發(fā)布時(shí)間：2018-05-16 08:09

  本文選題：風(fēng)力發(fā)電 + 點(diǎn)估計(jì)��； 參考：《華中科技大學(xué)》2014年博士論文

【摘要】：隨著煤、石油、天然氣這些化石能源的日益枯竭及其對(duì)環(huán)境污染的嚴(yán)重影響,作為可再生清潔能源中技術(shù)最為成熟的風(fēng)力發(fā)電得到了大規(guī)模的發(fā)展。然而隨著風(fēng)電在電力系統(tǒng)中滲透率的不斷增加,風(fēng)電所具有的強(qiáng)波動(dòng)性和不確定性將給電力系統(tǒng)調(diào)度運(yùn)行帶來越來越重大的影響,傳統(tǒng)的電力系統(tǒng)調(diào)度運(yùn)行方法可能不再適用,因此必須針對(duì)風(fēng)力發(fā)電自身的特點(diǎn),建立適用于含大規(guī)模風(fēng)電電力系統(tǒng)優(yōu)化調(diào)度運(yùn)行的模型,并研究相應(yīng)的求解和評(píng)估方法。本文首先回顧了電力系統(tǒng)優(yōu)化運(yùn)行相關(guān)的機(jī)組組合、經(jīng)濟(jì)調(diào)度和潮流計(jì)算三個(gè)關(guān)鍵問題的研究歷史與現(xiàn)狀,在此基礎(chǔ)之上,針對(duì)風(fēng)電對(duì)這三個(gè)問題產(chǎn)生的影響特性,重點(diǎn)研究了考慮風(fēng)電爬坡事件的機(jī)組組合問題、計(jì)及機(jī)組調(diào)節(jié)能力的含風(fēng)電電力系統(tǒng)的經(jīng)濟(jì)調(diào)度問題和基于風(fēng)電數(shù)據(jù)樣本的概率潮流問題。主要工作包括以下3個(gè)部分： (1)在含風(fēng)電的電力系統(tǒng)機(jī)組組合問題中,針對(duì)風(fēng)電功率可能會(huì)出現(xiàn)較大爬坡事件這一問題,建立了考慮風(fēng)電爬坡事件約束的機(jī)組組合模型,并首先通過精確線性化技術(shù)將隨機(jī)非線性混整模型轉(zhuǎn)化成隨機(jī)線性混整模型,然后考慮用風(fēng)電預(yù)測值以及區(qū)間預(yù)測上下限來描述其出力,利用線性魯棒優(yōu)化理論將隨機(jī)線性混整模型轉(zhuǎn)換成確定性線性混整模型,最后可利用商業(yè)軟件對(duì)其進(jìn)行大規(guī)模求解。仿真結(jié)果表明了該方法的可行性和有效性,且對(duì)于一般的風(fēng)電爬坡事件,爬坡事件約束是不起作用的,但對(duì)于風(fēng)電波動(dòng)速率較大的情況,考慮爬坡事件更能保證系統(tǒng)安全。 (2)在含風(fēng)電的電力系統(tǒng)經(jīng)濟(jì)調(diào)度問題中,針對(duì)風(fēng)電功率難以精確預(yù)測,在風(fēng)電預(yù)測值的基礎(chǔ)上,考慮風(fēng)電預(yù)測誤差上下限,建立了計(jì)及機(jī)組調(diào)節(jié)能力的含風(fēng)電電力系統(tǒng)的魯棒經(jīng)濟(jì)調(diào)度模型,其中風(fēng)電預(yù)測不準(zhǔn)所引起的誤差可通過常規(guī)機(jī)組的調(diào)節(jié)能力得到線性最優(yōu)補(bǔ)償,應(yīng)用線性魯棒優(yōu)化理論可將隨機(jī)問題轉(zhuǎn)化為確定性問題后利用商業(yè)軟件進(jìn)行求解,并保證系統(tǒng)安全,為含大規(guī)模風(fēng)電的電力系統(tǒng)經(jīng)濟(jì)調(diào)度提供種新的有效方法。 (3)在含風(fēng)電的電力系統(tǒng)概率潮流問題中,針對(duì)風(fēng)電功率分布特性難以用常見的概率密度函數(shù)進(jìn)行擬合,提出了一種基于風(fēng)電樣本數(shù)據(jù)的點(diǎn)估計(jì)方法,并在此基礎(chǔ)上利用Cholesky分解技術(shù)處理輸入變量的相關(guān)性,最后利用Gram-Charlier級(jí)數(shù)展開得到輸出隨機(jī)變量的累積分布,詳細(xì)分析了不考慮與考慮樣本數(shù)據(jù)相關(guān)性對(duì)仿真結(jié)果的影響。仿真結(jié)果表明該方法計(jì)算量小,精度高,且在實(shí)際工程應(yīng)用問題中應(yīng)考慮輸入變量的相關(guān)性,而忽略其相關(guān)性可能會(huì)造成較大的誤差。
[Abstract]:With the increasing depletion of fossil energy such as coal, oil and natural gas and its serious impact on environmental pollution, wind power generation, which is the most mature technology in renewable clean energy, has been developed on a large scale. However, with the increasing permeability of wind power in power system, the strong volatility and uncertainty of wind power will bring more important impact to the dispatching operation of power system, and the traditional dispatching operation method of power system may not be applicable. Therefore, according to the characteristics of wind power generation, a model suitable for optimal operation of wind power system with large-scale wind power should be established, and the corresponding solution and evaluation methods should be studied. This paper first reviews the research history and present situation of three key problems related to optimal operation of power system, such as unit combination, economic dispatch and power flow calculation. Based on this, the influence characteristics of wind power on these three problems are discussed. The problems of unit combination considering wind power climbing event, the economic dispatching problem of wind power system with unit regulation ability and the probabilistic power flow problem based on wind power data sample are studied in detail. The main work consists of the following three parts: 1) in the power system unit combination problem with wind power, in order to solve the problem that the wind power may occur a large slope climbing event, a unit combination model considering the wind power climbing event constraint is established. Firstly, the stochastic nonlinear blending model is transformed into the stochastic linear blending model by the exact linearization technique, and then the wind power prediction value and the upper and lower bounds of interval prediction are considered to describe the output force. The stochastic linear blending model is transformed into a deterministic linear blending model by using the linear robust optimization theory. Finally, it can be solved on a large scale by commercial software. The simulation results show that the method is feasible and effective, and that the constraint of climbing event is not effective for general wind power climbing event, but for the case of large wind power fluctuation rate, the system safety can be ensured by considering the climbing event. (2) in the economic dispatch problem of power system with wind power, considering the upper and lower limits of wind power prediction error, it is difficult to predict wind power accurately on the basis of wind power forecast value. A robust economic dispatching model of wind power system with unit regulation capacity is established, in which the error caused by the uncertainty of wind power prediction can be compensated linearly and optimally by the regulation ability of conventional units. By applying linear robust optimization theory, stochastic problems can be transformed into deterministic problems and solved by commercial software, and the system security can be guaranteed, which provides a new effective method for economic dispatch of power systems with large-scale wind power. In the probabilistic power flow problem of power system with wind power, a point estimation method based on wind power sample data is proposed to solve the problem that the distribution of wind power is difficult to fit with the common probability density function. On this basis, the Cholesky decomposition technique is used to deal with the correlation of input variables. Finally, the cumulative distribution of output random variables is obtained by using Gram-Charlier series expansion. The effect of not considering and considering the correlation of sample data on the simulation results is analyzed in detail. The simulation results show that the proposed method has the advantages of low computational complexity and high accuracy, and the correlation of input variables should be considered in practical engineering applications, while ignoring the correlation may result in large errors.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM614

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本文編號(hào)：1896104