Effectively managing aging assets: using a simulation model to improve O&M and capital planning processes.

• Author: Delaney, Patrick
• Position: Report

Electric utilities must manage a variety of aging distribution assets that are critical to system reliability. They are also faced with potentially huge costs when they need to replace these assets to maintain reliability. Making intelligent decisions about asset maintenance and replacement first requires that utilities have accurate information about the failure patterns of these assets over time. However, most data elements that could shed light on such patterns--asset condition, joint use, maintenance patterns, or results of stratified inspection--are not widely available. Still, utilities must forecast their capital and O&M spending requirements every year, regardless of their understanding of such asset failures.

In addition to these gaps in data, a lack of effective analytical tools and processes make it even more difficult to support such budget allocation decisions. For the most part, capital funding decisions are being made using a simple but potentially inaccurate forecasting method of taking the average of asset failures over a certain period of time. This often means existing replacement or maintenance strategies are not linked to the costs and reliability that will be experienced in several years. Thus, most utilities cannot quantitatively evaluate alternative strategies in order to select the best ones to implement. A more rigorous methodology is needed.

Understanding Asset Failures

To truly understand how an asset class fails over time, it is essential that utility companies capture and store historical data about asset failures. A database with critical asset attribute elements can provide insight to the pattern of failures and establish the framework for a probabilistic model that can replicate the failure patterns over time in a simulated environment.

Engineers often try to generate survivor curves for assets. If a utility can track an asset group from the time the assets were placed into service until the last remaining member of that asset year group has been taken out of service, then analyses such as survivor curves can applied to that group of assets. This approach is unrealistic as most utilities do not typically define the data needed to enable a "cradle-to-grave" analysis of asset failure patterns. Further, by the time a survivor curve has been generated for an asset, newer technologies are usually replacing that asset. On the other hand, basic asset life cycle characteristics, such as when an asset was put in service and when it failed, are for the most part available.

At a strategic level, this basic data does allow a robust analysis of failure patterns and provides the insight to required capital to replace failed assets, as the failures will likely occur with similar frequency. The key requirement is assessing the probabilistic nature of the failure, understanding the differences in mitigation options, and any associated...