hydropower variability in the western u.s.: consequences and opportunities
DESCRIPTION
Hydropower Variability in the Western U.S.: Consequences and Opportunities. Nathalie Voisin, Alan Hamlet, Phil Graham, Dennis P. Lettenmaier UW Water Resources Group Civil and Environmental Engineering University of Washington April 7, 2005. Background. Climate is predictable: - PowerPoint PPT PresentationTRANSCRIPT
Hydropower Variability in the Western U.S.: Consequences and Opportunities
Nathalie Voisin, Alan Hamlet, Phil Graham, Dennis P. Lettenmaier
UW Water Resources GroupCivil and Environmental Engineering
University of WashingtonApril 7, 2005
Background
Climate is predictable: Increasingly predictable up to 6 months (or more) in advance West coast U.S. climate more predictable than other regions, due to
strong ocean influence California and the Pacific Northwest are out of phase for some climate
events such as El Nino Southern Oscillation (ENSO)
Energy Demand is predictable: California has regular peaks in winter and summer while energy
consumption in the Pacific Northwest (PNW) has a strong winter peak
Question: How can climate predictions be used to manage West Coast energy transfers more efficiently?
In previous episodes …
Episodes I to III: Litterature review i) Precipitation, temperature and streamflow predictability based on climate (ENSO and PDO):extensive list
ii) Hydropower and Climate variability:Hamlet and Lettenmaier 1999, Cayan et al 2003
iii) Retrospective Analysis 1950-2000:Maurer et al 2002
iv) California summer temperature predictability :Alfaro et al 2005
Outline
Episode IV, A New Hopemeteorological data
hydrologic modelreservoir modelsenergy demand model
Episode V, VIC strikes backStreamflow and climate
Hydropower and climateElectricity demand and climate
Episode VI, return to modelingconstraintsbenefits and climatepotential for more benefit
A New Hope
Meteorological data Hydrologic Model Reservoir Models Energy Demand Model
The procedure in brief
Reservoir ModelColSim / CVMod
Monthly Natural Streamflow
Hydropower
Hydrologic ModelVIC
+ Routing Model + Bias Correction
Pre-processing(gridding, climatic trend, etc)
Temperature, Precipitation and wind observations
Meteorological Data
Station Data sources : National Climatic Data Center (NCDC)
Extended time series from 1916 to 2003
Forcing data sets gridded to the 1/8 degree
Adjustment of forcing data sets for orographic effects based on PRISM (Parameter-elevation Regressions on Independent Slopes Model ) approach (Daly and colleagues at Oregon State University)
Adjustment to reflect long-term trends that are present in the carefully quality controlled Hydroclimatic Network (HCN) and a similar network for the Canadian portion of the Pacific Northwest (PNW) region (Hamlet and Lettenmaier 2004)
Hydrologic Model: VIC (1/2)
1/ Water Balance 2/ Runoff Routing
Hydrological Model: VIC (2/3)
Simulated Flow = RedObserved = Black
Hydrological Model: VIC (2/3)
Simulated Flow = RedObserved = Black
Reservoir Models: CVMod and ColSim
Represent physical properties of the reservoir systems and their operation Assume fixed level of development Monthly time step
Monthly Natural Streamflow
Water Demand Flood Control, Energy Demand
CALIFORNIACVMod
(Van Rheenen et al 2004)
PACIFIC NORTHWESTColSim
(Hamlet and Lettenmaier 1999)
Hydropower
Monthly Natural Streamflow
Hydropower
Electricity Demand Model
Based on a regression of observed daily average or peak hour load with observed daily maximum temperature 1993-2000, and day type ( week end, national holyday, week day)
Derive 1916-2002 daily and peak hour electricity demand for California and the PNW
More skill in summer time in CA and in wintertime in the PNW
PNW CA
In Brief
1915-2002 meteorological data set in CA and PNW 1916-2002 naturalized bias corrected streamflow in CA and PNW 1917-2002 hydropower time series in CA and PNW 1917-2002 electricity demand in CA and PNW 1917-2002 ENSO and PDO climate signals
Episode V, VIC strikes back
Observed Covariability
Goal: confirm litterature review results with our time series derive new relationships
Streamflow and Climate Hydropower and Climate Energy demand and Climate Hydropower and Energy Demand
Streamflow covariability
Seasonal: North CA and the PNW are out of phase
Interannual based on ENSO and PDO: out of phase
Streamflow Covariability
CA NORTH (cfs)
Mean annual 22,353
std 9,880
CV 0.4
CA SOUTH (cfs)
Mean annual 7,709
std 4,128
CV 0.5
North CA: peak in winterSouth CA: peak in spring
ENSO: 17% annual flow differencePDO: 2%
Natural Streamflows in South California, San Joaquin River
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Flo
w (
cfs
)
Cold ENSO
Warm ENSO
Cold PDO
Warm PDO
Natural Streamflows in North California, Sacramento River
0
10,000
20,000
30,000
40,000
50,000
60,000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Flo
w (
cfs
)
Cold ENSOWarm ENSO
Cold PDOWarm PDO
Streamflow Covariability
DALLES (cfs)
Mean annual 181,063
std 33,066
CV 0.2
PNW: peak in early summer
ENSO/PDO: 12-16% annual flow difference
Natural Streamflows at the Dalles
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
450,000
500,000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Flo
w (
cfs
)
Cold ENSO
Warm ENSO
Cold PDO
Warm PDO
Columbia River Discharge at The Dalles, Or.
MAY-JUNE-JULY ANNUAL
Avg flow in cfs % of Average Avg flow in cfs % of Average
WARM ENSO 354,006 89% 172,501 92%
PDO 347,049 87% 172,063 91%
ENSO/PDO 330,980 83% 165,425 88%
COLD ENSO 416,866 105% 197,647 105%
PDO 438,924 110% 204,213 108%
ENSO/PDO 449,240 113% 209,527 111%
AVG 397,628 100% 188,271 100%
CALIFORNIA
NORTH, MAMJJ SOUTH, MJJAS
ANNUAL FLOW
NORTH SOUTH
Avg Flow
% of Avg
Avg Flow
% of Avg
Avg Flow
% of Avg
Avg Flow
% of Avg
WARM ENSO 13,272 110% 3,224 98% 8,481 107% 2,430 102%
PDO 13,221 110% 3,896 119% 8,495 107% 2,628 111%
ENSO/PDO 13,211 109% 3,524 108% 8,505 107% 2,579 109%
COLD ENSO 10,938 91% 3,166 97% 7,253 91% 2,277 96%
PDO 11,876 98% 2,830 86% 7,734 98% 2,154 91%
ENSO/PDO 10,673 88% 2,687 82% 7,175 90% 2,013 85%
AVG 12,074 100% 3,275 100% 7,933 100% 2,374 100%
Hydropower Covariability
PNW (avg MW)
mean 13,644
std 3,082
CV 0.2
CA (avg MW)
mean 976
std 399
CV 0.4
PNW: peak in JCA: peak in M
Hydropower Production in the PNW
0
2,000,000
4,000,000
6,000,000
8,000,000
10,000,000
12,000,000
14,000,000
16,000,000
18,000,000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
MW
h
cold ENSO
warm ENSO
cold PDO
warm PDO
Hydropower Production in CA
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
MW
h
cold ENSO
warm ENSO
cold PDO
warm PDO
Hydropower Covariability
53% correlation between PNW and CA hydropower
PNW April-May-June-July
Hydro. pdtion aMW
% of the avg
Demand on avg aMW
Hydro/ Demand
WARM ENSO 17,574 93% 17,458 101%
PDO 17,473 92% 17,466 100%
ENSO/PDO 16,737 88% 17,441 96%
COLD ENSO 19,708 104% 17,471 113%
PDO 20,586 108% 17,460 118%
ENSO/PDO 20,546 108% 17,466 118%
AVG 18,983 100% 17,470 109%
Energy Demand Covariability
I/ Seasonally:
Demands are out of phase in CA and in the PNW!!
II/ Interannually: ~1% variation of peak or daily electricity demand, underestimation of the variability due to monthly averaging
Daily Energy Demand (93-00)
0100,000200,000300,000400,000500,000600,000700,000800,000900,000
1,000,000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
MW
-hr
day PNW
day CA
Peak Hour Energy Demand (93-00)
05,000
10,00015,00020,00025,00030,00035,00040,00045,00050,000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
MW
-hr
peak PNW
peak CA
Climate based predictabilities
Predictable variables Streamflows Hydropower Electricity demand ENSO and PDO indices
Seasonal timing Energy demand is out of phase in CA and in the PNW PNW energy production and energy demand are out of phase PNW hydropower and CA peak energy demand are in phase
Interannual variability: Streamflows tend to be out of phase in CA and PNW Hydropower productions are correlated at 53% PNW hydropower and CA peak energy demand are in phase
Episode VI, Return To Modeling
Development of the transfer model Economic benefit New management timeline
The Pacific NW-SW Intertie
Slightly less than 8000 MW capacity Reliable transmission Southward transfer during peak hour Northward transfer overnight, if needed
Notes: The energy transfer follows the energy demand Transfers are decided on an hourly basis during
the day Currently climate information is not used in
planning West Coast energy transfers
Transfer Model
Based on conservative EXCESS PNW HYDROPOWER estimate( Production – daily demand)
Assumption: intertie used for California Peak Hour ~ 10 hour/day
Additional Constraints Capacity:
Excess Hydro. <= Intertie Transfer capacity (7500 MW)
Location:Excess Hydro. <= Hydro. Production at The Dalles and John Day
Price:Sell Price <= CA production price with conventionnal resources
Economic Benefit
ELECTRICITY RATES April May June July August
CA Natural Gas electricity $/MWh 59.52 59.52 59.52 59.52 59.52
PNW hydropower sale (HHL + load variance)
$/MWh 19.15 19.08 23.63 30.71 44.94
(PNW transmission) $/MWh 3.39 3.39 3.39 3.39 3.39
(PNW high load hour sale (HHL), PNW benefit)
$/MWh 18.05 17.98 22.53 29.61 43.84
Transfer Model
Constraint Excess Hydropower
PNW April-May-June-July
% of the avg
Hydro/ Demand
Avg available extra hydro over 10 hours aMW
% of the avg
WARM ENSO 93% 101% 3336 88%
PDO 92% 100% 3333 88%
ENSO/PDO 88% 96% 3017 80%
COLD ENSO 104% 113% 4321 114%
PDO 108% 118% 4900 129%
ENSO/PDO 108% 118% 4697 124%
AVG 100% 109% 3788 100%
Economic Benefit
Extra Benefits and Costs over April-May-June-July in millions $
CA BENEFIT PNW BENEFIT
WARM ENSO 132 77
PDO 135 78
ENSO/PDO 120 69
COLD ENSO 172 102
PDO 186 111
ENSO/PDO 189 112
AVG 159 95
Predictability Tools
Predictability Tools
A new Timeline
Based on
A Forecast Timeline
Jun Aug Jun
On ~June 1 we have:
•CurrentReservoirContents (PNW and CA)
•Current SummerStreamflow Forecast (PNW and CA)
•ENSO forecast•PDO forecast
CA Demand
Surplus PNWEnergy forComing Spring
PNW Winter Demand
Jan
Forecasts:
Conclusions (1)
1917-2002 retrospective Analysis CA and PNW hydropower covary 53% of the time
PNW hydropower is predictable using ENSO and PDO indices
Excess PNW hydropower is predictable
Electricity demands are predictable
Electricity tranfers are of the same order of magnitude as the CA hydropower (for a total of up to 20% of CA peak demand)
Predictable economic benefit, averaging $159 and $95 million for CA and the PNW
Conclusions (2)
Perspective for Forecasting Prediction of next winter ENSO available California summer electricity demand is predictable using MAM PDO
indices (Alfaro et al 2005 and our electricity demand model) Excess hydropower can be simulated
Thank You!
Meteorological Data : NCDC
Preprocessing Regridding
Lapse Temperatures
Correction to RemoveTemporal
Inhomogeneities
HCN/HCCD
Monthly Data
Topographic Correction forPrecipitation
Coop Daily Data PRISM Monthly
PrecipitationMaps
Extended time series from 1916 to 2003
Temperature &
Precipitation
Overall Covariability
TRENDS WARM ENSO PDO ENSO/
PDO
COLD ENSO PDO ENSO/
PDO
Temp CA JA - - - + + +PNW JFMA + + + - - -
Peak Hour Energy Demand
CA JA + + + - - -PNW JFMA - - - + + +
Daily Energy Demand
CA JA + + + - - -PNW JFMA - - - + + +
Hydro-power
CA JA + + + (-) (-) (-)PNW JJ - - - + + +
The scientific question
CLIMATEENSO / PDO
Streams
Electricity DemandHydropower production
IntertieMutual Benefit