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)

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 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

Additional slides for eventual questions

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