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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 3 1 ct -9 O 9 ct -9 O 7 ct -8 O 5 ct -8 O 3 ct -8 O 1 ct -8 O 9 ct -8 O 7 ct -7 O 5 ct -7 O 3 ct -7 O 1 ct -7 O 9 ct -7 O 7 ct -6 O 5 ct -6 O 3 ct -6 O 1 ct -6 O 9 ct -6 O 7 ct -5 O 5 ct -5 O 3 ct -5 O ct -5 O O ct -5 1 0 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 BN N AN BN 53** 29 18* N 29 42 29 AN 18* 29 53** BN N AN < 331 BN 53** 35 12*** CDD-JJA N 35 36 29 > 414 AN 12*** 29 59*** 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