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:  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 : Maurer et al 2002 iv) California summer temperature predictability : Alfaro et al 2005

Outline Episode IV, A New Hope meteorological data hydrologic model reservoir models energy demand model Episode V, VIC strikes back Streamflow and climate Hydropower and climate Electricity demand and climate Episode VI, return to modeling constraints benefits and climate potential for more benefit

A New Hope  Meteorological data  Hydrologic Model  Reservoir Models  Energy Demand Model

The procedure in brief Reservoir Model ColSim / CVMod Monthly Natural Streamflow Hydropower Hydrologic Model VIC + 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 Balance2/ Runoff Routing

Hydrological Model: VIC (2/3) Simulated Flow = Red Observed = Black

Hydrological Model: VIC (2/3) Simulated Flow = Red Observed = 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 CALIFORNIA CVMod (Van Rheenen et al 2004) PACIFIC NORTHWEST ColSim (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 , and day type ( week end, national holyday, week day)  Derive daily and peak hour electricity demand for California and the PNW  More skill in summer time in CA and in wintertime in the PNW

PNWCA

In Brief  meteorological data set in CA and PNW  naturalized bias corrected streamflow in CA and PNW  hydropower time series in CA and PNW  electricity demand in CA and PNW  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 annual22,353 std9,880 CV0.4 CA SOUTH (cfs) Mean annual7,709 std4,128 CV0.5 North CA: peak in winter South CA: peak in spring ENSO: 17% annual flow difference PDO: 2%

Streamflow Covariability DALLES (cfs) Mean annual181,063 std33,066 CV0.2 PNW: peak in early summer ENSO/PDO: 12-16% annual flow difference

Columbia River Discharge at The Dalles, Or. MAY-JUNE-JULYANNUAL Avg flow in cfs% of AverageAvg flow in cfs% of Average WARMENSO 354,00689%172,50192% PDO 347,04987%172,06391% ENSO/PDO 330,98083%165,42588% COLDENSO 416,866105%197,647105% PDO 438,924110%204,213108% ENSO/PDO 449,240113%209,527111% AVG 397,628100%188,271100% CALIFORNIA NORTH, MAMJJSOUTH, MJJAS ANNUAL FLOW NORTHSOUTH Avg Flo w % of Av g Avg Fl ow % of Av g Avg Flo w % of Av g Avg Fl ow % of Av g WARMENSO13,272110%3,22498%8,481107%2,430102% PDO13,221110%3,896119%8,495107%2,628111% ENSO/PDO13,211109%3,524108%8,505107%2,579109% COLDENSO10,93891%3,16697%7,25391%2,27796% PDO11,87698%2,83086%7,73498%2,15491% ENSO/PDO10,67388%2,68782%7,17590%2,01385% AVG 12,074100%3,275100%7,933100%2,374100%

Hydropower Covariability PNW (avg MW) mean13,644 std3,082 CV0.2 CA (avg MW) mean976 std399 CV0.4 PNW: peak in J CA: peak in M

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 WARMENSO17,57493%17,458101% PDO17,47392%17,466100% ENSO/PDO16,73788%17,44196% COLDENSO19,708104%17,471113% PDO20,586108%17,460118% ENSO/PDO20,546108%17,466118% AVG 18,983100%17,470109%

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

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 RATESAprilMayJuneJulyAugust CA Natural Gas electricity$/MWh59.52 PNW hydropower sale (HHL + load variance) $/MWh (PNW transmission)$/MWh3.39 (PNW high load hour sale (HHL), PNW benefit) $/MWh

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 WARMENSO93%101% % PDO92%100% % ENSO/PDO88%96% % COLDENSO104%113% % PDO108%118% % ENSO/PDO108%118% % AVG 100%109% %

Economic Benefit Extra Benefits and Costs over April- May-June-July in millions $ CA BENEFITPNW BENEFIT WARMENSO13277 PDO13578 ENSO/PDO12069 COLDENSO PDO ENSO/PDO AVG 15995

Predictability Tools

A new Timeline  Based on

Conclusions (1)  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 Remove Temporal Inhomogeneities HCN/HCCD Monthly Data Topographic Correction for Precipitation Coop Daily Data PRISM Monthly Precipitation Maps Extended time series from 1916 to 2003 Temperature & Precipitation

Overall Covariability TRENDSWARM ENSOPDOENSO/ PDO COLD ENSOPDOENSO/ PDO TempCA 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 CLIMATE ENSO / PDO Streams Electricity Demand Hydropower production Intertie Mutual Benefit