新能源系統(tǒng)模型及應用研究
發(fā)布時間：2018-10-29 17:35
【摘要】：隨著全球能源危機的進一步加深,新能源領域研究的重要性愈加凸顯。將可再生能源作為發(fā)電源的微電網(wǎng)作為傳統(tǒng)集中式大電網(wǎng)替代型技術研究發(fā)展?jié)摿薮�。微電網(wǎng)系統(tǒng)規(guī)模小,電網(wǎng)容量有限,而負荷序列明顯波動,具有高度的非平滑特性和非線性特性。同時風力、光伏發(fā)電受自然界客觀條件制約,其發(fā)電功率及供電質(zhì)量也受用戶負荷影響。因此如何提高微電網(wǎng)短時負荷預測精確度、提高供電質(zhì)量已經(jīng)成為當前研究熱點。本文基于風力發(fā)電、光伏發(fā)電及微電網(wǎng)系統(tǒng)結構設計做了深入研究,得出一些有意義的結論,這些研究成果為微電網(wǎng)技術研究提供了新思維和新路徑。 論文綜述微電網(wǎng)短時負荷預測的背景、意義和國內(nèi)外研究發(fā)展歷程。設計太陽能,風能互補的微電網(wǎng)系統(tǒng)結構。首先介紹了太陽能、風能電源的基本工作原理；而后依據(jù)其明顯的互補優(yōu)勢設計了太陽能、風能和儲能一體的微電網(wǎng)的主接線結構圖；剖析了主要環(huán)節(jié)的工作原理,給出了有特色的最大效率轉換太陽能裝置。其次,介紹了微電網(wǎng)電源數(shù)學模型,主要有光伏電池輸出特性和并網(wǎng)系統(tǒng)信號模型,蓄電池充放電模型,風力發(fā)電機模型。然后研究復雜系統(tǒng)的多模型切換預測控制在線性時變和非線性時變下的系統(tǒng)結構和切換控制策略。再次,本文提出了一種用量子粒子群(QPSO)優(yōu)化自適應神經(jīng)模糊推理系統(tǒng)(ANFIS)的預測算法,通過歸一化預處理負荷值的預測模型。在理論分析論證的基礎上,通過仿真,并結合某海島上的微電網(wǎng)實際負荷數(shù)據(jù)做模擬仿真論證,結果證明該方法的有效,為提高微電網(wǎng)系統(tǒng)的供電品質(zhì)及減低運行成本提供了新思路。最后,探討了微電網(wǎng)電能質(zhì)量問題的影響因素,從中選擇組合型無功補償裝置,建立分布式電源接入配電網(wǎng)后的無功優(yōu)化模型,提出了一種改進量子粒子群算法來進行無功優(yōu)化。最后進行仿真驗證了該模型和算法的有效性,說明接入分布式能源后,通過合理的無功優(yōu)化能夠降低網(wǎng)損,提高電能質(zhì)量。
[Abstract]:With the deepening of the global energy crisis, the importance of new energy research is becoming more and more prominent. There is great potential for the research and development of microgrid with renewable energy as power source as the substitute technology of traditional centralized power grid. The microgrid system is small in scale and limited in capacity, but the load sequence fluctuates obviously, and it has a high degree of nonsmooth and nonlinear characteristics. At the same time, wind and photovoltaic power generation is restricted by the objective conditions of nature, and its power generation and power supply quality are also affected by user load. Therefore, how to improve the accuracy of short-time load forecasting and improve the quality of power supply has become a hot topic. In this paper, based on wind power generation, photovoltaic generation and microgrid system structure design, some meaningful conclusions are drawn. These research results provide a new thinking and new path for the research of microgrid technology. This paper summarizes the background, significance and development of short-time load forecasting for micro-grid. The design of solar and wind energy complementary micro-grid system structure. Firstly, the basic working principle of solar and wind power supply is introduced, and then, according to its obvious complementary advantages, the main wiring structure of micro-grid with solar, wind and energy storage is designed. The working principle of the main link is analyzed, and the characteristic maximum efficiency conversion solar energy device is given. Secondly, the mathematical model of microgrid power supply is introduced, including photovoltaic cell output characteristics and grid-connected system signal model, battery charge and discharge model, wind turbine model. Then, the system structure and switching control strategy of multi-model switching predictive control for complex systems under linear and nonlinear time-varying conditions are studied. Thirdly, a prediction algorithm based on quantum particle swarm optimization (QPSO) for adaptive neural fuzzy inference system (ANFIS) is proposed. On the basis of theoretical analysis and demonstration, the simulation results show that the method is effective by simulation and combining with the actual load data of microgrid on a certain island. It provides a new way to improve the power supply quality and reduce the operation cost of microgrid system. Finally, the influence factors of power quality in microgrid are discussed, and the combined reactive power compensation device is selected to establish the reactive power optimization model after the distributed generation is connected to the distribution network. An improved Quantum Particle Swarm Optimization (QPSO) algorithm is proposed for reactive power optimization. Finally, the validity of the model and the algorithm is verified by simulation, which shows that the network loss can be reduced and the power quality can be improved by rational reactive power optimization after access to distributed energy.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM61
本文編號：2298353
[Abstract]:With the deepening of the global energy crisis, the importance of new energy research is becoming more and more prominent. There is great potential for the research and development of microgrid with renewable energy as power source as the substitute technology of traditional centralized power grid. The microgrid system is small in scale and limited in capacity, but the load sequence fluctuates obviously, and it has a high degree of nonsmooth and nonlinear characteristics. At the same time, wind and photovoltaic power generation is restricted by the objective conditions of nature, and its power generation and power supply quality are also affected by user load. Therefore, how to improve the accuracy of short-time load forecasting and improve the quality of power supply has become a hot topic. In this paper, based on wind power generation, photovoltaic generation and microgrid system structure design, some meaningful conclusions are drawn. These research results provide a new thinking and new path for the research of microgrid technology. This paper summarizes the background, significance and development of short-time load forecasting for micro-grid. The design of solar and wind energy complementary micro-grid system structure. Firstly, the basic working principle of solar and wind power supply is introduced, and then, according to its obvious complementary advantages, the main wiring structure of micro-grid with solar, wind and energy storage is designed. The working principle of the main link is analyzed, and the characteristic maximum efficiency conversion solar energy device is given. Secondly, the mathematical model of microgrid power supply is introduced, including photovoltaic cell output characteristics and grid-connected system signal model, battery charge and discharge model, wind turbine model. Then, the system structure and switching control strategy of multi-model switching predictive control for complex systems under linear and nonlinear time-varying conditions are studied. Thirdly, a prediction algorithm based on quantum particle swarm optimization (QPSO) for adaptive neural fuzzy inference system (ANFIS) is proposed. On the basis of theoretical analysis and demonstration, the simulation results show that the method is effective by simulation and combining with the actual load data of microgrid on a certain island. It provides a new way to improve the power supply quality and reduce the operation cost of microgrid system. Finally, the influence factors of power quality in microgrid are discussed, and the combined reactive power compensation device is selected to establish the reactive power optimization model after the distributed generation is connected to the distribution network. An improved Quantum Particle Swarm Optimization (QPSO) algorithm is proposed for reactive power optimization. Finally, the validity of the model and the algorithm is verified by simulation, which shows that the network loss can be reduced and the power quality can be improved by rational reactive power optimization after access to distributed energy.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM61
【參考文獻】
相關期刊論文 前10條
1 趙海青,牛東曉;灰色優(yōu)選組合預測模型及其應用[J];保定師范�？茖W校學報;2002年02期
2 李少林;姚國興;;風光互補發(fā)電蓄電池超級電容器混合儲能研究[J];電力電子技術;2010年02期
3 于渤,于浩;基于隨動思想的月度用電量時間序列預測模型[J];電力系統(tǒng)自動化;2000年14期
4 康重慶,夏清,張伯明;電力系統(tǒng)負荷預測研究綜述與發(fā)展方向的探討[J];電力系統(tǒng)自動化;2004年17期
5 魯宗相;王彩霞;閔勇;周雙喜;呂金祥;王云波;;微電網(wǎng)研究綜述[J];電力系統(tǒng)自動化;2007年19期
6 王成山;王守相;;分布式發(fā)電供能系統(tǒng)若干問題研究[J];電力系統(tǒng)自動化;2008年20期
7 袁越;李振杰;馮宇;左文霞;;中國發(fā)展微網(wǎng)的目的 方向 前景[J];電力系統(tǒng)自動化;2010年01期
8 蔣平，，鞠平;應用人工神經(jīng)網(wǎng)絡進行中期電力負荷預報[J];電力系統(tǒng)自動化;1995年06期
9 趙宏偉;吳濤濤;;基于分布式電源的微網(wǎng)技術[J];電力系統(tǒng)及其自動化學報;2008年01期
10 于建成;遲福建;徐科;李盛偉;劉洪;李昊揚;李達;;分布式電源接入對電網(wǎng)的影響分析[J];電力系統(tǒng)及其自動化學報;2012年01期
相關博士學位論文 前3條
1 胡義華;小型光伏系統(tǒng)及其關鍵問題研究[D];中國礦業(yè)大學;2011年
2 彭雙劍;微網(wǎng)運行和電能質(zhì)量控制研究[D];湖南大學;2011年
3 高曉芝;微網(wǎng)控制策略與電能質(zhì)量改善研究[D];天津大學;2012年
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