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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 Background Climate: 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? Outline 1/ Data and Models 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 1/ The Data 1/ Data and Models Meteorological data Hydrological model Reservoir models 2/ Observed covariability 3/ Opportunity: more efficient inter-regional energy transfers? 4/ Conclusions 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) Hydrologic Model: VIC (1/2) 1/ Water Balance 2/ Runoff Routing Hydrological Model: VIC (2/3) Simulated Flow Observed = Red = Black Hydrological Model: VIC (2/3) Simulated Flow Observed = Red = Black 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 Water Demand CALIFORNIA CVMod (Van Rheenen et al 2004) Flood Control, Energy Demand PACIFIC NORTHWEST ColSim (Hamlet and Lettenmaier 1999) Hydropower 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 Streamflow Covariability Natural Streamflows in North California, Sacramento River CA NORTH (cfs) 60,000 Cold ENSO Warm ENSO Cold PDO Warm PDO Flow (cfs) 50,000 40,000 Mean annual std 9,880 CV 0.4 30,000 20,000 CA SOUTH (cfs) 10,000 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Mean annual 7,709 std 4,128 CV 0.5 Natural Streamflows in South California, San Joaquin River 20,000 Cold ENSO Warm ENSO Cold PDO Warm PDO 18,000 16,000 Flow (cfs) 22,353 14,000 12,000 10,000 North CA: peak in winter South CA: peak in spring 8,000 6,000 4,000 2,000 0 Jan Feb Mar Apr May Jun Jul Month Aug Sep Oct Nov Dec ENSO: 17% annual flow difference PDO: 2% Streamflow Covariability DALLES (cfs) Natural Streamflows at the Dalles 500,000 Cold ENSO Warm ENSO Cold PDO Warm PDO 450,000 Flow (cfs) 400,000 350,000 Mean annual 181,063 std 33,066 CV 0.2 300,000 250,000 200,000 150,000 100,000 PNW: peak in early summer 50,000 0 Jan Feb Mar Apr May Jun Jul Month Aug Sep Oct Nov Dec ENSO/PDO: 12-16% annual flow difference Hydropower Covariability MWh Hydropower Production in the PNW cold ENSO warm ENSO cold PDO warm PDO 18,000,000 16,000,000 14,000,000 12,000,000 10,000,000 8,000,000 6,000,000 4,000,000 2,000,000 0 PNW (avg MW) mean Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 13,644 std 3,082 CV 0.2 Month Hydropower Production in CA 1,600,000 1,400,000 1,200,000 MWh CA (avg MW) cold ENSO warm ENSO cold PDO warm PDO 1,000,000 mean 976 std 399 CV 0.4 800,000 600,000 400,000 200,000 0 Jan Feb Mar Apr May Jun Jul Month Aug Sep Oct Nov Dec PNW: peak in J CA: peak in M Energy Demand Covariability 2 types of demand: Daily Energy Demand (93-00) 1,000,000 900,000 800,000 700,000 600,000 500,000 400,000 300,000 200,000 100,000 0 day PNW day CA Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Peak Hour Energy Demand (93-00) MW-hr Demands are out of phase in CA and in the PNW!! MW-hr Peak hour demand Daily total Demand 50,000 45,000 40,000 35,000 30,000 25,000 20,000 15,000 10,000 5,000 0 peak PNW peak CA Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Energy Demand Covariability How predictable is the energy demand? Regression of observed energy load with temperatures Daily Peak Hour Demand & Tmax R2=0.68 R2=0.60 Monthly average of daily total demand & Warming/Cooling degree days [ Σ (T-18.7)day ] 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 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 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 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 Climate Precipitation (timing) Energy Demand Hydropower ? Energy Transfers 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 Climate Forecast Temperature Precipitation (timing) Energy Demand Hydropower Derived daily and peak hour Energy Transfer Model Energy Transfers Disaggregation to daily based on temperature 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 Additional slides for eventual questions Meteorological Data : NCDC HCN/HCCD Monthly Data Preprocessing Regridding Lapse Temperatures Correction to Remove Temporal Inhomogeneities Coop Daily Data Topographic Temperature & Correction for Precipitation Precipitation PRISM Monthly Precipitation Extended time series from 1916 to 2003 Maps Energy Demand Model (1/2) Derived peak hour energy demand time series in the Pacific Northwest : skill in wintertime Energy Demand Model (2/2) Derived peak hour energy demand time series in California: skill in summer Overall Covariability WARM ENSO TRENDS Temp CA JA PNW JFMA PDO ENSO/ PDO COLD ENSO PDO ENSO/ PDO + + + + + + + - + - + - Peak Hour Energy Demand CA JA PNW JFMA - - - + + + Daily Energy Demand CA JA Hydropower CA JA + + - + + - + + - + (-) + + (-) + + (-) + PNW JFMA PNW JJ Energy transfer model (in progress) Scenario 1: total daily energy ( hydropower + Conventional Resources) meet PNW total daily and peak hour energy demands. Hydropower + Conventional Resources over peak hour period Daily time step Results aggregated to monthly time step Principles: • Assumes perfect forecast ( monthly hydropower production known) • Transmission line capacity limits the energy transfers Meet PNW Peak Hour Demand ? Compute Potential Transfer during Peak Hour How much energy needed to meet remaining daily energy demand? Enough time/capacity to send energy back eventually?
