Quality Assurance ofEIA C li S t D tEIACoolingSystemDataCha-Chi FanEnergy-Water Nexus Partners MeetingMarch 22 2012www.eia.govU.S. Energy Information AdministrationIndependent Statistics & AnalysisCollect dataReview partiallyReview finalCollectdata• Internet DataCollection (IDC)system edits – initiallogical checksReviewpartiallycollecteddatabase• Routine logicalReviewfinaldatabase• Final logical checks• StatisticalPublishdatalogicalchecksgchecksexaminationsReview comments and contact respondents abouti i d t d t i i d l timissingdata,data revisions, andexplanationsCha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 20122Types of quality checksTypesofqualitychecks• Logical checks• Statistical examinations• Other checksCha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 20123Logical checksCha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 20124IDC editsIDCedits• Automated data filters that are programmed in the IDCsystem– The hours in service cannot exceed the hours in the reporting month– Reported cooling ID was indicated as retired or out of service, while reportedhours in service were non-zero.hoursinservicewerenon– Reported monthly maximum water temperature at discharge is lower than thereported monthly maximum at withdrawal in a single month. Bypass this check,if hours in service = 0– Reported average monthly temperature atwithdrawal is greater than reportedmaximum monthly temperature at discharge– Reported average monthly temperature atdischarge is greater than reportedmaximum monthly temperature at dischargemaximummonthlytemperatureatdischargeCha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 20125Limitations of IDC editsLimitationsofIDCedits• Simple logic• Respondents can bypass the some edits by providingcomments–However comments don’always address the issuesHowever,commentsdon talwaysaddresstheissues• Not able to captures extreme values that meet the logicCha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 20126Problem flagsProblemflags• Logical checks that perform on the database that are buildoutside of the IDC systemoutsideoftheIDCsystem– Reported EIA923 withdrawal or diversion rate is higher than EIA860 designwithdrawal rate (for open loop system) or calculated engineer withdrawal upperbound (for close loop systems)– withdrawal rate= discharge rate= consumption rate– Reported cooling ID was indicated as retired or out of service, while reportedhours in service were non-zero or withdrawal/discharge/consumption activitywas reportedwasreported.– Reported monthly maximum water temperature at discharge is lower than thereported monthly maximum at withdrawal in a single month. Bypass this check,if hours in service = 0 or if EIA860water source = wells or municipals.– Reported values exceed engineer limits based on EIA860 net capacity (MW)and design withdrawal water flow rate (cfs)Cha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 20127Problem flagsProblemflags• Exceeding engineer limits based on EIA860 net capacity(MW) and design withdrawal water flow rate (cfs)(MW)anddesignwithdrawalwaterflowrate(Withdrawal rate limits Lower limit factor(cfs/MW)Upper limit factor(cfs/MW)Once through 0.75 1.25Cooling tower-wet 0.09 0.12Cooling pond0 751 25Coolingpond..Discharge rate limits Lower limit factor(cfs/cfs)Upper limit factor(cfs/cfs)Once through 0.98 1Cooling tower-wet 0.002 0.004Cooling pond0 0020 004Cha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 20128Coolingpond0.0020.004Problem flagsProblemflags• Exceeding engineer limits based on EIA860 net capacity(MW) and design withdrawal water flow rate (cfs)(MW)anddesignwithdrawalwaterflowrate(Consumption ratelimitsLower limit factor(cfs/cfs)Upper limit factor(cfs/cfs)Once through 0 0.02Cooling tower-wet 0.02 0.03Cooling pond0 010 02Annual amount ofchloride limitsLower limit factor(1000 lbs/MW)Upper limit factor(1000 lbs/MW)Coolingpond0.010.02Once through 0 0.81Cooling tower-wet 0 0.30Cooling pond00 31Cha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 20129Coolingpond00.31A screen shot of automated water data summary at a cooling system levelCha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 201210Limitations of problem flagsLimitationsofproblemflags• Could the reported values be in different units or could thedecimal points be misplaced?decimalpointsbemisplaced?• Could the reported values be aggregates at a plant level oraggregates of different coolingsystems?g g y• The logical checks are general, not specific to specialdesigns• Not able to captures extreme values that meet the logicCha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 201211Statistical examinationsCha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 201212Types of examinationsTypesofexaminationsUnivariateBivariateMultivariateHistogram / box plotScatter plotCluster analysisPercentile curveanalysisCluster analysisData miningStatistical tests (e.g.generalized extremeStudentizeddeviate)CorrelationPrincipal componentCha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 201213deviate)Example 1: Univariate/bivariateAverage withdrawal temperature vs. average discharge temperature

F) t ure (degree a rge tempera w ater dischAvgwater withdrawal temperature(degree F)

Avg wCha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 201214gp (g )Example 2: Univariate/bivariateWithdrawal rate vs. discharge rateWithdrawal rate (cfs) / discharge rate (cfs)Cha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 201215Withdrawalrate(cfs)/dischargerate(cfs)350400450500

cfs) / ( cfs)Example 2: Univariate/bivariateWithdrawal rate vs. discharge rate150200250300350

d rawal rate( c harge rate (0501001500% 50% 100%

With d

dis c300

/Percentile200250

r ate(cfs) / r ate (cfs)100150

hdrawal r s charge r05090%92%94%96%98%100%

Wit di s90%92%94%96%98%100%PercentileCha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 201216Example 3: Bivariate clusterAverage withdrawal temperature vs.average discharge temperature

degree F) e e F)

h arge temp ( e temp (degr e

v g water disc hAvgwater withdrawal temp (degree F) t er discharg e

A vAvgwaterwithdrawaltemp(degreeF)At ithd l t (d F)

Avg wa tCha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 201217Avg water withdrawaltemp(degreeF)Example 4: Bivariate clusterWithdrawal rate vs. chlorine amount

( cfs)

drawal rate With a l rate (cfs)Chl i t (1000 lb ) WithdrawChlorine amount(1000lbs)Cha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 201218Chlorine amount (1000 lbs)Example 5: Multivariate cluster, water rates, temperatures, chlorine amountChlorineamount(1000lbs)Withdrawalrate (cfs)Dischargerate (cfs)Consumptionrate (cfs)Avg waterwithdrawaltemperature(degree F)Cha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 201219Example 6: Data mining – clustering suggestionsChlorine amount vs. cooling type17.517.016 5

t (1000 lbs).16.0

o rine amoun t15.5

Chl15.0 D C H RI ltiple O C O F O S O T R C RF RI RNCha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 201220

D H mu O O O O RCooling system typesExample 6: Data mining – clustering suggestionsDifference in max withdrawal and discharge temperature vs. water source-15

p (degree F)-1617

s charge tem p--18

r awal and di s-19

n max withd r G W OT PD S W

Diff i nCha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 201221

G SCooling water sourcesExample 6: Data mining – examine relationshipsDiversion rate vs. consumption rate700600500

t e (cfs)400

Diversion ra300200 0 5 00 0 00 5 00 0 00 5 00 0 00100Cha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 201222

5 1 0 1 5 2 0 2 5 3 0Consumption rate (cfs)Examinations by clusters/groupsExaminationsbyclusters/groups• Latitude• Cooling system type• Cooling water source• Cooling water typeM th•on• Cooling status• StateCha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 201223Difficulties• Where the measurements were taken matters• Some plants report combined values without indications• Withdrawal and discharge of cooling pond system do notil h i th thnecessarilyhappeninthe same month• Respondents mistake withdrawal rate with tower water flow rate• Abnormal distribution doesn’t necessarily mean bad data• There is no one to one mapping of boilers and cooling systems• There are all kinds of combinations of cooling systems and howthings work is oftentimes beyond imaginationCha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 201224Other checksOtherchecks• Inputs and comments from data usersCha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 201225Quality Assurance ofEIA C li S t D tEIACoolingSystemDataCha-Chi Fanchachi.fan@eia.govwww.eia.govU.S. Energy Information AdministrationIndependent Statistics & AnalysisProblem flagsProblemflags• Reported EIA923 withdrawal or diversion rate is higher thanEIA860 design withdrawal rate (for open loop system) orEIA860designwithdrawalrate(foropenloopsystem)orcalculated engineer withdrawal upper bound (for close loopsystems)Once through/hybrid: 923 withdrawal rate vs 860 design cooling water flow rate–Oncethrough/hybrid:923withdrawalratevs.860designcoolingwaterflowrate– Cooling pond: 923 diversion rate vs. 860 design cooling water flow rate– Cooling pond: 923 withdrawal rate vs. calculated engineer withdrawal upperbound– Wet cooling tower: 923 withdrawal rate vs. calculated engineer withdrawalupper boundCha-Chi Fan, Energy-WaterNexus Partners Meeting,March 22 201227