Transcript Birch_Aq_ACPI_CalEnergy_2004-09

Climate and Energy in California
David W. Pierce
Tim P. Barnett
Eric Alfaro
Alexander Gershunov
Climate Research Division
Scripps Institution of Oceanography
La Jolla, CA
How we got started:
a typical climate change result
What does this mean to us?
IPCC, 2001
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
Standard reanalysis
Downscaled model
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:
Future:
Lance Vail,
PNNL
Climate change conclusions
• 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.
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
Typical effects of El Nino:
CA
hydro
Green et al., COAPS Report 97-1
Project Overview
Scripps Inst. Oceanography
University of Washington
Georgia Inst. Tech
PacifiCorp
San Diego Gas & Elec.
SAIC
Industrial
Partners
Academia
California Energy
Commission
California ISO
State
Partners
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 California
Central 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 largescale 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
6
13
20
27
Monday
Tuesday
Wednesday
Thursday
Friday
1
2
3
4
2990 MW
79 F
3031 MW
81 F
3389 MW
88 F
2958 MW
85 F
7
8
9
10
11
2814 MW
71 F
2766 MW
73 F
2791 MW
75 F
2906 MW
79 F
3106 MW
83 F
14
15
16
17
18
3130 MW
76 F
3089 MW
74 F
3046 MW
84 F
3102 MW
77 F
2888 MW
78 F
21
22
23
24
25
3317 MW
82 F
2867 MW
73 F
3055 MW
77 F
2991 MW
73 F
3006 MW
75 F
28
29
30
31
2935 MW
78 F
3165 MW
82 F
3398 MW
86 F
3176 MW
78 F
Average = 2916 MW
Saturday
5
12
19
26
July
Sunday
6
13
20
Monday
Wednesday
Thursday
Friday
1
2
3
4
2990 MW
79 F
3031 MW
81 F
3389 MW
2958 MW
85 F
7
8
9
10
11
2814 MW
71 F
2766 MW
73 F
2791 MW
75 F
2906 MW
79 F
3106 MW
83 F
14
15
16
17
18
3130 MW
76 F
3089 MW
74 F
3046 MW
84 F
3102 MW
77 F
2888 MW
78 F
21
22
23
24
25
3317 MW
2867 MW
73 F
3055 MW
77 F
2991 MW
73 F
3006 MW
75 F
28
29
30
31
2935 MW
78 F
3165 MW
82 F
3398 MW
3176 MW
78 F
82 F
27
Tuesday
Average = 2916 MW
86 F
88 F
Saturday
5
12
19
26
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
Sunday
6
13
20
Monday
Wednesday
Thursday
Friday
1
2
3
4
2990 MW
79 F
3031 MW
81 F
3389 MW
2958 MW
85 F
7
8
9
10
11
2814 MW
71 F
2766 MW
73 F
2791 MW
75 F
2906 MW
79 F
3106 MW
83 F
14
15
16
17
18
3130 MW
76 F
3089 MW
74 F
3046 MW
84 F
3102 MW
77 F
2888 MW
78 F
21
22
23
24
25
3317 MW
2867 MW
73 F
3055 MW
77 F
2991 MW
73 F
3006 MW
75 F
28
29
30
31
2935 MW
78 F
3165 MW
82 F
3398 MW
3176 MW
78 F
82 F
27
Tuesday
Average = 2916 MW
86 F
88 F
Saturday
5
12
19
26
July
Sunday
6
13
20
Monday
Wednesday
Thursday
Friday
1
2
3
4
2990 MW
79 F
3031 MW
81 F
3389 MW
2958 MW
7
8
9
10
11
2814 MW
71 F
2766 MW
73 F
2791 MW
75 F
2906 MW
79 F
3106 MW
83 F
14
15
16
17
18
3130 MW
76 F
3089 MW
74 F
3046 MW
84 F
3102 MW
77 F
2888 MW
78 F
21
22
23
24
25
3317 MW
2867 MW
73 F
3055 MW
77 F
2991 MW
73 F
3006 MW
75 F
28
29
30
31
2935 MW
78 F
3165 MW
82 F
3398 MW
3176 MW
78 F
82 F
27
Tuesday
Average = 2916 MW
86 F
88 F
Saturday
5
85 F
12
19
26
Warm days = 3237 MW (11 % more than avg)
Peak day electrical load savings
• If knew electrical loads in advance:
16%
• With event constraints:
14%
• If knew temperature in advance:
11%
(Load is relative to an average summer afternoon)
What can climate analysis say?
Peak day electrical load savings
• If knew electrical loads in advance:
16%
• With event constraints:
14%
• If knew temperature in advance:
11%
• Super simple scheme (24C, 0.5):
6%
(Load is relative to an average summer afternoon)
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
Flood control, navigation,
fish conservation
USBR
USBR:
Bureau of Reclamation
Lake Trinity
Water supply, hydropower,
fish conservation
USBR
DWR:
CA Dept Water Resources
Whiskeytown Reservoir
Flood control, hydropower
USBR
EBMUD:
East Bay Municipal District
Lake Oroville
Flood control, water supply,
hydropower, water quality,
environmental conservation
DWR
Folsom Lake
Flood control, water supply,
hydropower
USBR
TID:
Turlock Irrigation District
Pardee/Camanche Resv.
Flood control, water supply
EBMUD
COE:
US Army Corp of Engineers
New Hogan Reservoir
Flood control, water supply
COE
New Melones Reservoir
Flood control, water supply,
water quality, hydropower
Flood control, water supply
USBR
New Don Pedro Res./Lake
McClure
MC:
Merced County
TMID, MC
Millerton/Eastman/Hensley
Water supply, recreation
USBR, COE
Sacramento-San Joaquin Delta
Water supply, water quality
USBR, DWR
San Luis Reservoir
Water supply, hydropower
USBR, DWR
Van Rheenen et al.,
Climatic Change, 2004
Finally, make hydropower
Power Generation (megaW - Hr/month) at Shasta (Sacramento R.)
500,000
historical NRG final 01
vic NRG final 01
450,000
400,000
350,000
300,000
250,000
200,000
150,000
100,000
50,000
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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
<- Warmer than forecast
Colder than forecast ->
Dennis Gaushell,
Cal-ISO
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
-1.0
0.0
1.0
0
20
40
60
Correlations, Mode 1-PSST, MAM
150
Correlations, Mode 1Tmean, JJA =>
200
250
300
Contingency Analysis (conditional probabilities):
< 736
CDD-JJA
> 856
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53**
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N
29
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29
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35
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CDD-JJA
N
35
36
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> 414
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29
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BurbankGlendalePasadena
PDO
MAM
San Jose
PDO
MAM
 = 0.01 => ***, 0.05 => **, 0.10 => *
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