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Opening the Black Box
A new approach to utility asset management.
the different alternatives; and 4) the probability distribution of the cost of failure. Moreover, the optimal policy includes determining the optimal testing policy, based on the accuracy and the cost of alternative testing regimes. Thus, the Policy Model, shown in Figure 6, can determine the optimal policy as well as the expected benefit of alternative testing regimes. The Policy Model also forecasts the behavior of the asset inventory and the cash flows associated with implementing the optimal policy.
The Policy Model can be envisioned as a type of decision tree. For example, suppose we have a high-voltage transformer, which we believe is in fair condition today (Time =0). The transformer can be replaced, overhauled, or simply left alone (the “Do Nothing” alternative), as shown in Figure 7.
In the figure, after overhauling or doing nothing, there will remain uncertainty as to the transformer’s actual condition at Time = 1. Specifically, if the transformer is overhauled, its condition either will be good with probability PO (good) or fair with probability PO (fair). 11 However, if the transformer is left alone and doesn’t fail, next period it will be either in fair condition with probability PN (fair) or poor condition with probability PN (poor). The relative likelihoods of the resulting conditions in the Do-Nothing case are determined by the Condition Dynamics Model. The relative likelihoods associated with the overhauling procedure are based on utility-specific or industry-wide knowledge of the outcomes of overhauling
In actuality, of course, we are dealing with multiple uncertainties, including whether to test the transformer’s condition and, if so, what type of test to undertake. Moreover, the time horizon used by the model is infinite. The actual model uses dynamic optimization techniques to solve the model for each asset class and develop a recommended strategy, including a testing strategy. Moreover, the model can estimate the value of different testing regimes.
Spare Transformer Inventory Analysis
One aspect of ensuring a reliable electric system is quick restoration from forced outages. This type of repair-replace decision involves the value of spare equipment, similar to the spare tire example discussed above, with an additional geographical component.
For one RTO, a key issue was the best management policy for the step-down transformers on its system, which reduce voltages from 500 kV to 230 kV. Specifically, the RTO had four questions: 1) how often should these transformers be tested? 2) when should they be overhauled (refurbished)? 3) when should they be replaced? and 4) where should spare transformers be deployed to mitigate the consequences of transformer failures?
The expected value of a spare at a given location within the RTO is based on several factors. Not surprisingly, the first factor is the expected value of reduced outage duration. Thus, if the cost of a forced outage at location X is $OX per hour, then the expected value of the spare, E(VS,X), equals the probability of failure, PX(f), times the expected reduction in outage time because of locating the spare at X, ∆TX, times the outage value, i.e., E(VS,X) = Px (f)•∆TX•$0X.
In addition to this value, however,