Hydropower Variability in the Western U.S.: Consequences and Opportunities

1. 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

2. 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?

3. 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

4. 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

5. 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)

6. Hydrologic Model: VIC (1/2) 1/ Water Balance 2/ Runoff Routing

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

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

9. 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 Flood Control, Energy Demand Water Demand CALIFORNIA CVMod (Van Rheenen et al 2004) PACIFIC NORTHWEST ColSim (Hamlet and Lettenmaier 1999) Hydropower

10. 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

11. Streamflow Covariability North CA: peak in winter South CA: peak in spring ENSO: 17% annual flow difference PDO: 2%

12. Streamflow Covariability PNW: peak in early summer ENSO/PDO: 12-16% annual flow difference

13. Hydropower Covariability PNW: peak in J CA: peak in M

14. Energy Demand Covariability 2 types of demand: • Peak hour demand • Daily total Demand Demands are out of phase in CA and in the PNW!!

15. Energy Demand Covariability How predictable is the energy demand? Regression of observed energy load with temperatures Monthly average of daily total demand & Warming/Cooling degree days [ Σ (T-18.7)day ] Daily Peak Hour Demand & Tmax R2=0.68 R2=0.60

16. 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

17. 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

18. 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

19. 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 Precipitation Climate (timing) Energy Demand Hydropower ? Energy Transfers

20. 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 Temperature Precipitation Climate Forecast (timing) Energy Demand Hydropower Derived daily and peak hour Disaggregation to daily based on temperature Energy Transfers Energy Transfer Model

21. 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

22. Additional slides for eventual questions

23. Meteorological Data : NCDC HCN/HCCD Monthly Data Topographic Correction for Precipitation Correction to Remove Temporal Inhomogeneities Preprocessing Regridding Lapse Temperatures Temperature & Precipitation Coop Daily Data PRISM Monthly Precipitation Maps Extended time series from 1916 to 2003

24. Energy Demand Model (1/2) Derived peak hour energy demand time series in the Pacific Northwest : skill in wintertime

25. Energy Demand Model (2/2) Derived peak hour energy demand time series in California: skill in summer

26. Overall Covariability

27. Energy transfer model (in progress) • Daily time step • Results aggregated to monthly time step • Principles: • Assumes perfect forecast ( monthly hydropower production known) • Transmission line capacity limits the energy transfers Scenario 1: total daily energy ( hydropower + Conventional Resources) meet PNW total daily and peak hour energy demands. Hydropower + Conventional Resources over peak hour period Meet PNW Peak Hour Demand ? How much energy needed to meet remaining daily energy demand? Compute Potential Transfer during Peak Hour Enough time/capacity to send energy back eventually?