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June 18, 2018
Optimization: a new approach to investment planning for utilities
By: Olivier Ebert

Over the past decade, utilities have made significant investments in enterprise asset management (EAM) systems, with the goal of improving operational efficiency.

Traditionally, utilities have aimed to maximize revenues from capital expenditure and limit associated operational costs. But the increasing challenges brought by distributed energy resource (DER) systems and related non-wire alternatives, are disrupting this approach. More holistic performance frameworks now need to encompass a broader view of risk and the impact of a much wider set of costs.

In response, some utilities are discovering the saving potential of using optimized investment planning processes compared with the limited, but still widespread, prioritization.

Prioritization uses criteria such as risk scores and estimated financial return to rank investment needs and align them to constraints, such as budgetary, human resources, grid availability etc. Prioritization, however, is not dynamic. It does not include the ability to compare scenarios, including replacement, retrofit, increased maintenance, and to arbitrate between types of investment or expenditure.

Based solely on the current or predicted short-term condition of assets, prioritization ignores the longer-term consequences of investment strategies on risk, spending and overall grid performance.

But now, the development of strategic asset management tools for financial risk analysis and asset investment planning enables utilities to take advantage of truly optimized approaches. These are constantly updated and could deliver more robust investment decision-making and a more comprehensive picture of risk.

An optimized asset investment strategy relies on a few key components:

  • The ability to produce in high volumes and compare credible complex investment scenarios in the long run
    With the computing power available from the cloud, it’s possible for utilities to carry out multiple scenario simulations to assess the impact of all elements on all others. Scenarios can encompass thousands of assets and use approaches such as advanced Monte-Carlo modelling to determine a range of possible outcomes. This opens the door to efficient linear asset optimization by comparing situations where interdependent assets are replaced/maintained/ modernized (simultaneously or not), with their respective impacts on cost, grid performance and risk level.

  • A harmonized, measurable risk framework aligned to organizational values and objectives
    This allows comparisons across potentially competing asset investments by defining a common measure of an incident’s probability and its related consequences on various predefined dimensions like cost, safety, performance, environment and reputation, regardless of asset type. This new view of risk will include potential developments such as previously unexpected waves of replacements owing to historic investment deferrals or constraints on the availability of specialized human resources.

  • The ability to make the best of existing available data to draw high-value decisions
    Moving toward optimization entails a refocus on the data utilities collect. The IoT, proliferation of sensors and ubiquitous connectivity mean utilities are collecting more data than they can use. However, the demands of financial risk analysis using an optimization approach requires clarity about the precise data needed to generate the most useful outputs. CIOs have a clear role to play, and business case to pursue, for gathering and managing the relevant data.

    But investment decisions can also be improved quickly using only minimally cleaned and structured data. This can, as a starting point, provide incremental benefits for objective decisions. Rather than preventing calculations, probabilistic approaches that estimate what missing data values should be, enable conclusions with a margin of uncertainty that’s often lower than those asset managers previously tolerated. The truly relevant data to collect or improve quickly becomes obvious. Real-time analytics solutions add accuracy and solid, reliable data that feeds investment scenario modelling, improving accuracy exponentially.

  • Continual measurement and adjustment of asset investment strategy performance.
    A formalized feedback loop, based on objectivized risk measures using reliable data, can help asset managers continuously improve the investment process. And in a rapidly changing environment, that ability will become ever more critical.

Through constant assessment, using the data alongside revisited policies and processes and a refreshed risk framework, utilities can carry out simulations that will identify patterns of possible risks aligned to the long-term asset lifecycle. This establishes a solid common ground for productive discussions between finances and operations. It may also sometimes lead to decisions that initially appear to be counterintuitive and would never have been reached with conventional approaches to investment and risk planning. However, they may also uncover a wealth of new improvement opportunities.

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