Probabilistic Risk Assessment of Station Blackouts in Nuclear Power Plants

IEEE Adequate ac power is required for decay heat removal in nuclear power plants. Station blackout (SBO) accidents, therefore, are a very critical phenomenon to their safety. Though designed to cope with these incidents, nuclear power plants can only do so for a limited time, without risking core damage and possible catastrophe. Their impact on a plant's safety are determined by their frequency and duration, which quantities, currently, are computed via a static fault tree analysis that deteriorates in applicability with increasing system size and complexity. This paper proposes a novel alternative framework based on a hybrid of Monte Carlo methods, multistate modeling, and network theory. The intuitive framework, which is applicable to a variety of SBOs problems, can provide a complete insight into their risks. Most importantly, its underlying modeling principles are generic, and, therefore, applicable to non-nuclear system reliability problems, as well. When applied to the Maanshan nuclear power plant in Taiwan, the results validate the framework as a rational decision-support tool in the mitigation and prevention of SBOs

A Framework for Power Recovery Probability Quantification in Nuclear Power Plant Station Blackout Sequences

The safety of Generations II and III nuclear power plants relies on the availability of AC power, which is required for decay heat removal. This AC power (designated offsite power) is provided by sources outside the power plant via a grid that is susceptible to both random and induced failures. When offsite power is lost, alternative emergency sources on-site are started to drive the plant's safety systems. If, however, a situation arises where these sources are also unavailable or unable to provide the required power for the entire period the offsite sources are unavailable, a complete loss of power to the safety buses ensues. This phenomenon is known as Station Blackout (SBO), and its severity depends on its duration as well as, the plant's initial status. Consequently, the time-dependent non-recovery probability of AC power is a key parameter in the risk assessment and management of nuclear power plants. In this work, an easy-to-use and generally applicable reliability framework is proposed to model power recovery in station blackout sequences. It employs a load flow technique integrated into an efficient event-driven Monte-Carlo simulation algorithm. The resulting framework quantifies the probability of power recovery as a function of both time and power level, including other relevant indices. It, therefore, serves the purpose of a rational decision support tool in the mitigation of station blackout accidents. The proposed framework is used to analyse station blackouts emanating from grid and switchyard failures at the Maanshan nuclear power plant in Taiwan

Risk Assessment of Spent Nuclear Fuel Facilities Considering Climate Change

Natural hazards have the capability to affect technological installations, triggering multiple failures and putting the population and the surrounding environment at risk. Global climate change introduces an additional and not negligible element of uncertainty to the vulnerability quantification, threatening to intensify (both in terms of frequency and severity) the occurrence of extreme climate events. Sea level extremes and extreme coastal high waters are expected to change in the future as a result of both changes in atmospheric storminess and mean sea level rise, as well as extreme precipitation events. These trends clearly suggest a parallel increase in the risks affecting technological installations and the subsequent need for mitigation measures to enhance the reliability of existing systems and to improve the design standards of new facilities. In spite of this situation, the scientific research in this field lacks robust and reliable tools for this kind of assessment, often relying on the adoption of oversimplified models or strong assumptions, which affect the credibility of the results. The main purpose of this study is to provide a novel and general model for the evaluation of the risk of exposure of spent nuclear fuel stored in a facility subject to flood hazard, investigating the potential and limitations of Bayesian networks (BNs) in this field. The network aims to model the interaction between extreme weather conditions and the technological installation, as well as the propagation of failures within the system itself, taking into account the dependencies among the different components and the occurrence of human error. A real-world application concerning the nuclear power station of Sizewell B in East Anglia, in the United Kingdom, is extensively described, together with the models and data set used. Results are presented for three different time scenarios in which climate change projections have been adopted to estimate future risk

Electric System Vulnerabilities: Lessons from Recent Blackouts and the Role of ICT

In recent years, both Europe and America have experienced a significant number of major blackouts. This report specifically focuses on the events that affected Europe and North America during 2003, and provides a detailed analysis by critical comparison of diverse and authoritative information sources (UCTE, Eurelectric, national and international investigation committees, national authorities reports, etc).JRC.G.6-Sensors, radar technologies and cybersecurit

The role of the reactor size for an investment in the nuclear sector: an evaluation of not-financial parameters

The literature presents many studies about the economics of new Nuclear Power Plants (NPPs). Such studies are based on Discounted Cash Flow (DCF) methods encompassing the accounts related to Construction, Operation & Maintenance, Fuel and Decommissioning. However the investment evaluation of a nuclear reactor should also include not-financial factors such as siting and grid constraints, impact on the national industrial system, etc. The Integrated model for the Competitiveness Assessment of SMRs (INCAS), developed by Politecnico di Milano cooperating with the IAEA, is designed to analyze the choice of the better Nuclear Power Plant size as a multidimensional problem. In particular the INCAS’s module “External Factors” evaluates the impact of the factors that are not considered in the traditional DCF methods. This paper presents a list of these factors, providing, for each one, the rationale and the quantification procedure; then each factor is quantified for the Italian case. The IRIS reactor has been chosen as SMR representative. The approach and the framework of the model can be applied to worldwide countries while the specific results apply to most of the European countries. The results show that SMRs have better performances than LRs with respect to the external factors, in general and in the Italian scenario in particular

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Grid Reliability in the Electric Era

The United States has delegated the weighty responsibility of keeping the lights on to a self-regulatory organization called the North American Electric Reliability Corporation (NERC). Despite the fact that NERC is one of the largest and most important examples of industry-led governance—and regulates in an area that is central to our economy and basic human survival— this unusual institution has received scant attention from policymakers and scholars. Such attention is overdue. To achieve deep decarbonization, the United States must enter a new “electric era,” transitioning many sectors to run on electricity while also transforming the electricity system itself to run largely on clean but intermittent renewable resources. These new resources demand new approaches to electric grid reliability—approaches that the NERC model of reliability governance may inadequately deliver. This Article traces NERC’s history, situates NERC in ongoing debates about climate change and grid reliability, and assesses the viability of reliability selfregulation in the coming electric era. It may have made sense to delegate the task of maintaining U.S. electric grid reliability to a self-regulatory organization in prior decades, when regulated monopolies managed nearly every segment of electricity production. But many of the criteria that NERC used to justify selfregulation earlier in its history—electric utilities’ expertise, widespread agreement about the organization’s goals, and an industry structure in which regulated parties’ interests align with the public’s—no longer hold. The climate crisis creates a need for expertise beyond NERC’s domain, while the introduction of competition to large parts of the electricity sector blurs lines of accountability for reliability failures. NERC’s structure also perpetuates an incumbency bias at odds with public goals for the energy transition. These shifting conditions have caused NERC to fail to keep pace with the reliability challenges of the electric era. Worse still, outdated NERC standards often help entrench fossil fuel interests by justifying electricity market rules poorly suited to accommodate renewable resources. We therefore suggest a suite of reforms that would increase direct government oversight and accountability in electricity reliability regulation

Simulation Methods for the Analysis of Complex Systems

open access bookEveryday systems like communication, transportation, energy and industrial systems are an indispensable part of our daily lives. Several methods have been developed for their reliability assessment—while analytical methods are computationally more efficient and often yield exact solutions, they are unable to account for the structural and functional complexities of these systems. These complexities often require the analyst to make unrealistic assumptions, sometimes at the expense of accuracy. Simulation-based methods, on the other hand, can account for these realistic operational attributes but are computationally intensive and usually system-specific. This chapter introduces two novel simulation methods: load flow simulation and survival signature simulation which together address the limitations of the existing analytical and simulation methods for the reliability analysis of large systems