As is the case for many North American utilities serving major urban centers, the majority of Toronto Hydro's distribution system infrastructure in the city of Toronto, Canada, is approaching the end of its rated service life. In order to sustain and improve the power supply reliability to its 700,000 residential and commercial customers in a financially responsible manner, Toronto Hydro has adopted a risk-based asset management strategy for prioritizing investments in asset replacement and refurbishment.
The Need for Asset Management
Rooted in value-based reliability planning, the asset management strategy optimizes the intervention timing of investments in asset renewal and rehabilitation. The quantitative analysis underlying the new strategy serves as the decision-support tool, providing justification for the investments to internal and external stakeholders.
Traditionally, distribution utilities have managed aging assets in a reactive manner, repairing, replacing or rehabilitating assets only upon failure. At least on the surface, this practice may appear to be the least-cost solution. However, the life-cycle analysis of total costs, including the costs of customer interruptions, indicates the run-to-failure strategy is not the optimal strategy for most distribution system assets.
Utilities need to determine the optimal intervention timing by accurately assessing the probability and impacts of asset failures. In assessing the impacts, various direct and indirect cost attributes associated with in-service asset failures are taken into account, including the costs of customer interruptions, emergency repairs and asset replacements.
The asset management strategy is based on four basic building blocks:
- Calculation of asset failure probability
- Estimation of asset failure impacts
- Risk evaluations
- Determination of the optimal asset intervention timing.
These elements have been successfully incorporated into a decision-support tool known as the Feeder Investment Model (FIM). In order to implement this approach, an extensive amount of data for individual assets is required. Utilities traditionally have captured a very small amount of asset data in the past. Toronto Hydro is currently implementing programs to improve its asset data systems and data-collection processes to support short-term and long-term planning activities.
Probability of Failure
An assessment of the assets' probability of failure is required to prioritize and, in some cases, determine the exact timing of intervention. Two different approaches are commonly used to predict the probability of failure of a given asset.
One approach, the age-based failure probability assessment, determines the average failure probability for an asset, based on its age, through the calculation of the hazard-rate distribution function. First, formulate the basic probability distribution function (PDF) by calculating and plotting the percentage of asset failures per age group using historical failure data.
The cumulative distribution function (CDF) represents a running sum of this PDF function. The Survival Rate Curve is 1.0 minus the CDF and represents the number of assets that have survived up to a given age out of the total sample size. Finally, compute the Hazard Rate by dividing the original PDF by the survival rate.
A second approach, an asset condition assessment (ACA), computes a quantified condition score, or health index, for an asset based on several health-degradation factors. Appropriate health-degradation factors for an asset must meet the following requirements:
The selected degradation factors must contribute to the asset's failure.
The degradation factors must result in irreparable permanent damage (i.e., result in non-renewable degradation).
All selected degradation factors should have adequate available data in order to arrive at an accurate score.
Assign each degradation parameter an appropriate weight, based on its contribution to the assets' probability of failure. To calculate the health index, multiply each of the degradation scores with its selected weight and then sum up the overall score. Ideally, the final health index value should be represented on a scale from 0 to 100 to make it convenient to use.
Impacts of Failure
The ACA approach has obvious benefits over the age-based failure probability assessment. While the age-based failure probability assessment provides an easier means to calculating failure probability, it cannot differentiate between assets of similar age with differing conditions. The ACA approach takes into account multiple parameters indicative of an asset's health.
It is noteworthy that establishing an accurate correlation between the health index and the probability of failure requires a great deal of analysis and data. An ACA program must be in place for a reasonable length of time to capture sufficient asset failure data for correlating health indices with failure rates.
By prioritizing assets based on the probability-of-failure component alone, a utility cannot determine if the highest-priority assets are, in fact, the most-important assets that need to be intervened upon. For example, a transformer with a low health index score may be supplying lighting for a billboard, whereas an asset further down the prioritized list may supply a hospital.
Determining the importance of each asset or the impact of its failure is critical when evaluating risk or overall priority for asset intervention. Toronto Hydro employs the impact monetization approach, where a cost is assigned to each outage (impact). Monetary values can be determined through direct tangible utility costs as well as through customer costs related to power interruptions.
Direct tangible utility costs are those incurred by the utility when restoring service to the affected customers following an outage event, such as the costs of emergency repairs and lost revenue. Note that these costs on their own do not account for customer-related impacts.
The customer interruption cost (CIC) measures the monetary losses experienced by the customer due to an outage. CICs are best defined by breaking an outage down into three stages:
The first stage represents the initial occurrence of the outage where customers are forced to terminate activities that involve electricity, resulting in an immediate shock or impact to the customers.
Quantifying Cost of Risk
The second stage represents the time period following the initial outage occurrence when customers have adjusted their activities to account for the loss of electricity; therefore, the impact is less severe. However, as the outage duration continues to increase, so does the inconvenience and potential lost revenues incurred by the customers.
The third stage represents the time period when the system is repaired and services are restored to customers. During this stage, there are likely to be no further impacts to residential customers. For commercial customers, such as offices, overall productivity for the remainder of the day could be significantly reduced, depending on the length of the outage. For industrial customers, particularly those with sensitive processes that require startup time, there could be additional impacts incurred until these customers are fully operational.
Optimal Intervention Timing
Therefore, the CIC can be broken down into three key variables, one for each stage of the outage. The final impact cost formulation accounts for the CICs, the connected load and potential outage durations that may occur as well as the utility's direct impact costs.
Several approaches may be employed to determine the CICs, including application of customer surveys, lost revenue analysis using a worksheet approach, analysis of claims against the utility, blackout case studies and utility forums. Direct impacts associated with commercial- and industrial-class customers, such as lost revenues and worker productivity, can be more easily quantifiable in a monetary value using a worksheet approach. With respect to residential customers, it is far more difficult to effectively quantify the direct impacts associated with an outage, such as the spoilage of food or the loss of leisure or entertainment during an outage. Customer surveys provide a useful means of quantifying CICs.
The cost of risk associated with in-service failure of an asset can be quantified by multiplying the assets' probability of failure with their impact cost.
The main advantage of risk quantification is that a risk cost can be used as part of a life-cycle cost calculation in order to identify the assets' optimal timing of intervention. Alternative approaches, such as a risk score or index, force the utility to determine the intervention timing based on an acceptable risk threshold.
In the FIM employed by Toronto Hydro, life-cycle cost analysis is performed to determine the optimal intervention time for each distribution system asset.
The computed life-cycle cost represents the total operating cost of the asset, taking into account the annualized risk and capital across its entire life cycle. The best time to perform asset intervention is when these operating costs are at their lowest, as shown by the equivalent annualized cost (EAC) value.
Implementation and Path Forward
The FIM assumes the existing asset to be replaced and the new asset to go into service have different risk properties. As a result, the EAC from the life-cycle cost curve of the new asset must be cross-referenced with the risk cost curve of the existing asset to determine the optimal timing of intervention for the existing asset, which is approximately 30 years of age.
By pinpointing the assets' optimal intervention time based on the minimum operating costs, it is the life-cycle calculations and modeling that drive the selection of high-priority assets and the timing of their interventions. Therefore, the results produced by the FIM allow for a transparent, objective and cost-driven justification for the intervention of assets to regulatory bodies.
The final component within the FIM is to apply the individual asset prioritization and intervention timing results to a capital project. The FIM provides the ability to group high-priority, near-term intervention assets with lower-priority assets in the same geographic areas to produce cost-efficient projects for implementation.
In this manner, the FIM adds a practical layer to the final results by comparing the benefits of performing planning, design and construction activities at one time for a project area as opposed to performing each of the activities individually. If a positive net benefit is established, the project is approved accordingly and an optimal execution time is provided. FIM ensures Toronto Hydro is making the optimal decisions for its asset base at the optimal time.
Toronto Hydro's risk-based asset management strategy was successfully implemented with the aid of training programs, new processes and resources to support the analytical tools. In the coming years, Toronto Hydro will continue to further improve their asset data systems, data collection activities, analytical tools and processes to better support this strategy. These improvements ultimately will ensure that Toronto Hydro is making the most precise and informed decisions with respect to its distribution system investments.
Robert Otal (email@example.com) is a project manager in the system reliability planning department at Toronto Hydro. He has worked hands on in improving and optimizing Toronto Hydro's asset management plan. Otal was previously at Horizon Utilities, where he assisted with implementation of its asset management plan and condition assessment system for distribution system assets. He obtained his BSEE degree from Ryerson University. Otal takes an active role in the engineering profession and is a member of IEEE.
Thor Hjartarson (firstname.lastname@example.org) has more than 20 years of management and leadership experience in the electric distribution industry, in which he held progressive roles with Iceland State Electricity, Acres International and Kinectrics. He is currently manager of system reliability planning at Toronto Hydro. He holds a MSEE degree from the University of British Columbia and a BSEE degree from the University of Iceland. He is also a registered professional engineer in Ontario and a senior member of IEEE.