【摘要】：光伏電站群集中并網(wǎng)發(fā)電是太陽能大規(guī)模開發(fā)利用的重要途徑,隨著光伏電站裝機(jī)容量的不斷增加,高滲透率光伏功率的波動(dòng)將會(huì)對(duì)電網(wǎng)造成一系列消極的影響。對(duì)光伏電站以及光伏電站群出力波動(dòng)特性的全面分析以及光伏功率的準(zhǔn)確預(yù)測(cè)是研究光伏并網(wǎng)相關(guān)問題的基礎(chǔ)。本文基于青海省大型光伏發(fā)電基地的實(shí)測(cè)數(shù)據(jù),首先對(duì)光伏電站功率特性及其匯聚后電站群功率特性進(jìn)行了分析。研究了單一光伏電站的日出力特性以及天氣與季節(jié)變化對(duì)輸出功率的影響。構(gòu)建波動(dòng)特性指標(biāo),分析了不同時(shí)間尺度以及不同裝機(jī)容量下光伏功率波動(dòng)特性。從光伏電站間功率相關(guān)性的角度,揭示了光伏電站群的匯聚效應(yīng),提出匯聚系數(shù)概念衡量光伏電站群的匯聚效應(yīng)。指出匯聚效應(yīng)的應(yīng)用方向并提出計(jì)及匯聚效應(yīng)的光伏電站群輸出功率預(yù)測(cè)思路。介紹灰色神經(jīng)網(wǎng)絡(luò)模型應(yīng)用到光伏預(yù)測(cè)的預(yù)測(cè)原理,并對(duì)其運(yùn)用到光伏預(yù)測(cè)時(shí)的適用性進(jìn)行分析,根據(jù)分析結(jié)果對(duì)原始功率序列進(jìn)行平滑處理以改進(jìn)灰色模型,針對(duì)BP神經(jīng)網(wǎng)絡(luò)的缺陷采用粒子群算法對(duì)其進(jìn)行優(yōu)化,構(gòu)建改進(jìn)灰色神經(jīng)網(wǎng)絡(luò)組合模型實(shí)現(xiàn)對(duì)單一光伏電站提前一天的短期功率預(yù)測(cè)。算例結(jié)果表明,改進(jìn)后模型的預(yù)測(cè)精度較改進(jìn)前的灰色神經(jīng)網(wǎng)絡(luò)模型有明顯提高,并且滿足國(guó)家能源局設(shè)置的預(yù)測(cè)誤差標(biāo)準(zhǔn)�；诠夥娬救旱南嚓P(guān)性分析,提出一種計(jì)及匯聚效應(yīng)的區(qū)域光伏電站群短期功率預(yù)測(cè)方法。該方法根據(jù)相關(guān)性計(jì)算結(jié)果選出基準(zhǔn)光伏電站并對(duì)其進(jìn)行預(yù)測(cè),對(duì)預(yù)測(cè)值進(jìn)行線性放大得出光伏電站群預(yù)測(cè)的估計(jì)值,最后根據(jù)各光伏電站間的相關(guān)系數(shù)對(duì)估計(jì)值進(jìn)行修正,實(shí)現(xiàn)對(duì)光伏電站群的短期功率預(yù)測(cè)。算例結(jié)果表明,與常用的疊加法相比,該方法的預(yù)測(cè)結(jié)果更接近實(shí)際值并且預(yù)測(cè)精度有明顯的提高,并且區(qū)域光伏電站群的預(yù)測(cè)精度要高于單一光伏電站的預(yù)測(cè)精度。
[Abstract]:Centralized grid-connected photovoltaic power generation is an important way of large-scale development and utilization of solar energy. With the increasing of installed capacity of photovoltaic power station, the fluctuation of photovoltaic power with high permeability will cause a series of negative effects on power grid. The comprehensive analysis of the fluctuation characteristics of the output force of photovoltaic power stations and the accurate prediction of photovoltaic power are the basis of the research on grid-connected photovoltaic problems. Based on the measured data of large photovoltaic power station in Qinghai province, the power characteristics of photovoltaic power station and the power characteristics of converged power station group are analyzed in this paper. The daily output characteristics of a single photovoltaic power plant and the effects of weather and seasonal variations on the output power are studied. The fluctuation characteristics of photovoltaic power are analyzed under different time scales and different installed capacity. From the point of view of power correlation between photovoltaic power stations, the convergent effect of photovoltaic power station group is revealed, and the concept of convergence coefficient is proposed to measure the convergence effect of photovoltaic power plant group. The application direction of convergent effect is pointed out, and the forecast thought of output power of PV power station group considering convergent effect is put forward. This paper introduces the prediction principle of the grey neural network model applied to the photovoltaic prediction, and analyzes its applicability when it is applied to the photovoltaic prediction. According to the analysis results, the original power series is smoothed to improve the grey model. Particle swarm optimization (PSO) is used to optimize the BP neural network, and an improved grey neural network combination model is constructed to predict the short term power of a single photovoltaic power station one day in advance. The calculation results show that the prediction accuracy of the improved model is obviously higher than that of the grey neural network model before the improvement, and the prediction error standard set by the State Energy Bureau is satisfied. Based on the correlation analysis of photovoltaic power station group, a short-term power prediction method for regional photovoltaic power station group is proposed, which takes into account the convergent effect. The method selects and forecasts the reference photovoltaic power station according to the results of correlation calculation. The predicted value is obtained by linear amplification of the predicted value. Finally, the estimated value is revised according to the correlation coefficient among the photovoltaic power stations. The short-term power prediction of PV power station group is realized. The numerical results show that the prediction results of the proposed method are closer to the actual values and the prediction accuracy of the regional PV power stations is higher than that of the single photovoltaic power station compared with the common superposition method.
【學(xué)位授予單位】：東北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM615

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