Evaluation of Nuclear Microreactor Cost-competitiveness in Current Electricity Markets Considering Reactor Cost Uncertainties

• URL: http://arxiv.org/abs/2506.13361v1
• Date: Mon, 16 Jun 2025 11:04:48 GMT
• Title: Evaluation of Nuclear Microreactor Cost-competitiveness in Current Electricity Markets Considering Reactor Cost Uncertainties
• Authors: Muhammad R. Abdusammi, Ikhwan Khaleb, Fei Gao, Aditi Verma,
• Abstract summary: This paper evaluates the cost competitiveness of microreactors in today's electricity markets, with a focus on uncertainties in reactor costs.<n>A Genetic Algorithm (GA) is used to optimize key technical parameters, such as reactor fuel enrichment, tail enrichment, refueling interval, and discharge burnup.<n>Results show that microreactors can remain cost-competitive, with Ls ranging from $48.21/MWh to $78.32/MWh when supported by the Production Tax Credit (PTC).<n>Compared to conventional nuclear, coal, and renewable sources like offshore wind, hydro, and biomass, optimized microre
• Score: 2.2002244657481826
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper evaluates the cost competitiveness of microreactors in today's electricity markets, with a focus on uncertainties in reactor costs. A Genetic Algorithm (GA) is used to optimize key technical parameters, such as reactor capacity, fuel enrichment, tail enrichment, refueling interval, and discharge burnup, to minimize the Levelized Cost of Energy (LCOE). Base case results are validated using Simulated Annealing (SA). By incorporating Probability Distribution Functions (PDFs) for fuel cycle costs, the study identifies optimal configurations under uncertainty. Methodologically, it introduces a novel framework combining probabilistic cost modeling with evolutionary optimization. Results show that microreactors can remain cost-competitive, with LCOEs ranging from \$48.21/MWh to \$78.32/MWh when supported by the Production Tax Credit (PTC). High reactor capacity, low fuel enrichment, moderate tail enrichment and refueling intervals, and high discharge burnup enhance cost efficiency. Among all factors, overnight capital cost (OCC) has the most significant impact on LCOE, while O&M and fuel cost uncertainties have lesser effects. The analysis highlights how energy policies like the PTC can reduce LCOE by 22-24%, improving viability despite cost variability. Compared to conventional nuclear, coal, and renewable sources like offshore wind, hydro, and biomass, optimized microreactors show strong economic potential. This research defines a realistic design space and key trade-offs, offering actionable insights for policymakers, reactor designers, and energy planners aiming to accelerate the deployment of affordable, sustainable microreactors.

Related papers

• Demand Response Optimization MILP Framework for Microgrids with DERs [0.0]
  This paper presents a framework for optimizing demand response in a microgrid with solar generation and battery storage systems.<n>The framework incorporates load classification, dynamic price thresholding, and multi-period coordination for optimal DR event scheduling.
  arXiv  Detail & Related papers  (2025-02-12T20:10:51Z)
• Stochastic Bridges as Effective Regularizers for Parameter-Efficient Tuning [98.27893964124829]
  We propose regularizing PETs that use bridges as the regularizers (running costs) for the intermediate states. In view of the great potential and capacity, we believe more sophisticated regularizers can be designed for PETs.
  arXiv  Detail & Related papers  (2023-05-28T09:22:44Z)
• Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system [4.721325160754968]
  The adoption of CDRT is not economically viable at the current carbon price. The proposed DRL agent can meet the prosumers' multi-energy demand and schedule the CDRT energy demand economically. The configuration with PCCS and solid-sorbent DACS is considered the most suitable.
  arXiv  Detail & Related papers  (2023-01-18T20:22:44Z)
• PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design [102.9593507372373]
  Catalyst materials play a crucial role in the electrochemical reactions involved in industrial processes. Machine learning holds the potential to efficiently model materials properties from large amounts of data. We propose task-specific innovations applicable to most architectures, enhancing both computational efficiency and accuracy.
  arXiv  Detail & Related papers  (2022-11-22T05:24:30Z)
• (Private)-Retroactive Carbon Pricing [(P)ReCaP]: A Market-based Approach for Climate Finance and Risk Assessment [64.83786252406105]
  Retrospective Social Cost of Carbon Updating (ReSCCU) is a novel mechanism that corrects for limitations as empirically measured evidence is collected. To implement ReSCCU in the context of carbon taxation, we propose Retroactive Carbon Pricing (ReCaP) To alleviate systematic risks and minimize government involvement, we introduce the Private ReCaP (PReCaP) prediction market.
  arXiv  Detail & Related papers  (2022-05-02T06:02:13Z)
• Estimating air quality co-benefits of energy transition using machine learning [5.758035706324685]
  Estimating health benefits of reducing fossil fuel use from improved air quality provides important rationales for carbon emissions abatement. We develop a novel and succinct machine learning framework that is able to provide precise and robust annual average fine particle (PM2.5) concentration estimations. Our findings prompt careful policy designs to maximize cost-effectiveness in the transition towards a carbon-neutral energy system.
  arXiv  Detail & Related papers  (2021-05-29T14:52:57Z)
• Demand Responsive Dynamic Pricing Framework for Prosumer Dominated Microgrids using Multiagent Reinforcement Learning [59.28219519916883]
  This paper proposes a new multiagent Reinforcement Learning based decision-making environment for implementing a Real-Time Pricing (RTP) DR technique in a prosumer dominated microgrid. The proposed technique addresses several shortcomings common to traditional DR methods and provides significant economic benefits to the grid operator and prosumers.
  arXiv  Detail & Related papers  (2020-09-23T01:44:57Z)
• Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning [1.0765359420035392]
  This study used a novel technique, multi-agent deep reinforcement learning, to optimize dissolved oxygen and chemical dosage in a WWTP. The result shows that optimization based on LCA has lower environmental impacts compared to baseline scenario. The cost-oriented control strategy exhibits comparable overall performance to the LCA driven strategy.
  arXiv  Detail & Related papers  (2020-08-19T05:34:47Z)
• Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
  We study a risk-aware energy scheduling problem for a microgrid-powered MEC network. We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
  arXiv  Detail & Related papers  (2020-02-21T02:14:38Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.