本文選題：電動汽車 + 區(qū)域電網(wǎng)�。� 參考：《華北電力大學》2015年碩士論文

【摘要】：經(jīng)濟的增長和社會的進步,帶動人們生活水平提高的同時,也造成了深刻的能源環(huán)境問題,能源短缺和環(huán)境污染是當前人類社會面臨的重大問題。電動汽車的產(chǎn)生和發(fā)展,為人類解決能源環(huán)境問題提供了新的思路。然而,大規(guī)模電動汽車的投入運行與接入電網(wǎng),對城市交通、電網(wǎng)安全穩(wěn)定等是一個重大挑戰(zhàn)。本課題提出一種綜合考慮電網(wǎng)側(cè)電網(wǎng)負荷、充電公平性和用戶側(cè)便利性、快捷性等因素的多目標優(yōu)化充電模型,依此模型對區(qū)域電網(wǎng)內(nèi)電動汽車進行有序充電,在保證公平性的基礎上,實現(xiàn)最優(yōu)化有序充電,盡可能保證電網(wǎng)安全穩(wěn)定運行、提升用戶體驗和節(jié)約成本。運用迭代法、貪心法、優(yōu)先級法和多級反饋隊列等經(jīng)典算法,解決多目標優(yōu)化模型的最優(yōu)解問題。區(qū)域電網(wǎng)電動汽車多目標優(yōu)化充電模型的實現(xiàn)需要電力網(wǎng)、車聯(lián)網(wǎng)、充電站(樁)聯(lián)網(wǎng)及其他相關信息的融合。隨著行業(yè)的發(fā)展,在多信息源融合的過程中,會產(chǎn)生海量異構化數(shù)據(jù),呈大數(shù)據(jù)化,采用傳統(tǒng)的單機串行化處理模式已經(jīng)無法滿足時間和空間上的需求,其存儲和計算都將成為瓶頸。因此,本課題提出并實現(xiàn)了基于云計算平臺的電動汽車有序充電監(jiān)控系統(tǒng)。利用Hadoop開源云計算平臺,組建計算集群,實現(xiàn)此類大數(shù)據(jù)的并行化處理。系統(tǒng)以多目標優(yōu)化充電模型為核心,設計系統(tǒng)功能,包括數(shù)據(jù)接收、數(shù)據(jù)實時與離線處理、數(shù)據(jù)展示等。設計系統(tǒng)物理和邏輯框架,依此框架搭建Hadoop云計算平臺,利用HBase分布式數(shù)據(jù)庫存儲電網(wǎng)側(cè)和用戶側(cè)數(shù)據(jù),利用MapReduce編程框架實現(xiàn)基礎性算法,并實現(xiàn)系統(tǒng)功能。課題按照提出問題-需求分析-模型設計-系統(tǒng)設計-系統(tǒng)實現(xiàn)的順序逐步深入進行研究,提出、設計并實現(xiàn)基于云計算平臺的電動汽車有序充電監(jiān)控系統(tǒng)。
[Abstract]:The growth of economy and the progress of society bring about the improvement of people's living standard and at the same time cause profound problems of energy and environment. Energy shortage and environmental pollution are the major problems facing human society at present. The emergence and development of electric vehicles provide a new way for human beings to solve energy and environmental problems. However, the operation and connection of large-scale electric vehicles to the power grid is a major challenge to urban traffic, power grid safety and stability. In this paper, a multi-objective optimal charging model considering the load, charging fairness, convenience and rapidity of the power grid is proposed. According to this model, the electric vehicles in the regional power network are charged in an orderly manner. On the basis of fairness, we can realize the optimal and orderly charging, ensure the safe and stable operation of the power network as much as possible, improve the user experience and save the cost. The iterative method, greedy method, priority method and multilevel feedback queue are used to solve the optimal solution of multi-objective optimization model. The realization of multi-objective optimal charging model for electric vehicles in regional power grid requires the integration of power grid, vehicle network, charging station (pile) network and other related information. With the development of industry, mass isomerization data will be produced in the process of multi-information source fusion, and the traditional single-machine serialization processing mode can no longer meet the needs of time and space. Its storage and computing will become a bottleneck. Therefore, this paper proposes and implements an electric vehicle charging monitoring system based on cloud computing platform. Hadoop open-source cloud computing platform, set up a computing cluster to achieve this big data parallel processing. The system is based on the multi-objective optimized charging model. The functions of the system include data receiving, real-time and off-line data processing, data display and so on. The physical and logical framework of the system is designed, according to which the Hadoop cloud computing platform is built. The distributed database of HBase is used to store the data on the grid side and the user side, and the basic algorithm is realized by using the MapReduce programming framework, and the system functions are realized. According to the order of putting forward problem, requirement analysis, model design, system design and system implementation, the thesis puts forward, designs and implements an electric vehicle orderly charging monitoring system based on cloud computing platform.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U491.8;TP277

【引證文獻】

相關會議論文 前1條

1 李杰;王愛民;于金剛;;智能電網(wǎng)中云計算技術的應用研究[A];中國智能電網(wǎng)學術研討會論文集[C];2011年

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本文編號：1776609