本文關(guān)鍵詞：基于改進(jìn)BCC算法的電力系統(tǒng)魯棒動(dòng)態(tài)經(jīng)濟(jì)調(diào)度策略　出處：《燕山大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 經(jīng)濟(jì)調(diào)度 不確定性 壞場(chǎng)景集 魯棒調(diào)度 電動(dòng)汽車充放電 BCC算法 多代理

【摘要】：隨著越來(lái)越多的電動(dòng)汽車和風(fēng)電接入電網(wǎng),由于風(fēng)電固有的間歇性,電動(dòng)汽車充電在時(shí)間上和空間上具有不確定性,電力系統(tǒng)的運(yùn)行和調(diào)度將會(huì)受到影響。首先,不確定性的出現(xiàn)使得傳統(tǒng)經(jīng)濟(jì)調(diào)度的結(jié)果變得不穩(wěn)定,目標(biāo)函數(shù)易受擾動(dòng)。其次,經(jīng)濟(jì)調(diào)度的約束條件也出現(xiàn)了不確定性參數(shù),使得這些約束條件難以滿足。最后,大量電動(dòng)汽車的隨機(jī)充電可能會(huì)進(jìn)一步拉大系統(tǒng)負(fù)荷的峰谷差,而風(fēng)電的間歇性和反調(diào)峰特性導(dǎo)致其不能完全被利用,產(chǎn)生棄風(fēng)。針對(duì)上述問(wèn)題,本文從以下幾方面進(jìn)行了研究:首先,介紹了電力系統(tǒng)動(dòng)態(tài)經(jīng)濟(jì)調(diào)度問(wèn)題的數(shù)學(xué)模型,將多代理的思想引入到細(xì)菌群體趨藥性(bacterial colony chemotaxis,BCC)算法中。通過(guò)協(xié)調(diào)代理對(duì)各個(gè)分時(shí)代理進(jìn)行協(xié)調(diào),按照時(shí)間將電力系統(tǒng)動(dòng)態(tài)經(jīng)濟(jì)調(diào)度問(wèn)題分解為若干子優(yōu)化問(wèn)題由分時(shí)代理進(jìn)行優(yōu)化,提高了算法的尋優(yōu)能力和收斂能力。提出一種基于云模型的變異策略,提高了細(xì)菌的變異能力。其次,分析了風(fēng)電和私家電動(dòng)汽車充電的不確定性,利用蒙特卡洛模擬仿真了私家電動(dòng)汽車充電負(fù)荷,并建立了電動(dòng)汽車換電站模型。為了提高風(fēng)電利用率,減小風(fēng)電和電動(dòng)汽車不確定性給經(jīng)濟(jì)調(diào)度目標(biāo)函數(shù)和約束條件造成的影響,提出一種考慮解的最優(yōu)性和可行性的壞場(chǎng)景集電力系統(tǒng)魯棒動(dòng)態(tài)經(jīng)濟(jì)調(diào)度模型,并給出了求解方法。最后,介紹了電動(dòng)汽車雙向充放電,建立了電動(dòng)汽車雙向充放電模型,通過(guò)對(duì)電動(dòng)汽車充放電功率的控制,提高電力系統(tǒng)對(duì)風(fēng)電的消納能力。將魯棒區(qū)間優(yōu)化引入到電力系統(tǒng)壞場(chǎng)景集魯棒動(dòng)態(tài)經(jīng)濟(jì)調(diào)度模型中,優(yōu)化風(fēng)電出力允許區(qū)間,在減少棄風(fēng)的同時(shí)提高了系統(tǒng)的經(jīng)濟(jì)性。
[Abstract]:With more and more electric vehicles and wind power, wind power due to the inherent intermittency, with uncertainty in time and space of electric vehicle charging, operation and dispatching of power system will be affected. Firstly, the uncertainty of the traditional economic dispatch results becomes unstable, the objective function is vulnerable to disturbance secondly, the constraints of economic dispatching also appeared uncertain parameters, which is difficult to meet these constraints. Finally, a large number of electric vehicle charging random peak may further widen the system load, and the intermittent wind power and anti peaking characteristics led to its cannot be used completely, have abandoned the wind. In view of the above the problem, this article has conducted the research from the following aspects: firstly, the mathematical model of power system dynamic economic dispatch problem, multi agent is introduced into the bacterial chemotaxis group (of bacterial colony chemotaxis, BCC) algorithm. Through the coordination of the agent coordination each time, time will be in accordance with the economic dispatch of power system dynamic problem is decomposed into several sub optimization problems by time-sharing agent optimization algorithm to improve the searching ability and the convergence ability. This paper proposes a mutation strategy cloud model based on the improved mutation capability of bacteria. Secondly, analysis of the wind power and private electric vehicle charging uncertainty simulation of private electric vehicle charging load using Monte Carlo, and established the model of electric vehicle charging station. In order to improve the utilization of wind power, wind power and electric vehicle is affected by uncertainty to economic dispatch target function and constraint condition caused by a bad scene, proposed the optimality and feasibility of the set of robust dynamic economic dispatch model considering solutions, and give a solution. Finally, introduces the bidirectional electric automobile charging and discharging, established a two-way charge discharge model of electric vehicle, through the control of the electric vehicle charging and discharging power, improve the absorptive capacity of wind power in power system. The robust interval optimization is introduced to the power system bad scenario set robust dynamic economic scheduling model, the optimization of wind power output allowable interval in, reduce the abandoned wind and improve the system economy.

【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TM73

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 劉曉飛;張千帆;崔淑梅;;電動(dòng)汽車V2G技術(shù)綜述[J];電工技術(shù)學(xué)報(bào);2012年02期

2 王相勤;;當(dāng)前我國(guó)電動(dòng)汽車發(fā)展的瓶頸問(wèn)題及對(duì)策[J];能源技術(shù)經(jīng)濟(jì);2011年03期

3 趙俊華;文福拴;薛禹勝;董朝陽(yáng);辛建波;;計(jì)及電動(dòng)汽車和風(fēng)電出力不確定性的隨機(jī)經(jīng)濟(jì)調(diào)度[J];電力系統(tǒng)自動(dòng)化;2010年20期

4 陸凌蓉;文福拴;薛禹勝;辛建波;;計(jì)及可入網(wǎng)電動(dòng)汽車的電力系統(tǒng)機(jī)組最優(yōu)組合[J];電力系統(tǒng)自動(dòng)化;2011年21期

5 黃媛;劉俊勇;陳井銳;范康林;趙京虎;;計(jì)及電動(dòng)汽車入網(wǎng)的負(fù)荷頻率控制[J];電力系統(tǒng)自動(dòng)化;2012年09期

6 羅卓偉;胡澤春;宋永華;徐智威;陽(yáng)岳希;劉輝;;大規(guī)模電動(dòng)汽車充放電優(yōu)化控制及容量效益分析[J];電力系統(tǒng)自動(dòng)化;2012年10期

7 夏天;熊威;何永秀;;國(guó)外電動(dòng)汽車發(fā)展機(jī)制[J];電力需求側(cè)管理;2013年04期

8 丁然;李歧強(qiáng);孫同景;楊加敏;;考慮魯棒性的生產(chǎn)調(diào)度期望值模型[J];控制理論與應(yīng)用;2006年05期

9 王冰;羊曉飛;李巧云;;基于壞場(chǎng)景集的抗風(fēng)險(xiǎn)魯棒調(diào)度模型[J];自動(dòng)化學(xué)報(bào);2012年02期

10 武偉偉;王茜;;橢球不確定集下具有消費(fèi)的投資組合魯棒優(yōu)化模型[J];數(shù)學(xué)理論與應(yīng)用;2009年04期

相關(guān)碩士學(xué)位論文 前1條

1 何守龍;考慮風(fēng)電不確定性的電力系統(tǒng)多目標(biāo)魯棒調(diào)度策略研究[D];燕山大學(xué);2014年

，

本文編號(hào)：1378001