Forecasting Capital Expenditures for Transmission Utilities. Georgia Power Company

Transcription

1 Forecasting Capital Expenditures for Transmission Utilities Kelly Clute Remi Myers Georgia Power Company Transmission Maintenance & Reliability

2 Southern Company Alabama, Georgia, Gulf, and Mississippi Power Companies 42,000 megawatts generating capacity 4.3 Million Customers

3 Georgia Power 2.25 million customers 8,500 employees 155 of 159 counties Transmission Maintenance & Reliability Maintains information for 17,500 miles of transmission lines

4 Our GIS Use ESRI products (9.2) ArcView, ArcIMS, Citrix Maps Oracle Inventory DB 17,500 miles of transmission lines 46, 69, 115, 161, 230, and 500 kv See Map #16!

5 Inspection and Maintenance Program Workplan defined at Southern Company level How often do we look at what? Visit each structure every six years Treat wood every 12 years Inspections performed by contractors and transmission crews ABSIT s collected in the field, loaded back into Oracle DB

6 Abnormal Situations What is an ABSIT? Insulator, Conductor, ROW, Structure, etc Existed long before GIS Each ABSIT is assigned a priority Action Plan, ASAP, Critical, Information, Next Visit, Workplan Feed workplan Help to determine maintenance and capital projects Some handled system wide, others within individual territories

7 Transmission Maintenance Centers 12 TMC s 1 2 engineers 1 2 three and four man crews Perform regular and capital maintenance Key is balancing $$$

8 2006 Workplan Wood Pole Capital Absits Total Number by Support Install Date (In Decades)

9 2006 Workplan Wood Pole Capital Absits Total Number by Support Install Date (In Decades) Average Age of Wood Poles = 36 years

10 How do we allocate capital dollars now? Pole Blanket Committee Total 2007 Budget = $8 million 2006 Budget was $6 million 2006 Actuals was $8 million $4 million allocated equally to each TMC $4 million / 12 hdqtrs = $333,333 $4 million retained by Pole Blanket Committee

11 Pros Distribution of $ is somewhat equitable TMC s know their areas Documentation vs Cons

12 Pros Distribution of $ is somewhat equitable TMC s know their areas Documentation vs Cons Relies on engineer s Aging workforce Are all area s equal?

13 Pros Distribution of $ is somewhat equitable TMC s know their areas Documentation vs Cons Relies on engineer s Aging workforce Are all area s equal? Is there a better way?

14 Objectives of Our Study Use GIS to represent wood infrastructure on system Use statistical models to build a priority classification for change outs Use the classification data to examine key change out locations to project and maximize reliability for capital dollars spent

15 Searching for a Solution Identifying Key Variables Data Mining Inventory Assets Leveraging GIS

16 Using GIS to Identify Wood Poles on System Every year 1/6 of the Transmission facilities are inventoried and treated as part of a routine maintenance program In 2003, the Transmission Line Inspection System began to use GIS applications to inventory assets

17 Wood Structures and Cost of Replacement 60% of structures are wood Almost 50,000 poles Voltages from kv Pole heights from ft Average install date 1978 $38 million dollars to replace (not including labor and other equipment costs) Cost Density for Wood Poles at Georgia Power

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19 Building the Statistical Model For each wood pole (x) a severity score is calculated f(x). f ( x) 7,,,,,, 1 ) ( = = n i w r s c v a h x x x x x x x x f

20 The Statistical Model f ( x) = n i= 1 x h, x a, x v, x 7 c, x s, x r, x w Each class of x represents an attribute of the wood pole that may contribute to deterioration Each attribute is categorized by severity and assigned a class value from In most cases quartile techniques were used to classify

21 Visualizing the Model Age Voltage Pole Height Severity Nearby Transportation Location Sub Class Less Severe More Severe

22 Identifying Common Wood Problems Over 50% of current replacements are due to natural/environmental/climate deterioration Over 25% are due to insect or wildlife damage The remaining replacements are due to vehicle accidents or transportation conflicts

23 Pole Height Classifications Stresses are incurred based upon span length and guy tensions Most strongly correlated attribute with pole costs

24 Classifying Poles by Height (xh)

25 Classifying Poles by Age (xa)

26 Identifying Problems Associated with Voltage More hardware and greater span distances as voltage increases Increased probability of under build lines

27 Identifying Problems Associated with Voltage 230 KV 46 KV

28 Identifying Problems by Sub Class Angle poles incur greater stresses then other types Frequently have more hardware (Dead End Insulators) Angle Poles Tangent Dead End Poles

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30 Identifying Hazards Near Geographic Features Overhead crossings along transportation routes are Critical Locations Lines can be snagged by ships in waterways, or railcars at crossings. Poles sited near roads may be damaged by vehicular accidents.

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34 Classifying Poles by Transportation Variables (xs)

35 Classifying Poles by Transportation Variables (xr)

36 Classifying Poles by Transportation Variables (xw)

37 Adjusting for Costs Pole height is strongly correlated to Cost (r 2 =.8888) Pole type (class) and line voltage variables account for much of the remaining cost variance

38 Evaluating the model Most critical poles (Class 10) between 55 and 100 feet and installed between 1936 and 1984.

39 Evaluating the Model Most Critical Least Critical

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48 Evaluating the Model Most Critical Least Critical

49 Budget Impact of Change Out Plan Less Critical Most Critical

50 Testing the Model The total wood pole population was compared to 2008 pole change out plan A Chi-Squared evaluation was used to measure goodness of fit for selected problem types was the model useful in determining upcoming capital change outs

51 Evaluating the Results

52 Evaluating the Results ABSITYP_CA Total Class µ WOOD POLE, Mechanical Damage, Replace WOOD POLE, Natural Deterioration, Replace WOOD POLE, Other, Specify In Comments, Capit WOOD POLE, Other, Specify In Comments,Replace WOOD POLE, Public Accident, Replace WOOD POLE, Roadway Conflict, Replace WOOD POLE, Split, Termites, Replace WOOD POLE, Storm Damage, Replace WOOD POLE, Structure Upgrade, Replace WOOD POLE, W oodpecker Damage/Replace Grand Total

53 Evaluating the Results Total wood pole classification µ = replacement plan classification µ = 6.5 Good at predicting natural deterioration and woodpecker damage Fair at predicting storm damage and public accidents

54 Conclusions Age and pole height are best predictive variables Geography and pole location had little influence over replacements due to accidents More research into predictor variables would greatly improve accuracy

55 How does this apply to you? Leverage non-gis data Think about $$$ How can I bring extra value to management?

56 Questions? Remi Myers Kelly Clute