【摘要】：隨著近年來(lái)環(huán)境和能源短缺問(wèn)題日益嚴(yán)峻,世界各國(guó)對(duì)于能源結(jié)構(gòu)調(diào)整和環(huán)保技術(shù)研究的熱情都持續(xù)高漲。由于傳統(tǒng)的燃油燃?xì)馄?chē)是環(huán)境污染和能源消耗的主要來(lái)源,所以清潔的電力能源驅(qū)動(dòng)的電動(dòng)汽車(chē)受到了廣泛的關(guān)注和青睞,國(guó)內(nèi)外都有大量電動(dòng)汽車(chē)被投入市場(chǎng)作為傳統(tǒng)交通工具的替代品。然而由于電動(dòng)汽車(chē)技術(shù)的局限性,其續(xù)航里程較短、充電時(shí)間長(zhǎng),所以電動(dòng)汽車(chē)充電站無(wú)法使用傳統(tǒng)加油站的建設(shè)模式和同等的建站密度,導(dǎo)致了現(xiàn)有充電站難以滿足大量增長(zhǎng)的電動(dòng)汽車(chē)的充電需求,限制了電動(dòng)汽車(chē)的進(jìn)一步應(yīng)用和發(fā)展。本文針對(duì)北京市電動(dòng)出租車(chē)充電站選址的問(wèn)題,以乘客出行和電動(dòng)出租車(chē)運(yùn)行的特征參數(shù)進(jìn)行蒙特卡洛仿真,模擬了電動(dòng)出租車(chē)充電需求產(chǎn)生的過(guò)程,識(shí)別了在目前情況下能夠保障乘客正常出行的電動(dòng)出租車(chē)充電需求的時(shí)空分布;采用voronoi圖法劃分了充電站的服務(wù)范圍和容量,并在已知充電需求分布的基礎(chǔ)上,構(gòu)成了選址模型的約束條件,而充電站建設(shè)和運(yùn)行的成本函數(shù)則構(gòu)成了選址模型的目標(biāo)函數(shù);使用基本粒子群算法求出了低約束選址模型的解,與P中值模型的解對(duì)比驗(yàn)證了低約束選址模型的有效性,并引入禁忌粒子群算法提高了算法求解的精度;對(duì)影響選址模型結(jié)果的參數(shù)進(jìn)行了敏感性分析、求出了選址模型的最優(yōu)解。本文使用乘客出行距離和時(shí)間參數(shù)與電動(dòng)出租車(chē)電池電量狀態(tài)參數(shù)的概率分布,使模擬過(guò)程符合真實(shí)的事件發(fā)生概率,從而避免了對(duì)復(fù)雜電動(dòng)出租車(chē)運(yùn)行軌跡的研究,減少了數(shù)據(jù)獲取的難度和數(shù)據(jù)冗余,同時(shí)提高了充電需求預(yù)測(cè)的精度;構(gòu)建了基于粒子群算法求解的低約束選址模型,在保證模型解的精度能夠滿足需求的情況下,降低了模型約束的要求,簡(jiǎn)化了模型的復(fù)雜度,從而使得外部條件發(fā)生變化時(shí)模型需要調(diào)節(jié)的參數(shù)數(shù)量較少,模型能夠較好地應(yīng)對(duì)條件變化速度快的選址環(huán)境,避免了約束條件重新構(gòu)建的繁雜過(guò)程,增強(qiáng)了模型的適用性和易用性;使用禁忌粒子群算法,在保證算法求解速度的前提下,提高了算法的精度,較好地避免了算法陷入局部最優(yōu)的情況,為低約束條件下的選址模型提供了能夠滿足求解精度的理論依據(jù)。
[Abstract]:With the problem of environment and energy shortage becoming more and more serious in recent years, the enthusiasm for energy structure adjustment and environmental protection technology research in the world is continuously rising. Because the traditional fuel gas vehicle is the main source of environmental pollution and energy consumption, the clean electric vehicle driven by electric energy has received wide attention and favor. At home and abroad, a large number of electric vehicles have been put into the market as a substitute for traditional vehicles. However, due to the limitation of the electric vehicle technology, the electric vehicle charging station can not use the traditional gas station construction mode and the same station density because of its short mileage and long charging time. As a result, the existing charging stations are difficult to meet the increasing demand for electric vehicles, which limits the further application and development of electric vehicles. Aiming at the problem of location of electric taxi charging station in Beijing, this paper simulates the process of electric taxi charging demand by Monte Carlo simulation based on the characteristic parameters of passenger travel and electric taxi operation. The spatiotemporal distribution of charging demand of electric taxi which can guarantee the normal travel of passengers is identified, the service range and capacity of charging station are divided by voronoi diagram method, and the distribution of charging demand is known. The cost function of the charging station construction and operation constitutes the objective function of the location model, and the solution of the low constraint location model is obtained by using the basic particle swarm optimization algorithm. The comparison with the solution of P median model verifies the validity of the low constraint location model, and introduces Tabu Particle Swarm Optimization (Tabu) algorithm to improve the accuracy of the algorithm, and analyzes the sensitivity of the parameters that affect the result of the location model. The optimal solution of the location model is obtained. In this paper, the probability distribution of passenger travel distance and time parameters and electric taxi battery state parameters is used to make the simulation process accord with the true probability of occurrence of events, thus avoiding the study of complex electric taxi running trajectory. The difficulty of data acquisition and data redundancy are reduced, and the accuracy of charge demand prediction is improved. A low-constraint location model based on particle swarm optimization algorithm is constructed to ensure that the accuracy of the model solution can meet the requirements. The requirements of model constraints are reduced, and the complexity of the model is simplified, so that the number of parameters that the model needs to adjust when the external conditions change is less, and the model can better deal with the location environment where the conditions change quickly. It avoids the complicated process of reconstructing the constraint conditions, enhances the applicability and ease of use of the model, and improves the accuracy of the algorithm by using Tabu Particle Swarm Optimization (Tabu) algorithm under the premise of ensuring the speed of solving the algorithm. The algorithm can avoid falling into the local optimal condition and provide a theoretical basis for the location model under low constraint conditions to meet the accuracy of the solution.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：U491.8

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 舒雋;唐剛;韓冰;;電動(dòng)汽車(chē)充電站最優(yōu)規(guī)劃的兩階段方法[J];電工技術(shù)學(xué)報(bào);2017年03期

2 陳娟;黃元生;魯斌;;區(qū)域能源互聯(lián)網(wǎng)信息物理建模及控制策略[J];電力自動(dòng)化設(shè)備;2016年12期

3 劉丹;解強(qiáng);張?chǎng)?王海林;閆志勇;楊宏偉;郝鄭平;;北京冬季霧霾頻發(fā)期VOCs源解析及健康風(fēng)險(xiǎn)評(píng)價(jià)[J];環(huán)境科學(xué);2016年10期

4 陳奇芳;劉念;陳征;張建華;;考慮充電需求與隨機(jī)事件的光伏充電站實(shí)時(shí)運(yùn)行策略[J];電工技術(shù)學(xué)報(bào);2016年18期

5 李玉敏;張友國(guó);;中國(guó)碳排放影響因素的空間分解分析[J];中國(guó)地質(zhì)大學(xué)學(xué)報(bào)(社會(huì)科學(xué)版);2016年03期

6 Houssem Eddine Hadji;Malika Babes;;Integrating Tabu Search in Particle Swarm Optimization for the Frequency Assignment Problem[J];中國(guó)通信;2016年03期

7 姜大膀;劉葉一;;溫室效應(yīng)會(huì)使地球溫度上升多高?——關(guān)于平衡氣候敏感度[J];科學(xué)通報(bào);2016年07期

8 潘竟虎;張文;王春娟;;2011—2013年中國(guó)霧霾易發(fā)生期間API的分布格局[J];環(huán)境工程學(xué)報(bào);2016年03期

9 黃小慶;陳頡;田世明;曹一家;楊夯;江磊;;電動(dòng)汽車(chē)充電站規(guī)劃、運(yùn)行中的大數(shù)據(jù)集成應(yīng)用[J];電網(wǎng)技術(shù);2016年03期

10 龔;張杰;藍(lán)金輝;;霧霾情況下路網(wǎng)模型及霧霾對(duì)交通路網(wǎng)的影響[J];交通運(yùn)輸系統(tǒng)工程與信息;2015年05期

相關(guān)會(huì)議論文 前1條

1 張華民;;電動(dòng)汽車(chē)及充電站技術(shù)現(xiàn)狀及前景[A];中國(guó)化學(xué)會(huì)第27屆學(xué)術(shù)年會(huì)第10分會(huì)場(chǎng)摘要集[C];2010年

相關(guān)博士學(xué)位論文 前2條

1 馮亮;電動(dòng)汽車(chē)充電站規(guī)劃研究[D];天津大學(xué);2013年

2 姜星莉;經(jīng)濟(jì)全球化背景下中國(guó)能源安全問(wèn)題研究[D];武漢大學(xué);2010年

相關(guān)碩士學(xué)位論文 前5條

1 李亞芬;規(guī)�；瘏^(qū)域電動(dòng)乘用車(chē)負(fù)荷估算研究[D];北京交通大學(xué);2014年

2 王輝;電動(dòng)汽車(chē)充電站規(guī)劃與運(yùn)營(yíng)研究[D];浙江大學(xué);2013年

3 張國(guó)亮;城市內(nèi)和城市間電動(dòng)汽車(chē)充電站的選址布局研究[D];天津大學(xué);2012年

4 張菁菁;電動(dòng)汽車(chē)充電設(shè)施建設(shè)與發(fā)展的多方博弈分析[D];北京交通大學(xué);2011年

5 文凱;借鑒國(guó)際經(jīng)驗(yàn)發(fā)展我國(guó)新能源汽車(chē)產(chǎn)業(yè)研究[D];東北財(cái)經(jīng)大學(xué);2010年

，

本文編號(hào)：2137814