核电厂退役成本影响因素研究

Hyung-min Cha, Yongbeum Yoon, Soojin Park
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引用次数: 2

摘要

这是一篇在知识共享署名非商业许可(http://creativecommons.org/licenses/ by-nc/3.0)的条款下发布的开放获取文章,该许可允许在任何媒介上不受限制的非商业使用、分发和复制,前提是原始作品被适当引用。核电站产生放射性废物,这些废物的退役需要额外的成本;确定各种类型和规格的放射性废物的这些成本可能具有挑战性。本研究的目的是确定退役费用的主要决定因素及其对核电站的影响。为此,收集了已停用核电站的数据,并开发了2SLS(两阶段最小二乘)回归模型,根据反应堆类型,即压水堆(PWR)和沸水堆(BWR)来调查主要贡献者。此外,成本估计和蒙特卡罗模拟作为性能验证的一部分进行。我们的研究确定,退役成本主要受退役废物的放射性水平影响,这可以从运行周期、整体效率和工厂容量等操作因素中实现,也可以从退役时间和人工成本中实现。虽然我们的研究提供了一种改进的统计方法来识别这些因素,但我们承认,我们的模型在准确预测方面存在局限性,我们设想通过识别更多实质性因素来支持未来的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Study on the Determinants of Decommissioing Cost for Nuclear Power Plant (NPP)
This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited Nuclear power plants (NPPs) produce radioactive waste and decommissioning this waste entails additional cost; determining these costs for various types and specifications of radioactive waste can be challenging. The purpose of this study is to identify major determinants of the decommissioning cost and their impact on NPPs. To this end, data from defunct NPPs were gathered and 2SLS (Two Stage Least Squares) regression models were developed to investigate the major contributors depending on the reactor types, viz. PWR (Pressurized Water Reactors) and BWR (Boiling Water Reactors). Additionally, cost estimations and the Monte Carlo simulation were performed as part of performance validation. Our study established that the decommissioning costs are primarily influenced by the level of radioactivity in the decommissioned waste, which can be realized from operational factors like operation period, overall efficiency, and plant capacity, as well as from duration of decommissioning and labour cost. While our study provides an improved statistical approach to recognize these factors, we acknowledge that our models have limitations in forecasting accurately which we envisage to bolster in future studies by identifying more substantive factors.
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