5,255 research outputs found
Blockchain Solutions for Multi-Agent Robotic Systems: Related Work and Open Questions
The possibilities of decentralization and immutability make blockchain
probably one of the most breakthrough and promising technological innovations
in recent years. This paper presents an overview, analysis, and classification
of possible blockchain solutions for practical tasks facing multi-agent robotic
systems. The paper discusses blockchain-based applications that demonstrate how
distributed ledger can be used to extend the existing number of research
platforms and libraries for multi-agent robotic systems.Comment: 5 pages, FRUCT-2019 conference pape
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Factors Affecting Demand for Plug-in Charging Infrastructure: An Analysis of Plug-in Electric Vehicle Commuters
The public sector and the private sector, which includes automakers and charging network companies, are increasingly investing in building charging infrastructure to encourage the adoption and use of plug-in electric vehicles (PEVs) and to ensure that current facilities are not congested. However, building infrastructure is costly and, as with road congestion, when there is significant uptake of PEVs, we may not be able to “build out of congestion.” We modelled the choice of charging location that more than 3000 PEV drivers make when given the options of home, work, and public locations. Our study focused on understanding the importance of factors driving demand such as: the cost of charging, driver characteristics, access to charging infrastructure, and vehicle characteristics. We found that differences in the cost of charging play an important role in the demand for charging location. PEV drivers tend to substitute workplace charging for home charging when they pay a higher electricity rate at home, more so when the former is free. Additionally, socio-demographic factors like dwelling type and gender, as well as vehicle technology factors like electric range, influence the choice of charging location
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Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California
This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits
Control and Communication Protocols that Enable Smart Building Microgrids
Recent communication, computation, and technology advances coupled with
climate change concerns have transformed the near future prospects of
electricity transmission, and, more notably, distribution systems and
microgrids. Distributed resources (wind and solar generation, combined heat and
power) and flexible loads (storage, computing, EV, HVAC) make it imperative to
increase investment and improve operational efficiency. Commercial and
residential buildings, being the largest energy consumption group among
flexible loads in microgrids, have the largest potential and flexibility to
provide demand side management. Recent advances in networked systems and the
anticipated breakthroughs of the Internet of Things will enable significant
advances in demand response capabilities of intelligent load network of
power-consuming devices such as HVAC components, water heaters, and buildings.
In this paper, a new operating framework, called packetized direct load control
(PDLC), is proposed based on the notion of quantization of energy demand. This
control protocol is built on top of two communication protocols that carry
either complete or binary information regarding the operation status of the
appliances. We discuss the optimal demand side operation for both protocols and
analytically derive the performance differences between the protocols. We
propose an optimal reservation strategy for traditional and renewable energy
for the PDLC in both day-ahead and real time markets. In the end we discuss the
fundamental trade-off between achieving controllability and endowing
flexibility
Strategies for shifting technological systems : the case of the automobile system
Californian and Dutch efforts to produce electric vehicles are explored and compared. Three strategies are put forward that could turn electric vehicles from an elusive legend, a plaything, into a marketable product: technology forcing creating a market of early promises, experiments geared towards niche development and upscaling (strategic niche management), and the creation of new alliances (technological nexus) which bring technology, the market, regulation and many other factors together. These strategies deployed in the Californian and Dutch context are analysed in detail to explore their relative strengths and weaknesses and to argue in the end that a combined use of all three will increase the chances that the dominant technological system will change. The succesful workings of these strategies crucially depend on the coupling of the variation and selection processes, building blocks for any evolutionary theory of technical change. Evolutionary theory lacks understanding of these coupling processes. Building on recent insights from the sociology of technology, the authors propose a quasi-evolutionary model which underpins the analysis of suggested strategies
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems
Intelligent transportation systems (ITSs) have been fueled by the rapid
development of communication technologies, sensor technologies, and the
Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of
the vehicle networks, it is rather challenging to make timely and accurate
decisions of vehicle behaviors. Moreover, in the presence of mobile wireless
communications, the privacy and security of vehicle information are at constant
risk. In this context, a new paradigm is urgently needed for various
applications in dynamic vehicle environments. As a distributed machine learning
technology, federated learning (FL) has received extensive attention due to its
outstanding privacy protection properties and easy scalability. We conduct a
comprehensive survey of the latest developments in FL for ITS. Specifically, we
initially research the prevalent challenges in ITS and elucidate the
motivations for applying FL from various perspectives. Subsequently, we review
existing deployments of FL in ITS across various scenarios, and discuss
specific potential issues in object recognition, traffic management, and
service providing scenarios. Furthermore, we conduct a further analysis of the
new challenges introduced by FL deployment and the inherent limitations that FL
alone cannot fully address, including uneven data distribution, limited storage
and computing power, and potential privacy and security concerns. We then
examine the existing collaborative technologies that can help mitigate these
challenges. Lastly, we discuss the open challenges that remain to be addressed
in applying FL in ITS and propose several future research directions
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