【摘要】：電動(dòng)汽車產(chǎn)業(yè)的蓬勃發(fā)展為電網(wǎng)的發(fā)展帶來了機(jī)遇與挑戰(zhàn),電動(dòng)汽車在減少化石燃料燃燒,節(jié)能減排方面具有明顯優(yōu)勢(shì),然而大量無序接入電網(wǎng)的電動(dòng)汽車會(huì)對(duì)電網(wǎng)的穩(wěn)定運(yùn)行帶來巨大影響如電壓變化、電能質(zhì)量變差、諧波污染等;另一方面電動(dòng)汽車的電池具有儲(chǔ)能功能,通過先進(jìn)合理的控制策略管理電動(dòng)汽車的充放電行為可以減少電動(dòng)汽車接入對(duì)電網(wǎng)的影響甚至提高電網(wǎng)運(yùn)行的經(jīng)濟(jì)性和穩(wěn)定性。本文利用魯棒優(yōu)化算法對(duì)含可再生能源和電動(dòng)汽車的微網(wǎng)系統(tǒng)調(diào)度策略進(jìn)行研究,通過控制電動(dòng)汽車以及可再生能源在微網(wǎng)中協(xié)調(diào)互動(dòng)在保證電網(wǎng)穩(wěn)定運(yùn)行的前提下,達(dá)到提高經(jīng)濟(jì)性的目的。本文創(chuàng)新點(diǎn)可以總結(jié)如下:1.將魯棒優(yōu)化的方法應(yīng)用于電動(dòng)汽車在含可再生能源的微網(wǎng)系統(tǒng)有序充電策略中,在調(diào)度模型中以預(yù)測區(qū)間來描述可再生能源出力以及電動(dòng)汽車充電功率的不確定性,使調(diào)度策略對(duì)于不確定變量在其區(qū)間內(nèi)變化時(shí)具有較好的魯棒性。2.針對(duì)魯棒優(yōu)化理論的“過度保守”導(dǎo)致模型經(jīng)濟(jì)性較差的問題,引入“魯棒系數(shù)”的概念對(duì)前文所提傳統(tǒng)魯棒優(yōu)化進(jìn)行改進(jìn),使得調(diào)度策略在經(jīng)濟(jì)性和魯棒性中尋找折中,為決策者提供調(diào)度理論依據(jù)。并將改進(jìn)的魯棒優(yōu)化應(yīng)用于微網(wǎng)的電動(dòng)汽車與可再生能源協(xié)同調(diào)度中,利用電動(dòng)汽車的V2G功能,使得電動(dòng)汽車在可再生能源豐富的時(shí)段充電而在可再生能源匱乏時(shí)段將電能回饋給電網(wǎng),達(dá)到增強(qiáng)微網(wǎng)經(jīng)濟(jì)性的同時(shí)保證微網(wǎng)的穩(wěn)定性。3.針對(duì)魯棒優(yōu)化算法在微網(wǎng)協(xié)同優(yōu)化模型應(yīng)用中面臨的模型復(fù)雜度增加問題,本文使用基于電動(dòng)汽車到達(dá)時(shí)間的分類調(diào)度算法,將有相似充/放電行為的電動(dòng)汽車進(jìn)行分組統(tǒng)一調(diào)度,使得算法的復(fù)雜度不隨電動(dòng)汽車數(shù)量的增加而增加,提高了算法求解效率。4.魯棒優(yōu)化理論應(yīng)用于含有電動(dòng)汽車的V2G網(wǎng)絡(luò)的研究還處于初級(jí)階段,本文對(duì)如何利用魯棒優(yōu)化解決V2G網(wǎng)絡(luò)能量調(diào)度問題進(jìn)行探索,提出V2G應(yīng)用的三種場景,并針對(duì)每種場景問題的構(gòu)成提出相對(duì)應(yīng)的魯棒優(yōu)化解決方法。為學(xué)者運(yùn)用魯棒優(yōu)化解決V2G能量調(diào)度問題提供參考。
[Abstract]:The vigorous development of electric vehicle industry brings opportunities and challenges to the development of power grid. Electric vehicles have obvious advantages in reducing fossil fuel burning, energy saving and emission reduction. However, a large number of unordered electric vehicles connected to the power grid will have a great impact on the stable operation of the power grid, such as voltage changes, power quality deterioration, harmonic pollution, etc. On the other hand, the batteries of electric vehicles have the function of energy storage. The charging and discharging behavior of electric vehicles can be managed by advanced and reasonable control strategy, which can reduce the influence of electric vehicle access on the power grid and even improve the economy and stability of power grid operation. In this paper, the robust optimization algorithm is used to study the scheduling strategy of microgrid system with renewable energy and electric vehicle. By controlling the electric vehicle and the renewable energy in the microgrid, the coordination and interaction of the electric vehicle and the renewable energy in the microgrid can ensure the stable operation of the power grid. To achieve the purpose of improving economy. The innovations of this article can be summarized as follows: 1. The robust optimization method is applied to the ordered charging strategy of electric vehicles in microgrid systems with renewable energy. The prediction interval is used to describe the uncertainty of the renewable energy output and the charging power of electric vehicles in the scheduling model. The scheduling policy is robust to the uncertain variables changing in its interval. 2. In view of the problem that the model economy is poor due to the "excessive conservatism" of the robust optimization theory, the concept of "robust coefficient" is introduced to improve the traditional robust optimization mentioned in the previous paper, so that the scheduling strategy can find a compromise between economy and robustness. It provides a theoretical basis for decision makers. The improved robust optimization is applied to the cooperative scheduling of micro grid electric vehicle and renewable energy, and the V2G function of electric vehicle is utilized. The electric vehicle is charged in the renewable energy rich period, and the electric energy is fed back to the power network during the renewable energy shortage period, which can enhance the economic efficiency of the microgrid and ensure the stability of the microgrid. 3. Aiming at the problem of increasing the complexity of robust optimization algorithm in the application of microgrid cooperative optimization model, this paper uses a classification scheduling algorithm based on the arrival time of electric vehicles. In order to improve the efficiency of the algorithm, the complexity of the algorithm does not increase with the increase of the number of electric vehicles. The application of robust optimization theory to V2G networks with electric vehicles is still in its infancy. This paper explores how to solve the energy scheduling problem of V2G networks by robust optimization, and proposes three scenarios for V2G applications. A corresponding robust optimization solution is proposed for each scenario problem. It provides a reference for scholars to solve V2G energy scheduling problem by robust optimization.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM73

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