18 research outputs found

    Blockchain and artificial intelligence enabled peer-to-peer energy trading in smart grids

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    Peer-to-peer (P2P) energy trading allows smart grid-connected parties to trade renewable energy with each other. It is widely considered a scheme to mitigate the supplydemand imbalances during peak-hour. In a P2P energy trading system, users (e.g., prosumers, Electric Vehicles (EV)) increase their utility by trading energy securely with each other at a lower price than that of the main grid. However, three challenges hinder the development of secured P2P energy trading systems. First, there is a lack of implicit trust and transparency between trading participants because they do not know each other. Second, P2P energy trading systems cannot offer an intelligent trading strategy that could maximize users’ (agents’) utility. This is because the agents may lack previous trading experience data that enable them to select an optimal trading strategy. Third, the current energy trading platforms are mainly centralized, which makes them vulnerable to malicious attacks and Single point of failure (SPOF). This may interrupt the transaction validation mechanism when the system is compromised, and the central database is unavailable. [...

    From Structures to Services. The Path toBetter Infrastructure in Latin America and the Caribbean

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    To close its infrastructure gap, Latin America and the Caribbean needs more than investment in new structures. It needs to become more efficient at investing in infrastructure and regulating a new range of services that have the potential to disrupt the energy, transport, and water sectors. The technological revolution makes a future with quality services possible, but not inevitable. This book offers policy options for countries to improve the access, quality, and affordability of services today, to ensure that they will be sustainable in the future, and to harness emerging technological advances for the benefit of all. This report aims to provoke discussion and further research on those many important issues and mark a path that helps the region move from structures to services and improve infrastructure for all

    A Survey of Intelligent Network Slicing Management for Industrial IoT: Integrated Approaches for Smart Transportation, Smart Energy, and Smart Factory

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    This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordNetwork slicing has been widely agreed as a promising technique to accommodate diverse services for the Industrial Internet of Things (IIoT). Smart transportation, smart energy, and smart factory/manufacturing are the three key services to form the backbone of IIoT. Network slicing management is of paramount importance in the face of IIoT services with diversified requirements. It is important to have a comprehensive survey on intelligent network slicing management to provide guidance for future research in this field. In this paper, we provide a thorough investigation and analysis of network slicing management in its general use cases as well as specific IIoT services including smart transportation, smart energy and smart factory, and highlight the advantages and drawbacks across many existing works/surveys and this current survey in terms of a set of important criteria. In addition, we present an architecture for intelligent network slicing management for IIoT focusing on the above three IIoT services. For each service, we provide a detailed analysis of the application requirements and network slicing architecture, as well as the associated enabling technologies. Further, we present a deep understanding of network slicing orchestration and management for each service, in terms of orchestration architecture, AI-assisted management and operation, edge computing empowered network slicing, reliability, and security. For the presented architecture for intelligent network slicing management and its application in each IIoT service, we identify the corresponding key challenges and open issues that can guide future research. To facilitate the understanding of the implementation, we provide a case study of the intelligent network slicing management for integrated smart transportation, smart energy, and smart factory. Some lessons learnt include: 1) For smart transportation, it is necessary to explicitly identify service function chains (SFCs) for specific applications along with the orchestration of underlying VNFs/PNFs for supporting such SFCs; 2) For smart energy, it is crucial to guarantee both ultra-low latency and extremely high reliability; 3) For smart factory, resource management across heterogeneous network domains is of paramount importance. We hope that this survey is useful for both researchers and engineers on the innovation and deployment of intelligent network slicing management for IIoT.Engineering and Physical Sciences Research Council (EPSRC)Singapore University of Technology and Design (SUTD)Hong Kong RGC Research Impact Fund (RIF)National Natural Science Foundation of ChinaShenzhen Science and Technology Innovation Commissio

    Emerging Technologies

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    This monograph investigates a multitude of emerging technologies including 3D printing, 5G, blockchain, and many more to assess their potential for use to further humanity’s shared goal of sustainable development. Through case studies detailing how these technologies are already being used at companies worldwide, author Sinan Küfeoğlu explores how emerging technologies can be used to enhance progress toward each of the seventeen United Nations Sustainable Development Goals and to guarantee economic growth even in the face of challenges such as climate change. To assemble this book, the author explored the business models of 650 companies in order to demonstrate how innovations can be converted into value to support sustainable development. To ensure practical application, only technologies currently on the market and in use actual companies were investigated. This volume will be of great use to academics, policymakers, innovators at the forefront of green business, and anyone else who is interested in novel and innovative business models and how they could help to achieve the Sustainable Development Goals. This is an open access book

    Planning and Design for Intelligent and Secure Integration of Electric Vehicles into the Smart Grid

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    The transition to electric vehicles (EVs) is gaining momentum around the world and government initiatives to accelerate this transition range from major tax exemptions, lower insurance payments to convenient parking incentives at shopping malls. The major drivers for this acceleration are the rising awareness by the public for maintaining a clean environment, reducing pollutant emissions, breaking dependencies on oil, as well as tapping into cleaner sources of energies. EVs acceptance however is hindered by several challenges; among them is their shorter driving range, slower charging rates, and the ubiquitous availability of charging locations, collectively contributing to higher anxieties for EVs drivers. Governments of developed countries as well as major car manufacturers are taking solid steps to address these challenges and set ambitious goals to make EVs the major transportation mode within few years. Consequently, a significant number of EVs is going to connect to the existing smart grid and hence, the load pattern is expecting a paradigm shift. This immense load will challenge the generation, transmission and distribution sector of the grid along with being a potential cyber-physical attack platform. To attain a graceful EV penetration for curtailing GHG emission, along with the socioeconomic initiatives, an extensive research is required, especially to mitigate the range anxiety and ameliorate the load congestion on the grid. As a consequence, to reduce the range anxiety, we present a two-stage solution to provision and dimension a DC fast charging station (CS) network for the anticipated energy demand and that minimizes the deployment cost while ensuring a certain quality of experience for charging e.g., acceptable waiting times and shorter travel distances to charge. This solution also maintains the voltage stability by considering the distribution grid capacity, determining transformers’ rating to support peak demand of EV charging and adding a minimum number of voltage regulators based on the impact over the power distribution network. We propose, evaluate and compare two CS network expansion models to determine a cost-effective and adaptive CSs provisioning solution that can efficiently expand the CS network to accommodate future EV charging and conventional load demands. Though an adequate fast charging network may assist to reduce the range anxiety and propel the EV market, catering this large number of EVs using fuel based conventional grid actually shifts the carbon footprint from the transportation sector to the power generation sector. As a consequence, green energy needs to be promoted for EV charging. However, the intermittent behavior of renewable energy (RE) generation challenges to maintain a RE based stand alone CS. In order to address this issue, we consider a photovoltaic(PV) powered station equipped with an energy storage system (ESS), which is assumed to be capable of assigning variable charging rates to different EVs to fulfill their demands inside their declared deadlines at minimum price. To ensure fairness, a charging rate dependent pricing mechanism is proposed to assure a higher price for enjoying a higher charging rate. The PV generation profile and future load request are forecasted at each time slot, to handle the respective uncertainties. Whatever, the energy source is green or not of a CS, a static CS cannot offer the flexibility to charge an EV at any place at any time especially for an emergency case. Fortunately, the bidirectional energy transferring capability between vehicles (i.e., vehicle to vehicle (V2V)) might be a solution to charge an EV at any place and at any time without leaning on a stationary CS. Hence, we assume a market where charging providers each has a number of charging trucks equipped with a larger battery and a fast charger to charge a number of EVs at some particular parking lots. We formulate an integer linear program (ILP) to maximize the number of served EVs by determining the optimal trajectory and schedule of each truck. Owing to its complexity, we implement Dantzig-Wolfe decomposition approach to solve this. However, to build a prolific EV charging ecosystem, all its entities (e.g., EVs, CSs and grid) have to be connected through a communication link and that unveils a new cyber physical attack surface. As a consequence, we exploit the abundance of Electric Vehicles (EVs) to target the stability of the power grid by presenting a realistic coordinated switching attack that initiates inter-area oscillations between different areas of the power grid and assess the dire consequences over the power system. Finally, a back propagation neural network (BPNN) technique is used in a proposed framework to detect such switching attacks before being executed

    HJIC

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    Kootenay Express

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    Optimal control and approximations

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    Optimal control and approximations

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    Control of the Electron Energy Distribution Function (EEDF) in a Hall Thruster Plasma.

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    Further improvements to Hall-effect thruster (HET) efficiency in the low voltage regime will help enable more extensive space missions. HET efficiency depends on its ability to ionize and accelerate neutral propellant, which can be further improved when electrons with energies and trajectories that contribute to ionization are increased in the right locations. Therefore, electron energy distribution function (EEDF) tailoring is needed. However, predictive EEDF control in plasma devices is a challenging problem due to complex electromagnetic interactions that take place that lead to the turbulent nature of these plasmas. In an effort to control the HET’s EEDF to boost thruster efficiency, and to also uncover further insights into the operation and dynamics of these devices, various analyses were carried out. The last was a reverse-orientation cathode technique to control the EEDF by placing the cathode downstream and pointing towards the thruster to redirect high-energy electrons to a less circuitous path to ionization zones in the thruster’s channel. Total efficiency and its components were calculated from non-invasive thrust stand measurements and a suite of downstream thruster diagnostics respectively. For the test thruster’s nominal operating condition, total efficiency increased by 3 percentage points with the external, downstream cathode when compared to the standard, central cathode configuration; however, for an off-nominal condition, thruster efficiency decreased by 1 percentage point. For both operating conditions, the EEDFs revealed that external cathode electron temperatures were on average about half that of the corresponding central cathode values at the downstream data-taking locations. The results implied that the reverse-orientation cathode placed more control on directing high-energy electrons into the thruster channel since less were in downstream locations. These EEDF changes most likely correlated with an increase in efficiency for only the nominal condition due to larger ion beam divergence in the off-nominal condition. However, to confirm these inferences, internal ion current density and divergence measurements where thrust is produced are needed. Therefore, this EEDF control quest made clear the need for non-invasive, performance diagnostic measurements inside the channel to draw more direct conclusions not only about this method’s results, but also about thruster performance and dynamics in general.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135858/1/kimtrent_1.pd
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