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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning

We assume that we are given a time series of data from a dynamical system and our task is to learn the flow map of the dynamical system. We present a collection of results on how to enforce constraints coming from the dynamical system in order to accelerate the training of deep neural networks to represent the flow map of the system as well as increase their predictive ability. In particular, we provide ways to enforce constraints during training for all three major modes of learning, namely supervised, unsupervised and reinforcement learning. In general, the dynamic constraints need to include terms which are analogous to memory terms in model reduction formalisms. Such memory terms act as a restoring force which corrects the errors committed by the learned flow map during prediction. For supervised learning, the constraints are added to the objective function. For the case of unsupervised learning, in particular generative adversarial networks, the constraints are introduced by augmenting the input of the discriminator. Finally, for the case of reinforcement learning and in particular actor-critic methods, the constraints are added to the reward function. In addition, for the reinforcement learning case, we present a novel approach based on homotopy of the action-value function in order to stabilize and accelerate training. We use numerical results for the Lorenz system to illustrate the various constructions.

Stinis, Panagiotis↗
DOE OSTI2020

Resilient Autonomous Wind Farms: Preprint

With the advent of an increasing number of control strategies that seek to optimize wind turbine performance on a farm-level, taking account of individual wind turbine information to achieve wind farm-level objectives has become an increasingly important goal. Methods for controlling wind turbines on an individual and farm level have seen significant development, and an abundance of new implementations for gathering and using data from turbines have created potential for novel control mechanisms which can further optimize the performance and delivery characteristics of a wind farm. A key element of making these wind farms more efficient is to develop reliable algorithms that use local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. Making use of information from all wind turbines in a wind farm can enable such approaches as determining the atmospheric conditions across the farm, improving fault-finding, and enabling more efficient overall control of farm-wide optimizations through mechanisms such as wake-steering. However, these approaches typically involve a centralized communications and control center. In order to ensure the resilient operation of the farm, it is necessary to develop an approach which distributes the calculation and communication amongst multiple nodes throughout the farm. In this fashion, a redundant, robust, and secure network can be created, which can tolerate faults in calculation, communication, and even external attacks which seek to disrupt the operation of the wind farm. This paper introduces the use of the Raft Byzantine Fault Tolerance algorithm in the implementation of autonomous control of a wind farm. This implementation will allow for fault tolerance for malfunctioning nodes, sensors, transmitters, and connectors. This approach is equally extensible to account for malicious actors. It will be shown to achieve overall consensus, provided the number of faults/malicious nodes is less than 3$n$+1, where $n$ is the number of turbine cluster faults which may occur, and to be robust in the face of multiple arbitrary faults.

autonomous↗
DOE OSTI2021

Scaling Equitable Finance

Driven by dramatic declines in up-front cost, the U.S. solar photovoltaics (PV) industry has taken off over the past decade, growing from 1 gigawatt of installed capacity in 2009 to 89 gigawatts in 2020—or enough capacity to power roughly 19 million homes. The industry is expected to double in size over just the next 5 years.1 Much of the growth has been driven by large, utility-scale projects that can produce 5 mega- watts or more of power—enough to power at least 1,000 homes. The cost of electricity produced by these projects has decreased by more than 70 percent since 2010. As of Q3 2020, development costs of large, util- ity-scale solar PV power plants were under $1 per watt, down by more than 70 percent from 2010.2 A robust array of investors has come forward to efficiently deliver capital to these kinds of utility-scale projects including large banks, insurance companies, pension funds, and others. But low- and moderate-income communities, including communities of color, are at risk of being left behind in the transition to clean energy. Mission- driven solar project developers and financial institu- tions have been working alongside energy justice advocates to open up solar access for these communi- ties, using strategies ranging from community solar, to solar installations on affordable multifamily housing, to distributed solar and storage programs, and more. Their goals go beyond simply generating more green energy to advancing social equity by: • empowering communities to control their energy future • stabilizing energy prices, saving money, and build- ing wealth for low-income families • creating quality jobs • improving health by reducing pollution • providing energy resilience for vulnerable communities Mission-driven actors are successfully deploying a wide variety of strategies to meet these goals, from helping low-income homeowners get solar—and some- times battery storage, to developing solar projects serv- ing affordable rental housing and community facilities, to building larger “shared solar” projects to which households from across the community can subscribe. However, the financing ecosystem does not work nearly as well for these “mission driven” solar proj- ects as it does for utility-scale projects. For home rooftop solar, even if low-income consumers have a home and suitable roof, they may fail to qualify for federal tax incentives, lack adequate credit to qualify for a loan—or the mission-driven lenders seeking to serve them may not be adequately capitalized to make long-term loans. For mission-driven commercial or community-scale projects, assembling nearly every component of the project capital stack—whether bridging early-stage costs, attracting tax credit equity investors, securing long-term debt, or coming up with sponsor equity and filling gaps—can present challenges. A variety of obstacles contribute to the scarcity of financing for low-income solar, including small project sizes, lack of developer balance sheet capacity, both real and perceived issues with credit risk, elevated technical assistance needs, and greater subsidy requirements to pursue goals such as deep energy affordability, climate resilience, or job creation. Still other obstacles are regulatory: for example, not all states allow community solar projects or Power Purchase Agreements, common strategies used for providing low-income solar—and the potential for regulations to shift over time creates risks that mission-driven projects can ill afford. This report synthesizes information garnered from 47 key informant interviews, four focus group discus- sions involving 60 stakeholders, and a review of the substantial existing literature on low-income solar finance to assess the current landscape of mission- driven solar development in the United States, examine the roles that community-based financial institutions could play, and recommend public invest- ments and policy changes that could help to scale the provision of equitable solar finance. Key recommen- dations for policymakers and funders in the renew- able energy and community development fields that emerge from this process include the following: • Help to capitalize and support community-based lenders to provide flexible, low-cost, and long- term financing to mission-driven solar projects— including providing guarantees or other forms of credit enhancement. • Provide federal support for equitable solar, including a grant-in-lieu-of-credits option for the Investment Tax Credit to improve access to this critical government subsidy. • Develop pools of government and philanthropic support that can complement financing from community-based lenders to complete the capi- tal stack for mission-driven projects, as well as to support education and technical assistance to both consumers and potential project sponsors. • Create a national Renewable Energy Credits pro- gram that includes social equity targets to provide a baseline of support for clean energy generation. • Change utility regulations to remove barriers to low-income solar projects; lower permitting costs; provide greater certainty for developers, consumers and owners; and measure progress toward equity in renewable energy policy implementation.

14 SOLAR ENERGY↗
DOE OSTI2021

Artificial Intelligence for Energy Systems Cybersecurity

Artificial intelligence and machine learning systems have the potential to influence the future design and implementation of cybersecurity systems for the power grid. These systems may enhance the overall operation of the power system by leveraging and making sense of massive amounts of data. However, we must also understand how AI/ML will need to be protected from cyber threat actors. We discuss the existing insights the NREL team has developed using AI/ML systems and then present resources including ESIF and the Cyber Energy Emulation Platform that can be used to generate training data and insights. We end by offering suggestions on priority research paths for AI in cybersecurity.

artificial intelligence↗
DOE OSTI2021

Cyberbiosecurity and Public Health in the Age of COVID-19

Cyberbiosecurity, the aspect of biosecurity involving the digital representation of biological data, had already been emerging as a matter of public concern even prior to the onset of the COVID-19 pandemic. Key issues of concern include, among others, the privacy of patient data, the security of public health databases, the integrity of diagnostic test data, the integrity of public biological databases, the security implications of automated laboratory systems and the security of proprietary biological engineering advances. With the onset of the COVID-19 pandemic, and the importance of digital resources in combatting it, concern about the potential for cyber attacks by state-based or non-state actors has been elevated. To illuminate the challenges, we focus on the cyber vulnerabilities that need to be addressed in public health activities such as disease surveillance and outbreak management. In particular, we examine cyber issues raised by the accelerated pace of development for COVID mitigations, treatments, and vaccines.

cybersecurity, biosecurity↗
DOE OSTI2022

Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks

We present a novel method for graph partitioning, based on reinforcement learning and graph convolutional neural networks. Our approach is to recursively partition coarser representations of a given graph. The neural network is implemented using SAGE graph convolution layers, and trained using an advantage actor critic (A2C) agent. We present two variants, one for finding an edge separator that minimizes the normalized cut or quotient cut, and one that finds a small vertex separator. The vertex separators are then used to construct a nested dissection ordering to permute a sparse matrix so that its triangular factorization will incur less fill-in. The partitioning quality is compared with partitions obtained using METIS and SCOTCH, and the nested dissection ordering is evaluated in the sparse solver SuperLU. Our results show that the proposed method achieves similar partitioning quality as METIS and SCOTCH. Furthermore, the method generalizes across different classes of graphs, and works well on a variety of graphs from the SuiteSparse sparse matrix collection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Unified Testing Platform to Mature Blockchain Applications for Grid Emulation Environments: Preprint

Blockchain technology is a relatively novel technology that can be used to develop more decentralized, autonomous and tamper-evident solutions. A feature that can aid Transactive Energy Systems to reach their goals by enabling individual actors to communicate and reach consensus with other participants in a more decentralized fashion. However, technical barriers to evaluate and adopt this type of technology within the electrical domain still exist. To facilitate this task, BLOSEM Unified Testing Platform (UTP), a DOE-sponsored, multi-lab effort intends to accelerate the development of solutions by offering a common set of reusable services that can be used to interconnect existent grid tools with blockchain services. UTP is intended to serve as development platform that can provide application engineers with the technical means to evaluate potential blockchain solutions, by enabling them to concentrate on the actual application functionalities while at the same time abstracting the connectivity and performance measurement tasks. The use of BLOSEM UTP is further demonstrated by implementing two potential use cases that are intended to validate both the feasibility of implementing these applications as blockchain-based solutions while also demonstrating the features provided by UTP.

blockchain↗
DOE OSTI2022

Development of a Configurable DERMS Test System in GridAPPS-D

Distributed Energy Resources and their increased penetration within the power grid are a suitable method to provide essential reliability services. In order to do so, they must operate in aggregate. Aggregation of a wide variety of systems with varying standards and protocols is a complex task, which could benefit from rules and standards to support interoperability. To this end, the Portland State University Power Lab has developed the Energy Grid of Things project, which includes a Distributed Energy Resource Management System to provide aggregation and demonstrate the usage of an Energy Services Interface: a set of rules governing the interface between resource owners and aggregator businesses. This thesis presents a Modeling Environment that provides a simulation and test platform reflecting the complex needs of an aggregator. A grid simulator is required that is able to reflect the effects of thousands of Distributed Energy Resources operating in tandem; furthermore, these effects must be communicated to external actors such as the Grid Operator, which provides grid service requests to the aggregator and requires feedback. The Modeling Environment provides all of this functionality by leveraging the GridAPPS-D platform to develop an interoperable class-based system that provides configurable simulations with modular input and output APIs. The Modeling Environment uses abstractions to allow any feasible type of input to be reflected within the model as a Distributed Energy Resource, and serves as a proof-of-concept for the development of aggregation simulation and testing within GridAPPS-D.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Cybersecurity concerns for the energy sector in the maritime domain

The world has seen a number of high-profile maritime disasters in recent months and years, and has felt the impact of them. At the same time, the world has also seen a number of high-profile cyberattacks. It has felt their impact, as well. And, likely no sector has been more affected by the maritime and cyber incidents than the energy sector, as fuel prices often spike or trough, and access to energy resources can become an instant source of concern, tension, or even conflict. As energy sectors—in all their forms—continue to rely on the maritime domain or even increase that reliance, they must be mindful that traditional maritime threats—like piracy, theft, and weather events—are not the only threats they face today. Maritime cybersecurity concerns are among the most potentially disruptive to energy-sector interests and, yet, are among the least understood and least addressed. This paper identifies nine areas in which the energy sector faces harmful cyber vulnerabilities in the maritime domain, to provide enough insight and examples to allow for action to be taken to reduce the risk of harm from these different vulnerabilities. The paper develops the example of offshore wind energy to model how to assess cyber considerations more fully. Ultimately, it concludes with a series of recommendations that offer policymakers, energy-sector actors, and security and law-enforcement professionals steps to minimize the exposure of the maritime energy sector to harmful cyberattacks.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Distributed Power Allocation for 6-GHz Unlicensed Spectrum Sharing via Multi-agent Deep Reinforcement Learning

We consider the problem of power allocation over the 6 GHz Unlicensed National Information Infrastructure (UNII)- 5 spectrum. We propose a novel deep Reinforcement Learning (DRL)-based distributed power allocation scheme which utilizes the multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm. In particular, we model the base stations (BSs) as DRL agents that simultaneously determine the transmit powers to their scheduled user equipment (UE) in a synchronized manner. The power decision of each BS is based on its own observation of the radio environment, which consists of several local interference measurements and a limited amount of information obtained from other BSs. One advantage of the proposed scheme is that it addresses the single-agent non-stationarity problem of RL in the multi-agent scenario by incorporating the actions and observations of other BSs into each BS’s own critic which helps it to gain a more accurate perception of the overall radio environment. A centralized-training-distributed execution framework is used to train the policies where the critics are trained over the joint actions and observations of all BSs while the actor of each BS only takes the local observation as input in order to produce the transmit power. Simulation shows that the proposed power allocation scheme can achieve better throughput performance than several state-of-the-art approaches.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Analyzing Insider Risk Threat to the Internet of Things (IoT)

Recent technological advancement has created a growing convergence of innovation. From machine learning to ubiquitous computing to wireless networks and automation, the world is seeing new technology increasingly capable of connecting with each other. Devices and systems use open communications networks to interact, process information, and react. This is called the Internet of Things (IoT) and is comprised of physical devices that exchange data over networks, creating revolutionary possibilities. The most common way most people interact with an IoT is through ‘smart home’ products like Amazon’s Alexa, which use microphones, speakers, and phones to control a variety of devices, from lights and thermostats, to cameras, to appliances and vacuum cleaners. But the open nature of IoT networks—necessary for their ability to communicate and operate—also introduces privacy and security concerns. At a personal level, this might mean a hack into a home to steal private information, but when applied in broader industries like healthcare, transportation, manufacturing, or the military, this vulnerability can have serious consequences. As IoT usage and interconnectivity increases, so too does the susceptibility to malicious actors. And the entire system is only as secure as its least secure member. This creates particular risk and vulnerability to radiological material industries, as a competent insider adversary could utilize the IoT to potentially steal or access classified or sensitive information about employees, sites, or systems; or simply sabotage security or maintenance from a more remote—and less secure—device. The IoT relies on a secure network across the entire system, especially in transport which may lack the security of more permanent locations; if one device fails, it can create a ripple effect and an insider threat may seek to exploit that connectivity. While IoT benefits drive increased innovation and usage, there are also vulnerabilities an insider threat could exploit; this risk of an IoT to radiological material must be addressed in any mitigation effort.

Kinney, Justin↗
DOE OSTI2023

Two-Stage Deep Reinforcement Learning for Distribution System Voltage Regulation and Peak Load Management: Preprint

The growing integration of distributed solar photovoltaic (PV) in distribution systems could result in adverse effects during grid operation. This paper develops a soft actor critic-based deep reinforcement learning (SAC-DRL) solution to simultaneously control PV inverters and battery energy storage systems for voltage regulation and peak load demand shaving. The novel two-stage framework, featured with two different control agents, is applied for daytime and nighttime operation to enhance the control performance. Comparison results with other control methods on a real feeder in Western Colorado demonstrate that the proposed method can provide advanced voltage regulation with modest active power curtailment for peak demand reduction.

deep reinforcement learning↗
DOE OSTI2023

Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning

Constrained multiagent reinforcement learning (C-MARL) is gaining importance as MARL algorithms find new applications in real-world systems ranging from energy systems to drone swarms. Most C-MARL algorithms use a primal-dual approach to enforce constraints through a penalty function added to the reward. In this paper, we study the structural effects of this penalty term on the MARL problem. First, we show that the standard practice of using the constraint function as the penalty leads to a weak notion of safety. However, by making simple modifications to the penalty term, we can enforce meaningful probabilistic (chance and conditional value at risk) constraints. Second, we quantify the effect of the penalty term on the value function, uncovering an improved value estimation procedure. We use these insights to propose a constrained multiagent advantage actor critic (C-MAA2C) algorithm. Simulations in a simple constrained multiagent environment affirm that our reinterpretation of the primal-dual method in terms of probabilistic constraints is effective, and that our proposed value estimate accelerates convergence to a safe joint policy.

chance constraints↗
DOE OSTI2013

First Steps Toward a Quality of Climate Finance Scorecard (QUODA-CF): Creating a Comparative Index to Assess International Climate Finance Contributions

Are climate finance contributor countries, multilateral aid agencies and specialized funds using widely accepted best practices in foreign assistance? How is it possible to measure and compare international climate finance contributions when there are as yet no established metrics or agreed definitions of the quality of climate finance? As a subjective metric, quality can mean different things to different stakeholders, while of donor countries, recipients and institutional actors may place quality across a broad spectrum of objectives. This subjectivity makes the assessment of the quality of climate finance contributions a useful and necessary exercise, but one that has many challenges. This work seeks to enhance the development of common definitions and metrics of the quality of climate finance, to understand what we can about those areas where climate finance information is available and shine a light on the areas where there is a severe dearth of data. Allowing for comparisons of the use of best practices across funding institutions in the climate sector could begin a process of benchmarking performance, fostering learning across institutions and driving improvements when incorporated in internal evaluation protocols of those institutions. In the medium term, this kind of benchmarking and transparency could support fundraising in contributor countries and help build trust with recipient countries. As a feasibility study, this paper attempts to outline the importance of assessing international climate finance contributions while describing the difficulties in arriving at universally agreed measurements and indicators for assessment. In many cases, data are neither readily available nor complete, and there is no consensus on what should be included. A number of indicators are proposed in this study as a starting point with which to analyze voluntary contributions, but in some cases their methodologies are not complete, and further research is required for a robust measurement tool to be created.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

Public Resistance is Waste-Based and What to Do About That - 13412

'Nuclear Communicators' connect a highly advanced sci-tech world with the world of everyday living. One challenge is helping stakeholders fit together three Big Ideas: (1) the valuable nuclear energy resource, (2) nuclear energy's invisible mortal dangers and potential bad-guy threats, and (3) critical scientific and engineering knowledge that is far over the heads of average (grade 8) USA reading levels. This article provides an overview of what does - and does not - work in our public communications. What does not work: 1. Going off topic. Address what concerns people most: how to manage nuclear wastes. 2. Underestimating public intelligence. What works: 1. Doing your homework on community history regarding nuclear materials. 2. Having discussion forums and public meetings with special guests from industry, government, and local leaders. 3. Regular cycles of communication with community groups to build a long-term dialogue. Solutions to some communication challenges require handling four gaps and one jungle: 1. Facts gap - Honest information about waste disposition. 2. Time gap - The timelines associated with waste management include 300 years, 10,000 years, and 703.8 million years. How do we talk about this? 3. Money gap - Who pays for new waste management challenges and technologies? 4. Confidence gap - Link local options to regional, national, and global knowledge. 5. Decision jungle - How can we make waste management rules and infrastructure more logical and transparent? Public communication needs to be grounded in facts for people who want to be credible actors in the new nuclear world. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

An Information Building on Radioactivity and Nuclear Energy for the French CEA Cadarache Research Center - 13492

The CEA Cadarache research center is one of the 10 research centers of the French Alternative Energies and Atomic Energy Commission (CEA). Distributed throughout various research platforms, it focuses on nuclear fission, nuclear fusion, new energy technologies (hydrogen, solar, biomass) and fundamental research in the field of vegetal biology. It is the most important technological research and development centers for energy in Europe. Considering the sensitive nature of nuclear activities, the questions surrounding the issue of radioactive waste, the nuclear energy and the social, economic and environmental concerns for present and future generations, the French Government asked nuclear actors to open communication and to give all the information asked by the Local Information Commission (CLI) and the public [1]. In this context, the CEA Cadarache has decided to better show and explain its expertise and experience in the area of nuclear energy and nuclear power plant design, and to make it available to stakeholders and to the public. CEA Cadarache receives each year more than 9000 visitors. To complete technical visits of the research facilities and laboratories, a scientific cultural center has been built in 2011 to inform the public on CEA Cadarache research activities and to facilitate the acceptance of nuclear energy in a way suited to the level of knowledge of the visitors. A modern interactive exhibition of 150 m{sup 2} allows visitors to find out more about energy, CEA Cadarache research programs, radioactive waste management and radiological impact on the research center activities. It also offers an auditorium for group discussions and for school groups to discover science through enjoyment. This communication center has received several thousand visitors since its opening on October 2011; the initial results of this experience are now available. It's possible to explain the design of this exhibition, to give some statistics on the number of the visitors, their characteristics and their perception after their center visits. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

An agent-based framework for fuel cycle simulation with recycling

Simulation of the nuclear fuel cycle is an established field with multiple players. Prior development work has utilized techniques such as system dynamics to provide a solution structure for the matching of supply and demand in these simulations. In general, however, simulation infrastructure development has occurred in relatively closed circles, each effort having unique considerations as to the cases which are desired to be modeled. Accordingly, individual simulators tend to have their design decisions driven by specific use cases. Presented in this work is a proposed supply and demand matching algorithm that leverages the techniques of the well-studied field of mathematical programming. A generic approach is achieved by treating facilities as individual entities and actors in the supply-demand market which denote preferences amongst commodities. Using such a framework allows for varying levels of interaction fidelity, ranging from low-fidelity, quick solutions to high-fidelity solutions that model individual transactions (e.g. at the fuel-assembly level). The power of the technique is that it allows such flexibility while still treating the problem in a generic manner, encapsulating simulation engine design decisions in such a way that future simulation requirements can be relatively easily added when needed. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2012

The EU Approach for Responsible and Safe Management of Spent Fuel and Radioactive Waste - 12118

In July 2011 legislation on responsible and safe management of spent fuel and radioactive waste was adopted in the European Union (EU). It aims at ensuring a high level of safety, avoiding undue burdens on future generations and enhancing transparency. EU Member States are responsible for the management of their spent fuel and/or radioactive waste. Each Member State remains free to define its fuel cycle policy. The spent fuel can be regarded either as a valuable resource that may be reprocessed or as radioactive waste that is destined for direct disposal. Whatever option is chosen, the disposal of high level waste, separated at reprocessing, or of spent fuel regarded as waste should be considered. The storage of radioactive waste, including long-term storage, is an interim solution, but not an alternative to disposal. To this end, each Member State has to establish, maintain and implement national policy, framework and programme for management of spent fuel and/or radioactive waste in the long term. Member States will invite international peer reviews to ensure that high safety standards are achieved. The EU approach is anchored in internationally endorsed principles and requirements of the IAEA safety standards and the Joint Convention and in this context makes them legally binding and enforceable in the EU. The EU approach of regulating the management of spent fuel and radioactive waste is anchored in the competence of the national regulatory authorities and in the internationally endorsed principles and requirements of the IAEA Safety Standards and the Joint Convention. Member States have to report to the Commission on the implementation of Directive 2011/70/Euratom for the first time by 23 August 2015, and every 3 years thereafter, taking advantage of the review and reporting under the Joint Convention. On the basis of the Member States' reports, the Commission will submit to the European Parliament and the Council a report on progress made and an inventory of radioactive waste and spent fuel present in the EU territory and the future prospects. Directive 2011/70/Euratom is a logical next step after the Council Directive 2009/71/Euratom on the nuclear safety of nuclear installations. The EU is the first major regional actor providing a binding legal framework on nuclear safety and on responsible and safe management of spent fuel and radioactive waste, and thus is a real model to progress spent fuel and waste management in a safe and responsible manner. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗