The World Nuclear Industry Status Report 2019

Schneider, Mycle; Froggatt, Antony; Hazemann, Julie; Katsuta, Tadahiro; Stirling, Andy; Wealer, Ben; Johnstone, Philip; Ramana, M. V.; Stienne, Agnès

doi:10.1142/9789811213953_0021

Cited by 28 publications

(22 citation statements)

References 1 publication

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“…They also sit worse than the median in terms of their economic performance. Concerningly, both nations have development agreements in place with Russia; Nigeria has agreed in principle to a two‐plant build in 2017, while in Bangladesh, construction is ongoing on Rooppur 1 and 2, both of which comprise Gigawatt‐scale reactors (Schneider et al, 2019). In both cases, Russia is providing significant technical and financial support, financing 90% of the project in Bangladesh and proposing a BOO model for Nigeria (WNA, 2019).…”

Section: Resultsmentioning

confidence: 99%

“…The development of large, capital-intensive projects carries significant technical, financial, and institutional risks. Over the past few decades, and perhaps because of its inherent safety and security implications, nuclear power has emerged as a clear example of this phenomenon (The Economist, 2017;Schneider et al, 2019). The oversight required to safely and securely build and operate nuclear power plants is substantial, including standards assessment As with Vietnam, Saudi Arabia's economy has become stronger, while its relative need for low-carbon solutions has always been high, given its large electric grid's complete dependence on fossil fuel sources (first oil, and now natural gas).…”

Section: Discussionmentioning

confidence: 99%

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New Methods for Evaluating Energy Infrastructure Development Risks

Ford

Abdulla

2021

Risk Analysis

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Many energy technologies that can provide reliable, low‐carbon electricity generation are confined to nations that have access to robust technical and economic capabilities, either on their own or through geopolitical alliances. Equally important, these nations maintain a degree of institutional capacity that could lower the risks associated with deploying emergent energy technologies such as advanced nuclear or carbon capture and storage. The complexity, expense, and scrutiny that come with building these facilities make them infeasible choices for most nations. This paradigm is slowly changing, as the pressing need for low‐carbon electricity generation and ongoing efforts to develop modular nuclear and carbon capture technologies have opened the door for potentially wider markets, including in nations without substantial institutional capacity. Here, using advanced nuclear technologies as our testbed, we develop new methods to evaluate national readiness for deploying complex energy infrastructure. Specifically, we use Data Envelopment Analysis—a method that eliminates the need for expert judgment—to benchmark performance across nations. We find that approximately 80% of new nuclear deployment occurs in nations that are in the top two quartiles of institutional and economic performance. However, 85% of potential low‐carbon electricity demand growth is in nations that are in the bottom two quartiles of performance. We offer iconic paradigms for deploying nuclear power in each of these clusters of nations if the goal is to mitigate risk. Our research helps redouble efforts by industry, regulators, and international development agencies to focus on areas where readiness is low and risk correspondingly higher.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

New Methods for Evaluating Energy Infrastructure Development Risks

Ford

Abdulla

2021

Risk Analysis

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“…Nuclear Reactor Control. Nuclear power is the second-largest source of low-carbon electricity, supplying 10.2% of global electricity in 31 countries in 2018 [48]. It provided 55% of America's low-carbon electricity in 2019, making it by far the largest source of clean energy [56].…”

Section: Nuclear Reactor Control In Offline Environmentmentioning

confidence: 99%

Time-Aware Q-Networks: Resolving Temporal Irregularity for Deep Reinforcement Learning

Kim,

Chi

2021

Preprint

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Deep Reinforcement Learning (DRL) has shown outstanding performance on inducing effective action policies that maximize expected long-term return on many complex tasks. Much of DRL work has been focused on sequences of events with discrete time steps and ignores the irregular time intervals between consecutive events. Given that in many real-world domains, data often consists of temporal sequences with irregular time intervals, and it is important to consider the time intervals between temporal events to capture latent progressive patterns of states. In this work, we present a general Time-Aware RL framework: Timeaware Q-Networks (TQN), which takes into account physical time intervals within a deep RL framework. TQN deals with time irregularity from two aspects: 1) elapsed time in the past and an expected next observation time for time-aware state approximation, and 2) action time window for the future for time-aware discounting of rewards. Experimental results show that by capturing the underlying structures in the sequences with time irregularities from both aspects, TQNs significantly outperform DQN in four types of contexts with irregular time intervals. More specifically, our results show that in classic RL tasks such as CartPole and MountainCar and Atari benchmark with randomly segmented time intervals, time-aware discounting alone is more important while in the real-world tasks such as nuclear reactor operation and septic patient treatment with intrinsic time intervals, both time-aware state and time-aware discounting are crucial. Moreover, to improve the agent's learning capacity, we explored three boosting methods: Double networks, Dueling networks, and Prioritized Experience Replay, and our results show that for the two real-world tasks, combining all three boosting methods with TQN is especially effective.

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“…Nuclear power takes approximately one-third of the low-carbon electricity generation worldwide [1], supplying energy efficiently for households and industries. However, nuclear power plants (NPPs) can significantly impact the public and the environ-JRPR ment in case of unexpected nuclear accidents.…”

Section: Introductionmentioning

confidence: 99%

Development of a Dynamic Downscaling Method for Use in Short-Range Atmospheric Dispersion Modeling Near Nuclear Power Plants

Lee¹,

Oh²,

Kim³

et al. 2023

J Radiat Prot Res

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Background: High-fidelity meteorological data is a prerequisite for the realistic simulation of atmospheric dispersion of radioactive materials near nuclear power plants (NPPs). However, many meteorological models frequently overestimate near-surface wind speeds, failing to represent local meteorological conditions near NPPs. This study presents a new high-resolution (approximately 1 km) meteorological downscaling method for modeling short-range (< 100 km) atmospheric dispersion of accidental NPP plumes.Materials and Methods: Six considerations from literature reviews have been suggested for a new dynamic downscaling method. The dynamic downscaling method is developed based on the Weather Research and Forecasting (WRF) model version 3.6.1, applying high-resolution land-use and topography data. In addition, a new subgrid-scale topographic drag parameterization has been implemented for a realistic representation of the atmospheric surface-layer momentum transfer. Finally, a year-long simulation for the Kori and Wolsong NPPs, located in southeastern coastal areas, has been made for 2016 and evaluated against operational surface meteorological measurements and the NPPs’ on-site weather stations.Results and Discussion: The new dynamic downscaling method can represent multiscale atmospheric motions from the synoptic to the boundary-layer scales and produce three-dimensional local meteorological fields near the NPPs with a 1.2 km grid resolution. Comparing the year-long simulation against the measurements showed a salient improvement in simulating near-surface wind fields by reducing the root mean square error of approximately 1 m/s. Furthermore, the improved wind field simulation led to a better agreement in the Eulerian estimate of the local atmospheric dispersion. The new subgrid-scale topographic drag parameterization was essential for improved performance, suggesting the importance of the subgrid-scale momentum interactions in the atmospheric surface layer.Conclusion: A new dynamic downscaling method has been developed to produce high-resolution local meteorological fields around the Kori and Wolsong NPPs, which can be used in short-range atmospheric dispersion modeling near the NPPs.

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The World Nuclear Industry Status Report 2019

Cited by 28 publications

References 1 publication

New Methods for Evaluating Energy Infrastructure Development Risks

New Methods for Evaluating Energy Infrastructure Development Risks

Time-Aware Q-Networks: Resolving Temporal Irregularity for Deep Reinforcement Learning

Development of a Dynamic Downscaling Method for Use in Short-Range Atmospheric Dispersion Modeling Near Nuclear Power Plants

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