# Presentation: 200138

##  Climate Forecasts for Improving Management of Energy and Hydropower  Resources in the Western U.S.

- Scripps Institution of Oceanography
- T. Barnett, D. Pierce
- University of California, Merced
- A. Westerling
- University of Washington
- D. Lettenmaier, A. Hamlet, A. Steinemann
- PNNL
- Ross Gutromson, Ning Lu

-  Climate Forecasts for Improving Management of Energy and Hydropower  Resources in the Western U.S.

## Conjunctive Hydropower Management

- streamflow available for western hydropower production is dominated by cool season precipitation
- the coefficient of variation, autocorrelation, and regional synchronicity have all increased markedly since about 1975
- these results suggest reduced opportunity for conjunctive management of the West’s hydropower resources in recent decades

## PRECIP

- Regionally Averaged Cool Season Precipitation Anomalies

**PRECIP**

**Notes:**
Despite a lack of clear trends in cool season precipitation volume over time, cool season precipitation variability in the West has changed markedly since the mid 1970s.  The coefficient of variation, autocorrelation, and regional synchronicity have all increased markedly since about 1975. 

## Annual hydropower production in the West has become more variable and more regionally synchronous in the period 1976-2002 in comparison with the rest of the 20th century. 

- Correlation:
- CRB-SSJ = 0.07
- CRB-PNW = 0.08
- SSJ-PNW = 0.36

- Correlation:
- CRB-SSJ = 0.14
- CRB-PNW = -0.14
- SSJ-PNW = 0.06

- Correlation:
- CRB-SSJ = 0.73
- CRB-PNW = 0.51
- SSJ-PNW = 0.65

**Notes:**
Annual hydropower production has followed suit.  These results suggest reduced opportunity for conjunctive management of the West’s hydropower resources in recent decades.  three-basin comparison across Columbia, Colorado, and Sacramento/San Joaquin.

## Modeling Climatic Influences on the Western Electricity Grid

**Notes:**
control areas for Western Electricity Coordinating Council member utilities.  these utilities contribute load and generation information to power planning scenarios.  we have adapted a 2004 summer peak planning case to model electricity generation and transmission around the western grid in summer.

## Modeling effects of reduced streamflow on power generation

- reduce hydropower production on the Grand Coulee dam 10% - 30%
- replace lost production via nearby thermal generation

## Dams along the Columbia River

## An Example of Weather Impacts

- If the hydro generation is reduced
- Economically: what is the cost of the replacement generation?
- Technically: will we get more power from Canada? Can we transport the power into the states?
- If the hydro generation is increased
- Economically: who is going to reduce generation?
- Technically: can we transport these hydro generation out to other states?

- B.C. Hydro

- Puget Sound Energy
- (Thermal)

- The Hydro plants

- A Summer Case

## An Example Case

- Reduce the power output of a hydro plant.
- Let Puget Sound Energy (a generator company in Seattle) pick up the power.
- Adjust the power outputs of the thermal generators
- Satisfy the transmission constraints (no congestion)
- Power flow converge
- Based on the cost curves, calculate the cost of the replacement energy.

- Decreased Hydro

- Increased
- Thermal

## Simulation in WECC systems

- Adjust Hydro and Thermal Outputs

- A WECC Planning Case
- (A summer day)

**Notes:**
Using GE software PSLF, we load in a WECC planning case of a summer day.  By adjusting the Hydro outputs and thermal outputs, we re-dispatch the system.  We can calculate the cost based on cost curves for each type of generator.

## Cost Curves

- The cost curve of Hydro electric plants vary with respect to the hydraulic heads.
- Thermal power plants usually have startup costs. When capacity limits are reached, the cost goes up significantly.

## Replacing Reduced Hydro with Thermal Generation

| 
Scenario | Hydro
(Coulee)* | Thermal
(PGE)* | Net 
Gain/Loss* |
| --- | --- | --- | --- |
| Base | 1800 MW | 1443 MW | na |
| - 10% | 1620 MW | 1614 MW |   -$5,198 |
| - 20% | 1440 MW | 1794 MW | -$13,670 |
| - 30% | 1260 MW | 1974 MW | -$32,316 |

- (Peak Summer Day - WECC Planning Scenario)

- *per hour

## Modeling Peak Electricity Demand as a Function of max Temperature and Day of Week

- hourly electric loads by service areas for western utilities as reported annually to FERC
- We use the daily peak for a representative sample of utilities with “clean” data
- daily TMAX on 1/8 degree grid averaged for four regions:  NW, NoCA, SoCA, SW
- peak daily load is fit to TMAX and weekday/weekend
- Result is scaled to loads reported in WECC planning cases

**Notes:**
primary issue here is that detailed load data is proprietary.  publicly available data is often poor quality.  what we have assembled here is likely the highest quality publicly available data.

## Northwest Peak Hourly Electricity Demand as a function of TMAX

**Notes:**
points are observed peak daily electricity demand (from hourly data reported to FERC) for northwest utilities versus daily tmax averaged over a 1/8 degree grid covering the northwest (tmax data are from the VIC input data set (see Maurer et 2002 J Clim 3237).  Lines are 2nd order polynomial fit of weekday and weekend peak electric loads to tmax.
winter loads are higher than summer loads in the northwest... heating demand is bigger than cooling demand.  consequently, peak summer load is substantially below generation capacity in the northwest.

## Northern CA Peak Hourly Electricity Demand as a function of TMAX

**Notes:**
summer heating demand is bigger than winter cooling demand in northern california.  (this is not just due to climate, but also built infrastructure, e.g. gas heating and electric air conditioning)  the opposite of the northwest.

## Southern CA Peak Hourly Electricity Demand as a function of TMAX

**Notes:**
winter electricity demand is more variable (and less temperature sensitive) in southern california than in northern california.  if we could more easily separate out coastal from inland demand, in southern california they would likely look somewhat different... and the fit in winter (lower tmax) would be tighter.

## Southwest Peak Hourly Electricity Demand as a function of TMAX

**Notes:**
summer cooling demand is high in the southwest, but winter demand is also more tightly linked to tmax than in southern california. fit is spline instead of polynomial.

## Coupling Energy Demand and Generation Models

- Electricity Generation from WECC model given Demands as a function of TMAX
- optimization given generic thermal, hydro cost curves
- constrained by existing transmission capacity
- WECC model is modified
- from peak hour planning case to 24 hours
- from non-coincident to coincident peak demand

## WECC Peak Generation - Peak Load

- TMAX at 90th %tile

- TMAX at 100th %tile

- Additional demand in California is met by increased production
- in the Northwest and Southwest (cheaper)

**Notes:**
in this example we use the modified WECC model to estimate surplus generation by service area (represented by the bubbles) for (left) all four western regions’ TMAX at the 90th percentile versus all four regions’ TMAX at the 100th percentile.  increased temperature increases demand everywhere, but especially in california and the southwest.  As demand increases, exports of power from the Northwest and Southwest to northern and southern CA increase.  The price per kw/hour for peak power increases as well.

## WECC Peak Generation - Peak Load

- TMAX at 95th %tile in CA,
- 50th %tile in NW & SW

- TMAX at 95th %tile in CA,
- 95th %tile in NW & SW

- Additional demand in Northwest & Southwest reduces exports
- to California, more power produced in California (more expensive)

**Notes:**
here we hold the summer tmax (and thus demand) constant in California at the 95th percentile, but vary tmax (demand) elsewhere from the 50th percentile to the 95th percentile.  More demand in Southern California is met by production in southern california as demand increases elsewhere.  note that while tmax and demand in northern CA are held constant, production increases there and a small surplus is exported to southern CA to compensate for reduced exports from the northwest in particular.

## Forecasting Summer Tmax

- Summer Tmax influenced by Pacific climate
- Modest skill
- CCA methodology
- Trend
- Parametric approach:  PDF conditional on climate indices

## Use Patterns in March Sea Surface Temperature and PDSI

- to forecast patterns in
- spring and summer
- temperatures

*after Alfaro, Gershunov and Cayan 2005 *

**Notes:**
incorporates trend and interannual/spatial variability associated with pacific climate indices

## Probability Density for Southern California

- Detrended Summer TMAX Conditional on ENSO & PDO

**Notes:**
a relatively simple forecast scheme is probability density functions for DETRENDED summer tmax  (1950-2003) conditional on Pacific sst modes.  this is less skillful in some places than schemes that incorporate soil moisture, but it may be easier to transfer to stakeholders.  ENSO and PDO are indices they can readily obtain off the web.   a small but significant difference in these pdfs implies a substantial change in the probability of an extreme temperature.  exploiting the trend in summer tmax (it has been removed from the data shown here) could increase forecast skill.  

## Example:  probability of western TMAX in top 95 percentile

- Out of 92 day season

**Notes:**
shift in probability of all four regions (NW, NoCal, SoCal, SW) being simultaneously in 95th percentile tmax in summer, implied by a joint skew-normal PDF across all four regions.  This parameterizationl overestimates the mean frequencies for both conditional distributions, but not the relative difference between them.

## Management Strategies conditional on a T forecast

- Demand:
- 	curtailment
- 	variable pricing
- Supply:
- 	forward contracts

## Forecasting Streamflow, Hydropower

- Climate forecasts dominate planning in fall and early winter
- by late winter, spring, observed snowpack dominates planning
- ENSO/PDO basis of long lead forecasts for both hydropower (supply) and temperature (demand)

## December

- Winter Climate
- Forecasts Dominate

- Hydrologic State
- Variables Dominate

- June

- March

- April 1 SWE (mm)

## Climate Forecast

- Reservoir
- Model

**VIC**
- Hydrology Model

## Linkage to Reservoir Models

- Streamflow
- Forecast

- Bias Correction

- Reservoir Model

- Observed Reservoir
- Contents

- Storage
- Ensemble

- Demand Scenarios

## Natural Streamflow (cfs)

- Red = Unconditional mean
- Blue = Ensemble mean
- Black = 2005 Observed

- Bias Corrected Long Range Streamflow Forecast for the Columbia River at The Dalles
- Nino3.4 index between 0.2 and 1.2 and warm PDO (interannual)

## Management Strategies conditional on a Streamflow/ Hydropower forecast

- Demand:
- 	curtailment (advance warning)
- 	variable pricing
- Supply:
- 	forward contracts
- 	conserve cheaper hydro supply
- 	export hydro