【摘要】：摘要:隨著新一輪能源革命的興起,電網(wǎng)的發(fā)展已經(jīng)進入到能源互聯(lián)、智能的階段。含分布式電源(distributiongeneration,DG)的配電網(wǎng)作為未來電網(wǎng)的一種重要形態(tài),其規(guī)劃發(fā)展對節(jié)約能源、推進地區(qū)城市建設(shè)、解決新興技術(shù)并網(wǎng)等一系列問題都起著至關(guān)重要的作用。然而,DG本身帶有的隨機性、間歇性等不確定性特點卻給配電網(wǎng)規(guī)劃帶來了一定的沖擊和挑戰(zhàn)。為此,如何做好這種形態(tài)下的配電網(wǎng)規(guī)劃,解決各種不確定性因素對其經(jīng)濟性、安全可靠性帶來的影響,提高配電網(wǎng)對可再生能源的接納能力,獲得配電網(wǎng)建立的最佳性能等一系列問題,變得尤其重要。本文基于不確定理論,對含DG的配電網(wǎng)網(wǎng)架進行規(guī)劃,主要完成工作如下:考慮到含DG的配電網(wǎng)中存在的不確定因素,對不確定規(guī)劃理論進行概述。在模糊規(guī)劃中給出模糊集、模糊期望值的概念并進行模糊模擬;在隨機規(guī)劃中給出隨機變量、隨機期望值的概念和隨機機會約束的數(shù)學(xué)原理。對不確定潮流計算和優(yōu)化方法進行研究。首先考慮分布式電源出力和負荷功率的特性,并對應(yīng)模糊規(guī)劃和隨機規(guī)劃分別進行模糊潮流和隨機潮流計算。模糊潮流以牛-拉法為基礎(chǔ),利用泰勒級數(shù)展開求解狀態(tài)變量的模糊增量;隨機潮流采用基于半不變量的方法,求出待求變量各節(jié)點電壓和各支路功率的概率分布情況。其次重點介紹自適應(yīng)遺傳算法、單親遺傳算法和樹形結(jié)構(gòu)編碼遺傳算法這三種改進的遺傳算法,比較其各自的應(yīng)用特點。建立含DG的配電網(wǎng)網(wǎng)架規(guī)劃模型。對風(fēng)力發(fā)電、光伏發(fā)電進行建模,并建立了相應(yīng)的模糊期望規(guī)劃模型和機會約束規(guī)劃模型。在模糊期望規(guī)劃模型中,采用梯形模糊數(shù)對DG和負荷的不確定進行模糊模擬,以投資建設(shè)費用模糊期望值最小為目標(biāo)函數(shù);在機會約束規(guī)劃中,考慮到節(jié)點電壓、支路功率約束條件的置信水平。為說明以上模型的合理性,采用18節(jié)點系統(tǒng)進行分析驗證。分別對是否含有DG接入系統(tǒng)和是否采用不確定規(guī)劃理論方法驗證,并采用基于樹形結(jié)構(gòu)編碼的單親遺傳算法(TSE-PGA)進行優(yōu)化求解。由建設(shè)投資費用最小和網(wǎng)損費用最小得出,含有DG接入、采用不確定規(guī)劃理論,可以有效解決不確定因素對配電網(wǎng)規(guī)劃的影響,降低網(wǎng)損,減少網(wǎng)絡(luò)規(guī)劃費用,從而獲得可觀的經(jīng)濟效益。通過以上研究,基于不確定理論,提供了解決含DG的配電網(wǎng)網(wǎng)架規(guī)劃問題的解決思路,并采用案例計算說明了方法的可行性。
[Abstract]:Absrtact: with the rise of new energy revolution, the development of power grid has entered the stage of energy interconnection and intelligence. Distribution network with distributed generation (distributiongeneration,DG) is an important form of power grid in the future. Its planning and development plays an important role in saving energy, promoting the construction of regional cities, solving a series of problems such as connecting with new technologies and so on. However, the uncertain characteristics of DG itself, such as randomness and intermittence, have brought some challenges and challenges to distribution network planning. Therefore, how to do well the distribution network planning in this form, solve the impact of various uncertain factors on its economy, safety and reliability, improve the distribution network's ability to accept renewable energy, A series of problems, such as obtaining the best performance of the distribution network, have become particularly important. Based on the uncertainty theory, the main work of this paper is as follows: considering the uncertain factors in the distribution network with DG, the uncertain planning theory is summarized. In fuzzy programming, the concepts of fuzzy set and fuzzy expected value are given and fuzzy simulation is carried out. In random programming, the concepts of random variables, random expected values and the mathematical principles of stochastic chance constraints are given. The calculation and optimization methods of uncertain power flow are studied. Firstly, the characteristics of distributed power generation and load power are considered, and fuzzy power flow and stochastic power flow are calculated for fuzzy programming and random programming, respectively. The fuzzy power flow is based on the Niu-pull method and the Taylor series expansion is used to solve the fuzzy increment of the state variable, and the random power flow is based on the semi-invariant method to obtain the probability distribution of the voltage and branch power of each node. Secondly, three improved genetic algorithms, namely adaptive genetic algorithm, parthenogenetic genetic algorithm and tree structure coding genetic algorithm, are introduced, and their respective application characteristics are compared. The distribution network planning model with DG is established. The wind power generation and photovoltaic power generation are modeled, and the corresponding fuzzy expectation programming model and chance constrained programming model are established. In the fuzzy expectation programming model, the trapezoidal fuzzy number is used to simulate the uncertainty of DG and load, and the minimum expected value of investment and construction cost is taken as the objective function, and the node voltage is considered in the chance constrained programming. The confidence level of branch power constraints. In order to explain the rationality of the above model, the 18-bus system is used to analyze and verify the model. Whether the DG access system is included or not and whether the uncertain programming theory is adopted are verified, and the algorithm based on tree structure coding (TSE-PGA) is used to optimize the solution. From the minimum construction investment cost and the minimum network loss cost, including DG access, the uncertain programming theory can effectively solve the influence of uncertain factors on distribution network planning, reduce network loss, and reduce network planning costs. Thus, considerable economic benefits are obtained. Based on the theory of uncertainty, this paper provides a solution to the distribution network frame planning problem with DG, and the feasibility of the method is illustrated by a case study.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM715

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