Research on Resource Allocation Optimization of Smart City Based on Big Data

Junling Zhou,Pohsun Wang,Lingfeng Xie

doi:10.1109/access.2020.3017765

Junling Zhou, Pohsun Wang + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.3017765

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Abstract

The resource allocation of charging stations is an important part of promoting the development of renewable energy in modern cities. It can promote the scientific and modern construction of urban resource allocation and promote the intelligent transformation of cities. In view of the existing problems in the resource allocation process of urban charging stations, such as a single planning method, considering the actual travel demand. Based on the smart city transportation network information, this article will consider the impact of charging station construction costs, user driving and waiting costs on the location of charging stations, construct a charging station configuration optimization model, and introduce charging convenience coefficients to modify the model. Secondly, this paper establishes a systematic clustering model based on principal component analysis, selecting factors such as per capita GDP, population, and civilian car ownership as indicators, clustering analysis of different regions and assigning different charging convenience coefficients. Finally, the shortest distance matrix between any two nodes is calculated by the Voronoi diagram to concentrate the regional charging load to the traffic node, and the Floyd algorithm is used to analyze and evaluate the effect of the established charging station configuration optimization model. This technology provides a basis for promoting the modernization of urban green transportation.

Highlights

With the increasing demand of social development and scientific and technological progress, the fossil energy crisis and environmental pollution are becoming worse and worse
The in-depth development of high-tech technologies such as smart cities and the Internet of Things provides information research ideas for the construction of smart city charging station resource allocation optimization technology based on big data [5]
By analyzing the research of domestic and foreign scholars on the planning of charging stations, we find that the current methods basically stay in the theoretical stage, not in the actual data or just based on the assumption of actual calculation examples, ignoring the application requirements of the planning of charging stations in reality [12,13]

Summary

INTRODUCTION

With the increasing demand of social development and scientific and technological progress, the fossil energy crisis and environmental pollution are becoming worse and worse. Mak et al Studied the location problem of electric vehicle switching station by establishing two kinds of robust optimization models with the lowest cost and the highest service level, and solved it with CPLEX software [8]. Andrew et al Considered that under the limitation of charging station capacity and charging time, aiming at the minimum driving distance between electric vehicle and charging station, established a MIP location optimization model [9]. From the perspective of smart city, this paper proposes a big data-driven model based on the actual data, using the real urban traffic data to explore and solve the charging station planning problem and establish the corresponding optimization system, in order to inject new scientific power into the modernization of urban green traffic

OVERVIEW OF SMART CITY TECHNOLOGY

Smart city traffic data analysis

REGIONAL DIFFERENCE CLASSIFICATION BASED ON CLUSTER ANALYSIS

SYSTEM APPLICATION ANALYSIS AND EVALUATION

Findings

CONCLUSION