【摘要】：太陽能以其清潔、無污染、可再生等特點(diǎn)越來越受到關(guān)注，光伏發(fā)電已成為當(dāng)今世界可再生能源領(lǐng)域的研究熱點(diǎn)。然而，光伏功率具有不確定性和間歇性等特點(diǎn)，大規(guī)模光伏并網(wǎng)運(yùn)行會(huì)增加電網(wǎng)調(diào)度的難度，影響電力系統(tǒng)的安全、穩(wěn)定及經(jīng)濟(jì)運(yùn)行。精確預(yù)測(cè)光伏功率是有效減緩大規(guī)模光伏并網(wǎng)對(duì)電網(wǎng)不利影響的前提，對(duì)電網(wǎng)調(diào)度計(jì)劃、常規(guī)能源規(guī)劃和光伏發(fā)電規(guī)劃等具有重要的指導(dǎo)意義。 本文以光伏發(fā)電功率為研究對(duì)象，通過分析光伏發(fā)電功率的影響因素，對(duì)光伏發(fā)電系統(tǒng)發(fā)電能力預(yù)測(cè)問題展開研究。首先，分析了季節(jié)類型、天氣類型及輻照強(qiáng)度、環(huán)境溫度、云量等氣象因子對(duì)光伏功率的影響，確定了預(yù)測(cè)模型的輸入變量，并提出利用相似日理論確定訓(xùn)練樣本的方法；其次，分析了傳統(tǒng)BP算法的優(yōu)缺點(diǎn)，提出了基于動(dòng)量項(xiàng)-陡度因子-自適應(yīng)學(xué)習(xí)率的改進(jìn)BP算法，并建立了相應(yīng)的光伏功率預(yù)測(cè)模型；然后，針對(duì)改進(jìn)BP算法和PSO算法的不足，將混沌搜索和自適應(yīng)變異思想引入到粒子群算法中，以提高算法的全局收斂概率和速度，，建立了基于混沌搜索的AMPSO-BPNN的光伏功率預(yù)測(cè)模型，并提出利用相似日功率修正模型預(yù)測(cè)結(jié)果的方法；最后，依托實(shí)際光伏電站和氣象觀測(cè)站對(duì)預(yù)測(cè)模型進(jìn)行了實(shí)例驗(yàn)證，并通過分析光伏出力與負(fù)荷用電間的相關(guān)性，進(jìn)一步明確了光伏電站調(diào)度運(yùn)行的研究方向。 在Microsoft Visual C++6.0環(huán)境下編制了光伏發(fā)電系統(tǒng)功率預(yù)測(cè)軟件，對(duì)比分析了不同光伏功率預(yù)測(cè)模型的優(yōu)化性能，預(yù)測(cè)結(jié)果表明所提模型及算法具有較高的預(yù)測(cè)精度和收斂速度，且基于相似日功率的修正方法具有一定的可行性。
[Abstract]:Solar energy has attracted more and more attention because of its clean, pollution-free and renewable characteristics. Photovoltaic power generation has become a research hotspot in the field of renewable energy in the world. However, photovoltaic power has the characteristics of uncertainty and intermittency. Large-scale photovoltaic grid-connected operation will increase the difficulty of power grid dispatching and affect the security, stability and economic operation of power system. Accurate prediction of photovoltaic power is the premise to effectively mitigate the adverse effects of large-scale photovoltaic grid connection, and has important guiding significance for grid scheduling, conventional energy planning and photovoltaic generation planning. In this paper, the photovoltaic power generation as the research object, through the analysis of the factors affecting photovoltaic generation power, PV power generation capacity prediction problem is studied. Firstly, the effects of seasonal type, weather type and radiation intensity, ambient temperature, cloud amount on photovoltaic power are analyzed, and the input variables of the prediction model are determined, and a method to determine the training samples by using the similarity day theory is proposed. Secondly, the advantages and disadvantages of the traditional BP algorithm are analyzed, and an improved BP algorithm based on momentum term, steepness factor and adaptive learning rate is proposed, and the corresponding photovoltaic power prediction model is established. Chaotic search and adaptive mutation are introduced into particle swarm optimization to improve the global convergence probability and speed of the algorithm. A photovoltaic power prediction model based on chaotic search for AMPSO-BPNN is established. The method of using the similar daily power correction model to forecast the results is put forward. Finally, the prediction model is verified by the actual photovoltaic power station and the meteorological observation station, and the correlation between the photovoltaic output and the load consumption is analyzed. The research direction of photovoltaic power plant dispatching and operation is further clarified. The power prediction software of photovoltaic power generation system is developed under Microsoft Visual C 6.0. The optimized performance of different photovoltaic power prediction models is compared and analyzed. The prediction results show that the proposed model and algorithm have higher prediction accuracy and convergence speed. And the correction method based on similar day power has certain feasibility.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM615

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