creates over-confidence in archaic models and weakens the credibility of capital planning.
What’s usually missing is a grid model. Sometimes called a topological or a connectivity model, it represents how assets are connected, how power flows through the grid, and how it responds under normal and abnormal conditions. Rather than treating assets as isolated sources of risk, it provides a way to model grid assets as part of an interconnected system, where the impact of a failure depends on where it happens and how the network is configured.
Grid models need to become central to asset management. They provide a more defensible basis for estimating consequences and link asset failures to customer outages. This lets asset managers evaluate investment scenarios in terms of reliability metrics (like SAIDI) and customer interruptions.
In 2020, a team of grid planners were under this exact pressure to justify the outage impacts of their capital plans. Together, they developed a comprehensive grid model that became ENGIN, Engineered Intelligence’s flagship planning solution. The team’s solutions addressed concerns for many utilities around the world.
For utilities, grid models lead to much more confident planning decisions. Regulators are increasingly asking utilities to transparently share the trade-off between what customers pay for and what they receive in return. By integrating grid modeling tools like ENGIN, utilities can provide this transparency and better defend investment decisions.
If you’re ready for defensible investment decisions and accurate grid planning, reach out to the team at Engineered Intelligence.