303 research outputs found

    Long term individual load forecast under different electrical vehicles uptake scenarios

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    More and more households are purchasing electric vehicles (EVs), and this will continue as we move towards a low carbon future. There are various projections as to the rate of EV uptake, but all predict an increase over the next ten years. Charging these EVs will produce one of the biggest loads on the low voltage network. To manage the network, we must not only take into account the number of EVs taken up, but where on the network they are charging, and at what time. To simulate the impact on the network from high, medium and low EV uptake (as outlined by the UK government), we present an agent-based model. We initialise the model to assign an EV to a household based on either random distribution or social influences - that is, a neighbour of an EV owner is more likely to also purchase an EV. Additionally, we examine the effect of peak behaviour on the network when charging is at day-time, night-time, or a mix of both. The model is implemented on a neighbourhood in south-east England using smart meter data (half hourly electricity readings) and real life charging patterns from an EV trial. Our results indicate that social influence can increase the peak demand on a local level (street or feeder), meaning that medium EV uptake can create higher peak demand than currently expected

    The Political Economy of Myanmar's Transition

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    This is an Author's Original Manuscript of an article whose final and definitive form, the Version of Record, has been published in the JOURNAL OF CONTEMPORARY ASIA, 07 Feb 2013, copyright Taylor & Francis, available online at: http://www.tandfonline.com/10.1080/00472336.2013.764143.Since holding elections in 2010, Myanmar has transitioned from a direct military dictatorship to a formally democratic system and has embarked on a period of rapid economic reform. After two decades of military rule, the pace of change has startled almost everyone and led to a great deal of cautious optimism. To make sense of the transition and assess the case for optimism, this article explores the political economy of Myanmar's dual transition from state socialism to capitalism and from dictatorship to democracy. It analyses changes within Myanmar society from a critical political economy perspective in order to both situate these developments within broader regional trends and to evaluate the country's current trajectory. In particular, the emergence of state-mediated capitalism and politico-business complexes in Myanmar's borderlands are emphasised. These dynamics, which have empowered a narrow oligarchy, are less likely to be undone by the reform process than to fundamentally shape the contours of reform. Consequently, Myanmar's future may not be unlike those of other Southeast Asian states that have experienced similar developmental trajectories

    VANET Coverage Analysis for GPS Augmentation Data in Rural Area

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    The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Enhanced position accuracy is key for modern navigation systems, location based services and applications based on Inter-Vehicle Communication (IVC). Position data are the foundation for deriving vehicle trajectories used for assessing a situation's criticality in vehicle safety. Thus, especially Advanced Driver Assistance Systems (ADASs) and integral safety applications bene t from nearby vehicles spreading their positions periodically with high accuracy. Positioning based on Global Navigation Satellite System (GNSS) measurements can be enhanced by established Cooperative Positioning (CP) methods like Real-Time Kinematic (RTK) and Di fferential GNSS (DGNSS). Conventional CP relies on positioning correction data from a third party, whereas this paper introduces a self-su fficient CP system based on Precise Point Positioning (PPP) and Vehicular Ad-Hoc Network (VANET) technology requiring no infrastructure. Furthermore, the data dissemination process and achievable coverage are analysed by a simulation study for a rural area in Bavaria, Germany. For this purpose, the simulation employs the European IVC protocol stack ITS-G5. While the general feasibility of this CP approach could be assured, some remaining issues regarding employed network protocols were discovered as well

    Low-complexity three-dimensional AOA-cross geometric center localization methods via multi-UAV network

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    The angle of arrival (AOA) is widely used to locate a wireless signal emitter in unmanned aerial vehicle (UAV) localization. Compared with received signal strength (RSS) and time of arrival (TOA), AOA has higher accuracy and is not sensitive to the time synchronization of the distributed sensors. However, there are few works focusing on three-dimensional (3-D) scenarios. Furthermore, although the maximum likelihood estimator (MLE) has a relatively high performance, its computational complexity is ultra-high. Therefore, it is hard to employ it in practical applications. This paper proposed two center of inscribed sphere-based methods for 3-D AOA positioning via multiple UAVs. The first method could estimate the source position and angle measurement noise at the same time by seeking the center of an inscribed sphere, called the CIS. Firstly, every sensor measures two angles, the azimuth angle and the elevation angle. Based on that, two planes are constructed. Then, the estimated values of the source position and the angle noise are achieved by seeking the center and radius of the corresponding inscribed sphere. Deleting the estimation of the radius, the second algorithm, called MSD-LS, is born. It is not able to estimate angle noise but has lower computational complexity. Theoretical analysis and simulation results show that proposed methods could approach the Cramér–Rao lower bound (CRLB) and have lower complexity than the MLE

    Towards the solution of variants of Vehicle Routing Problem

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    Some of the problems that are used extensively in -real life are NP complete problems. There is no any algorithm which can give the optimal solution to NP complete problems in the polynomial time in the worst case. So researchers are applying their best efforts to design the approximation algorithms for these NP complete problems. Approximation algorithm gives the solution of a particular problem, which is close to the optimal solution of that problem. In this paper, a study on variants of vehicle routing problem is being done along with the difference in the approximation ratios of different approximation algorithms as being given by researchers and it is found that Researchers are continuously applying their best efforts to design new approximation algorithms which have better approximation ratio as compared to the previously existing algorithms

    Applying reinforcement learning to network management

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    This project seeks to find if in the actual scenario Reinforcement Learning could help Vehicle Networks to get better performances, concretely applied in the field of resource allocation. It would be tried to allocate a varied number of requests in a network with multiple datacenters, modeling an actual road and city track. To do so, 4 algorithms were implemented, a heuristic and 3 RL approaches, in which we defined a simple DQN and the remaining two that run the same DQN but also include a parameter sharing method. It will be seen that a more sophisticated model must be done in order to demonstrate that Reinforcement Learning is worthwhile, and also, that parameter sharing is a tool that would be very useful for these types of networks as it could work in a very efficient manner.Este proyecto busca encontrar si en un escenario real, el aprendizaje por refuerzo podría ayudar a las nuevas redes de vehículos a obtener mejores rendimientos, aplicadas concretamente en el ámbito de la asignación de recursos. Esto se intentaría hacer asignando un número variado de peticiones a varios centros de datos, modelando una carretera real y un tramo de ciudad. Para ello, se implementaron 4 algoritmos, una heurística y 3 enfoques RL, en los que definimos un DQN simple y los dos restantes que ejecutan el propio DQN, pero también incluyen un método para compartir parámetros. Se verá que debe hacerse un modelo sofisticado para demostrar que el aprendizaje por refuerzo vale la pena, y también, que compartir parámetros es una herramienta que será muy útil para este tipo de redes ya que podría funcionar de una manera muy eficiente.Aquest projecte busca trobar si en un escenari real, l'aprenentatge per reforç podria ajudar a les noves xarxes de vehicles a obtenir millors rendiments, aplicades concretament en l'àmbit de l'assignació de recursos. Això s'intentaria fer assignant un nombre variat de peticions a diversos centres de dades, modelant una carretera real i un tram de ciutat. Per fer-ho, es van implementar 4 algorismes, una heurística i 3 enfocaments RL, en els quals vam definir un DQN simple i els dos restants que executen el mateix DQN, però també inclouen un mètode per compartir paràmetres. Es veurà que s'ha de fer un model sofisticat per demostrar que l'aprenentatge per reforç val la pena, i també, que compartir paràmetres és una eina que serà molt útil per a aquests tipus de xarxes ja que podria funcionar d'una manera molt eficient

    Cooperation as a Service in VANET: Implementation and Simulation Results

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    The past decade has witnessed the emergence of Vehicular Ad-hoc Networks (VANET), specializing from the well-known Mobile Ad Hoc Networks (MANET) to Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) wireless communications. While the original motivation for Vehicular Networks was to promote traffic safety, recently it has become increasingly obvious that Vehicular Networks open new vistas for Internet access, providing weather or road condition, parking availability, distributed gaming, and advertisement. In previous papers [27,28], we introduced Cooperation as a Service (CaaS); a new service-oriented solution which enables improved and new services for the road users and an optimized use of the road network through vehicle\u27s cooperation and vehicle-to-vehicle communications. The current paper is an extension of the first ones; it describes an improved version of CaaS and provides its full implementation details and simulation results. CaaS structures the network into clusters, and uses Content Based Routing (CBR) for intra-cluster communications and DTN (Delay and disruption-Tolerant Network) routing for inter-cluster communications. To show the feasibility of our approach, we implemented and tested CaaS using Opnet modeler software package. Simulation results prove the correctness of our protocol and indicate that CaaS achieves higher performance as compared to an Epidemic approach

    Population-based simulation optimization for urban mass rapid transit networks

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    In this paper, we present a simulation-based headway optimization for urban mass rapid transit networks. The underlying discrete event simulation model contains several stochastic elements, including time-dependent demand and turning maneuver times as well as direction-dependent vehicle travel and passenger transfer times. Passenger creation is a Poisson process that uses hourly origin–destination-matrices based on anonymous mobile phone and infrared count data. The numbers of passengers on platforms and within vehicles are subject to capacity restrictions. As a microscopic element, passenger distribution along platforms and within vehicles is considered. The bi-objective problem, involving cost reduction and service level improvement, is transformed into a single-objective optimization problem by normalization and scalarization. Population-based evolutionary algorithms and different solution encoding variants are applied. Computational experience is gained from test instances based on real-world data (i.e., the Viennese subway network). A covariance matrix adaptation evolution strategy performs best in most cases, and a newly developed encoding helps accelerate the optimization process by producing better short-term results. Document type: Articl

    Control Strategies for Smart Charging and Discharging of Plug- In Electric Vehicles

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    This chapter aims to provide an overview of the plug-in electric vehicle (PEV) charging and discharging strategies in the electric power system and the smart cities, as well as an application benefiting both consumers and power utility. The electric vehicle technology will be introduced. Then, the main impacts, benefits and challenges related to this technology will be discussed. Following, the role of the vehicles in smart cities will be presented. Next, the major methods and strategies for charging and discharging of plug-in electric vehicles available in the literature will be described. Finally, a new strategy for the intelligent charging and discharging of electric vehicles will be presented, which aims to benefit the consumer and the power utility
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