1. Hydropower Variability in the Western U. S
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: Energy Demand:
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?
Climate and its variability have large effects on energy supply and consumption in ways that are now predictable.  Detailed forecasts can provide lead times from days to as long as six months.  As yet, however, government and industries have not exploited this capability in power management.  Furthermore, California’s residential electrical consumption has regular peaks in winter and summer, while energy consumption in the Pacific Northwest has a strong winter peak.  Seasonal to inter annual climate along the west coast varies in such a way that California and the Pacific Northwest are often out of phase.  For instance, warm and dry winters in the Pacific Northwest (and hence reduced hydropower production) often occur at the same time as cool and wet conditions in California.  These conditions can now be forecast with some accuracy as much as a year in advance.
The University of Washington has developed models that simulate both the hydrology and operations of the Columbia River reservoir system, and the Sacramento-San Joaquin (combined State Water Project and federal Central Valley Project).  These models are driven by gridded daily historical climate data, now complete from 1916 to current. These sequences of historical climate data were used to address the following questions:
1) If the current system of hydropower reservoirs, and thermal energy plants in both the Pacific Northwest (PNW) and California had existed throughout the period 1916 to present, how frequently would winter and summer power production and demand have been out of phase?
2) What is the potential for use of weather and climate forecasts with lead times from a few weeks out to a year or more for improving joint operation of Pacific Northwest and California energy generation, considering specifically the potential for incorporation of weather and climate forecasts in projection of both supply (hydropower) and energy demand?

3. Outline 1/ Data and Models 2/ Observed covariability
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 2/ Observed covariability
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)
The National Climatic Data Center (NCDC) has just completed a major project which has converted the pre-1950 Gridded NCDC Cooperative Observer data to electronic form. Procedures described in Maurer et al (2002) have been used to create the gridded forcing data sets for the PNW and California at the same 1/8 degree spatial resolution used by Maurer et al. We use the Maurer et al procedures that adjust the gridded data for orographic effects based on the PRISM approach developed by Daly and colleagues at Oregon State University. A technique (Hamlet and Lettenmaier, 2004 submitted) was also developed to adjust the gridded data 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 PNW region

6. Hydrologic Model: VIC (1/2)
1/ Water Balance
2/ Runoff Routing
The forcing data developed as reported above were used to run the Variable Infiltration Capacity (VIC) model over the PNW and California regions for the period Simulations were conducted in water balance mode using a daily time step. Results have been obtained for streamflows at 23 locations (Figure 1) in California and 16 over the PNW. This allows for comparison of the VIC simulations and naturalized observed streamflows, for the periods for the San Joaquin-Sacramento and for the Columbia.
Techniques were also applied to correct streamflow simulations for model bias prior to using them as drivers for our Columbia River and California reservoir simulation models (see Hamlet and Lettenmaier 1999). The bias correction of daily streamflows is based on the full period when naturalized observed streamflows are available, i.e. from for the San Joaquin-Sacramento and for the Columbia.

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
Both the Columbia River Reservoir Model ColSim ( Hamlet and Lettenmaier, 1999) and the Central Valley reservoir model CVmod, developed by the Alphaus group at the University of Washington (Van Rheenen et al, 2004) represent the physical properties of the water resources systems and their performance under current operational policies respectively in the PNW and California.. The models assume that facilities, land use, water supply contracts and regulatory requirements are constant over this period, representing a fixed level of development, here CVmod and ColSim operate at a monthly time step.
CVmod is driven by the water demand, which is based on seasonal streamflow, temperature, water rights, level of hydrologic development and carryover storage among others. The water demand scenario in CVmod is set up in this study to represent the demand amount of The demand variability is set up to correspond to the period variability. These data sets are extracted from the reservoir model CALSIM. The water demand scenario has the same climate sensitivity of that of the period and is adjusted depending on the annual water year type varying from “critically dry” to “wet”. A different rule curve corresponds to each water year type.
ColSim is driven primarily by flood control rule curves and power demand, with power demand currently divided into “firm” and “non-firm” components that are constant in each year of the simulation. This represents the amount of hydropower currently supplied by hydropower projects within multi-objective management framework simulated by the model. During the simulation these fixed demands are subject to the various constraints in the reservoir operating policies. “Firm” resources are 100% reliable (by definition) and non-firm energy is about 90% reliable in the simulations. The model first ensures that firm resources are met and then allocates remaining reservoir storage to the production of non-firm energy. In periods of high flow, energy production is sometimes larger than the energy targets. To capture this situation the model reports the total available energy, firm resources met, and non-firm resources met in each month.
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
CA NORTH (cfs)
Mean annual
22,353
std
9,880 CV
0.4
CA SOUTH (cfs)
7,709
4,128
0.5
Both the Columbia River Reservoir Model ColSim ( Hamlet and Lettenmaier, 1999) and the Central Valley reservoir model CVmod, developed by the Alphaus group at the University of Washington (Van Rheenen et al, 2004) represent the physical properties of the water resources systems and their performance under current operational policies respectively in the PNW and California.. The models assume that facilities, land use, water supply contracts and regulatory requirements are constant over this period, representing a fixed level of development, here CVmod and ColSim operate at a monthly time step.
CVmod is driven by the water demand, which is based on seasonal streamflow, temperature, water rights, level of hydrologic development and carryover storage among others. The water demand scenario in CVmod is set up in this study to represent the demand amount of The demand variability is set up to correspond to the period variability. These data sets are extracted from the reservoir model CALSIM. The water demand scenario has the same climate sensitivity of that of the period and is adjusted depending on the annual water year type varying from “critically dry” to “wet”. A different rule curve corresponds to each water year type.
ColSim is driven primarily by flood control rule curves and power demand, with power demand currently divided into “firm” and “non-firm” components that are constant in each year of the simulation. This represents the amount of hydropower currently supplied by hydropower projects within multi-objective management framework simulated by the model. During the simulation these fixed demands are subject to the various constraints in the reservoir operating policies. “Firm” resources are 100% reliable (by definition) and non-firm energy is about 90% reliable in the simulations. The model first ensures that firm resources are met and then allocates remaining reservoir storage to the production of non-firm energy. In periods of high flow, energy production is sometimes larger than the energy targets. To capture this situation the model reports the total available energy, firm resources met, and non-firm resources met in each month.
North CA: peak in winter
South CA: peak in spring
ENSO: 17% annual flow difference
PDO: 2%

12. Streamflow Covariability
DALLES (cfs)
Mean annual
181,063
std
33,066 CV
0.2
Both the Columbia River Reservoir Model ColSim ( Hamlet and Lettenmaier, 1999) and the Central Valley reservoir model CVmod, developed by the Alphaus group at the University of Washington (Van Rheenen et al, 2004) represent the physical properties of the water resources systems and their performance under current operational policies respectively in the PNW and California.. The models assume that facilities, land use, water supply contracts and regulatory requirements are constant over this period, representing a fixed level of development, here CVmod and ColSim operate at a monthly time step.
CVmod is driven by the water demand, which is based on seasonal streamflow, temperature, water rights, level of hydrologic development and carryover storage among others. The water demand scenario in CVmod is set up in this study to represent the demand amount of The demand variability is set up to correspond to the period variability. These data sets are extracted from the reservoir model CALSIM. The water demand scenario has the same climate sensitivity of that of the period and is adjusted depending on the annual water year type varying from “critically dry” to “wet”. A different rule curve corresponds to each water year type.
ColSim is driven primarily by flood control rule curves and power demand, with power demand currently divided into “firm” and “non-firm” components that are constant in each year of the simulation. This represents the amount of hydropower currently supplied by hydropower projects within multi-objective management framework simulated by the model. During the simulation these fixed demands are subject to the various constraints in the reservoir operating policies. “Firm” resources are 100% reliable (by definition) and non-firm energy is about 90% reliable in the simulations. The model first ensures that firm resources are met and then allocates remaining reservoir storage to the production of non-firm energy. In periods of high flow, energy production is sometimes larger than the energy targets. To capture this situation the model reports the total available energy, firm resources met, and non-firm resources met in each month.
PNW: peak in early summer
ENSO/PDO: 12-16% annual flow difference

13. Hydropower Covariability
PNW (avg MW)
mean
13,644
std
3,082 CV
0.2
CA (avg MW)
976
399
0.4
Both the Columbia River Reservoir Model ColSim ( Hamlet and Lettenmaier, 1999) and the Central Valley reservoir model CVmod, developed by the Alphaus group at the University of Washington (Van Rheenen et al, 2004) represent the physical properties of the water resources systems and their performance under current operational policies respectively in the PNW and California.. The models assume that facilities, land use, water supply contracts and regulatory requirements are constant over this period, representing a fixed level of development, here CVmod and ColSim operate at a monthly time step.
CVmod is driven by the water demand, which is based on seasonal streamflow, temperature, water rights, level of hydrologic development and carryover storage among others. The water demand scenario in CVmod is set up in this study to represent the demand amount of The demand variability is set up to correspond to the period variability. These data sets are extracted from the reservoir model CALSIM. The water demand scenario has the same climate sensitivity of that of the period and is adjusted depending on the annual water year type varying from “critically dry” to “wet”. A different rule curve corresponds to each water year type.
ColSim is driven primarily by flood control rule curves and power demand, with power demand currently divided into “firm” and “non-firm” components that are constant in each year of the simulation. This represents the amount of hydropower currently supplied by hydropower projects within multi-objective management framework simulated by the model. During the simulation these fixed demands are subject to the various constraints in the reservoir operating policies. “Firm” resources are 100% reliable (by definition) and non-firm energy is about 90% reliable in the simulations. The model first ensures that firm resources are met and then allocates remaining reservoir storage to the production of non-firm energy. In periods of high flow, energy production is sometimes larger than the energy targets. To capture this situation the model reports the total available energy, firm resources met, and non-firm resources met in each month.
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!!
In order to assess the economic benefit of hydropower covariability, two energy demand models have been developed respectively for California and the PNW; a daily peak hour demand model based on peak hour demand time series and a monthly energy demand based on monthly energy demand.
A data base of hourly energy use was obtained by SIO form the California Energy Commission. This data set was constructed from the FERC 714 archives for the period , and covers both the PNW and CA. This data set was used to construct aggregate demand models for the two regions at both monthly and daily timescales based on linear relationships between monthly heating and cooling degree days (monthly model, Figure X) and daily maximum temperature and day of week (daily peak model) and energy load as reported in the CEC data set. These demand models were then used to produce a long time series from of energy demand in the two regions.
Similarly we have developed a monthly regression model for regional demand based on population weighted monthly heating or cooling degree days in the major urban centers (Figure X) and also based on day type ( week day, week end, holiday). Holidays include New Year, July 4th, Thanksgiving and Christmas. This model is only skillful in summer (July-August) in CA and in winter (January-April) in the PNW and is set to the monthly average for the remaining months. The model shows some interesting relationships to seasonal to interannual climate predictors like ENSO, as explained later

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
In order to assess the economic benefit of hydropower covariability, two energy demand models have been developed respectively for California and the PNW; a daily peak hour demand model based on peak hour demand time series and a monthly energy demand based on monthly energy demand.
A data base of hourly energy use was obtained by SIO form the California Energy Commission. This data set was constructed from the FERC 714 archives for the period , and covers both the PNW and CA. This data set was used to construct aggregate demand models for the two regions at both monthly and daily timescales based on linear relationships between monthly heating and cooling degree days (monthly model, Figure X) and daily maximum temperature and day of week (daily peak model) and energy load as reported in the CEC data set. These demand models were then used to produce a long time series from of energy demand in the two regions.
Similarly we have developed a monthly regression model for regional demand based on population weighted monthly heating or cooling degree days in the major urban centers (Figure X) and also based on day type ( week day, week end, holiday). Holidays include New Year, July 4th, Thanksgiving and Christmas. This model is only skillful in summer (July-August) in CA and in winter (January-April) in the PNW and is set to the monthly average for the remaining months. The model shows some interesting relationships to seasonal to interannual climate predictors like ENSO, as explained later
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
The National Climatic Data Center (NCDC) has recently created digital archives of daily climatological data (primarily precipitation and daily temperature maxima and minima) for the continental U.S. going back to the beginning of the period of instrumental records. Previous electronic archives were typically available only back to about 1948, with a few stations digitized back to the 1930's. Using the newly available data merged with the previous archive ( ), we have created a 1/8 degree data set of precipitation, temperature, and derived radiative forcings and other surface variables needed to drive the Variable Infiltration Capacity (VIC) macroscale hydrology model over the western U.S. (soon to be extended to the entire conterminous U.S.).
The first step though consists of developing a method, based an carefully quality controlled HCN (Historical Climatology Network; see Easterling et al, “United States Historical Climatology Network (U.S. HCN) Monthly Temperature and Precipitation Data”, ORNL/CDIAC-87, NDP-019/R3. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee) stations to control for drift in the gridded data that otherwise results from changes in the stations included over time. We proceeded as follows:
Using the station meta data for indexing, the data were gridded to a uniform 1/8 degree grid over the study domain.
Precipitation data were then rescaled for each month and each grid location by comparing the long term mean of the raw data from to the PRISM (Daly et al. , 1984, J. of Applied Met. (33) pp ) means for the same location and time period.
The temporal variability of the precipitation data was directly derived from the station data, but the spatial distribution was forced to match the PRISM results in the long-term mean for each month at each location. Earlier records were corrected by the same scaling fraction, even though the exact group of stations that define the raw precipitation values are not guaranteed to be the same in different periods. No attempt was made in this preliminary analysis to remove temporal inhomogeneities in the precipitation time series.
Wind data were gathered from NCAR reanalysis, and the daily climatological mean value for each grid cell was used to define the daily mean wind speed. This approach was used because reanalysis data are not available prior to 1948.
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
TRENDS
WARM
ENSO
PDO
ENSO/ PDO
COLD ENSO
Temp
CA JA - +
PNW JFMA
Peak Hour Energy Demand
Daily Energy Demand
Hydro-power
(-)
PNW JJ

27. Energy transfer model (in progress)
Scenario 1: total daily energy ( hydropower + Conventional Resources) meet PNW total daily and peak hour energy demands.
Daily time step
Results aggregated to monthly time step
Principles:
Assumes perfect forecast ( monthly hydropower production known)
Transmission line capacity limits the energy transfers
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?