Impact of RD&D on the diffusion of particular technologies

The overall benefits of increased RD&D investments (reductions in energy CO₂ emissions, oil imports, and carbon prices) were discussed in section 2.8.1. We now turn to a discussion of the impact of differ-ent RD&D scenarios—of increasing investmdiffer-ents from the BAU scenario to the experts’ recommended level (which corresponds to approximately a two- to four-fold budget increase over the BAU scenario), and of increasing investments from the recommended budget scenario to a scenario with an invest-ment 10-times greater (about 23–38 times greater than the BAU funding scenario)—on the deployinvest-ment of different technologies. In the case of buildings, we use emissions reductions from the commercial buildings sector as a proxy for technology deployment.

FIGURE 2.19. Average increase in consumer and producer surplus per $RD&D invested when comparing MFULLCO2 to MBAUCO2 and MTENCO2 to MFULLCO2 in 2030 and 2050. The dot corresponds to the 50^th percentile, the black rectangle encompasses the 25^th and 75^th percentiles, and the line encompasses the 5^th and 95^th percentiles.

2030 2050

BAU-Full Full-Ten

-150 -100 -50 0 50

Rate of Consumer and Producer Surplus Change per RD&D Spending Change ($Consumer and Producer Surplus / $ RD&D)

100 150 200

As in the previous section, we discuss the impact of RD&D under three policy scenarios: (1) current poli-cies only, (2) a CO2 cap-and-trade policy, and (3) sectoral policies. In some scenarios, the construction of new nuclear power plants is restricted. Under current policies only, no additional policies beyond those in place today are put into place. The CO2 cap scenario is described in Table 2.4 and reduces energy-related CO2 emissions by 83% compared to 2005 levels by 2050 without allowing international offsets.

The sectoral policies include a clean-energy standard (CES), and more stringent vehicle fuel economy standards, and commercial building standards. The particular implementations of these policies are de-scribed in sections 2.2.2 and 2.5.4. The scenarios in the discussion below use the legend in Table 2.6.

We discuss the impact of RD&D on the median result of the deployment of different technologies in terms of installed capacity of solar, nuclear, fossil, storage, and biomass electricity, in terms of total en-ergy use for different types of vehicles, and in terms of CO2 emissions from the commercial buildings sector all in a particular year (either 2030 or 2050). The figures in section 2.8.2 also show the impact of RD&D on the uncertainty range of technology deployment, which we define as the difference between the 95^th and the 5^th percentile deployment results. The vertical white line in the figure corresponds to the median result, and the edges correspond to the 5^th and 95^th percentiles of deployment. (Shading does not represent the exact probability distribution and is only an aesthetic effect.)

In some scenarios, deployment of a particular technology is projected to decrease with increases in RD&D funding. This is because other technologies also improve in terms of lower costs and better performance with more RD&D funding. Other competing technologies may have relatively greater im-provements from one funding level to the next, making the particular technology less competitive and resulting in less deployment for the particular technology. This is true in the case of CAES technology and commercial buildings.

At the end of each of the following sub-sections, we make a recommendation about the level of RD&D funding for the technology area in question based on technology deployment under the different fund-ing and policy scenarios, and also based on insights of the optimization of RD&D allocation under dif-ferent policy scenarios discussed in section 2.8.3.

Bioenergy

Increasing RD&D funding from BAU to FULL has significant impacts on the deployment of biodiesel from thermochemical plants and cellulosic ethanol production plants, and on the deployment of bio-mass power plants.

• Under current policies only, increasing RD&D from MBAU to MFULL results in a lower me-dian deployment of biodiesel. This is driven by the fact that under the MFULL scenario there is a greater increase in the deployment of PHEVs. On the other hand, under CO2 and SEC, increasing RD&D results in increased deployment of biodiesel (Figure 2.20).

• For cellulosic ethanol, increasing RD&D funding from MBAU to MFULL results in less median deployment under current policies, CO2, and SEC. This decrease in cellulosic ethanol deploy-ment is driven by the fact that under MFULL there is an increase in the deploydeploy-ment of plug-in hybrid electric vehicles. Under OBAUCO2, 2050 cellulosic ethanol deployment plug-increases dramatically, but increasing RD&D funding from OBAUCO2 to OFULLCO2 has a negligible impact. Deployment does increase slightly from MBAUSEC to MFULLSEC (Figure 2.21).

• Increasing funding from MBAU to MFULL under current policies would result in an increase in median deployment of bioelectricity plants in 2050 from 7 GW to 11 GW. This is about 1%

of electricity capacity in 2009 (961.5 GW). Under the CO2 cap case, bioelectricity deployment increases to 57 and 58 GW under MBAUCO2 and MFULLCO2, respectively.

Increasing funding from FULL to TEN also has an impact on deployment, although at significantly greater RD&D costs per unit of deployment.

• Biodiesel deployment in 2050 increases from 371 MBOE under MFULL to 449 MBOE un-der MTEN (from 39% to 47% of 2009 diesel consumption, respectively). Increases also occur from MFULLCO2 to MTENCO2 (from 663 MBOE to 770 MBOE) and from MFULLSEC to MTENSEC (from 448 MBOE to 500 MBOE) (Figure 2.20).

• The deployment of cellulosic ethanol plants would increase marginally from 125 MBOE under MFULL to 144 MBOE under MTEN. Under CO2 and SEC, increasing funding beyond MFULL would not result in additional cellulosic ethanol deployment (Figure 2.21).

• The deployment of biopower plants stays constant or decreases from MFULL to MTEN, from MFULLCO2 to MTENCO2, from MFULLSEC to MFULLCO2, and from OFULLCO2 to OTENCO2. The exception is a slight increase in deployment from MFULLCO2NN to MTENCO2NN (from 75 GW to 81 GW) and from MFULLSECNN to MTENSECNN (from 56 GW to 59 GW) because, with nuclear removed, the competition from electric and hybrid vehicles is reduced.

To illustrate the significance of these biofuel production volumes, it is useful to view them in conjunc-tion with US oil import dependence. In our models, U.S. crude oil imports in 2050 under MBAU and MBAUCO2 were 3,460 MBOE and 2,060 MBOE, respectively. Oil imports in 2010 were approximately 3,980 MBOE. This means that, under the MBAU scenario by 2050 the United States would import 13%

less oil than in 2010, and under MBAUCO2 the United States would import 48% less oil than in 2010. It is important to note that, under MBAU, by 2020 the United States would import 16% less oil than in 2010,

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FIGURE 2.20. Deployment of thermochemically produced biodiesel in 2030 (blue) and 2050 (red) under various scenarios in million barrels of oil equivalent (MBOE) per year. The white line corresponds to the median result, and the edges correspond to the 5^th and 95^th percentiles of deployment. U.S. diesel consumption for transportation in 2009 was 955 MBOE.

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FIGURE 2.21. Deployment of cellulosic ethanol in 2030 (blue) and 2050 (red) under various scenarios in MBOE. U.S. motor gasoline consumption for transportation in 2009 was 2,900 MBOE.

0 500 1,000 1,500

which falls short of President Obama’s goal³⁷ of cutting oil imports by one third by 2021. Even by 2030, oil imports would be reduced by only 24% when compared to 2010 import volumes under this scenario.

This analysis indicates that funding increases for bioenergy RD&D result in an increase in the deploy-ment of biodiesel under a CO2 policy scenario and a sectoral policies scenario, and to a lesser extent lead to an increase in deployment of cellulosic ethanol under the sectoral policies scenario. The funding increase from MBAU to MFULL has larger benefits in terms of the deployment of bioenergy technolo-gies when compared to the funding increase from MFULL to MTEN. The average allocation of RD&D from all experts to gasification, pyrolysis, and hydrolysis is similar to the relative deployment of ther-mochemical and biochemical technologies. Specifically, on average experts allocated more funding to thermochemical technologies, and the modeling results project more deployment of thermochemical biodiesel. It is also important to note that experts allocated 12% of their recommended RD&D invest-ments to ‘other’ technologies, which included the development of novel feedstock materials. Break-throughs in feedstock development could dramatically affect the deployment of cellulosic ethanol and are not considered in the modeling, which used switchgrass as the main feedstock.

Overall, we recommend increasing biomass funding levels to the mean recommended by all the experts, or $680 million, which is over twice as large as what the expert whose estimates were used for the middle technology assumptions recommended. The $680 million recommendation largely agrees with the me-dian recommendation among all experts of $600 million. An increase in bioenergy RD&D is further supported by the results from the optimization of a $5 billion budget to six of the seven RD&D areas in-vestigated (see section 2.8.3) under different policy scenarios—namely a no-new-policy scenario, a CO2

cap policy scenario, and a sectoral policy scenario. This optimization showed that there is additional value associated with investing in bioenergy RD&D because, in a no-new-policy scenario, an increase in bioenergy RD&D could be an important contributor to CO2 emissions reductions. Under the other two policy scenarios bioenergy RD&D would play a smaller role in the two benefits considered: reducing CO₂ prices or CES credit prices, respectively.

Grid-Scale Energy Storage

Due to the limited number of experts and the diversity of energy storage technologies, for each type of battery the same experts were used as middle, optimistic, and pessimistic experts (the costs are the same across these scenarios but different across battery types). For CAES, one expert’s projections were used for the optimistic scenario, while another expert’s projections were used for the middle and pessimistic

37 President Obama’s goal could be reached through more stringent vehicle fuel economy standards and further subsidies related to advanced vehicles, which are not yet established policies and thus not modeled under “current policies”.

scenarios. This model explicitly represents a time-of-use load profile for electricity to better model power plant dispatch.  Therefore the availability of storage technologies allows for inter-temporal arbi-trage—the storage of electricity generated at off-peak hours when prices are low and subsequent release at peak times when prices are high. In addition, the model includes a capacity requirement, which acts as a proxy for reliability markets. Storage technologies can thus be used as a source of firm power in place of other resources (e.g., natural gas turbines). This can provide significant incentive for investment in storage, particularly in scenarios where there is a high deployment of technologies with low capacity values (e.g., wind or solar), which tends to increase the value of firm capacity and also to widen the price spread between on-peak and off-peak power.

However, the model does not directly represent the intermittency and short-term variability in output of renewable generation technologies such as wind and solar. For this reason, it does not capture the ben-efits that storage technologies can offer for mitigating problems related to variability when integrating significant amounts of intermittent resources onto a system. Similarly, the geographic granularity is not sufficient to model local transmission congestion, and consequently the value that storage can provide by alleviating these constraints is not captured in this analysis.

The MFULL scenario has little impact on the median deployment of Li-ion batteries under all scenarios, but it has a slightly larger impact on CAES deployment in 2050 when compared to the MBAU scenario.

• Median CAES deployment decreases with increased RD&D investment under most scenarios in 2050, except under optimistic technology assumptions and a CO2 cap policy (from 8 GW under OBAUCO2 to 12 GW under OFULLCO2). (Recall that two different experts’ projections were used for the three CAES technology assumptions.) (Figure 2.22).

• Li-ion battery deployment would increase from 45 GW to 49 GW in 2050 from MBAU to MFULL. In other words, the deployment of Li-ion batteries in 2050 under current policies and BAU funding is large—5% of the 961.5 GW of total U.S. electricity generation capacity in 2009—

but increasing RD&D from MBAU to MFULL does not make a large impact. In addition, under SEC and CO2, deployment increases with increases in RD&D funding from MBAU to MFULL.

As opposed to CAES, median deployment decreases from the OBAUCO2 to OFULLCO2.

Recall that the same technology assumptions for Li-ion batteries were used for all technology assumption scenarios (M, O, and P), while more optimistic assumptions were used for CAES in the optimistic scenarios (O). Therefore, it is unsurprising that CAES would show a relative advantage over Li-ion in the optimistic scenarios (Figure 2.23).

The TEN scenario has a larger impact on storage deployment over the FULL scenario when compared to the impact of FULL over BAU.

• Again, CAES deployment decreases with increased funding to TEN under most scenarios except from MFULLSEC to MTENSEC (from 1 GW to 14 GW in 2050), MBAUCO2NN to MTENCO2NN (a significant increase from 1 GW to 234 GW), and OFULLCO2 to OTENCO2 (another significant increase from 12 to 247 GW in 2050, with most of the growth occurring be-tween 2030 and 2050). Assuming the optimistic expert’s CAES cost projections and an RD&D level beyond FULL, CAES could play an important role in reducing electricity costs by utiliz-ing baseload power to meet the peak, thereby reducutiliz-ing the need for some additional capacity (Figure 2.22).

• The median deployment of Li-ion batteries in 2050 increases under all the scenarios from FULL to TEN (except MBAU to MTEN when nuclear expansion is not possible), though deployment at the 95^th percentile case is a decrease between MFULL and MTEN (Figure 2.23).

We recommend increasing RD&D funding for energy storage to $240 million per year because there are deployment benefits for CAES and Li-ion batteries with increased funding beyond the median recom-mended funding level across all experts, which is $120 million. In particular, if sufficiently supported by government RD&D, CAES could play a large role in multi-hour power arbitrage. Li-ion batteries are also important to support as they have a large potential for deployment, even though RD&D plays a smaller role in the scenarios investigated. The mean of all experts’ recommendations was $240 million, as a result of an outlier that had recommended $2 billion, which is $1.5 billion larger than the second largest budget recommendation. The experts who provided the battery technology and middle CAES assumptions recommended an average of $103 million, and the average recommendation from the op-timistic CAES expert and the battery technology experts was $128 million. Thus increasing funding to the mean of the experts recommendation would correspond to technology improvements consistent with funding increases beyond the middle and optimistic experts’ recommendations. In addition, the results discussed in section 2.8.3 indicate that RD&D for storage will be particularly valuable if a CO2

cap policy is implemented, especially under the optimistic technology assumptions, or if nuclear power generation expansion is not possible. This further supports a significant increase for storage.

Even though Li-ion batteries and CAES seem to be the most promising technologies for the 2030-2050 timeframe, experts also recommended devoting 7% of funding to other storage technologies. Flywheels and electrochemical capacitors received 8% and 9% of the average funding for storage, respectively, but it is not expected that they will serve for grid-scale multi-hour applications. Government support for these technologies should probably be on the very early stage research, given that short-duration storage technologies already have an established market and many private sector players.

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FIGURE 2.22. Deployment of CAES in 2030 (blue) and 2050 (red) under various scenarios in GW. Note the extended hori-zontal axis and change in scale.

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FIGURE 2.23. Deployment of grid-scale Li-ion batteries in 2030 (blue) and 2050 (red) under various scenarios in GW.

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Nuclear

Increasing RD&D funding from the BAU to the FULL scenario has no impact (or limited impact in the case of SMRs under a no-new-policy case) on the median deployment of Gen III/III+, Gen IV, and SMR reactors under the no-new-policy case and the CO2 cap case with middle technology assumptions.

Under optimistic technology assumptions and in the CO2 cap policy case, however, increasing funding from the BAU to the FULL scenario results in an increase in the deployment of Gen III/III+ reactors from 298 GW to 390 GW. Under a sectoral policy case, the FULL scenario has a positive impact on the deployment of Gen III/III+ and SMRs in 2050.

• Under the BAU with middle assumptions in the no-new-policy case, no Gen III/III+ plants are built by 2050 under any of the three RD&D scenarios considered (BAU, FULL, and 10-times).

In the MBAUCO2 and MFULLCO2 cases, 16 GW and 13 GW of nuclear capacity is built, re-spectively. This would represent a 13% increase over installed nuclear capacity in 2010 (101 GW). Increasing RD&D funding from the OBAUCO2 to the OFULLCO2 scenario would result in a median increase in Gen III/III+ deployment from 298 GW to 390 GW. Both levels of new Gen III/III+ reactor deployment by 2050 are extremely high, equivalent to almost 3 times and almost 4 times 2010 installed capacity, respectively. This deployment under the BAU scenario, a CO₂ policy, and optimistic assumptions would require an average construction per year of 7.6 GW, or about 8 nuclear reactors, over the next 39 years. The deployment under the FULL scenario would require an average construction of about 10 nuclear reactors per year for almost four decades. This rate of construction is over twice as large the rate of construction in the United States from 1972 to 1990, when installed capacity increased from 3 GW to 90 GW, and for twice the amount of time³⁸ (Figure 2.24).

• Just as in the case of Gen III/III+ plants, under the no-policy case and middle assumptions there is no Gen IV deployment at the median level by 2050 under any of the three funding levels. The MCO2 scenarios show no 2050 Gen IV deployment at the median, but significant deployment in the 95^th percentile; moving from the MBAUCO2 to the MFULLCO2 scenario results in a significant decrease in the 95th percentile of 2050 Gen IV deployment (from 190 GW to 115 GW). This decrease under the FULL scenario is caused by the fact that in this scenario, other technologies have costs that are low enough to experience increased deployment: coal cogenera-tion plants, pulverized coal plants, and natural gas plants with and without CCS. These levels of new Gen IV deployment under the BAU and FULL funding scenarios would imply almost

38 Note that in the case of nuclear power, the optimistic assumptions are in contrast to the historical cost increases of nuclear plants over time. Under the sectoral policy case and middle assumptions, there would be 10 GW of nuclear under the BAU scenario and 12 GW under the full scenario in 2050. The small impact of RD&D on Gen III/III+ deployment under the middle assumptions with all scenarios is a good representation of the full set of elicitation results; only 6 out of 22 experts expected that public funding for RD&D would have an impact on Gen III/III+ costs.

a tripling and doubling, respectively, of 2010 nuclear capacity (101 GW), but only take place at the 95^th percentile of the simulation results. Under OBAUCO2 and OFULLCO2 scenarios, no Gen IV reactors are deployed by 2050 at the median level. At the 95^th percentile, there is no Gen IV deployment under the OBAUCO2 scenario and 258 GW under the OFULLCO2 scenario.

In the MSEC policy case, the median result shows no Gen IV deployment in 2050 under any of

In the MSEC policy case, the median result shows no Gen IV deployment in 2050 under any of