Climate and Energy in California

Climate and Energy in CaliforniaClimate and Energy in California

David W. PierceDavid W. Pierce

Tim P. BarnettTim P. BarnettEric AlfaroEric Alfaro

Alexander GershunovAlexander Gershunov

Climate Research DivisionClimate Research DivisionScripps Institution of OceanographyScripps Institution of Oceanography

La Jolla, CALa Jolla, CA

How we got started: a typical climate change result

IPCC, 2001

What does this mean to us?

Effect of Climate Change on Western U.S.

• Large and growing population in a semi-arid region

• How will it impact water resources?

• Use an “end-to-end” approach

Project overview

Tim Barnett, SIO; R. Malone, LANL; W. Pennell, PNNL; A. Semtner, NPS; D. Stammer, SIO; W. Washington, NCAR

Step 1

• Begin with current state of global oceans

Why initialize the oceans?

• That’s where the heat has gone

Data from Levitus et al, Science, 2001

Step 2

• Estimate climate change due to emissions

Global Climate Change Simulation

• Parallel Climate Model (PCM)

• Business as Usual Scenario (BAU)

• 1995-2100

• 5 ensemble members

How well does the PCM work over the Western United States?

Dec-Jan-Feb total precipitation (cm)

Step 3

• Downscaling and impacts

Why downscale?

Global model (orange dots) vs. Regional model grid

(green dots)

How good is downscaling?

El Nino rainfall simulation

Observations Downscaled modelStandard reanalysis

Ruby Leung, PNNL

Change in California snowpack

Projected change by 2050

River flow earlier in the year

Runoff already coming earlier

Columbia Basin Options

Hydropower

Or

Salmon

Los Angeles water shortage

Christensen et al., Climatic Change, to appear

Miss water treaty obligations to Mexico

Christensen et al., Climatic Change, to appear

More wildfires

100% more acres burned in 2100

Less time for Salmon to reproduce

Now:

Lance Vail,PNNL

Future:

• A reduction of winter snowpack. Precipitation more likely to fall as rain, and what snow there is melts earlier in the year.

• River flow then comes more in winter/spring than in spring/summer – implications for wildfires, agriculture, recreation, and how reservoirs are managed.

• Will affect fish whose life cycle depends on the timing of water temperature and spring melt.

• Will also change salinities in the San Francisco bay.

Climate change conclusions

More heat waves

Dan Cayan and Mike Dettinger,Scripps Inst. Oceanography

August daily high temperature, Sacramento, CA

On a warm summer afternoon, 40% of all electricity in California goes to air conditioning

California Energy Project

Objective:

Determine the economic value of climate and weather forecasts to the energy sector

Climate & weather affect energy demand

Source: www.caiso.com/docs/0900ea6080/22/c9/09003a608022c993.pdf

…and also energy supply

Green et al., COAPS Report 97-1

Typical effects of El Nino:

CAhydro

Project Overview

Scripps Inst. OceanographyUniversity of Washington

Georgia Inst. Tech

California Energy Commission California ISO

PacifiCorpSan Diego Gas & Elec.

SAIC

Academia

StatePartners

IndustrialPartners

Why aren’t climate forecasts used?

• Climate forecasts are probabilistic in nature – sometimes unfamiliar to the user

What climate forecasts mean

Why aren’t climate forecasts used?

• Climate forecasts are probabilistic in nature – sometimes unfamiliar to the user

• Lack of understanding of climate forecasts and their benefits

• Language and format of climate forecasts is hard to understand – need to be translated for end-users

• Aversion to change – easier to do things the traditional way

1. California "Delta Breeze"

• An important source of forecast load error (CalISO)

• Big events can change load by 500 MW (>1% of total)

• Direct cost of this power: $250K/breeze day (~40 days/year: ~$10M/year)

• Indirect costs: pushing stressed system past capacity when forecast is missed!

NO delta Breeze

Sep 25, 2002: No delta breeze; winds carrying hot air down CaliforniaCentral valley. Power consumption high.

Delta Breeze

Sep 26, 2002: Delta breeze starts up; power consumption drops >500 MW compared to the day before!

Weather forecasts of Delta Breeze

1-day ahead prediction of delta breeze wind speed from ensemble average of NCEP MRF, vs observed.

Statistical forecast of Delta Breeze

(Also uses large-scale weather information)

By 7am, can make a determination with >95% certainty, 50% of the time

Delta Breeze summary

• Using climate information can do better than dynamic weather forecasts

• Possible savings of 10 to 20% in costs due to weather forecast error. Depending on size of utility, will be in range of high 100,000s to low millions of dollars/year.

2. Load demand management

• Induce customers to reduce electrical load on peak electrical load days

• Prediction challenge: call those 12 days, 3 days in advance

• Amounts to calling weekdays with greatest "heat index" (temperature/humidity)

Why shave peak days?

http://www.energy.ca.gov/electricity/wepr/2000-07/index.html

Price vs. Demand

http://www.energy.ca.gov/electricity/wepr/1999-08/index.html

July

Sunday Monday Tuesday Wednesday Thursday Friday Saturday

1

2990 MW79 F

2

3031 MW81 F

3

3389 MW88 F

4

2958 MW85 F

5

6 7

2814 MW71 F

8

2766 MW73 F

9

2791 MW75 F

10

2906 MW79 F

11

3106 MW83 F

12

13 14

3130 MW76 F

15

3089 MW74 F

16

3046 MW84 F

17

3102 MW77 F

18

2888 MW78 F

19

20 21

3317 MW82 F

22

2867 MW73 F

23

3055 MW77 F

24

2991 MW73 F

25

3006 MW75 F

26

27 28

2935 MW78 F

29

3165 MW82 F

30

3398 MW86 F

31

3176 MW78 F

Average = 2916 MW

July


1

2990 MW79 F

2

3031 MW81 F

3

3389 MW88 F

4

2958 MW85 F

5

6 7

2814 MW71 F

8

2766 MW73 F

9

2791 MW75 F

10

2906 MW79 F

11

3106 MW83 F

12

13 14

3130 MW76 F

15

3089 MW74 F

16

3046 MW84 F

17

3102 MW77 F

18

2888 MW78 F

19

20 21

3317 MW82 F

22

2867 MW73 F

23

3055 MW77 F

24

2991 MW73 F

25

3006 MW75 F

26

27 28

2935 MW78 F

29

3165 MW82 F

30

3398 MW86 F

31

3176 MW78 F

Average = 2916 MW Top days = 3383 MW (16 % more than avg)

Peak day electrical load savings

• If knew electrical loads in advance: 16%

• With event constraints: 14%

(Load is relative to an average summer afternoon)

July


1

2990 MW79 F

2

3031 MW81 F

3

3389 MW88 F

4

2958 MW85 F

5

6 7

2814 MW71 F

8

2766 MW73 F

9

2791 MW75 F

10

2906 MW79 F

11

3106 MW83 F

12

13 14

3130 MW76 F

15

3089 MW74 F

16

3046 MW84 F

17

3102 MW77 F

18

2888 MW78 F

19

20 21

3317 MW82 F

22

2867 MW73 F

23

3055 MW77 F

24

2991 MW73 F

25

3006 MW75 F

26

27 28

2935 MW78 F

29

3165 MW82 F

30

3398 MW86 F

31

3176 MW78 F

Average = 2916 MW

July


1

2990 MW79 F

2

3031 MW81 F

3

3389 MW88 F

4

2958 MW

85 F

5

6 7

2814 MW71 F

8

2766 MW73 F

9

2791 MW75 F

10

2906 MW79 F

11

3106 MW83 F

12

13 14

3130 MW76 F

15

3089 MW74 F

16

3046 MW84 F

17

3102 MW77 F

18

2888 MW78 F

19

20 21

3317 MW82 F

22

2867 MW73 F

23

3055 MW77 F

24

2991 MW73 F

25

3006 MW75 F

26

27 28

2935 MW78 F

29

3165 MW82 F

30

3398 MW86 F

31

3176 MW78 F

Average = 2916 MW Warm days = 3237 MW (11 % more than avg)




• If knew temperature in advance: 11%


What can climate analysis say?




• If knew temperature in advance: 11%

• Super simple scheme (24C, 0.5): 6%


Optimizing the process

Peak day summary

• Might ultimately be a real-time program

– Driven by "smart" electric meters

– Main benefit would be avoided cost of peaker generation plants ~$12M/yr.

• Until then, climate prediction:

– Far less deployment cost

– Cost of avoided procurement ~$1.3M/yr

-> Climate analysis can give expected benefits to a program

3. Irrigation pump loads

• Electricity use in Pacific Northwest strongly driven by irrigation pumps

• When will the pumps start?

• What will total seasonal use be?

Irrigation pump electrical use

Pump start date

Eric Alfaro, SIO

Total use over summer

Idaho Falls, ID

Total load affected by soil moisture

Eric Alfaro, SIO

Irrigation load summary

• Buying power contracts 2 months ahead of a high-load summer saves $25/MWh (over spot market price)

• Use: about 100,000 MWh

• Benefit of 2 month lead time summer load forecast: $2.5 M

4. NPO and winter heating

Why the NPO matters

Higher than usual pressure associated with the NPO…

generates anomalous winds from the north west…

…which bring more cold, arctic air into the western U.S. during winter

NPO affects summer, too!

Summer temperature, NPO above normal in spring

Possible benefits: better planning, long term contracts vs. spot market prices

5. Hydropower

• CalEnergy work done by U.W. hydrology group (Dennis Lettenmaier, Alan Hamlet, Nathalie Voisin)

Develop historic precipitation fields…

… then apply precipitation to a runoff model

Major components of CA model

Lake Shasta

Lake Trinity

Whiskeytown Reservoir

Lake Oroville

Folsom Lake

Pardee/Camanche Resv.

New Hogan Reservoir

New Melones Reservoir

New Don Pedro Res./Lake McClure

Millerton/Eastman/Hensley

Sacramento-San Joaquin Delta

San Luis Reservoir

Flood control, navigation, fish conservation

Water supply, hydropower, fish conservation

Flood control, hydropower

Flood control, water supply, hydropower, water quality, environmental conservation

Flood control, water supply, hydropower

Flood control, water supply

Flood control, water supply

Flood control, water supply, water quality, hydropowerFlood control, water supply

Water supply, recreation

Water supply, water quality

Water supply, hydropower

USBR

USBR

USBR

DWR

USBR

EBMUD

COE

USBR

TMID, MC

USBR, COE

USBR, DWR

USBR, DWR

USBR: Bureau of Reclamation

DWR: CA Dept Water Resources

EBMUD: East Bay Municipal District

MC: Merced County

TID: Turlock Irrigation District

COE: US Army Corp of Engineers

Van Rheenen et al., Climatic Change, 2004

Finally, make hydropower

Power Generation (megaW - Hr/month) at Shasta (Sacramento R.)

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

500,000

Oct-51

Oct-53

Oct-55

Oct-57

Oct-59

Oct-61

Oct-63

Oct-65

Oct-67

Oct-69

Oct-71

Oct-73

Oct-75

Oct-77

Oct-79

Oct-81

Oct-83

Oct-85

Oct-87

Oct-89

Oct-91

Oct-93

historical NRG final 01

vic NRG final 01

N. Voisin et al., Univ. Wash., 2004

Economic value of climate forecasts to the energy sector

1. Improved bay area and delta breeze forecasts: $100K’s to low $millions/yr

2. Peak day load management: ~$1-10M/yr

3. Pump loads: ~$2M/yr

4. Pacific SSTs: benefits of the information might include risk reduction, improved reliability, and improved planning

5. Hydropower: better water management, reduced costs

El Nino/La Nina

Why does that affect other places?

Global atmospheric pressure pattern “steers” weather

Horel and Wallace, 1981

Climate change

Some of it is straightforward

Other parts are harder

Clouds have competing effects

How good is the Hydrological Model?

Andrew Wood, Univ. of Washington

Predicted change by 2050

Columbia River flow

Andrew Wood, Univ. of Washington

The problem:

• Proposal to breach 4 Snake River dams to improve salmon habitat

• Those dams provide 940 MW of hydropower generation

Historical Global Temperatures

MSU (microwave sounding unit)

A difficult data set…

Problem: Orbit decay

MSU versus Jones

Paleo temperature history

Mann et al, 2001

Effect of Economic Assumptions

IPCC, 2001

Natural vs. Human Influences

IPCC, 2001

Predicting summer temperature based on spring temperature

Dennis Gaushell,Cal-ISO

<- Warmer than forecast Colder than forecast ->

Cost of forecast errors

NPO and heating degree days

Positive NPO Negative NPO

Difference is about 150 HDD, or 5% of total HDD

Extreme events

Same temperature threshold (e.g. 95 °F) =>

Same percentile threshold (e.g. 95th) =>

Spring SST predicting summer temperatures

CDD Tmax-95th percentile

Relationship PDO => California Summertime Temperatures

150 200 250 300

02

04

06

0 -1.0 0.0 1.0

Correlations, Mode 1-Tmean, JJA =>

Correlations, Mode 1-PSST, MAM

Contingency Analysis (conditional probabilities):

San Jose < 331 CDD-JJA > 414 BN N AN

PDO BN 53** 35 12*** MAM N 35 36 29

AN 12*** 29 59***

= 0.01 => ***, 0.05 => **, 0.10 => *

Burbank-Glendale-Pasadena

< 736 CDD-JJA > 856

BN N AN PDO BN 53** 29 18* MAM N 29 42 29

AN 18* 29 53**

Step 3. Verify streamflow

Nathalie Voisin et al., Univ. Washington, 2004

Step 4. Apply to reservoir model

• ColSim (Columbia Simulation) for the Pacific Northwest

• CVmod (Central Valley model) for Sacramento-San Joaquin basin

• Use realistic operating rules:– Energy content curves (ECC) for allocating hydropower– US Army Corp of Engineers rule curves for flood prevention– Flow for fish habitat under Biological Opinion Operating Plan– Agricultural withdrawal estimated from observations– Recreational use of Grand Coulee Dam reservoir

Step 2: Apply to soil/streamflow model

Nathalie Voisin et al., Univ. Washington, 2004

Strong year to year variability

Climate and Energy in California

Documents

Transcript of Climate and Energy in California