Because of their economical operation and low environmental pollution, PHEVs (Plug-in Hybrid Electric Vehicles) are rapidly substituting gasoline vehicles. However, there still exist obstacles to proliferating their use, such as their relatively short driving range and long battery charging time. At the same time, it is recognized that the current increasing trend of PHEV use will have a serious impact on the stability of power grids (i.e., electricity providers). Along with improving the performance of PHEVs, the installation of charging stations, which addresses such problems, is essentially required in smart grid communities. This paper proposes an operational framework for multiple PHEV charging stations. To maintain the power grid stability, regulating electric supply for charging stations through support planning is an attractive approach. In this direction, we determine a condition under which customers can receive improved QoS (Quality of Service), and provide an algorithm which allocates PHEVs into the condition.Our analysis is based on a multi-queue system, used as a model of charging stations whose dynamics we investigate. Specifically, our interest is the performance change when demand responses (i.e., the behavior of customers) are controlled. We proceed with our investigation in two steps: In the first step, we consider the PHEV allocation problem. We formulate an optimization problem which can minimize the waiting time of customers and obtain its solution. Then, we additionally regard the size constraint of charging stations and propose an optimal PHEV allocation algorithm. In the second step, we modify this algorithm to work in realistic scenarios. If PHEVs do not receive any incentives (or penalties), there is no restriction to control their allocation. At the station side, we suggest price control methods and show that the optimal allocation can be attained by them. For each step, we provide test results to validate our analysis.

Abstract-Modern mobile devices are equipped with multiple network interfaces, including 3G/LTE and WiFi. Bandwidth aggregation over LTE and WiFi links offers an attractive opportunity of supporting bandwidth-intensive services, such as high-quality video streaming, on mobile devices. However, achieving effective bandwidth aggregation in mobile environments raises several challenges related to deployment, link heterogeneity, network fluctuation, and energy consumption. We present GreenBag, an energy-efficient bandwidth aggregation middleware that supports real-time data-streaming services over asymmetric wireless links, requiring no modifications to the existing Internet infrastructure and servers. GreenBag employs several techniques, including medium load balancing, efficient segment management, and energy-aware mode control, to resolve such challenges. We implement a prototype of GreenBag on Android-based mobile devices which hosts, to the best knowledge of the authors, the first LTE-enabled bandwidth aggregation prototype for energyefficient real-time video streaming. Our experiment results in both emulated and real-world environments show that GreenBag not only achieves good bandwidth aggregation to provide QoS in bandwidth-scarce environments but also efficiently saves energy on mobile devices. Moreover, energy-aware GreenBag can minimize video interruption while consuming 14-25% less energy than the non-energy-aware counterpart in real-world experiments.

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