STEPS Symposium December 8, 2018

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Presentation transcript:

STEPS Symposium December 8, 2018 H2 Sustainable Transportation Energy Pathways (STEPS) HD Truck Technology Transitions: Comparing an Energy Model with a Choice Model Christopher Yang Research Scientist This presentation will weave together two different threads of research that we have been working on at ITS. The goal of this talk is to compare different approaches for modeling the transition of trucks, though this would also be relevant for other sectors like LDVs (which we’ve done already). STEPS Symposium December 8, 2018 www.steps.ucdavis.edu

Transition Models for Transport Decarbonization Presentation Goal: To explore the transition to a lower-carbon HDV sector, with emphasis on vehicle efficiency, advanced technologies, and alternative fuels To understand how different modeling approaches can help us answer different questions about this transition Methods: Use different models of different types to explore the transition from conventional HDV vehicles to low-carbon vehicles and fuels Truck Choice Model – Discrete choice simulation CA-TIMES – Energy system optimization model 2

Truck Choice Model Researchers: Marshall Miller, Qian Wang, Lew Fulton Core model: Nested multinomial logit (NMNL) discrete choice model Brings in three important additional behavioral elements relative to optimization ( diversity in tech. adoption) Consumer heterogeneity (i.e. segmentation) Variation in preferences (probability distribution) Adoption is driven by overall generalized cost Capital and operating costs (over planning horizon) Inclusion of non-monetary utility factors (besides costs) Environmental perception Uncertainty (Risk) Model Availability Vehicle Range Refueling Time Station Availability First I want to talk about the choice model. Marshall miller and I have talked about this in previous symposia, so I wont rehash all of the details, but will review the overall approach. Disutility due to lost driver time for finding stations and fueling vehicle 3

CA-TIMES Energy System Model Students: Kalai Ramea, Saleh Zakerinia Core model: Linear optimization (cost minimization) of investment and operating costs of entire energy system from 2010-2050 primary resources conversion technologies (fuels and electricity production) end-use technologies (vehicles, appliances) Minimize cost of building and operating energy system to meet demand for energy services Capital Cost Operating costs (fuel use, maintenance) Incentives Carbon price/constraints Global decision-maker Constraints are critical to shaping technology adoption Carbon caps Policy constraints (CAFE, ZEV mandate, RPS) 4

Choice model vs Optimization Attribute Choice Model Optimization Model Focus of Analysis Vehicle adoption behavior Energy system linkages – vehicle adoption coupled with upstream supply infrastructure and resources Representation of decision-maker(s) Consumer heterogeneity – many individuals maximizing utility Global decision-maker - a single decision-maker designing the system Decision factors Utility, including non-monetary factors Hybrid, primarily economic cost factors (coupled with implications in other sectors) with high discount rates are used to approximate non-monetary factors* Results Probabilistic purchase behavior Often see “winner-take-all” behavior (one technology is the lowest cost) A little bit about the differences between the choice model and the optimization model in terms of approach - Vehicles only vs an interconnected system Many individuals random distribution vs a single deterministic decision maker optimizing with perfect foresight Utility factors, vs hybrid Yo ucan get a distribution of outcomes vs winner take all * We have included non-monetary factors in the decision-making for LDV purchases (COCHIN) 5

Truck Choice Model Results Scenarios: (L) BAU, (R) HDV ZEV mandate scenario (25% by 2050) includes carbon tax ($150/tonne by 2050) FCV Diesel Diesel Here are some results in the truck choice model. Even though we have 8 heavy and medium duty truck/bus types (LH, SH, Heavy Vocational, Medium Vocational, Medium duty delivery, transit buses, other buses, hd pickups), I’m only showing LH and SH so as not to overwhelm you. The scenario that was investigated was a BAU scenario and also a ZEV mandate scenario which required 25% ZEV sales in each of the truck categories by 2050. The choice model cannot impose adoption on consumers so the only way to elicit adoption is to lower the cost (i.e. provide subsidies) and we don’t talk about which way they are subsidized (government or industry cross subsidies). We see FCVs at 25% in LH and a mix of BEV and FCV truck in SH. SH exceeds the 25% ZEV target in 2050. LNG BEV LNG FCV Diesel Diesel 6

CA-TIMES Results Scenarios: (L) BAU, (R) Carbon cap (80% reduction by 2050) FCV Diesel Diesel These are the results for the same two truck categories in CA-TIMES. In this case, the differences between the BAU and Carbon cap scenario are driven by the need to reduce emissions state wide by 80%. SO this is not just trucks or transportation, but electricity, buildings, industry etc. . . We see that we have significant ZEV adoption in the 80% cap scenario FCVs in LH and FCVs + BEVs in short haul. The contributions are much higher because the target is more stringent not just 25% ZEVs but really low carbon emissions even with growing activity. FCV Diesel Diesel BEV 7

Truck Choice Analysis Identifying how to achieve a specific adoption target Target of 25% ZEV adoption in each truck category by 2050 Carbon tax of $150/tonne CO2 (~$1.90/Diesel Gal) in 2050. LH incentives: cost total $2.8B for 29k extra ZEVs  $95k/veh SH incentives: cost total $0.2B for 10k extra ZEVs  $18k/veh Economic costs diff. (capital and fuel) vs BAU is $820 million for LH & SH <$20,000 per vehicle (lifetime cost) Difference of about $2.2 Billion used to overcome non-monetary utility factors $300k/veh $140k/veh $50k/veh $0/veh Now I’m going to talk about the analysis of the truck choice scenarios. We want to identify what it takes to achieve the ZEV target of 25%. Some targets are easier to meet than others. Don’t get totally fixated on the exact number, but just that these incentives are high – to counteract the barriers to adoption - higher prices, infrastructure costs and the non-monetary issues: the risk, model availability, and refueling inconvenience. This incentive level calculated is the one needed to get 25% of the purchases to be a ZEV. It is high and adds to the consumer surplus as many of the people buying the ZEV would have bought it at a lower incentive price. We calculate just the economic cost difference between BAU and ZEV scenario (as only the difference in vehicle capital and fuel costs) and the cost differential is much lower as it doesn’t include any of these non-monetary factors. 8

CA-TIMES Transportation Decarbonization CA-TIMES GHG reduction scenario is driven primarily by the goal of meeting carbon reduction goal of 80% by 2050 Requires decarbonization across supply and end-use sectors Non-energy emissions are challenging to reduce Transport > 90% reduction Energy System Cost and Emissions (2010-2050) Cost ↑ vs BAU: $137B Emissions ↓ vs BAU: 2804 MMT $49/tonne CO2e abatement Relatively low average cost of emissions reduction But this only includes economic costs and ignores other factors Transportation Commercial Industrial Residential Non-Energy Looking at CA-TIMES, it is important to again note that the model results are driven by the carbon target. This requires substantial decarbonization across all sectors. If you look at the graph here, you’ll see that the energy sector actually reduces emission by more than 80% because there is this wedge of non-energy emissions, that is difficult to reduce (methane leaks, cement, enteric fermentation, land emissions, etc.). Non-energy emissions (high GWP gases) 431 MMT 258 in 2030.  86 in 2050 (target is 86 MMT in 2050) for entire state. 1.7tonnes/person (equivalent to ~6000 miles/year in a 40mpg car). The broader point is that given this requirement, we need to have lots of adoption of ZEVs in heavy duty as we saw a couple of slides ago. In calculating costs, looking at the entire energy system we see that if you look at the difference in costs and emissions between the two scenarios we can calculate the abatement cost of $49/tonne, which is relatively low. Of course this is an average across all years. Generally it is lower in early years (or even negative) and the grows as thigns get more stringent and you have to do more and more things (think of a carbon reduction supply curve, like a McKinsey curve). But again this cost ignores things other than capital and operating costs. 9

Discussion of modeling insights We have two models that have very different costs results: Choice model - Very high levels of subsidies are required for carbon abatement, even though extra capital and fuel costs are significantly lower Energy system model – Modest costs associated with carbon reductions across all sectors Carbon tax/price can have two meanings: a calculation of the estimated extra economic costs associated with policies (may not include non-monetary factors) as an policy instrument that can help to change behavior in the adoption of low-carbon technologies and fuels (should account for non-monetary factors) Highlights a tension in modeling between results that are societally optimal and behaviorally realistic It comes down to how we view costs. 1st approach is what we do in TIMES and just compares the amount that people paid for capital and fuel in the two scenarios and ignores any utility differences (what people would have preferred even if the costs were the same). 2nd approach is a differnet question, which is important for policy and regulation, what do we need to do to change behavior. 10

Conclusions and Takeaways In either BAU scenario, we don’t see adoption of ZEVs and low-carbon technologies Choice Model Results A ZEV mandate can achieve ZEV adoption but requires significant additional incentives (subsidies and carbon tax in our approach) CA-TIMES Results Decarbonization of the energy system is a huge undertaking Requires significant adoption of ZEVs in transportation Fuel cell vehicles are chosen in the long-haul sector while FCVs and BEVs are chosen in short-haul Cost of emissions reduction is relatively modest mainly due to cost savings from efficiency (highlighting the efficiency gap) Can be thought of as difference between simulation and optimization (forward looking vs back-casting). 11

Conclusions and Takeaways (2) Adding behavioral elements to a model makes the results more relevant to the real-world These real-world, behavioral elements are barriers to adoption Capital and fuel costs Refueling/charging inconvenience and costs Model availability and risk/uncertainty Technology readiness The goal is to understand what is needed to drive adoption Monetary incentives (subsidies, carbon tax) Infrastructure deployment Technology maturation and perception 12

Thank You ccyang@ucdavis.edu 13