If you are concerned with the planning of an outage for a specific unit, then you would be concentrating on when would be the optimal financial time for this outage and what major
maintenance would this outage need to contain. The demonstration here is to show that even on a unit level, the process is the same as that for a system or a plant—there are just fewer history data to process. The risk analysis will still determine the risk-critical components for the unit. In addition, we will determine when the overhaul is needed for this unit and what components are driving when the overhaul needs to occur.
Analysis Process
Gather
The last five years for NERC-GADS submissions were downloaded as indicated in Figure 5-1.
The last five years of forced outage data were selected to represent the recent problems on this unit.
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5-2 Process
The raw data were processed by removing all but U1-U3 and D1-D3 outage types5. The zero equivalent hour entries were deleted. The columns headed “Unit/Cause Code,” “Year,” “Net Maximum Capacity,” and “Equivalent Hours” were sorted by “Unit/Cause Code” and “Year” in ascending order. The data for these individual forced outage events are shown in the left side of Figure 5-2. On the right of this figure are the consolidated forced outage event data by
“Unit/Cause Code” by “Year” showing the total annual number of occurrences and total equivalent hours.
Figure 5-2
Processed NERC-GADS Data for Each Forced Outage Event on the Left and the Consolidated Data for Each Cause Code by Year on the Right for Unit AA1
Risk Rank and Risk Plot
All columns and rows of data from the right side of Figure 5-2 were copied and “paste special/
value” pasted into the yellow background area of the risk-rank workbook as shown in Figure 5-3 on the left side. The aggregation of last five years of data by “Unit/Cause Code” for all years is shown on the right after running the aggregation macro.
5 U1 – Unplanned (Forced) Outage – Immediate U2 – Unplanned (Forced) Outage – Delayed U3 – Unplanned (Forced) Outage – Postponed D1 – Unplanned (Forced) Derate – Immediate D2 – Unplanned (Forced) Derate – Delayed D3 – Unplanned (Forced) Derate – Postponed MO – Maintenance Outage
SF – Startup Failure
Figure 5-3
Unit AA1 Input Data in the “Raw Data” Tab in the Risk-Rank Workbook Is on the Left, and the Cause Code for All Years Is Shown on the Right
The aggregated data are copied and “paste special/value” pasted into the “Rank” tab where they are then sorted by risk in descending order as shown in Figure 5-4.
Figure 5-4
Risk-Ranked System Data for Unit AA1
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5-4
All four columns of data are now copied and “paste special/value” pasted into the risk-plot workbook as shown in Figure 5-5. Note the risk rank and cumulative risk of each cause code are calculated on the right-most columns.
Figure 5-5
Risk-Ranked Data Inserted Into Risk-Plot Workbook
From this risk-ranked data, a log-log risk plot was produced as shown in Figure 5-6 and the diminishing-risk plot in Figure 5-7 by clicking the “Label Plot Points” button.
Figure 5-6
Log-Log Risk Plot of Unit AA1 Forced Outage Data by Component Cause Code
Figure 5-7
Diminishing-Risk Plot for Unit AA1 Showing Up to the 10th Ranked Component as the Highest Contributors to Incremental Cumulative Risk
Select
The rapid reduction of incremental cumulative risk occurred with the 10th ranked component as indicated in Figure 5-7. Based on the rapid reduction of incremental cumulative risk beyond the 10th ranked component, the top 10 risk components were chosen as risk-critical for this unit at this time. To produce the final version of Figure 5-6, the component identifiers are changed to one blank for all components, the plot points are relabeled, and then the identifiers for the top 10 components are replaced and the points relabeled again. These labeled points assist in the
placement of the line of constant risk.
The four columns of the risk-critical components, sorted by risk, are shown in Figure 5-8. The total risk for components associated with this unit is also shown. We will pursue the planning of boiler overhaul for unit AA1 in this example with these components.
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Unit Example
5-6
Figure 5-8
Unit AA1 Risk-Critical Components Sorted by Risk
Estimate
The probability-of-failure curves for the run or “without overhaul” case were generated for the 10 critical components to be considered on Unit AA1. In this example, the maintenance planned is what was performed during the last major outage. For that reason, only the failures since the last outage were used to create the probability-of-failure curves. The annual change in
probability is calculated for component cause code 3110, Condenser Tubes, in Figure 5-9. In Figure 5-10, the operation year and probability change by year has been entered along with the base year into Baycom11, “Fit of History” tab. Then a Weibull curve can be fitted by selecting
“Tools, Fit of History.” That will produce the comparison plot in Figure 5-11. Again, if the fit is not satisfactory, then change the base year and try a refit.
Figure 5-9
ProbCalc Workbook Calculating the Change in Probability by Year for Cause Code 3110, Condenser Tube Leaks
Figure 5-10
Input Worksheet in Baycom11, “Fit of History” Tab, Where the Weibull Shape and Scale Parameters Are Calculated for Input Into the Boiler OIO for Cause Code 3110, Condenser Tube Leaks
Figure 5-11
The Cumulative Probability-of-Failure Plot in Baycom11 That Compares the Weibull Fitted Curve to a Curve Linking the Failure History Points for Cause Code 3110, Condenser Tube Leaks
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5-8
When all component curves are fitted, then the Weibull alpha, beta, and base year values for this
“without overhaul” probability curve can be input for each component into the Boiler OIO. The resulting probability-of-failure versus time curves for all 10 components to be used in the Boiler OIO analysis of Unit AA1 for the “without overhaul” case is shown in Figure 5-12.
Figure 5-12
The Resulting Fitted Probability-of-Failure Curves for the 10 Risk-Critical Components
Input and Optimize
The inputs for the 10 components for Unit AA1 for the “with overhaul” case are shown in
Figure 5-13 with the other necessary unit input information. For the “with overhaul” case, it was assumed for each component that the Weibull base year parameter was increased by the time since the last outage to the present, leaving the scale and shape parameter the same, because the same type of maintenance activity is planned for this overhaul as was pursued in the last major outage.
Figure 5-13
Boiler OIO Input “With Overhaul” for Unit AA1
As can be seen in Figure 5-14, the heat-rate change is being considered for the condenser tube component as well as the forced outage created by a tube leak. The annual change in heat rate expected for this component after the overhaul occurs is input in the “With Overhaul” tab in the area of column L starting at row 60.
Figure 5-14
Boiler OIO Input “With Overhaul” for Heat Rate Change for Unit AA1
The inputs for the “Without Overhaul” tab are shown in Figure 5-15 for forced outage and Figure 5-16 for heat rate. The forced outage inputs were derived from the history data for these 10 components and processed with ProbCalc and Baycom11.
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5-10
Figure 5-15
Boiler OIO Input “Without Overhaul” for Unit AA1
Figure 5-16
Boiler OIO Input “Without Overhaul” for Heat-Rate Change for Unit AA1
The operation parameters by year (unit replacement power cost, projected capacity factor, and service factor) were input in the respective tabs as well as the financial assumptions for time value of money and taxes. The annual budget limits, forced outage rate limit, and probability of safety flag limit are input in the respective columns in the “Summary” tab as shown in
Figure 5-17. After loading all the data, the “Launch Optimization” button is clicked and the outage business plan for Unit AA1 is produced. This figure shows the overhaul year that will produce the highest NPV within the constraints and when and if the safety limit is exceeded. To
the right top are the totals of the present value cash flows for this analysis period, in this case of 10 years, for the outage performed in 2009. In this case, the power company wanted to use a 10-year analysis period as opposed to a 20-year analysis period. These present value totals come from columns D, F, and K, respectively. The current value totals before taxes are shown to the left of each of these columns. To the far right is the total expected NPV for planning the overhaul in each year of the analysis period.
Figure 5-17
Boiler OIO Summary Worksheet for Unit AA1
These overhaul year expected NPVs are plotted in Figure 5-18. Note the increase in NPV if the outage was delayed.
Figure 5-18
Boiler OIO NPV Versus Overhaul Year Results for Unit AA1
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5-12 Examine
Upon examination of Boiler OIO inputs for the 10 components for Unit AA1, it was determined that projected service factor is constant at around 93% and projected replacement energy value is forecast to fluctuate significantly year to year. Note that the probability-of-failure curve for 3110, Condenser Tubes, and 1080, Economizer Tubes, in Figure 5-12 is rising fairly linearly as are the other component curves. Examining the expected consequential cash flows for these components indicates they have the highest consequential cost through time. The reason that the two
components dominate in consequential forced outage cost is that their population of elements is significantly higher than the other components. With probability curves near linear shape, the significant fluctuation in the projected replacement energy values is reflected in the NPV curve shape because the service factor is constant. The lower forced outage duration time for condenser tube failures as well as a moderate unit capacity result in a lower NPV for all years when
compared to the other examples. However, note that a unit expected retirement date significantly beyond the end of the analysis window has a major contribution to the NPV.
Figure 5-19
Boiler OIO Cost “With Overhaul” Worksheet Showing the Annual Consequential Cost for the 10 Components Selected for Unit AA1
Conclusion
Unit AA1 does not need an overhaul until 2009 to reap a $9,000,000 net present value savings.
The large fluctuations in the NPV versus overhaul year provide some nearly-as-valuable times for the overhaul. However, the trend is for the NPV curve to increase with time for this unit.
Taking the time and effort to find the corporate projection on annual replacement values, as well as capacity and service factors, has a significant effect on the maximum value timing of the overhaul.