hydropower variability in the western u.s.: consequences and opportunities
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Hydropower Variability in the Western U.S.: Consequences and Opportunities. Nathalie Voisin, Alan Hamlet, Phil Graham, Dennis P. Lettenmaier UW-UBC Fall Hydrology Workshop University of Washington October 1, 2004. Background. Climate: - PowerPoint PPT PresentationTRANSCRIPT
Hydropower Variability in the Western U.S.: Consequences and Opportunities
Nathalie Voisin, Alan Hamlet, Phil Graham, Dennis P. Lettenmaier
UW-UBC Fall Hydrology WorkshopUniversity of Washington
October 1, 2004
Background
Climate: 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: 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?
Outline
1/ Data and Models Meteorological data Hydrological model Reservoir models
2/ Observed covariability Streamflow and Climate Hydropower and Climate Energy demand and Climate Hydropower and Energy Demand
3/ Opportunity: more efficient inter-regional energy transfers? Currently climate information is not used in planning West Coast
energy transfers Some ideas for an energy transfer model that exploits climate
information
4/ Conclusions
1/ The Data
1/ Data and Models Meteorological data Hydrological model Reservoir models
2/ Observed covariability
3/ Opportunity: more efficient inter-regional energy transfers?
4/ Conclusions
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)
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 StreamflowWater Demand
Flood Control, Energy Demand
CALIFORNIACVMod
(Van Rheenen et al 2004)
PACIFIC NORTHWESTColSim
(Hamlet and Lettenmaier 1999)
Hydropower
2/ Observed Covariability
1/ Data and Models
2/ Observed Covariability Streamflow and Climate Hydropower and Climate Energy demand and Climate Hydropower and Energy Demand
3/ Opportunity: more efficient inter-regional energy transfers?
4/ Conclusions
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
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
Energy Demand Covariability
2 types of demand: Peak hour demand Daily total Demand
Demands are out of phase in CA and in the PNW!!
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
Energy Demand Covariability
How predictable is the energy demand?
Regression of observed energy load with temperatures
Daily Peak Hour Demand & TmaxMonthly average of daily total demand & Warming/Cooling degree days [ Σ (T-18.7)day ]
R2=0.60
R2=0.68
Timing
Interannual variability: winter and summer 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: ENSO events ENSO warm: Higher temperatures and less precipitation in the PNW
ENSO cold: Higher energy demand in the PNW in winter and higher summer hydropower production
3/ Energy Transfers
1/ Data and Models
2/ Observed Covariability
3/ Opportunity: more efficient inter-regional energy transfers? Currently climate information is not used in planning
West Coast energy transfers Some ideas for an energy transfer model that exploits
climate information
4/ Conclusions
The Pacific NW-SW Intertie
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
More efficient energy transfers?
Based on a decision making process following the demand, a relation exists between climate and a 10 year intertie time series :
BUT complications appears when using the above climate-intertie
Temperature Climate Precipitation
Energy Transfers
HydropowerEnergy Demand
?
(timing)
Energy transfer model (in progress)
Monthly time step, daily sub time step ( peak hour complication) Principles:
• Assumes perfect forecast ( monthly hydropower production known)
• Transmission line capacity limits the energy transfers
TemperatureClimate Forecast
Precipitation
Energy Transfers
HydropowerEnergy Demand
(timing)
Derived daily and peak hour Disaggregation to
daily based on temperature
Energy Transfer Model
Conclusions
Observed Covariability: Streamflow and Climate (precipitation, temperature) Hydropower and Climate (precipitation and temperature) Energy Demand and Temperature
Consequences : Energy supply and demand are out of phase within the same Region ( California or PNW)
Opportunities: Temperature is (relatively) highly predictable. How can long-range (out to a year) forecasts of air temperature anomalies be used to better manage energy transfers between the two regions?
Future work Evaluate the potential for increased transfers using statistical methods, combined with a
simple model for incorporating (uncertain) forecasts of energy demand and supply for lead times up to one year
Evaluate the worth of (energy production and demand) forecasts via an economic analysis based on the price difference between hydropower and conventional resources
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
Energy Demand Model (1/2)
Derived peak hour energy demand time series in the Pacific Northwest : skill in wintertime
Energy Demand Model (2/2)
Derived peak hour energy demand time series in California: skill in summer
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 - - - + + +
Energy transfer model (in progress)
Compute Potential Transfer during Peak Hour
Hydropower +
Conventional Resources over peak hour period
Meet PNW Peak Hour Demand ?
How much energy needed to meet remaining daily
energy demand?
Scenario 1: total daily energy ( hydropower + Conventional Resources) meet PNW total daily and peak hour energy demands.
Enough time/capacity to send energy back eventually?
Daily time step Results aggregated to monthly time step Principles:
• Assumes perfect forecast ( monthly hydropower production known)
• Transmission line capacity limits the energy transfers