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首页> 外文期刊>Intelligent Transportation Systems, IEEE Transactions on >Joint Transportation and Charging Scheduling in Public Vehicle Systems—A Game Theoretic Approach
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Joint Transportation and Charging Scheduling in Public Vehicle Systems—A Game Theoretic Approach

机译:公共车辆系统中的联合运输和收费调度-一种博弈论方法

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摘要

Public vehicle (PV) systems are promising transportation systems for future smart cities which provide dynamic ride-sharing services according to passengers' requests. PVs are driverless/self-driving electric vehicles which require frequent recharging from smart grids. For such systems, the challenge lies in both the efficient scheduling scheme to satisfy transportation demands with service guarantee and the cost-effective charging strategy under the real-time electricity pricing. In this paper, we study the joint transportation and charging scheduling for PV systems to balance the transportation and charging demands, ensuring the long-term operation. We adopt a cake cutting game model to capture the interactions among PV groups, the cloud and smart grids. The cloud announces strategies to coordinate the allocation of transportation and energy resources among PV groups. All the PV groups try to maximize their joint transportation and charging utilities. We propose an algorithm to obtain the unique normalized Nash equilibrium point for this problem. Simulations are performed to confirm the effects of our scheme under the real taxi and power grid data sets of New York City. Our results show that our scheme achieves almost the same transportation performance compared with a heuristic scheme, namely, transportation with greedy charging; however, the average energy price of the proposed scheme is 10.86% lower than the latter one.
机译:公共车辆(PV)系统是面向未来智慧城市的有前途的交通系统,可根据乘客的要求提供动态的乘车共享服务。光伏发电是无人驾驶/自动驾驶的电动汽车,需要频繁地从智能电网进行充电。对于这样的系统,挑战在于既要有有效的调度方案来满足具有服务保证的运输需求,又要在实时电价下实现具有成本效益的充电策略。在本文中,我们研究了光伏系统的联合运输和充电计划,以平衡运输和充电需求,确保长期运行。我们采用切蛋糕游戏模型来捕获PV组,云和智能电网之间的交互。云宣布了协调光伏集团之间运输和能源分配的策略。所有的光伏集团都试图最大程度地发挥联合运输和充电的作用。我们提出了一种算法来获得该问题的唯一归一化Nash平衡点。在纽约市的真实出租车和电网数据集下进行了仿真,以确认我们的计划的效果。我们的结果表明,与启发式方案相比,我们的方案实现了几乎相同的运输性能。但是,该方案的平均能源价格比后一种方案低10.86%。

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