Unsplittable Load Balancing in a Network of Charging Stations Under QoS Guarantees

The operation of the power grid is becoming more stressed, due to the addition of new large loads represented by Electric Vehicles (EVs) and a more intermittent supply due to the incorporation of renewable sources. As a consequence, the coordination and control of projected EV demand in a network of fast charging stations becomes a critical and challenging problem. In this paper, we introduce a game theoretic based decentralized control mechanism to alleviate negative impacts from the EV demand. The proposed mechanism takes into consideration the non-uniform spatial distribution of EVs that induces uneven power demand at each charging facility, and aims to: (i) avoid straining grid resources by offering price incentives so that customers accept being routed to less busy stations, (ii) maximize total revenue by serving more customers with the same amount of grid resources, and (iii) provide charging service to customers with a certain level of Quality-of-Service (QoS), the latter defined as the long term customer blocking probability. We examine three scenarios of increased complexity that gradually approximate real world settings. The obtained results show that the proposed framework leads to substantial performance improvements in terms of the aforementioned goals, when compared to current state of affairs.Comment: Accepted for Publication in IEEE Transactions on Smart Gri

Guest editorial—special issue on service modeling and design for the future Internet

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Electric Power Allocation in a Network of Fast Charging Stations

In order to increase the penetration of electric vehicles, a network of fast charging stations that can provide drivers with a certain level of quality of service (QoS) is needed. However, given the strain that such a network can exert on the power grid, and the mobility of loads represented by electric vehicles, operating it efficiently is a challenging problem. In this paper, we examine a network of charging stations equipped with an energy storage device and propose a scheme that allocates power to them from the grid, as well as routes customers. We examine three scenarios, gradually increasing their complexity. In the first one, all stations have identical charging capabilities and energy storage devices, draw constant power from the grid and no routing decisions of customers are considered. It represents the current state of affairs and serves as a baseline for evaluating the performance of the proposed scheme. In the second scenario, power to the stations is allocated in an optimal manner from the grid and in addition a certain percentage of customers can be routed to nearby stations. In the final scenario, optimal allocation of both power from the grid and customers to stations is considered. The three scenarios are evaluated using real traffic traces corresponding to weekday rush hour from a large metropolitan area in the US. The results indicate that the proposed scheme offers substantial improvements of performance compared to the current mode of operation; namely, more customers can be served with the same amount of power, thus enabling the station operators to increase their profitability. Further, the scheme provides guarantees to customers in terms of the probability of being blocked by the closest charging station. Overall, the paper addresses key issues related to the efficient operation of a network of charging stations.Comment: Published in IEEE Journal on Selected Areas in Communications July 201

Modeling and simulation of self-similar variable bit rate compressed video: A unified approach

Variable bit rate (VBR) compressed video is expected to become one of the major loading factors in high-speed packet networks such asATM-based B-ISDN. However, recent measurements based on long empirical traces (complete movies) revealed that VBR video tra c possesses self-similar (or fractal) characteristics, meaning that the dependence in the tra c stream lasts much longer than traditional models can capture. In this paper, we present a uni ed approach which, in addition to accurately modeling the marginal distribution of empirical video records, also models directly both the short and the long-term empirical autocorrelation structures. We also present simulation results using synthetic data and compare with results based on empirical video traces. Furthermore, we extend the application of e cient estimation techniques based on importance sampling that we had used before only for simple fractal processes. We use importance sampling techniques to e ciently estimate low probabilities of packet losses that occur when a multiplexer is fed with synthetic tra c from our self-similar VBR video model.

Lightweight mobile and wireless systems: technologies, architectures, and services

1Department of Information and Communication Systems Engineering (ICSE), University of the Aegean, 81100 Mytilene, Greece 2Department of Information Engineering and Computer Science (DISI), University of Trento, 38123 Trento, Italy 3Department of Informatics, Alexander Technological Educational Institute of Thessaloniki, Thessaloniki, 574 00 Macedonia, Greece 4Centre Tecnologic de Telecomunicacions de Catalunya (CTTC), 08860 Barcelona, Spain 5North Carolina State University (NCSU), Raleigh, NC 27695, US

Post-Quantum Authentication in TLS 1.3: A Performance Study

The potential development of large-scale quantum computers is raising concerns among IT and security research professionals due to their ability to solve (elliptic curve) discrete logarithm and integer factorization problems in polynomial time. All currently used public key algorithms would be deemed insecure in a post-quantum (PQ) setting. In response, the National Institute of Standards and Technology (NIST) has initiated a process to standardize quantum-resistant crypto algorithms, focusing primarily on their security guarantees. Since PQ algorithms present significant differences over classical ones, their overall evaluation should not be performed out-of-context. This work presents a detailed performance evaluation of the NIST signature algorithm candidates and investigates the imposed latency on TLS 1.3 connection establishment under realistic network conditions. In addition, we investigate their impact on TLS session throughput and analyze the trade-off between lengthy PQ signatures and computationally heavy PQ cryptographic operations. Our results demonstrate that the adoption of at least two PQ signature algorithms would be viable with little additional overhead over current signature algorithms. Also, we argue that many NIST PQ candidates can effectively be used for less time-sensitive applications, and provide an in-depth discussion on the integration of PQ authentication in encrypted tunneling protocols, along with the related challenges, improvements, and alternatives. Finally, we propose and evaluate the combination of different PQ signature algorithms across the same certificate chain in TLS. Results show a reduction of the TLS handshake time and a significant increase of a server\u27s TLS tunnel connection rate over using a single PQ signature scheme

Revenue optimization frameworks for multi-class PEV charging stations

The charging power of plug-in electric vehicles (PEVs) decreases significantly when the state of charge (SoC) gets closer to the fully charged state, which leads to a longer charging duration. Each time when the battery is charged at high rates, it incurs a significant degradation cost that shortens the battery life. Furthermore, the differences between demand preferences, battery types, and charging technologies make the operation of the charging stations a complex problem. Even though some of these issues have been addressed in the literature, the charging station modeling with battery models and different customer preferences have been neglected. To that end, this paper proposes two queueing-based optimization frameworks. In the first one, the goal is to maximize the system revenue for single class customers by limiting the requested SoC targets. The PEV cost function is composed of battery degradation cost, the waiting cost in the queue, and the admission fee. Under this framework, the charging station is modeled as a M/G/S/K queue, and the system performance is assessed based on the numerical and simulation results. In the second framework, we describe an optimal revenue model for multi-class PEVs, building upon the approach utilized in the first framework. Two charging strategies are proposed: 1) a dedicated charger model and 2) a shared charger model for the multi-class PEVs. We evaluate and compare these strategies. Results show that the proposed frameworks improve both the station performance and quality of service provided to customers. The results show that the system revenue is more than doubled when compared with the baseline scenario which includes no limitations on the requested SoC

Optimal design of electric vehicle charging stations for commercial premises

Influx of plug-in electric vehicles (PEVs) creates a pressing need for careful charging infrastructure planning. In this paper, the primary goal is to devise a closed-form expression for the PEV charging station capacity problem. Two types of commercial charging stations are considered. The first problem is related to the calculation of the optimal service capacity for charging lots located at workplaces where PEV parking statistics are given as a priori. The second problem, on the other hand, is related to the optimisation of arrival rates for a given station capacity. In the second part, the mathematical models are expanded for the case where multiple charger technologies serve customer demand. This time the goal is to calculate the optimal customer load for each charger type according to its rate. Calculations are carried out for both social and individual optimality cases. Markovian queues are used to model the charging station system to capture the complex interactions between customer load, service waiting times, and electricity cost. The related optimisation problems are solved using convex optimisation methods. Closed-form expressions of station capacity and optimal arrival rates are explicitly derived. Both analytical calculations and discrete-event simulations are carried out and the results show that 60% of the waiting times and 42% of the queue length can be reduced by optimal capacity planning