Exploring the Reliability of U.S. Electric Utilities

Transcription

1 Exploring the Reliability of U.S. Electric Utilities Peter Larsen (Lawrence Berkeley National Laboratory/Stanford University) Kristina Hamachi LaCommare (LBNL) Joe Eto (LBNL) Jim Sweeney (Stanford) International Association of Energy Economics

2 Presentation outline 1. Study context, outage causes, measuring reliability 2. Research questions 3. Generalized method 4. Sources and coverage of data 5. What is the preferred model? 6. Regression diagnostics and preliminary results 7. Interpretation and next steps 2

3 Study context Factors that affect the long term reliability of U.S. electric utilities is an important and under studied research topic Eto et al. (212) developed a basic panel dataset and preliminary econometric framework to evaluate some factors that may be correlated with more frequent and lengthy service interruptions Eto et al. (212) found that the frequency and duration of reliability events increased ~2% annually; increases in cooling degree days are correlated with increased frequency of outages; outage management systems are initially correlated with longer outages, but LSEs appear to be learning from these systems over time 3

4 Study context (cont.) Eto et al. (212) paper acknowledged that additional factors should be considered including: a larger sample of utilities and more disaggregate measures of weather variability (e.g., lightning strikes and severe storms), utility characteristics (e.g., the number of rural versus urban customers, and the extent to which transmission and distribution lines are overhead versus underground), and utility spending on transmission and distribution maintenance and upgrades, including advanced technologies. Eto, J. K. H. LaCommare, P. Larsen, A. Todd, and E. Fisher An Examination of Temporal Trends in Electricity Reliability Based on Reports from U.S. Electric Utilities. Energy Policy 49 (October). 4

5 Power outage causes? What causes increase the duration of reliability events? What causes increase the frequency of reliability events? 5

6 How is reliability measured? System Average Interruption Duration Index (SAIDI) (without major events) (with major events) 6

7 How is reliability measured? (cont.) System Average Interruption Frequency Index (SAIFI) (without major events ) 3 (with major events ) 2.5 SAIFI (with major events) th Percentile Median (U.S.) 25th Percentile

8 Research questions Are there utility specific differences in reported electricity reliability? Is the share of underground distribution miles to total line miles correlated with reliability? Do reliability improvements occur after the installation of an automated outage management system (OMS)? Is the total amount of electricity delivered correlated with changes in utility reliability? How does abnormal weather affect the frequency and duration of system outages? Are previous year annual transmission and distribution expenditures correlated with the frequency and duration of system outages? Are there unexplained trends in reported electricity reliability over time? 8

9 General method Annual utility reliability is represented by the dependent variable: Y it. Electric utility and reporting year are represented by subscript i and t, respectively. Subscript d and f are used to differentiate between observed and unobservable variables, respectively and X di and Z fi represent observed and unobservable variables. Finally, Ɛ it represents the model error term and t is a variable to capture a time trend. As indicated above, the array of Z fi variables are unobservable. Accordingly, I define a new term, α i, which represents the combined effect of the unobservable variables on the dependent variable, Y it. 9

10 Possible Regressors Ideal ( I ) vs. readily available ( R ) independent variables Average Weather Extreme Weather Heating degreedays I,R I,R Cooling degreedays I,R I,R Windspeed I,R I,R Total precipitation I,R I,R Lightning strikes I,R I,R # of days with snowfall I I # of days with rainfall I I # of days with sleet/ice I I # of days with high wind I I % share of lines buried R R R R Reported Causes Wildlife Vegetation Human Error T&D Equip. Other/ Unknown/ External Population of squirrels near overhead T&D corridors I Population of birds near overhead T&D corridors I Average distance from leaftip to T&D line I Annual vegetation maintenance budget I,R # of professional degrees/certifications per employee I Presence of outage management systems I?,R? I?,R? Employee morale I Count and duration of customercaused interruptions Number of customers per line mile Current and lagged T&D expenditures Remaining lifespan of T&D equipment Count/duration of unknown/other/external interruptions Sales and electricity delivered per customer Count/duration of planned outages Note: This matrix needs work I R I,R I I I,R Planned Outage I1

11 Data sources and coverage Data Number of Years Original units Source utilities available Reliability (frequency of 147 Varies by SAIFI LBNL (213) outage with major events) utility Reliability (duration of outage 149 Varies by SAIDI LBNL (213) with major events) utility Delivered electricity, revenue, MWh, nominal EIA/Ventyx (214) and number of customers dollars, number of customers Weather (degreedays) HDDs, CDDs NCDC/Ventyx (213) Weather (lightning strikes) Number of NLDN/LBNL (213) positively and negativelycharged strikes Weather (total precipitation) Inches NCDC/Ventyx (214) Weather (average windspeed) Miles per hour NCDC/Ventyx (214) Presence of outage Binary and LBNL (213) management system installation year Adoption of IEEE Binary LBNL (213) reporting standard Transmission and distribution extent Line miles above ground, underground, FERC/EIA/RUS/Ventyx (214) Transmission and distribution expenses and other Nominal dollars of fixed and variable costs FERC/EIA/RUS/Ventyx (214) 11

12 Preferred model in two steps. Oneway fixed effect (utility) Reliability metric Fvalue Degrees of freedom Reject null of Prob. > F (numerator/denominator) no effects? Log of SAIDI without major events /48 <.1 Yes Log of SAIDI with major events /326 <.1 Yes Log of SAIFI without major events /48 <.1 Yes Log of SAIFI with major events /33 <.1 Yes Oneway random effect (utility) Reliability metric mvalue Degrees of freedom Prob. > m Reject null of random effects at p.1? Log of SAIDI without major events No Log of SAIDI with major events No Log of SAIFI without major events Yes Log of SAIFI with major events No 12

13 Regression diagnostics: SAIDI SAIDI (without major events) SAIDI (with major events) 13

14 Regression Method: Pooled Intercept (15.384) Electricity delivered (MWh per customer).16*** (.6) Lagged T&D expenditures.516** ($212 per customer) (.2) Years since outage management.36 system installation (.85) Outage management system?.49 (.63) Abnormally cold weather (%.12 above average HDDs) (.43) Abnormally cold weather squared () Abnormally warm weather (%.5 above average CDDs) (.58) Abnormally warm weather squared (.2) Abnormally high # of lightning.8 strikes (% above average strikes) (.8) Abnormally windy (% above.184 average wind speed) (.149) Abnormally windy squared.3 (.13) Abnormally wet (% above.74* average total precipitation) (.38) Abnormally wet squared.1* Abnormally dry (% below.6 average total precipitation) (.5) Abnormally dry squared.1 Year.1 (.75) Number of customers per line mile.98*** Share of underground T&D miles to total T&D miles (.13).65*** (.15) Log of SAIDI (without major events) Fixed Effects ( ).128 (.9).328 (.379).92 (.9).819* (.491).14 (.3) ().44 (.39).2.12** (.5).224** (.91).26*** (.9).31 (.28).1.3 (.39).5 (.67).2 (.39).12 (.52) Log of SAIDI (with major events) Random Effects Pooled Fixed Effects (12.525).1 (.21).89 (.253).14 (.79).747 (.488).12 (.28) ().52 (.38).2*.12*** (.5).237*** (.91).25*** (.9).34 (.28).1.25 (.37).82 (.62).68*** (.25).27 (.35) *** (38.99).2 (.77).328 (.313).195 (.185).774 (.1233).152 (.256).19 (.23).21 (.16).3 (.2).9 (.15).994*** (.36).45** (.21).158 (.97) (.114).5** (.2).753*** (.19).34 (.37).149*** (.34) *** ( ).342 (.429).3714 (.5268).15 (.32).143 (.1527).238 (.268).32 (.24).14 (.97).2 (.2).16 (.15).166*** (.294).58*** (.2).177** (.81).2.86 (.17).2 (.2).786*** (.279).354** (.169).47 (.117) Random Effects *** ( ).28 (.18).212 (.762).138 (.244).6 (.1363).27 (.247).31 (.22).31 (.96).2 (.2).1 (.14).176*** (.277).55*** (.18).173** (.85).1.13 (.15).2 (.2).886*** (.237).85 (.85).126 (.79) Lightning Wind/ Wind 2 Wet Year Customers per line mile % underground 14

15 Above average wind and duration of outages 45 4 SAIDI (without major events) SAIDI (with major events) Outage Duration (Minutes) % Duration + 69% Duration + 6% Duration 2% Duration 1 5 +% +5% +1% % Above Average Windspeed (2212)

16 Regression diagnostics: SAIFI SAIFI (without major events) SAIFI (with major events) 16

17 Regression Method: Intercept (14.377) Electricity delivered (MWh per.3 customer) (.6) Lagged T&D expenditures ($212 per customer).565*** (.149) Years since outage management.46 system installation (.66) Outage management system?.16*** (.492) Abnormally cold weather (%.37 above average HDDs) (.43) Abnormally cold weather squared () Abnormally warm weather (%.72* above average CDDs) (.43) Abnormally warm weather.1 squared Abnormally high # of lightning.3 strikes (% above average strikes) (.7) Abnormally windy (% above.79 average wind speed) (.129) Abnormally windy squared.1 (.9) Abnormally wet (% above.7 average total precipitation) (.29) Abnormally wet squared () Abnormally dry (% below.18 average total precipitation) (.37) Abnormally dry squared Year.3 (.72) Number of customers per line mile.61*** Share of underground T&D miles to total T&D miles Log of SAIFI (without major events) Fixed Random Pooled Effects Effects (.1).54*** (.13) *** (8.1318).58 (.48).96 (.311).145*** (.54).84 (.274).18 (.2) ().11 (.2).7** (.3).136** (.58).16*** (.5).16 (.16) ().14 (.19) ().129*** (.41).6 (.23).47* (.28) ** (7.9643).32* (.18).116 (.246).1** (.49).144 (.274).19 (.19) ().1 (.19).6** (.3).14** (.61).16*** (.6).15 (.15) ().11 (.19) ().98** (.4).25 (.18).21 (.21) Log of SAIFI (with major events) Fixed Random Pooled Effects Effects *** ( ).1 (.19).437*** (.151).143* (.84).543 (.61).18 (.111).18** (.9) (.45).1.14* (.8).239 (.151) (.1).12 (.43).4 (.44).232*** (.74).34* (.18).1*** (.18) (17.672).67 (.18).1273 (.1823).28 (.18).474 (.476).34 (.16).6 (.1).23 (.33).1.15** (.6).369*** (.11).23*** (.7).37 (.32) ().41 (.32) (.89).83 (.53).5 (.43) ** (15.819).2 (.47).175 (.416).57 (.84).547 (.463).53 (.89).6 (.7).18 (.32).1.13** (.6).368*** (.16).2*** (.7).33 (.31) ().35 (.3).179** (.76).2 (.35).52 (.33) Lightning Wind/Wind 2 Year % underground 17

18 Above average wind and frequency of outages SAIFI (without major events) SAIFI (with major events) Frequency of Outages % Frequency + 3% Frequency + 18% Frequency 2% Frequency % +5% +1% % Above Average Windspeed (2212)

19 Preliminary interpretation A5% increase in average wind speed is correlated with increased frequency (+15%) and duration (+5%) of reliability events. Higher than average wind speeds and lightning are consistently correlated with increases in the duration and frequency of reliability problems regardless of whether major events are included in the calculation of the performance metric. Statistically significant and increasing time trend in the frequency (+2%/year) and duration (+8%/year) of reliability events, but results are sensitive to whether or not major events (i.e., natural disasters) are included in the panel regression.

20 Preliminary interpretation A5% increase in average wind speed is correlated with increased frequency (+15%) and duration (+5%) of reliability events. Higher than average wind speeds and lightning are consistently correlated with increases in the duration and frequency of reliability problems regardless of whether major events are included in the calculation of the performance metric. Statistically significant and increasing time trend in the frequency (+2%/year) and duration (+8%/year) of reliability events, but results are sensitive to whether or not major events (i.e., natural disasters) are included in the panel regression.

21 Next steps Decompose expenditures related regressors into proactive and reactive costs Estimate reliability improvement from increased share of underground lines Forecast frequency and duration of U.S. power outages under alternative scenarios

22 Thank you Peter Larsen