CA-TIMES California Energy System Optimization Model

Transcription

1 NextSTEPS (Sustainable Transportation Energy Pathways) CA-TIMES California Energy System Optimization Model Christopher Yang, Alan Jenn, Saleh Zakerinia, Kalai Ramea, David Bunch, Sonia Yeh STEPS Lookback Modeling Workshop December 9,

2 What is CA-TIMES? An integrated model of California s energy system that tracks energy flows, economic costs and environmental emissions Projections of energy service demands to 2050 Energy demand sectors (transportation, buildings, industry) Energy supply sectors (resource extraction, fuel production, electricity generation) Integration of demand and supply sectors It is used to design the state s future energy systems to meet the demand for energy services Outputs include technology investments, and operational decisions Constrain these decisions based upon policy considerations Emissions, technology or sector specific policies Key metrics: End-use and supply technology mix, technology costs, resource utilization, emissions, mitigation costs, etc...

3 CA-TIMES can be used to answer many questions How does a cap on GHG emissions influence the evolution of the energy system in terms of technology adoption and system costs? What technologies are adopted in each sector? What is the optimal mix of efficiency and fuel switching? Which sectors reduce emissions more? What is the incremental cost (or savings) of GHG mitigation? How can policies and the availability of technologies and resources influence the mitigation strategies and costs? How sensitive are the results (technology mix and cost of mitigation) to uncertain input assumptions? 3

4 CA-TIMES Energy Sectors Value of integrated model of California s energy system Resource supplies used across many sectors Electricity used across many sectors, timing Understanding tradeoffs and minimizing mitigation costs Economy wide policies

5 Key approach, assumptions and caveats to understand when evaluating the model and results? Linear economic optimization (minimize system costs) to 2050 Model requires specification of all service demands, technology and resource options to 2050 Quantity, availability, cost, efficiency, lifetime Optimization and adoption of technologies is based primarily on a lifecycle economic analysis of various alternatives Policies are modeled as system constraints Markets are not represented Decisions are made on providing fuels/electricity/services at lowest cost Single, global decision-maker with perfect foresight Tradeoffs are made across sectors Incorporation of behavioral elements via discount/hurdle rates Also include maximum share and/or growth constraints on adoption Working on preference variability (heterogeneity) and non-cost factors Model is deterministic

6 Transportation Sector Energy System Example of the decisions that the model makes across the energy system Refinery NGCC NG Turbine Ethanol Plant Wind Electrolysis Hydro Solar Biomass Thermal Solar Natural PV Gas SMR Biomass Onsite SMR Refueling station EV charger Refueling station Gasoline Diesel E85 Electricity 6 H2 Gasoline ICE Gasoline HEV Diesel ICE PHEV10 PHEV20 PHEV40 BEV100 FCV Light duty Medium Duty Heavy Duty Bus Rail Passenger Freight Aviation Instate Interstate Marine Instate Interstate Ag Off-road

7 CA-TIMES GHG emissions results GHG emissions in BAU and GHG scenario (GHG-Line) GHG-Line scenario meets the emissions target up to 2045 and achieves 75% GHG reduction in 2050 CCS and nuclear power are not available in this scenario Ignores out-of-state aviation and marine travel and excludes offsets Significant emissions reduction across all sectors (44 to 81% reduction) Transportation continues to contribute majority of emissions BAU GHG-Line 2020 Target (391 MtCO2e) 26.7% reduction in Target 78 MtCO2e

8 Total Transport Fuel Use GHG-LineScenario Decrease in petroleum and increase in biofuels, hydrogen and electricity Difficulty in electrifying most transport sectors Liquid fuels requirements and limited biofuels availability (7.3 billion GGE) makes it hard to fully decarbonize sector Includes fuels for interstate/international aviation and marine Fuel 2010 share (%) 2030 share (%) Petroleum 95% 75% BAU Electricity <0.1% 0.6% Hydrogen 0% 0.7% Biofuels 5% 20% Natural gas 0% 4.5% 2030 Petroleum usage (vs 2010) 25% reduction across transport 31% reduction for On-Road 38% reduction for LDV 8

9 Light-Duty Vehicles LDVs are primarily electrified to battery electric (BEVs) and H 2 fuel cell vehicles (FCVs) by million vehicles (~1.1M BEV/PHEV, 1.3MFCV) in % of VMT supplied by electric-drive vehicles (FCV/BEV/PHEV) in 2030 On-road fuel economy is 36 mpgge in 2030 (110 mpgge in 2050) LDV fuel demand is 36% lower than 2010 in 2030 and 77% lower in 2050 Petroleum usage is 38% lower than 2010 in 2030 and 98% lower in 2050

10 Electricity Supply Increases in GHG scenario Electricity demand increases significantly due to electrification in end-use sectors (residential, commercial, industrial, transport) Without nuclear or CCS in primary scenario, renewable electricity sources are needed to decarbonize electricity generation GHG-Line generation (TWh) Wind Solar Geo Tidal 8 22 Biomass <1 0 Natural gas NG is marginal generator RPS renewables 38% in 2030 (Step) 49% in 2030 (Line) 80% in

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12 California Energy and Greenhouse Gas Models (including CA-TIMES) Evolution/History

13 California emissions reductions analysis context 2005 Executive Order S Governor Schwarzenegger announces goal of reducing GHG emissions by 80% below 1990 levels in Global Warming Solutions Act (AB32) passed 2008 ARB Scoping Plan released Analyses for deep reductions in CA greenhouse gas emissions 80in50 Transportation Scenarios ( ) ITS-Davis scenarios of transportation sub-sector emissions reduction potential from travel demand, low-carbon fuels, and advanced vehicle technologies California s Energy Future ( ) Expert panel from California Council on Science and Technology (CCST) looking at options for energy system GHG reductions for the state. California s Carbon Challenge ( ) UC Berkeley/Davis/LBL analysis for CEC, developing scenarios for energy system GHG reductions

14 CA-TIMES Underlying Modeling Framework Based upon MARKAL/TIMES modeling framework Developed by ETSAP, an arm of the International Energy Agency MARKAL dates back over 30 years TIMES The Integrated MARKAL EFOM System Worldwide set of users across many countries EPA 9-region MARKAL model IEA ETP TIMES model CA-TIMES v1 Focused on transportation and energy supply sectors (electricity, fuels) David McCollum s PhD Thesis ( ) Funding via STEPS and CARB McCollum, David L., Christopher Yang, Sonia Yeh, Joan M. Ogden (2012) Deep Greenhouse Gas Reduction Scenarios for California - Strategic Implications from the CA-TIMES Energy-Economic Systems Model. Energy Strategy Reviews 1, 19-32

15 CA-TIMES History CA-TIMES v2 Updated to add residential and commercial sectors, upgraded electric sector representation Saleh Zakerinia Master s Thesis, Kalai Ramea, Christopher Yang ( ) Funding via STEPS and CARB Yang, Christopher, Sonia Yeh, Saleh Zakerinia, Kalai Ramea, David L. McCollum (2015) Achieving California's 80% Greenhouse Gas Reduction Target in 2050: Technology, Policy and Scenario Analysis Using CA-TIMES Energy Economic Systems Model. Energy Policy 77, CA-TIMES v3 Learning and heterogeneity in light-duty vehicle sector, demand response and energy storage in electricity sector, parameter uncertainty Saleh Zakerinia and Kalai Ramea PhD Thesis, Christopher Yang ( ) Funding via STEPS and PG&E

16 California Climate Policy Modeling (CCPM) Forum (2015) Modeling comparison for CA GHG emissions (2050) CA-TIMES (UCD) CALGAPS (LBL) BEAR (UCB) PATHWAYS (E3) Very different modeling approaches Understanding consistencies and differences between models

17 Model evolution and changes Activity projections work began in timeframe post-2012, projections changed significantly due to recession and other trends State population projections reduction (60 million vs 50 million in 2050) VMT projections (per capita growth became unclear) Resource projections oil and gas prices dropped significantly Models typically rely on AEO price projections Technology projections rapid changes in analysis of many different low-carbon technologies Electric, hydrogen and natural gas vehicles Solar PV costs Modeling and methodological changes interest in improving the capabilities of the model Focus on improving representation of specific sectors Additional policies and analyses

18 Modeling Improvements Improved model representation of key sectors Residential and commercial sectors (v1 v2) Improved electricity sector (v1 v2) (v2 v3) Electricity demand response Update data inputs and assumptions Population and activity (v1 v2) Vehicle costs (v1 v2) Oil and gas prices (v1 v2) (v2 v3) Industrial sector representation (v2 v3) Structural/methodological changes Partial equilibrium mode (v2) Consumer heterogeneity and consumer choice (v3) Learning (v3) Parameter uncertainty (v3) Computational limitations

19 Thanks!