本文關(guān)鍵詞： 經(jīng)濟(jì)調(diào)度 多目標(biāo) 風(fēng)電場(chǎng) 電動(dòng)汽車 差分進(jìn)化算法 Pareto最優(yōu)解　出處：《華北電力大學(xué)(北京)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：經(jīng)濟(jì)調(diào)度是指在滿足一定的約束條件的基礎(chǔ)上,通過(guò)調(diào)度機(jī)組出力滿足模型目標(biāo)函數(shù)的基本問(wèn)題,它在本質(zhì)上是一種最優(yōu)化問(wèn)題。隨著電動(dòng)汽車和可再生能源的發(fā)展,傳統(tǒng)經(jīng)濟(jì)調(diào)度模型不再局限于傳統(tǒng)火電機(jī)組,經(jīng)濟(jì)調(diào)度問(wèn)題有了新的發(fā)展；同時(shí),基于對(duì)可再生能源和電動(dòng)汽車等多種因素的考慮,原先的單目標(biāo)函數(shù)已經(jīng)不能滿足要求,因此,研究智能電網(wǎng)背景下的多目標(biāo)動(dòng)態(tài)經(jīng)濟(jì)調(diào)度模型具有重要意義。針對(duì)智能電網(wǎng)背景下的多目標(biāo)動(dòng)態(tài)經(jīng)濟(jì)調(diào)度問(wèn)題,分析了新能源和電動(dòng)汽車并網(wǎng)對(duì)傳統(tǒng)經(jīng)濟(jì)調(diào)度問(wèn)題帶來(lái)的影響；提出了一種改進(jìn)的差分進(jìn)化算法,并利用該算法對(duì)含風(fēng)電場(chǎng)動(dòng)態(tài)經(jīng)濟(jì)調(diào)度模型求解；融入混沌和量子的思想提出了一種混合優(yōu)化算法,并利用該算法對(duì)含風(fēng)電場(chǎng)和電動(dòng)汽車的多目標(biāo)動(dòng)態(tài)經(jīng)濟(jì)調(diào)度進(jìn)行求解。具體工作如下：1.在差分進(jìn)化算法的基礎(chǔ)上結(jié)合人工蜂群算法(ABC)的觀察蜂加速進(jìn)化操作、偵查蜂隨機(jī)搜索操作提出了一種改進(jìn)差分進(jìn)化算法(IDE)；并在此基礎(chǔ)上,引入量子思想和混沌思想,提出了一種混合改進(jìn)量子差分進(jìn)化算法(HIQDE),最終實(shí)現(xiàn)了降低種群規(guī)模、防止早熟收斂、提高局部和全局搜索能力的目的。2.提出了基于IDE算法的含風(fēng)電場(chǎng)的動(dòng)態(tài)經(jīng)濟(jì)調(diào)度模型。利用本文提出的IDE算法,以IEEE-30節(jié)點(diǎn)系統(tǒng)為例,對(duì)模型進(jìn)行MATLAB仿真,并將IDE算法實(shí)驗(yàn)結(jié)果與其他ABC、DE、PSO算法進(jìn)行對(duì)比。通過(guò)對(duì)目標(biāo)函數(shù)值(費(fèi)用)和功損的比較,驗(yàn)證了本文提出的改進(jìn)算法的有效性。3.提出了基于HIQDE算法的含電動(dòng)汽車和風(fēng)電場(chǎng)經(jīng)濟(jì)調(diào)度的多目標(biāo)動(dòng)態(tài)經(jīng)濟(jì)調(diào)度模型。以IEEE-39節(jié)點(diǎn)系統(tǒng)為例,利用前文提出的基于Pareto最優(yōu)的混合改進(jìn)量子差分進(jìn)化算法,對(duì)模型進(jìn)行MATLAB仿真實(shí)驗(yàn)。實(shí)驗(yàn)結(jié)果表明本文提出的研究方案能很好的解決含新能源和電動(dòng)汽車的經(jīng)濟(jì)調(diào)度問(wèn)題。
[Abstract]:Economic dispatch refers to the basic problem of satisfying the objective function of the model by dispatching the generating units on the basis of satisfying certain constraint conditions. It is an optimization problem in essence. With the development of electric vehicles and renewable energy, the traditional economic scheduling model is no longer confined to traditional thermal power units, and the economic scheduling problem has a new development. At the same time, based on the consideration of renewable energy and electric vehicles, the original single-objective function can not meet the requirements. It is of great significance to study the multi-objective dynamic economic dispatching model in the context of smart grid, aiming at the multi-objective dynamic economic dispatching problem in the context of smart grid. The influence of new energy and electric vehicle grid connection on the traditional economic scheduling problem is analyzed. An improved differential evolutionary algorithm is proposed and used to solve the dynamic economic scheduling model of wind farm. In this paper, a hybrid optimization algorithm is proposed, which is based on the theory of chaos and quantum. The algorithm is used to solve the multi-objective dynamic economic scheduling with wind farm and electric vehicle. The main work is as follows: 1. Based on the differential evolutionary algorithm (DEA), the artificial bee colony algorithm (ABC) is used to solve the problem. To watch bees accelerate evolution. In this paper, an improved differential evolutionary algorithm (IDEN) is proposed for random search. On the basis of this, a hybrid improved quantum differential evolution algorithm (HIQDEA) is proposed by introducing quantum thought and chaos theory, which can reduce the population size and prevent premature convergence. The purpose of improving the local and global search ability. 2. A dynamic economic scheduling model with wind farm based on IDE algorithm is proposed. Using the IDE algorithm proposed in this paper, the IEEE-30 node system is taken as an example. The model is simulated by MATLAB, and the experimental results of the IDE algorithm are compared with those of other ABCs de PSO algorithms. The value of the objective function (cost) and the power loss are compared. The effectiveness of the improved algorithm proposed in this paper is verified. 3. A multi-objective dynamic economic scheduling model including electric vehicle and wind farm economic scheduling based on HIQDE algorithm is proposed. The IEEE-39 node system is used as the node system. For example. The hybrid improved quantum differential evolution algorithm based on Pareto is proposed in this paper. The model is simulated by MATLAB. The experimental results show that the proposed scheme can solve the economic scheduling problem with new energy sources and electric vehicles.
【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】：2016
【分類號(hào)】：TM73;TP18

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