【摘要】：電動汽車行業(yè)作為清潔環(huán)保的新型交通出行載具,有著普通燃油車無法比擬的優(yōu)勢。近年來,在國家政策的扶持之下飛速發(fā)展�？梢灶A(yù)期的是,在未來電動汽車充電負(fù)荷將成為電網(wǎng)用電負(fù)荷不可忽視的一個重要組成部分。然而電動汽車的充電行為會給電網(wǎng)造成很大的影響,出于電動汽車有序調(diào)度,能量管理和配網(wǎng)規(guī)劃的考慮,對電動汽車的負(fù)荷預(yù)測提出了更多的預(yù)測需求和更高的預(yù)測精度要求。 本分選取了快換式電動公交車、快充式電動出租車和快換式電動出租車三種預(yù)測場景樣本數(shù)據(jù),分別對基于灰色理論、概率模型和BP神經(jīng)網(wǎng)絡(luò)的三種電動汽車負(fù)荷預(yù)測方法的預(yù)測原理、預(yù)測數(shù)學(xué)模型和預(yù)測結(jié)果的適用性進(jìn)行分析研究。 對灰色預(yù)測模型的輸入數(shù)據(jù)條件和短期負(fù)荷預(yù)測精度的關(guān)系進(jìn)行考量,其一為輸入數(shù)據(jù)量的不同與預(yù)測精度的關(guān)系分析,其二為輸入數(shù)據(jù)的離散程度與預(yù)測精度的關(guān)系。 應(yīng)用基于灰色理論和BP神經(jīng)網(wǎng)絡(luò)的兩種模型分析研究二者在電動汽車超短期和短期負(fù)荷預(yù)測時間尺度下的適用性,得到的結(jié)果表明在實(shí)際應(yīng)用中,BP神經(jīng)網(wǎng)絡(luò)的預(yù)測效果優(yōu)于灰色預(yù)測,尤其是在超短期負(fù)荷預(yù)測時間尺度下,可考慮預(yù)測點(diǎn)前一時刻的電動汽車負(fù)荷預(yù)測值的BP神經(jīng)網(wǎng)絡(luò)模型能有效的減少預(yù)測的平均誤差和最大負(fù)荷相對誤差。 應(yīng)用基于灰色理論和基于概率模型的電動汽車負(fù)荷預(yù)測方法對比分析二者在電動汽車中長期負(fù)荷預(yù)測時間尺度下的適用性。結(jié)果表明在中長期負(fù)荷預(yù)測尺度下,基于概率模型和基于灰色理論的電動汽車負(fù)荷預(yù)測方法各有預(yù)測側(cè)重面�；诟怕誓Ｐ偷呢�(fù)荷預(yù)測方法在原理之上更為適用于中長期負(fù)荷預(yù)測中考慮國家政策和未來電動汽車發(fā)展規(guī)模的典型日預(yù)測�；诨疑淼呢�(fù)荷預(yù)測方法適用于電動汽車中長期負(fù)荷預(yù)測中日用電量和日最大負(fù)荷預(yù)測的應(yīng)用。 由于配網(wǎng)的規(guī)劃需求電動汽車的時空負(fù)荷預(yù)測,本文利用電動汽車充電負(fù)荷與其空間分布的位置不相關(guān)這一特性,通過分別建立電動汽車時間維度的負(fù)荷預(yù)測模型和利用OD矩陣建立電動汽車空間負(fù)荷分配數(shù)學(xué)模型,結(jié)合二者得到時空聯(lián)合分布的電動汽車充電負(fù)荷預(yù)測模型。
[Abstract]:Electric vehicle industry as a clean and environmental-friendly new type of transport vehicles, has an unparalleled advantage over ordinary fuel vehicles. In recent years, under the support of national policies, rapid development. It can be expected that the electric vehicle charge load will become an important part of the power grid load in the future. However, the charging behavior of electric vehicles will have a great impact on the power grid. Due to the consideration of the orderly scheduling, energy management and distribution network planning of electric vehicles, More forecasting demands and higher precision requirements are put forward for load forecasting of electric vehicles. This paper selects the sample data of three kinds of prediction scenarios, which are fast changing electric bus, fast charging electric taxi and fast changing electric taxi, respectively based on grey theory. The probabilistic model and BP neural network are used to predict the load of electric vehicle, and the applicability of forecasting mathematical model and forecasting results are analyzed and studied. The relationship between the input data condition of grey forecasting model and short-term load forecasting accuracy is considered. One is the analysis of the relationship between the difference of input data volume and the prediction accuracy, and the other is the relationship between the discrete degree of input data and the prediction accuracy. Two models based on grey theory and BP neural network are applied to analyze the applicability of the two models in the time scale of ultra-short term and short term load forecasting of electric vehicles. The results show that the prediction effect of BP neural network is better than that of grey forecasting in practical application, especially in the time scale of ultra-short-term load forecasting. The BP neural network model, which can consider the load forecasting value of electric vehicle at the previous moment, can effectively reduce the average error and the maximum load relative error. Based on grey theory and probabilistic model, the applicability of the two methods in the medium and long term load forecasting time scale of electric vehicles is compared and analyzed. The results show that under the medium and long term load forecasting scale, the probabilistic model and the grey theory based load forecasting method have different emphases. The probabilistic model based load forecasting method is more suitable for the typical daily forecasting considering the national policy and the future development scale of electric vehicles in the medium and long term load forecasting based on the principle. The method of load forecasting based on grey theory is suitable for the application of daily electricity consumption and daily maximum load forecasting in medium and long term load forecasting of electric vehicles. Because of the planning demand of distribution network, this paper makes use of the characteristic that the charge load of electric vehicle is not related to the location of its spatial distribution. The time dimension load forecasting model of electric vehicle and the mathematical model of space load distribution of electric vehicle are established by using OD matrix, and the charge load forecasting model of electric vehicle with time-space joint distribution is obtained by combining the two models.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TM715

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王震坡;孫逢春;林程;;電動公交客車充電站容量需求預(yù)測與仿真[J];北京理工大學(xué)學(xué)報(bào);2006年12期

2 陳亞紅,穆鋼,段方麗;短期電力負(fù)荷預(yù)報(bào)中幾種異常數(shù)據(jù)的處理[J];東北電力學(xué)院學(xué)報(bào);2002年02期

3 康重慶,夏清,張伯明;電力系統(tǒng)負(fù)荷預(yù)測研究綜述與發(fā)展方向的探討[J];電力系統(tǒng)自動化;2004年17期

4 徐立中;楊光亞;許昭;F.MARRA;C.TR■HOLT;;電動汽車充電負(fù)荷對丹麥配電系統(tǒng)的影響[J];電力系統(tǒng)自動化;2011年14期

5 徐志剛;王超;;基于灰色關(guān)聯(lián)投影法的短期負(fù)荷預(yù)測相似日選擇算法[J];電氣開關(guān);2010年04期

6 史德明,李林川,宋建文;基于灰色預(yù)測和神經(jīng)網(wǎng)絡(luò)的電力系統(tǒng)負(fù)荷預(yù)測[J];電網(wǎng)技術(shù);2001年12期

7 田立亭;史雙龍;賈卓;;電動汽車充電功率需求的統(tǒng)計(jì)學(xué)建模方法[J];電網(wǎng)技術(shù);2010年11期

8 高賜威;張亮;;電動汽車充電對電網(wǎng)影響的綜述[J];電網(wǎng)技術(shù);2011年02期

9 謝瑩華;譚春輝;張雪峰;盧奕城;;電動汽車充放電方式對深圳電網(wǎng)日負(fù)荷曲線的影響[J];廣東電力;2011年12期

10 劉鵬;劉瑞葉;白雪峰;郭志忠;;基于擴(kuò)散理論的電動汽車充電負(fù)荷模型[J];電力自動化設(shè)備;2012年09期

，

本文編號：2227263