认知智能电网中基于能效优化的频谱分配策略
作者:
作者单位:

贵州大学大数据与信息工程学院

作者简介:

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中图分类号:

TN939

基金项目:

贵州省自然科学基金资助项目(黔科合基础[2017]1047号)


Spectrum Allocation Strategy Based on Energy Efficiency Optimization in Cognitive Smart Grid
Author:
Affiliation:

College of Big Data and Information Engineering of GuizhouUniversity,

Fund Project:

the Natural Science Foundation of Guizhou Province, China (Grant No. [2017]1047)

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    摘要:

    针对智能电网的无线通信环境存在频谱短缺、资源利用效率低等问题,本文将认知无线电技术应用于智能电网的邻域网络通信中。引入认知智能电网概念以保证业务传输的公平性和有效性,提出了一种基于改进二进制蝴蝶优化算法(BOA)的频谱分配策略,此方案考虑了通信过程中的信噪比和路径损耗后,选择系统能量效率作为信道效益,并且在拓扑结构固定的城市居民小区进行建模仿真。首先使用基于改进时变转换函数和扰动策略的二进制蝴蝶优化算法(IBBOA)为认知智能电网用户进行频谱分配,再采用基于接收信噪比的闭环功率控制算法动态调整用户的传输功率,减少了认知智能电网用户和主要用户之间存在的干扰。最后,以系统能量效率和两个用户公平性指数为优化目标,与遗传算法(GA)和二进制粒子群算法(BPSO)进行了对比实验,仿真实验表明,联合闭环功率控制的IBBOA算法所获得的系统能量效率比未联合闭环功率控制的NBOA算法高33.2%,IBBOA算法最终的系统能量效率和用户公平性指数Fair比效果表现最差的GA算法分别高出47.8%和62.6%;比未改进前的BBOA算法分别高出17.6%和26.7%。结果表明该方案能够有效抑制认知智能电网中用户间的干扰,大大提高了频谱利用率和系统能量效率。

    Abstract:

    In view of the problems such as spectrum shortage and low utilization efficiency of wireless communication environment of smart grid, this paper applies cognitive radio technology to the neighborhood network communication of smart grid. The concept of cognitive smart grid is introduced to ensure the fairness and effectiveness of business transmission, and a spectrum allocation strategy based on the improved binary Butterfly Optimization Algorithm (BOA) is proposed, which takes into account the signal-to-noise ratio and path loss in the communication process, and selects the system energy efficiency as the channel benefit. The modeling and simulation are carried out in urban residential areas with fixed topology. Firstly, spectrum are allocated for cognitive smart grid users with the Binary Butterfly Optimization Algorithm based on Improved time-varying conversion function and disturbance strategy(IBBOA), and then the transmission power of the user is dynamically adjusted by the closed-loop power control algorithm based on the received signal-to-noise ratio, which reduces the interference between the cognitive smart grid users and the main users. Finally, with the system energy efficiency and two fairness indexes of users as the optimization objectives, Genetic Algorithm (GA) and the Binary Particle Swarm Optimization (BPSO) has carried on the contrast, and the simulation experiment shows that the IBBOA algorithm joint closed-loop power control obtained 33.2% higher system energy efficiency than the NBOA algorithm without joint closed-loop power control, and the final system energy efficiency and the user fairness index Fair of the IBBOA algorithm were 47.8% and 62.6% higher than the worst-performing GA algorithm respectively. Compared with the previous BBOA algorithm, which was 17.6% and 26.7% higher respectively. And it"s also that the scheme can effectively suppress the interference between users in the cognitive smart grid, and greatly improve the spectrum utilization and system energy efficiency.

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  • 收稿日期:2019-10-15
  • 最后修改日期:2020-12-25
  • 录用日期:2020-03-06
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