electric portfolio modeling with stochastic water - climate interactions: implications for...

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UMI 3522945Copyright 2012 by ProQuest LLC.

UMI Number: 3522945

DEDICATION

ACKNOWLEDGEMENT

1. Introduction1.1.Population,UrbanizationandSustainability

1.2.ClimateChangeandWater

1.3.WaterConstraintsonElectricityGeneration

1.4.CoManagementofWaterandElectricServices

1.5.NeedforStochasticElectricPortfolioModel

1.6.ThesisObjectives

2. CaseStudyArea:ColoradoSpringsUtilities(CSU)2.1.HistoryofColoradoSpringsUtilities

2.2.CSUElectricitySystem

2.3.CSUWaterSystem

Load(M

Wh)

Days

2.4.MunicipalWaterDemandofCSU

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

Annualmunicipalwaterde

mand(Mgal)

Years

3. SeasonalityofWaterIntensityFactorsforElectricPowerGeneration:ImplicationforRiskAssessmentattheWater

EnergyClimateNexus

3.1. Introductionintensity

3.2. Objectives

3.3. Methodology3.3.1. SeasonalityandStochasticTrendsinWWIF

ComputationofObservedWWIF:

QuantitativeWWIFTrendsandProjections:StochasticsimulationThefuturevariabilityandtrends

3.3.2. QuantitativeRiskAssessmenttoPowerGenerationattheWaterEnergyClimateNexus

WWIF:

TSF:

MD:

EG:

RISK:

TSF:ForthefutureprojectionsofTSF

OtherMunicipalWaterDemand

MonthlyElectricityGeneration(EG):

3.4. ResultsandDiscussions3.4.1. SeasonalityinWWIF

CharacterizingseasonalityinWWIF

TrendAnalysis

0 10 20 30 40 50 60

0.55

0.60

0.65

0.70

0.75

Months (2006-2010)

WW

IF (g

al/K

Wh)

17%

17%

0.531

0.751

0.64

t:representsp:w:rh:

0 10 20 30 40 50 60

0.55

0.60

0.65

0.70

0.75

Months (2006-2010)

WW

IF (g

al/k

wh)

FutureProjections

30 40 50 60 70

0.55

0.60

0.65

0.70

0.75

Monthly average temperature (Deg F) (2006-2010)

WW

IF (g

al/k

wh)

3.4.2. ResultsoftheTSFAnalysisSeasonalityofTSF:

TrendsinTSF

0 200 400 600 800 1000

050

0010

000

1500

0

Months (1960-2050)

Mon

thly

stre

am fl

ows

(MG

M)

Jan.2004

FutureProjectionofTSF:WaterSupply(TSF)versusCompetingDemands

3.4.3. HypotheticalRiskAnalysisofDrakePlantsElectricityGenerationLoss

0

2000

4000

6000

8000

10000

12000

Milliong

allonspe

rmonth

(MGM

)

Months(2015)

MonthlyriskApril(%)

WaterstoragecarryoverfrompreviousyearinMay(Mgal)

Annualrisk20

15(%

)

WaterstoragecarryoverfrompreviousyearinMay(Mgal)

4. IncorporatingWaterConstraintsintoElectricityPortfolioModels:ACaseStudyofApplyingMARKAL/TIMEStoa

MunicipalOwnedUtility

4.1. Introduction4.1.1. MunicipalOwnedUtilities

4.1.2. ElectricPowerSectorStrategiestoReduceWaterUse4.1.2.1. ShiftofElectricGenerationTechnologies

4.1.2.2. PermanentRetrofitsofThermalPlantsCoolingSystems

4.1.2.3. UseofDegradedWaterforThermalPowerPlantsCooling

4.1.3. NeedforPortfolioModeling

4.2. PaperobjectivesandScenarios4.2.1. Objectives

x x

x x x x

4.2.2. RiskManagementScenarios

4.2.2.1. UnplannedRiskManagementScenario(URM)toAddressSeasonalRisk

4.2.2.2. ZeroInfrastructureInvestmentWaterMinimizingScenario(ZInvWM)inPermanentWaterScarcity

4.2.2.3. ConstrainedInfastructureInvestmentWaterMinimizingScenario(CInvWM)inPermanentWaterScarcity

4.2.2.4. UnlimitedInvestmentWaterMinimizingScenario(UInvWM)inPermanentWaterScarcity

4.2.2.5. UnlimitedInvestmentWaterandGHGEmissionsMinimizingScenario(UInvWM_GHG)inPermanentWaterScarcity

4.2.3. ModelOutputs

x x x x x x x x

x

x

in

4.3. Method4.3.1. TIMESElectricityPortfolioModel

4.3.2. BusinessasUsual(BAU)ScenarioDevelopmentusingtheTIMESModel

www.dsireusa.org/

4.3.3. IncorporatingWaterConstraintsintotheTIMESModel

CoalDistillateOilWRECWAT

N.GasWRECWAT

CoalN.Gas

MRECWAT

ElectricityCOSONOxHg

DrakeCoal

Nixon1Coal

FRPNGCC

Input Process Output

4.3.4. AlternativeWaterMinimizingScenariosDevelopment

4.4. ResultsoftheVariousWaterMinimizingScenarioAnalyses4.4.1. VariousModelsRuns

4.4.1.1. SeasonalWaterShortageScenario(URM)

4.4.1.2. ZeroInfrastructureInvestmentWaterMinimizingScenario(ZInvWM)

4.4.1.3. ConstrainedInfrastructureInvestmentWaterMinimizingScenario(CInvWM)

4.4.1.4. UnlimitedInfrastructureInvestmentWaterMinimizingscenario(UInvWM)

4.4.1.5. UnlimitedInfrastructureInvestmentWaterandGHGMinimizingScenario(UInvWM_GHG)

4.4.2. ComparativeAnalysisoftheVariousModelOutputs4.4.2.1. AdditionalCapitalInvestmentsoftheVariousScenariosagainsttheBAU

4.4.2.2. GridMixoftheVariousScenariosinYear2010and2040

AnnualAv

g.additionalinvestment,

M$)

CSUgridmix(PJ)

Scenarios

4.4.2.3. DiscountedSystemCostoftheVariousScenarios

CSUgridmix(PJ)

Scenarios

4.4.2.4. LevelizedCostoftheVariousScenarios

2,000

4,000

6,000

8,000

10,000

12,000

NPV(M

)($2

010$)

4.4.2.5. LevelizedCostofElectricityoftheVariousScenarios

Levelizedcost(M

$)

Period

4.4.2.6. SystemwidewatersavingsoftheVariousScenarios

Avg.LCOE(/kw

h)

4.4.2.7. SystemWideGHGemissionsReductionbytheVariousScenarios

Watersavings(Mgal)

Period(2010 2050)

0.0

1.0

2.0

3.0

4.0

5.0

6.0

GHGe

missions(mt)

Periods

GHGe

missionsreduction(m

t)

4.4.2.8. SystemWideWWIFoftheVariousScenarios

4.4.2.9. MarginalCostofWaterSavingbytheVariousScenarios

WWIFs(gal/kw

h)

4.4.2.10. MarginalCostofGHGEmissionsReduction

Avg.costofwatersavings($/Mgalyr)

Electricsectoralternativewaterminimizingscenarios

4.5. DiscussionsandConclusions

CostofGH

Greduction($)

5ComparingUnitCostandOverallWaterSavingsinCoManagedWaterandElectricUtilities:WaterWithdrawalReductionatPowerPlantsand

WaterConservationatMunicipalWaterDemandSectors

5.1. Introduction

LCOE($/MWh)

5.2. Objective

5.3. AlternativeWaterSavingStrategiesinMunicipalOwnedUtilities

5.3.1. WaterSavingInterventionStrategiesinElectricitySector

5.3.2. WaterSavingInterventionStrategiesinWater/WastewaterSector

5.3.2.1. WaterDemandManagementStrategy

5.3.2.2. WaterSavingsStrategyusingDegradedWaterSources

5.3.2.3. NewWaterResourceDevelopmentStrategy

5.4. ResultsofWaterSavingInterventionsfromElectricandWaterSectors

5.4.1. ResultsofWaterSavingInterventionStrategiesfromElectric

Sector

0

50,000

100,000

150,000

200,000

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

URM ZInvWM CInvWW UInvWM UInvWM_GHG Avg.costofwatersavings($/Mgalyr)

Avg.watersavings(Mgal/year)

5.4.2. ResultsofWaterSavingInterventionStrategiesfromWater/WastewaterSector

Annualizedcostofwatersavings(andwateraugmentation)($/Mgalyear)

5.4.3. ResultsofWaterSavingInterventionStrategiesfromUseofReclaimed/Recycled/WellWaterfortheCSUElectricSector

Avg.co

stofwatersavings($/Mgalyr)

Avg.watersavings(Mgal/year)

5.4.4. WaterReductionEffectivenessoftheIndividualWaterSavingStrategies

Avg.watersavings(Mgalyr)

Avg.costofwatersavings($K/M

galyr)

5.5. SummaryandConclusions

6. ContributionsandConclusions6.1.Conclusions

6.2. ThesisNewContributionsx

x

x

x

x

x

x

x

x

x

x

6.3. RecommendationsforFutureWork

AppendixA.CSUInputData

B.MunicipalWaterWithdrawalShareofDrakePlants

C.WWIFsandTSFStatisticalAnalyses

Jan Mar May Jul Sep Nov Annual

0.55

0.65

0.75

Means Boxplots


0.00

0.04

0.08

Standard Deviation


0.55

0.65

0.75

Maximum box plots


0.50

0.60

0.70

Minimum box plots

Histogram of x11

x11

Den

sity

-0.10 -0.05 0.00 0.05

04

812

-2 -1 0 1 2

-0.0

50.

05

Normal Q-Q Plot

Theoretical QuantilesS

ampl

e Q

uant

iles

0 5 10 15

-0.2

0.2

0.6

1.0

Lag

AC

F

Series x11

0.55 0.65 0.75

0.60

0.70

observation Vs GLM-fit

y-observation

GLM

-fit

Histogram of x11

x11

Den

sity

-0.10 -0.05 0.00 0.05

05

1015

-2 -1 0 1 2

-0.0

8-0

.04

0.00

0.04

Normal Q-Q Plot

Theoretical QuantilesS

ampl

e Q

uant

iles

0 5 10 15

-0.2

0.2

0.6

1.0

Lag

AC

F

Series x11

0.55 0.60 0.65 0.70 0.75

0.60

0.65

0.70

ant WI using LOCFIT model Vs observe

y

LOC

FIT

Vs

obse

rvat

ions


0.25

0.35

0.45

0.55

Means Boxplots


0.00

0.05

0.10

0.15

Standard Deviation


0.3

0.4

0.5

0.6

0.7

Maximum box plots


0.1

0.2

0.3

0.4

0.5

Minimum box plots


0.55

0.65

0.75

Means Boxplots


0.00

0.04

0.08

0.12

Standard Deviation


0.55

0.65

0.75

Maximum box plots


0.40

0.50

0.60

0.70

Minimum box plots

0 100 200 300 400 500

050

0010

000

1500

0

Months (1960-2003)

Mon

thly

stre

am fl

ow (M

GM

)

Histogram of x11

x11

Den

sity

-2 0 2 4

0.0

0.2

0.4

-3 -1 0 1 2 3

-3-1

13

Normal Q-Q Plot

Theoretical Quantiles

Sam

ple

Qua

ntile

s


040

0080

0012

000

Means Boxplots


020

4060

8010

0

Standard Deviation


040

0080

0012

000

Maximum box plots


020

0060

0010

000

Minimum box plots

020

0040

0060

0080

0010

000

1200

0

Monthly average streamflow 1960-2003 (MGM)

Month

Mea

n flo

w M

GM

1 2 3 4 5 6 7 8 9 10 11 12


1500

2500

3500

Means Boxplots


020

040

060

080

0

Standard Deviation


2000

4000

Maximum box plots


500

1500

2500

3500

Minimum box plots

45000

50000

55000

60000

65000

70000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecMonthlywatersurplus(MGM

)

Months(2015)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

1 2 3 4 5 6 7 8 9 10 11 12

gal/kw

h

Months

2006

2007

2008

2009

2010

0 5 10 15 20 25

0.60

0.65

0.70

Surface air temperature (C)

Dra

ke p

lant

s W

WIF

(gal

/kw

h)

D.AnnualWaterAdditionsfromtheReservoirSystem

Reservoirw

ater(M

gal)

Period(2010 2050)

AspenMunicipal Electri csystem 1903 6,671 2,845 70 2 5.5 MEAN Yes No

Burl ingtonMunicipa l Light&Power

1920 4,264 1,742 31 4 7.5 Xcel Energy

Yes Yes

CenterMunicipal Gas Light&Power 1941 2,382 775 19 2 1.5

Xcel Energy

Yes Yes a,b

ColoradoSprings Uti l i ties

1927 473,2463 192,476 5,182 15 611 Sel f Yes Yes b

Delta Municipal Light&Power

1937 8,325 2,840 57 7 5 MEAN Yes a

Estes ParkLight&Power

1945 6,315 10,008 130 0 0 PRPA Yes

FlemingElectricLightDepartment

1917 437 201 3 0 0 MEAN Yes Yes

FortCol l ins Uti l i ties 1935 136,427 64,694 1,485 0 0 PRPA Yes Yes d,eFortMogranElectricLight

1906 10,834 5,658 235 0 0 MEAN Yes Yes b

FountainMunicipal LightSystem 1919 23,049 15,359 210 0 0 MEAN Yes

FrederickMunicipal LightSystem 1950 8,211 3,510 46 0 0

UnitedPower

GlenwoodSprings Electri cSystem 1953 9,026 5,848 136 0 0 MEAN Yes Yes c,d

Granada Uti l i ties 1953 594 231 3 0 0 SECPAGunnison Light&WaterDepartment

1907 5,600 3,700 72 0 0 MEAN Yes Yes

HaxtunMunicipal Light&Power 1909 992 569 9 1 4 MEAN

Hol lyLight&Power 1949 894 584 6 2 1.1 ARPA Yes YesHolyoke Municipa l Light&Power 1909 2,272 946 23 3 0.875 MEAN Yes Yes

JulesburgMunicipal Light&Power 1911 1,336 604 11 3 2.1 MEAN Yes Yes

La Junta Municipa l Uti l i ties

1939 7,133 3,760 76 7 17.3 ARPA Yes Yes a

Las Animas Mun.Light&Power 1941 2,432 1,635 31 5 5.6 ARPA Yes Yes a

LamarLight&Power 1919 8,312 5,726 95 3 31.5 ARPA Yes Yes a ,cLongmontPower&Communication

1911 86,047 39,043 830 2 0.5 PRPA Yes Yes a,d,e

LovelandWater&Power

1925 65,824 30,911 696 6 0.7 PRPA Yes Yes a,e

Lyons MunicipalLight&Power 1974 1,957 1,056 13 0 0 MEAN Yes Yes

OakCreekMunicipal Uti l i ties

1907 979 539 6 1 1.3 MEAN

SpringfieldMunicipa l Uti l i ties

1947 1,420 962 12 4 2.8 ARPA

TrinidadMunicipal Power&Light 1948 9,542 4,639 57 6 12 ARPA Yes Yes b

WrayLight&Power 1903 2,234 1,244 21 0 0 YWElectri cYuma MunicipalLight&Power

1928 3,391 1,531 30 4 0.74 MEAN Yes Yes a

TOTAL 890,146 403,636 9,598 77 711

GridmixWWIFs(gal/kw

h)

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

2010 2020 2030 2040 2050Annu

alizedca

pitalin

vestmen

t(M$)

BAU/URM/ZInvWMCInvWM

UInvWM/UInvWM_GHG

E.AnnualWaterWithdrawal,WaterSavingsandGridMixesofCSUunderVariousScenarios

Waterwith

draw

al(M

gal)

Periods(2010 2050)

Waterwithdrawals(Mgal/year)

2010 2020 2030 2040 2050

Watersavings(Billiongallons)

Periods

UInvWM_GHG

Deg_UInvWM_GHG

SoutherndeliverySystem(SDS)Municipalwatersavings

URM Electric 410 1966 Wholepopulation 6.6%ZInvWM Electric 72,952 2262 Wholepopulation 7.5%CInvWM Electric 65,622 2259 Wholepopulation 7.5%UInvWM Electric (27,312) 2325 Wholepopulation 7.8%UInvWM_GHG Electric (25,923) 2331 Wholepopulation 7.8%

References

electric portfolio modeling with stochastic water - climate interactions: implications for...

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