【摘要】：智能電網(wǎng)通過布置大量傳感器以及數(shù)據(jù)采集裝置,提高對電網(wǎng)相關(guān)數(shù)據(jù)的采集及實時監(jiān)控的能力,以實現(xiàn)智能化的輸電和配電,是未來電網(wǎng)發(fā)展的必然趨勢。然而,智能電網(wǎng)的運行卻帶來了所采集的數(shù)據(jù)量的爆炸性增長,具備了大數(shù)據(jù)特征。當(dāng)前大規(guī)模電網(wǎng)的最優(yōu)潮流計算中,傳統(tǒng)的計算方法面對具備大數(shù)據(jù)特征的電力系統(tǒng)數(shù)據(jù)時,便出現(xiàn)計算速度慢,任務(wù)執(zhí)行效率低等缺點,難以滿足智能電網(wǎng)的實時計算需求;而現(xiàn)有的并行計算方法大多運行于專用并行機,性價比較低。因此,如何以高性價比,快速地實現(xiàn)最優(yōu)潮流計算,成為智能電網(wǎng)發(fā)展中所需要解決的一個重要問題。本文研究了云環(huán)境下大規(guī)模配電網(wǎng)最優(yōu)潮流的分布式并行計算方法。提出的方法借助Map-Reduce分布式并行編程框架,能夠運行于性價比較高的Hadoop集群之上。具體來說,本文首先提出了面向Map-Reduce框架的最優(yōu)潮流算法性能模型。該模型能夠分析和量化在不同的集群配置下算法的執(zhí)行時間,并為算法中電網(wǎng)的分解和計算粒度提供指導(dǎo)。基于此性能模型,本文提出了最優(yōu)潮流計算的負載均衡算法。在給定的集群資源情況下,通過模擬退火算法確定最優(yōu)的算法分解方式和計算粒度;并通過饋線重組算法實現(xiàn)負載均衡,從而優(yōu)化最優(yōu)潮流在云環(huán)境下的計算速度和效率。實驗方面,本文將提出的方法與傳統(tǒng)的串行最優(yōu)潮流計算進行了比較。實驗結(jié)果表明,提出的方法相對于串行方法,能夠減少68.3%的計算時間。同時,本文也驗證了負載均衡和不均衡情況下最優(yōu)潮流算法的計算時間。實驗數(shù)據(jù)表明,相比較于負載不平衡方法,本文提出的負載平衡算法能夠?qū)⒆顑?yōu)潮流的計算時間減少43.7%。
[Abstract]:By arranging a large number of sensors and data acquisition devices, smart grid can improve the ability of collecting and monitoring the related data of power grid in real time, so as to realize intelligent transmission and distribution, which is the inevitable trend of power grid development in the future. However, the operation of smart grid has brought about the explosive growth of the amount of data collected, with the characteristics of big data. At present, in the optimal power flow calculation of large-scale power grid, when the traditional calculation method faces the power system data with big data characteristics, the calculation speed is slow and the task execution efficiency is low, so it is difficult to meet the real-time computing requirements of smart grid, while most of the existing parallel computing methods run on special parallel computers, and the performance and price are relatively low. Therefore, how to realize the optimal power flow calculation quickly with high performance-price ratio has become an important problem to be solved in the development of smart grid. In this paper, the distributed parallel computing method for optimal power flow of large-scale distribution network in cloud environment is studied. With the help of Map-Reduce distributed parallel programming framework, the proposed method can run on Hadoop clusters with high performance and price. Specifically, this paper first proposes an optimal power flow algorithm performance model for Map-Reduce framework. The model can analyze and quantify the execution time of the algorithm under different cluster configurations, and provide guidance for the decomposition and calculation granularity of the power grid in the algorithm. Based on this performance model, a load balancing algorithm for optimal power flow calculation is proposed in this paper. In the case of given cluster resources, the optimal algorithm decomposition method and computational granularity are determined by simulated annealing algorithm, and the load balancing is realized by feeder reorganization algorithm, so as to optimize the computing speed and efficiency of optimal power flow in cloud environment. In the aspect of experiment, the proposed method is compared with the traditional serial optimal power flow calculation. The experimental results show that the proposed method can reduce the computing time by 68.3% compared with the serial method. At the same time, the calculation time of the optimal power flow algorithm under load balancing and imbalance is verified. The experimental data show that compared with the load imbalance method, the load balancing algorithm proposed in this paper can reduce the calculation time of the optimal power flow by 43.7%.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM744

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