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To mitigate global warming, phasing out coal in the global energy system orderly and rapidly is an important near-term strategy. However, the majority of coal-fired plants in China have operated for less than 15 years. Accelerated coal power plant retirements would lead to substantial asset stranding. Coal-to-nuclear (C2N) technology offers a potential solution by replacing coal boilers in existing coal-fired plants with nuclear reactors. In this study, the G4-ECONS model was used to assess the economics of repowering a 600 MW supercritical coal-fired power plant with two 272 MWe high-temperature gas-cooled reactors. The timeline for the C2N project and the additional cost of dispatching electricity from the grid during retrofitting were discussed. Results showed that the C2N total capitalized costs are 19.4% (baseline estimate, USD 5297.6/kW) and 11.1% (conservative estimate, USD 5847.2/kW) lower than the greenfield project (USD 6576.5/kW), respectively. And C2N projects need to reduce LUEC by at least 20% to become competitive. This study can inform engineering design decisions leading to more precise and cost-effective C2N projects.

To mitigate global warming, phasing out coal in the global energy system orderly and rapidly is an important near-term strategy. However, the majority of coal-fired plants in China have operated for less than 15 years. Accelerated coal power plant retirements would lead to substantial asset stranding. Coal-to-nuclear (C2N) technology offers a potential solution by replacing coal boilers in existing coal-fired plants with nuclear reactors. In this study, the G4-ECONS model was used to assess the economics of repowering a 600 MW supercritical coal-fired power plant with two 272 MWe high-temperature gas-cooled reactors. The timeline for the C2N project and the additional cost of dispatching electricity from the grid during retrofitting were discussed. Results showed that the C2N total capitalized costs are 19.4% (baseline estimate, USD 5297.6/kW) and 11.1% (conservative estimate, USD 5847.2/kW) lower than the greenfield project (USD 6576.5/kW), respectively. And C2N projects need to reduce LUEC by at least 20% to become competitive. This study can inform engineering design decisions leading to more precise and cost-effective C2N projects.

Decarbonizing heavy-duty vehicles is becoming increasingly important, yet multiple renewable-based decarbonization pathways have not been compared systematically. To fill this gap, this study develops a techno-economic and environmental model that integrates the fuel cycle and vehicle cycle to assess these pathways for China. The model includes two energy storage technologies: batteries and hydrogen, three energy transmission options, and two vehicle types: fuel cell electric vehicles and battery electric vehicles. Five distinct low-carbon pathways are evaluated on a ton-kilometer basis, including cost, greenhouse gas emissions, and abatement cost relative to conventional diesel trucks. Results indicate that the most cost-effective battery electric vehicle pathway offers a 7 % lower ton-km cost than diesel trucks for a 49-ton gross vehicle weight. Battery electric vehicle charging combined with hydrogen for power generation is more cost-effective than direct use of hydrogen in fuel cell electric vehicles. All studied pathways can achieve over 80 % emission reduction compared to diesel trucks, with fuel cell electric vehicle pathways having the highest abatement costs of 103–117 USD/tonCO2. Analysis of the impact of gross vehicle weight and vehicle range indicates that battery electric vehicle pathways outperform fuel cell electric vehicle pathways across most dimensions. Costs of battery electric vehicle pathways do increase sharply for vehicle ranges beyond about 700 km, and fuel cell electric vehicles become more cost-effective relative to battery electric vehicles for ranges above approximately 1100 km, although all pathways are much more costly than diesel trucks at long ranges. The sensitivity analysis highlights the importance of powertrain efficiency and cost projections show that fuel cell electric heavy-duty vehicles can achieve cost-parity before 2030. These results highlight the desirability of promoting the adoption of heavier battery electric vehicles and providing more targeted subsidies for long-range fuel cell electric vehicles.

Decarbonizing heavy-duty vehicles is becoming increasingly important, yet multiple renewable-based decarbonization pathways have not been compared systematically. To fill this gap, this study develops a techno-economic and environmental model that integrates the fuel cycle and vehicle cycle to assess these pathways for China. The model includes two energy storage technologies: batteries and hydrogen, three energy transmission options, and two vehicle types: fuel cell electric vehicles and battery electric vehicles. Five distinct low-carbon pathways are evaluated on a ton-kilometer basis, including cost, greenhouse gas emissions, and abatement cost relative to conventional diesel trucks. Results indicate that the most cost-effective battery electric vehicle pathway offers a 7 % lower ton-km cost than diesel trucks for a 49-ton gross vehicle weight. Battery electric vehicle charging combined with hydrogen for power generation is more cost-effective than direct use of hydrogen in fuel cell electric vehicles. All studied pathways can achieve over 80 % emission reduction compared to diesel trucks, with fuel cell electric vehicle pathways having the highest abatement costs of 103–117 USD/tonCO2. Analysis of the impact of gross vehicle weight and vehicle range indicates that battery electric vehicle pathways outperform fuel cell electric vehicle pathways across most dimensions. Costs of battery electric vehicle pathways do increase sharply for vehicle ranges beyond about 700 km, and fuel cell electric vehicles become more cost-effective relative to battery electric vehicles for ranges above approximately 1100 km, although all pathways are much more costly than diesel trucks at long ranges. The sensitivity analysis highlights the importance of powertrain efficiency and cost projections show that fuel cell electric heavy-duty vehicles can achieve cost-parity before 2030. These results highlight the desirability of promoting the adoption of heavier battery electric vehicles and providing more targeted subsidies for long-range fuel cell electric vehicles.

Effective decision making to allocate public funds for energy technology research, development, and demonstration (R&D) requires considering alternative investment opportunities that can have large but highly uncertain returns and a multitude of positive or negative interactions. This paper proposes and implements a method to support R&D decisions that propagates uncertainty through an economic model to estimate the benefits of an R&D portfolio, accounting for innovation spillovers and technology substitution and complementarity. The proposed method improves on the existing literature by: (a) using estimates of the impact of R&D investments from one of the most comprehensive sets of expert elicitations on this topic to date; (b) using a detailed energy-economic model to estimate evaluation metrics relevant to an energy R&D portfolio: e.g., system benefits, technology diffusion, and uncertainty around outcomes; and (c) using a novel sampling and optimization strategy to calculate optimal R&D portfolios. This design is used to estimate an optimal energy R&D portfolio that maximizes the net economic benefits under an R&D budget constraint. Results parameterized based on expert elicitations conducted in 2009-2011 in the United States provide indicative results that show: (1) an expert-recommended portfolio in 2030, relative to the BAU portfolio, can reduce carbon dioxide emissions by 46 million tonnes, increase economic surplus by $29 billion, and increase renewable energy generation by 39 TWh; (2) uncertainty around the estimates of R&D benefits is large and overall uncertainty increases with greater investment levels; (3) a 10-fold expansion from 2012 levels in the annual R&D budget for utility-scale energy storage, bioenergy, advanced vehicles, fossil energy, nuclear energy, and solar photovoltaic technologies can be justified by returns to economic surplus; (4) the greatest returns to public R&D investment are in energy storage and solar photovoltaics; and (5) the current allocation of energy R&D funds is very different from optimal portfolios. Taken together, these results demonstrate the utility of applying new methods to improve the cost-effectiveness and environmental performance in a deliberative approach to energy R&D portfolio decision making.

Effective decision making to allocate public funds for energy technology research, development, and demonstration (R&D) requires considering alternative investment opportunities that can have large but highly uncertain returns and a multitude of positive or negative interactions. This paper proposes and implements a method to support R&D decisions that propagates uncertainty through an economic model to estimate the benefits of an R&D portfolio, accounting for innovation spillovers and technology substitution and complementarity. The proposed method improves on the existing literature by: (a) using estimates of the impact of R&D investments from one of the most comprehensive sets of expert elicitations on this topic to date; (b) using a detailed energy-economic model to estimate evaluation metrics relevant to an energy R&D portfolio: e.g., system benefits, technology diffusion, and uncertainty around outcomes; and (c) using a novel sampling and optimization strategy to calculate optimal R&D portfolios. This design is used to estimate an optimal energy R&D portfolio that maximizes the net economic benefits under an R&D budget constraint. Results parameterized based on expert elicitations conducted in 2009-2011 in the United States provide indicative results that show: (1) an expert-recommended portfolio in 2030, relative to the BAU portfolio, can reduce carbon dioxide emissions by 46 million tonnes, increase economic surplus by $29 billion, and increase renewable energy generation by 39 TWh; (2) uncertainty around the estimates of R&D benefits is large and overall uncertainty increases with greater investment levels; (3) a 10-fold expansion from 2012 levels in the annual R&D budget for utility-scale energy storage, bioenergy, advanced vehicles, fossil energy, nuclear energy, and solar photovoltaic technologies can be justified by returns to economic surplus; (4) the greatest returns to public R&D investment are in energy storage and solar photovoltaics; and (5) the current allocation of energy R&D funds is very different from optimal portfolios. Taken together, these results demonstrate the utility of applying new methods to improve the cost-effectiveness and environmental performance in a deliberative approach to energy R&D portfolio decision making.

The allowance allocation under the European Emission trading schemes differs fundamentally from earlier cap and trade programs, like SO2 and NOx in the USA. Because of the iterative nature of negotiations of the overall budget, the allocation also has to follow an iterative process. If power generators anticipate that their current behaviour will affect future allowance allocation, then this can distort todayâ  s decisions. Furthermore, the National Allocation Plans (NAPs) contain multiple provisions dealing with existing installations, what happens to allocation when they close, and allocations to new entrants. We provide a framework to assess the economic incentives and distortions that provisions in NAPs can have on market prices, operation and investment decisions. To this end, we use both analytic models to illustrate the incentives effects and results from numerical simulation runs that estimate the magnitude of impacts from different allocation rules.

The allowance allocation under the European Emission trading schemes differs fundamentally from earlier cap and trade programs, like SO2 and NOx in the USA. Because of the iterative nature of negotiations of the overall budget, the allocation also has to follow an iterative process. If power generators anticipate that their current behaviour will affect future allowance allocation, then this can distort todayâ  s decisions. Furthermore, the National Allocation Plans (NAPs) contain multiple provisions dealing with existing installations, what happens to allocation when they close, and allocations to new entrants. We provide a framework to assess the economic incentives and distortions that provisions in NAPs can have on market prices, operation and investment decisions. To this end, we use both analytic models to illustrate the incentives effects and results from numerical simulation runs that estimate the magnitude of impacts from different allocation rules.