H 2. Webinar: Inter-Model Comparison of California Energy Models. 27 February, 2014 UC Davis

Transcription

1 H 2 Webinar: Inter-Model Comparison of California Energy Models Geoff Morrison Anthony Eggert Sonia Yeh Raphael Isaac Christina Zapata 27 February, 2014 UC Davis

2 MMT CO2e/yr California s Goals: Reach 1990 levels by 2020 and 80% reduction by MMT CO2e/yr MMT CO2e = Million metric tonnes of carbon dioxide equivalent Levels? 80% below 1990 Levels MMT COe/yr /21

3 Model Questions How will California s energy system evolve to 2030 & 2050: Greenhouse Gas (GHG) trajectories? Fuel mix and technology mix? Infrastructure build rate? Air quality? What assumptions drive these results? What are common insights across models? Where do they diverge? 3/21

4 Need for Mid-term GHG Target Update to AB 32 Scoping Plan (2014): A mid-term statewide emission limit will ensure that the State stays on course to meet our long-term goal and continues the success it has achieved thus far in reducing emissions. (CARB, 2014, p. 39) Governor s Environmental Goals and Policy Report (2013): the state needs a mid-term emission reduction target to provide a goalpost to guide near-term investment and policy development. A mid-term target will allow us to gauge current actions relative to our climate goals and serve to provide a clear sign of the state s commitment to achieving longterm climate stabilization. This commitment will send a strong signal of support for the innovators and entrepreneurs to drive technology and development to tackle the challenge of climate change. (OPR, 2014, p. 6) 4/21

5 Why Do Inter-Model Comparisons? Sweeney, 1983 Model comparisons benefit the modeling community through identification of errors, clarification of disagreements, and guidance for model selection Weyant, 2012 Understand Strength/weaknesses of existing methodologies Identify high priority areas for development of new data, analyses, and modeling methodologies Two levels of model comparisons: Level 1: compare & contrast inputs & outputs (e.g. review article) Level 2: standardize inputs, compare outputs (SRES, SSPs) 5/21

6 CA Energy Models/Reports Reviewed Model ARB VISION BEAR CA-TIMES CCST View to 2050 CCST (Bioenergy) E-DRAM Energy 2020 GHGIS IEPR 2013/CED 2013 LEAP-SWITCH MRN-NEEM PATHWAYS Wind Water Solar (WWS) Group (lead) California Air Resources Board (CARB) UC Berkeley (Roland-Holst) UC Davis (Yang, Yeh) CCST (Long) CCST (Youngs) UCB/CARB (Berck) ICF/CRA LBNL (Greenblatt) California Energy Commission (CEC) UC Berkeley/LBNL (Nelson, Wei) EPRI/CARB E3/LBNL (Williams) Stanford/UCD (Jacobson, Delucchi) 6/21

7 Qualitative Comparison Yes/Represented Limited None/Not represented 7/21

8 Population (Mil) Population Assumptions BEAR DOF (2013) Wei et al., CA U.S. Census (2005) CA-TIMES - DOF (2013) 50 Berck et al., 2008 Williams et al., E-DRAM - DOF (2003) ICF/SSI, 2010 IEPR WWS 2013, mid Roland-Holst, 2012 Greenblatt, 2013 Nelson/Wei et al., 2013 Yang et al., 2014 Energy IEPR (2009) GHGIS - DOF (2013) IEPR IHS Global Insight for Mid projection LEAP-SWITCH - AEO (2011) VISION - AEO (2011) WWS - U.S. Census (2009) 8/21

9 MMT CO2e/yr Business As Usual (BAU) Scenarios ARB Scoping Plan, 2008 Historic Long et al., 2011 Williams et al., 2012 Nelson/Wei et al., 2013 Yang et al., Roland-Holst, 2012 ARB Scoping Plan, in '50 AB32 Target /21

10 MMT CO2e/yr Reaching 80 in 50 Goals 1, Linear Reduction to 80% Constant Rate to 80% Pathways (Hi Nuke) Pathways (Hi renew) Linear CA TIMES Reduction (Line) to 80% Constant CA TIMES Rate (CCS-C) to 80% GHGIS (Case 2) GHGIS (Case 3) LEAP-SWITCH (Base) /21

11 MMT CO2e/yr Reaching 80 in 50 Goals 1, Linear Reduction to 80% Constant Rate to 80% Williams et al., 2012 (Nuke) Williams et al., 2012 (Hi Renew) Yang et al., 2014 (Line) Yang et al., 2014 (CCS) GHGIS (Case 2) Greenblatt, 2013 (Case 3) Nelson/Wei et al., 2013 (Base) Nelson/Wei et al., 2013 (-40% BioCCS) /21

12 MMT CO2/yr MMT CO2 Annual vs. Cumulative Emissions? Annual Cumulative ,000 12,000 10,000 8,000 6,000 4,000 2, Linear Reduction to 80% Constant Rate to 80% Williams et al., 2012 (Nuke) Williams et al., 2012 (Hi Renew) Yang et al., 2014 (Line) Yang et al., 2014 (CCS) GHGIS (Case 2) Greenblatt, 2013 (Case 3) Nelson/Wei et al., 2013 (Base) Nelson/Wei et al., 2013 (-40% BioCCS) 12/21

13 MMT CO2e/yr MMT CO2e Annual vs. Cumulative Emissions? Annual Emissions Cumulative Emissions ,000 12,000 12,528 14, ,000 8,000 6,000 4,000 2, ,149 4,070 9,205 8,578 6,492 8,473 10,357 Large difference in Climate Impacts! % Reduction 13/21

14 LDV Energy (PJ) Light-Duty Vehicle Energy Use, 2030 & Hydrogen Electric Liquid Fuels All Transport LEGEND Bars = LDV energy use by source Red triangles = total transport energy use VISION VISION CA-TIMES CA-TIMES LEAP-SWITCH CCST PATHWAYS WWS (Case 3) (Case 2) (Hi Bio) (GHG-M) (Agg. Elect) (PEV+H2) (Mitigation) In deep reduction scenarios, electricity and hydrogen provide 3-13% of Light Duty Vehicle (LDV) fuel in 2030 and 57-87% by 2050 Total transportation energy drops by as much as 70% from due to increased efficiency. Vehicle Miles Traveled (VMT) assumptions range from 275 billion miles to 695 billion miles Models differ dramatically in total energy use for LDVs and total transportation in /21

15 Electricity Generation (TWh/yr) Electricity Generation and Renewable Fraction in 2030 & % 30-45% 38-74% 38-55% 42-94% 33-39% 38-81% GHGIS 2050 WWS CCST2050 LEAP-SWITCH CA-TIMES PATHWAYS GHGIS WWS CCST (Case 3) 51% 81% % 100% 36% LEGEND Box plot = quartiles (box) and max/mins (whiskers) across mitigation scenarios in given year Red squares = individual scenarios Percentages above boxes are percent renewable (nonhydro) across mitigation scenarios Renewable fraction (non-hydro) ranges from 30-51% in 2030 and 36-96% in 2050 (non-wws) Total generation goes from 306 TWh in 2013 to in 2030 and in 2050 Implied renewable build rate is Gigawatts per year (GW/yr) between today and 2030 and GW/yr between /21

16 Liquid Biofuels are Important but Assumptions Matter! Delivered Bioenergy in 2050 Williams et al., 2012; PATHWAYS Neslon/Wei et al., 2013; LEAP-SWITCH Greenblatt, 2013; GHGIS (Case 3) Greenblatt, 2013; GHGIS (Case 2) Youngs, 2013; CCST-Bio (Hi) Youngs, 2013; CCST-Bio (Base) Long et al., 2011; CCST (Hi) Long et al., 2011; CCST (Low) Yang et al., 2013, CA-TIMES ARB, 2013; VISION Unspecified In-state (unspecified) Out-of-state (unspecified) Generic "energy crops" In-state residues Conventional Herbaceous Energy Crops Forest Residue Landfill Tallow/Grease Ag Residue Billion Gallons Gasoline Equivalent (BGGE) Advanced bio-liquids could power up to ~40% of transportation sector in 2050 Bioenergy goes to transportation, not to electricity Large carbon savings from bioenergy+ccs (more modeling needed!) 16/21

17 Criteria Emissions Coordination needed between 2032 criteria emission goals and 2030/2050 climate goals Including detailed criteria and GHG emissions in a single model can be very difficult WWS estimates that a 100% renewable energy system would eliminate approximately 16,000 state air pollution deaths per year and avoid $131 billion per year in health care costs. 17/21

18 Observations Models built to examine pathways to 2050 not specifically focused on maximizing climate benefits by 2030 (except GHGIS) Many models lack economic indicators to consider economic feedback and benefits/costs of policy options Poor representation of uncertainty (version 2 of E3 model improves on this) Criteria emissions not part of the optimization process Modelers need to work with policy makers more closely to represent the details of the policy design Data availability and data/model transparency is absolutely essential. 18/21

19 Key Takeaways Annual emissions in deep reduction scenarios (i.e. 80 in 50): MMT CO2e/yr in % reduction by 2030 from 1990 levels Cumulative emissions vary by as much as 40% in % renewable grid by % renewable grid by 2050 Electrification of end uses and expansion of grid are key Need to expand grid by times its current capacity Need greater understanding about how to utilize biomass for energy and fuel More modeling of bioenergy+ccs More modeling of life cycle emissions and other sustainability factors Better long-term modeling of policies and technologies addressing non-energy related GHG emissions BAU scenarios have non-energy GHG emissions >2050 target Coordination is key! 19/21

20 Thank you! Please see our CCPM summary document and forthcoming white paper here: 20/21