本文選題：最優(yōu)潮流　切入點(diǎn)：智能優(yōu)化算法　出處：《北京交通大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：隨著分布式電源的大規(guī)模并網(wǎng)以及電動汽車等柔性負(fù)荷的快速發(fā)展使得現(xiàn)代電力系統(tǒng)變得越來越復(fù)雜。最優(yōu)潮流作為電力系統(tǒng)規(guī)劃、經(jīng)濟(jì)調(diào)度和市場交易等方面的分析工具,可以有效地對復(fù)雜電力系統(tǒng)的安全性、經(jīng)濟(jì)性及穩(wěn)定性進(jìn)行綜合優(yōu)化計算,但其本質(zhì)是一個多約束的、離散連續(xù)變量共存的、多維的非線性優(yōu)化問題,選擇合適的求解方法直接決定了最優(yōu)潮流解的有效性及優(yōu)越性。人工蜂群算法作為一種新穎的智能優(yōu)化算法,在處理非線性、多約束、多變量、非連續(xù)、非凸等優(yōu)化問題上具有一定優(yōu)勢,已在人工神經(jīng)網(wǎng)絡(luò)訓(xùn)練、圖像識別、語音識別等領(lǐng)域得到廣泛應(yīng)用。然而,人工蜂群算法與其它智能優(yōu)化算法發(fā)展類似,在初始研究階段依然存在一些問題需要解決,例如提高該算法的收斂速度及計算精度。為此,本文以基于人工蜂群算法的最優(yōu)潮流相關(guān)技術(shù)研究作為課題,通過對人工蜂群算法的深入研究,為電力系統(tǒng)的單目標(biāo)／多目標(biāo)最優(yōu)潮流問題提供一種新的求解方法,從而為采用最優(yōu)潮流作為計算工具的電力系統(tǒng)問題提供更加豐富的分析及決策信息。本文的主要研究內(nèi)容如下：(1)分析了人工蜂群算法尋優(yōu)時各階段的數(shù)學(xué)模型,采用幾個標(biāo)準(zhǔn)數(shù)值測試函數(shù)對人工蜂群算法的優(yōu)化性能進(jìn)行了仿真計算,結(jié)果表明：人工蜂群算法具有較高的收斂特性,能有效地處理數(shù)值優(yōu)化問題。(2)針對人工蜂群算法在處理低維度優(yōu)化問題時具有較高的尋優(yōu)能力,但求解高維度優(yōu)化問題時易陷入局部最優(yōu)的缺點(diǎn),提出了一種混沌差分人工蜂群算法。該改進(jìn)算法采用差分進(jìn)化算法的變異、交叉操作代替標(biāo)準(zhǔn)人工蜂群算法新蜜源的搜索操作,以提高算法的局部搜索能力；利用混沌映射中的Tent映射生成算法的初始種群、變異操作中的參考蜜源以及交叉操作中的參考維數(shù),以增加種群的多樣性。(3)分析了典型最優(yōu)潮流的數(shù)學(xué)模型,分別從經(jīng)濟(jì)、環(huán)保、電能質(zhì)量三方面建立最優(yōu)潮流的目標(biāo)函數(shù),即總發(fā)電成本、有功網(wǎng)損、總污染物排放量及電壓偏離量。針對多個目標(biāo)函數(shù),采用最大模糊滿意度法將其模糊處理,形成模糊多目標(biāo)最優(yōu)潮流模型。建立了基于混沌差分人工蜂群算法的模糊多目標(biāo)最優(yōu)潮流求解模型。仿真結(jié)果表明：所建立的求解模型可以有效地、可靠地解決最優(yōu)潮流問題,并且得到的最優(yōu)潮流運(yùn)行方案可以進(jìn)一步提高系統(tǒng)的經(jīng)濟(jì)性、電壓水平以及降低對環(huán)境的污染程度。(4)為了獲得質(zhì)量更高的Pareto最優(yōu)前沿,研究并提出了一種改進(jìn)的多目標(biāo)人工蜂群算法。該改進(jìn)算法通過變異和交叉操作獲得新可行解,采用快速非支配排序獲得各可行解支配信息以及更新外部存檔,利用目標(biāo)函數(shù)以及擁擠距離的綜合信息來計算可行解被待工蜂選擇的概率值,通過計算擁擠距離來實(shí)時控制外部存檔的大小,利用外部存檔中的Pareto最優(yōu)前沿作為算法尋優(yōu)時的參考蜜源。在此基礎(chǔ)上,建立了基于改進(jìn)的多目標(biāo)人工蜂群算法的多目標(biāo)最優(yōu)潮流求解模型。該求解模型先采用所提出的算法獲得Pareto最優(yōu)前沿,再利用K均值聚類法對最優(yōu)前沿進(jìn)行聚類,最后采用模糊集理論方法進(jìn)行決策分析。仿真計算表明：所提出的改進(jìn)的多目標(biāo)人工蜂群算法在求解Pareto最優(yōu)前沿時具有有效性、可靠性及優(yōu)越性；所建立的多目標(biāo)最優(yōu)潮流求解模型可以從Pareto最優(yōu)前沿中選擇出更滿意、更優(yōu)異的運(yùn)行決策方案。(5)針對系統(tǒng)含有風(fēng)電及負(fù)荷不確定因素的最優(yōu)潮流問題,建立了考慮風(fēng)電接入及負(fù)荷隨機(jī)變化的多目標(biāo)概率最優(yōu)潮流模型。該模型以發(fā)電成本的期望值及標(biāo)準(zhǔn)差作為目標(biāo)函數(shù),將狀態(tài)變量違反程度的期望值以懲罰項形式加入到目標(biāo)函數(shù)中。針對所建立的模型,提出了兩種求解方法。第一種方法：將多目標(biāo)概率最優(yōu)潮流采用模糊數(shù)學(xué)轉(zhuǎn)換成單目標(biāo)概率最優(yōu)潮流,再利用基于拉丁超立方采樣的改進(jìn)人工蜂群算法進(jìn)行求解。第二種方法：直接通過基于拉丁超立方采樣的多目標(biāo)人工蜂群算法獲得多目標(biāo)概率最優(yōu)潮流的Pareto最優(yōu)前沿。用改進(jìn)IEEE30節(jié)點(diǎn)測試系統(tǒng)的仿真計算表明：所建立的模型可以有效地處理含有風(fēng)電及負(fù)荷不確定性因素的概率最優(yōu)潮流問題,同時也驗證了所提出的兩種方法在求解多目標(biāo)概率最優(yōu)潮流問題上的有效性及優(yōu)越性。
[Abstract]:With the rapid development of large-scale distributed power grid and electric vehicle flexible load makes modern power system becomes more and more complicated. The optimal power flow in power system planning, analysis tools of economic dispatch and market transactions, can be effective for complex power system safety, economy and stability of the integrated optimization, but its essence is a multi constraint, discrete continuous variables, multidimensional nonlinear optimization problem, select the appropriate algorithm directly determines the effectiveness and superiority of the optimal power flow solution. Artificial bee colony algorithm is a novel intelligent optimization algorithm in dealing with nonlinear, multi constraint, multi variable, non continuous. Non convex optimization problem has certain advantages, has image recognition in artificial neural network, and is widely used in the field of speech recognition. However, artificial bee colony algorithm Method and other intelligent optimization algorithm, need to solve some of the problems still exist in the initial stage of research, such as improving the convergence speed and accuracy of the algorithm. Therefore, the optimal power flow based on artificial bee colony algorithm research as the subject, through in-depth study of the artificial bee colony algorithm, provides a new method to solve the problem for single target / multi-objective optimal power flow of power system, so as to provide analysis and decision making more information for the optimal power flow calculation of power system as tools. The main contents of this paper are as follows: (1) analysis of the mathematical model of each stage of artificial bee colony algorithm, using several standard numerical test function the artificial bee colony algorithm optimization performance were simulated. The results show that the convergence characteristics of artificial bee colony algorithm is high, can effectively. Physical and numerical optimization problems. (2) based on artificial bee colony algorithm is high in processing low dimension optimization optimization capabilities, but to solve the high dimension optimization problem is easy to fall in local optima, a chaotic differential artificial bee colony algorithm. The improved algorithm uses deviation algorithm, crossover operator instead of the standard artificial bee colony algorithm new nectar search operation, in order to improve the local search ability of the algorithm; using Tent mapping algorithm to generate the initial population in the chaotic mapping, mutation and cross reference nectar in the reference dimension, in order to increase the diversity of the population. (3) analyzed the typical mathematical model of optimal power flow, respectively from the economic, environmental protection, power quality three aspects to establish the objective function of optimal power flow, the total cost of power generation, power loss, the total pollutant discharge quantity and voltage deviation. For multiple target function The number of the fuzzy satisfaction maximizing method fuzzy processing, fuzzy multi-objective optimal power flow model is established. Based on chaotic differential fuzzy multi-objective optimal power flow solution model of artificial bee colony algorithm. The simulation results show that the model can solve effectively and reliably solve optimal power flow problem, optimal power flow and operation plan can further improve the economic system, the voltage level and reduce the degree of pollution of the environment. (4) in order to obtain the Pareto optimal front for higher quality, research and proposes an improved multi-objective artificial bee colony algorithm. The improved algorithm by mutation and crossover operation to obtain a new feasible solution, the fast non dominated the sort of feasible solution of controlling information and update the external archive, comprehensive information of the objective function and the crowding distance to calculate the feasible solution by Daigong bee selection The probability value by calculating the crowding distance to the real-time control of the external archive size, using the Pareto optimal front external archive as algorithm reference nectar. On this basis, the establishment of multi-objective optimal power flow model to solve multi-objective artificial bee colony algorithm based on improved. The model is solved by using the proposed algorithm to obtain the first Pareto optimal front, then cluster the optimal frontier using K means clustering method, the fuzzy set theory in decision analysis method. The simulation results indicate that the multi-objective improved artificial bee colony algorithm proposed is efficient in solving Pareto optimal front, superiority and reliability; multi-objective optimal power flow solution model you can choose from the Pareto optimal front with more excellent operation decision. (5) for the system with wind power and load the most uncertain factors Optimal power flow problem, a multi objective optimal power flow probability of wind power and the load of model. In this model, the generation cost of expected value and standard deviation as the objective function, the degree of violation of state variable expectations to punish a form to join in the objective function. According to the model, put forward two methods. The first method: the multi-objective probabilistic optimal power flow using fuzzy mathematics into single objective probabilistic optimal power flow, then using the improved artificial bee colony algorithm to solve the Latin hypercube sampling based on second methods: directly through the Pareto optimal front to obtain optimal power flow of multi objective probabilistic multi-objective artificial bee colony algorithm Latin hypercube sampling based on the simulation. The results indicate that the improved IEEE30 node test system: model can effectively deal with the uncertainty of wind power and load The probabilistic optimal power flow problem with qualitative factors is also verified. It also verifies the effectiveness and superiority of the two methods proposed for solving multi-objective probabilistic optimal power flow problem.

【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TM744;TP18

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 ;“基于運(yùn)行模式的最優(yōu)潮流結(jié)構(gòu)選擇”通過鑒定[J];哈爾濱理工大學(xué)學(xué)報;2000年03期

2 李志民,李衛(wèi)星,王永建;基于熵理論的最優(yōu)潮流代理約束算法[J];電力系統(tǒng)自動化;2001年11期

3 袁貴川,王建全;考慮了動態(tài)約束和穩(wěn)定約束的最優(yōu)潮流[J];電力系統(tǒng)及其自動化學(xué)報;2003年03期

4 黃玉;;基于改進(jìn)原對偶對數(shù)障礙法的最優(yōu)潮流算法[J];山東大學(xué)學(xué)報(工學(xué)版);2006年04期

5 胡澤春;王錫凡;;考慮負(fù)荷概率分布的隨機(jī)最優(yōu)潮流方法[J];電力系統(tǒng)自動化;2007年16期

6 劉曉東;;常見最優(yōu)潮流算法分析[J];科技信息;2009年02期

7 付敏;毛晨峰;楊永旺;;電力系統(tǒng)最優(yōu)潮流算法綜述[J];中國新技術(shù)新產(chǎn)品;2009年23期

8 張粒子,楊以涵;求最優(yōu)潮流的網(wǎng)流參量法(英文)[J];哈爾濱工業(yè)大學(xué)學(xué)報;1990年02期

9 薛忠;趙晉泉;羅衛(wèi)華;趙軍;;含離散和連續(xù)混合決策變量最優(yōu)潮流求解方法綜述[J];自動化技術(shù)與應(yīng)用;2014年04期

10 任奇;用最優(yōu)潮流法解決運(yùn)城電網(wǎng)無功補(bǔ)償問題的研究[J];山西電力;2002年06期

相關(guān)會議論文 前5條

1 郭振興;彭顯剛;;隨機(jī)最優(yōu)潮流綜述[A];武漢(南方九省)電工理論學(xué)會第22屆學(xué)術(shù)年會、河南省電工技術(shù)學(xué)會年會論文集[C];2010年

2 王燦;盧錦玲;鄭振華;;最優(yōu)潮流的GAAA算法[A];中國高等學(xué)校電力系統(tǒng)及其自動化專業(yè)第二十四屆學(xué)術(shù)年會論文集（中冊）[C];2008年

3 范曉丹;郭金蓮;趙洪山;;基于NRAL技術(shù)的外點(diǎn)最優(yōu)潮流新算法[A];中國高等學(xué)校電力系統(tǒng)及其自動化專業(yè)第二十四屆學(xué)術(shù)年會論文集（上冊）[C];2008年

4 潘雄;周明;孔曉民;吳s，

本文編號：1650681