Joint intelligent optimizing economic dispatch and electric vehicles charging in 5G vehicular networks

IF 4.4 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Gang Pan , Xin Guan , Haiyang Jiang , Yongnan Liu , Huayang Wu , Hongyang Chen , Tomoaki Ohtsuki , Zhu Han
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引用次数: 0

Abstract

In recent years, with the rapid development of 5G networks, the road traffic network composed of vehicles with different energy sources has become more and more complex, and the problems of environmental pollution and road congestion have also become increasingly serious. Electric vehicles are favored by people due to their environmental protection and energy-saving characteristics. However, improper charging dispatching will cause excess energy in charging stations, affecting the power grid and road traffic, such as energy shortages and lower traffic throughput. Therefore, how to design a reasonable charging strategy that can maximize the user’s charging satisfaction and consume the energy of the charging station as much as possible becomes a challenge. Meanwhile, this strategy should consider power economic dispatch to reduce power generation costs and polluting gas emissions. With the support of 5G’s high-bandwidth and low-latency characteristics, this paper designs an intelligent charging model which indirectly reflects the charging satisfaction through the time cost, energy consumption cost, charging cost, and the user’s range anxiety, while consuming the remaining energy of the charging station as much as possible. Due to the uncertainty of wind and photovoltaic power generation, this paper proposes a two-stage economic dispatch model to improve the accuracy of power dispatch and reduce power generation costs and carbon emissions. Due to the highly variable traffic environment and energy demand, we employ proximal policy optimization-based deep reinforcement learning algorithms to realize electric vehicle charging dispatching and charging station power dispatching. Numerical results show the efficiency of our proposed strategy for electric vehicle charging in terms of the convergence speed.
5G 车辆网络中的联合智能优化经济调度和电动汽车充电
近年来,随着 5G 网络的快速发展,由不同能源车辆组成的道路交通网络变得越来越复杂,环境污染和道路拥堵问题也日益严重。电动汽车因其环保节能的特点受到人们的青睐。然而,充电调度不当会造成充电站能量过剩,影响电网和道路交通,如能源短缺、交通吞吐量降低等。因此,如何设计一种合理的充电策略,既能最大限度地满足用户的充电需求,又能最大限度地消耗充电站的能量,成为一个难题。同时,该策略应考虑电力经济调度,以降低发电成本和污染气体排放。在 5G 的高带宽、低延迟特性支持下,本文设计了一种智能充电模型,通过时间成本、能耗成本、充电成本和用户的续航焦虑间接反映充电满意度,同时尽可能消耗充电站的剩余能量。由于风力和光伏发电的不确定性,本文提出了两阶段经济调度模型,以提高电力调度的准确性,降低发电成本和碳排放。由于交通环境和能源需求变化很大,我们采用了基于近端策略优化的深度强化学习算法来实现电动汽车充电调度和充电站电力调度。数值结果表明,我们提出的电动汽车充电策略在收敛速度方面非常有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computer Networks
Computer Networks 工程技术-电信学
CiteScore
10.80
自引率
3.60%
发文量
434
审稿时长
8.6 months
期刊介绍: Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.
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