资源环境科技发展态势分析平台

Policies and funding programs that support research, development, and demonstration (RD&D) for advanced energy technologies are critical elements of an overall strategy for advancing clean energy and decarbonization of the economy. In contrast to direct emissions reduction and technology deployment policies (e.g., standards, financial incentives), however, there have been limited efforts to quantitatively assess the impacts of proposed energy RD&D policies to help inform decisions. An important reason for this information gap is that it has been difficult to estimate (1) how much proposed policies will advance the technologies of interest and (2) how much such technology advances will be worth to society.

This study addresses the latter of these questions by estimating the net benefits to society that come from reducing the costs of five different advanced energy technologies. (We refer to these five technologies as the AETs). The AETs are each included in the American Energy Innovation Act (AEIA), which has been introduced in the US Senate, and are as follows:

• advanced nuclear fission power generation (hereafter, “nuclear”)
• natural gas power generation with carbon capture and sequestration (NG-CCS)
• enhanced geothermal power generation (“geothermal”)
• grid-connected diurnal electricity storage (“storage”)
• direct air capture of carbon dioxide (DAC)

This study estimates the net benefits of reducing the costs of these AETs under scenarios with and without a national clean electricity standard. The components of total net benefits include reduced electricity consumer bills, changes in generator profits and government revenue, health benefits from reduced air pollution, and climate benefits from reduced greenhouse gas emissions. We will refer to the sum of the estimated dollar values of all these impacts as the “net benefits.” Sometimes one or more of the components is negative. When that is the case, it reduces the net benefits. However, the term “net benefits,” as we use it here, does not account for the costs of the technology innovation itself, such as RD&D spending. Instead, one can compare the net benefits estimated here with the associated RD&D spending necessary to achieve those cost reductions, in order to get an overall sense of net value to society.

Analytic Approach

This assessment employs the Engineering, Economic, and Environmental Electricity Simulation Tool (E4ST), a highly realistic simulation model of the US electric power sector. We use the model to determine how different magnitudes of cost reduction for each AET would influence its deployment in the electricity system, as well as the consequences of that deployment. E4ST predicts construction and retirement of grid-serving electricity generating units and hourly operation of the grid and the generating units in future years, under policies, prices, and other conditions specified by the user. The model begins with a highly detailed representation of the current grid, generating units, electricity demand, and hourly, location-by-location wind and solar data. Among the model’s outputs are hourly locational electricity prices; emissions of carbon dioxide (CO₂), methane, sulfur dioxide (SO₂), and nitrogen oxides (NOₓ); and all the components of total net benefits noted above.

The five AETs are incorporated in E4ST using high-quality data and an emphasis on rep-resenting how they will respond to different potential circumstances. For example, for any given NG-CCS or DAC plant, the cost of transporting and sequestering carbon dioxide is determined by a network model of the potential future carbon dioxide transportation system and of the estimated supply curve for sequestration in each state or offshore area with high sequestration potential. For enhanced geothermal, we use the model of enhanced geothermal supply curves in 134 US zones from the National Renewable Energy Laboratory (NREL). For diurnal storage, we explicitly model optimal charging and discharging for 52 representative hours and 16 representative days. New nuclear plants can be built at only about 300 locations that pass a suitability screen.

For each AET, we simulate five cost levels while holding the costs of the other four AETs constant. The five cost levels are the four shown in Table ES-1 plus a fifth which is higher than ”high” and is sufficiently high that none of that technology can be built. We simulate the year 2050, and all cost levels shown are projections for 2050. For each technology, “high” is a projection, based on a highly credible source in the literature, of the cost of each technology in 2050 if it undergoes only minimal learning-by-doing. For storage, the ”high” cost level assumes some commercial deployment. For the other four AETs, high costs do not assume any commercial deployment. For all AETs except geothermal, the high 2050 cost is still lower than current cost due to assumed cost reductions resulting from a baseline amount of RD&D. “Low” cost is based on the lowest estimate of potential future cost we found from a highly credible source. The low cost tends to include substantial learning through deployment in addition to RD&D. Appendix B gives the sources of these estimates. “Medium” cost is halfway in between the high and low costs. As a further sensitivity, “very low” cost is uniformly 12.5 percent below low cost and can be motivated as a moderate degree of additional learning beyond low cost (see, e.g., Larsen et al. (2019), in which 12.5% is the central estimate of cost reduction from a doubling of cumulative capacity built).

Note that the high and low cost assumptions assessed for each technology are only rough indicators of how levelized costs might evolve with differing magnitudes of RD&D and deployment. This is partly because it is impossible to know with certainty how easily the cost of each technology can be reduced and partly because our low-cost projections for different technologies come from different sources, since no single source provides suitable projections for all the AETs.