本文選題：光伏發(fā)電功率預測 + 組合預測�。� 參考：《華北電力大學》2014年碩士論文

【摘要】：光伏功率預測可以有效的避免并網(wǎng)光伏發(fā)電系統(tǒng)輸出功率間歇性和不可控性等缺點對電網(wǎng)的沖擊,因此對光伏發(fā)電系統(tǒng)進行發(fā)電量預測具有十分重要的意義。本文綜述了光伏功率預測研究現(xiàn)狀和預測方法,針對光伏功率預測精度問題,提出了基于熵權法的光伏輸出功率組合預測模型和基于組合權重相似日選取方法的光伏輸出功率預測模型,并提出基于光伏功率預測結果的風光儲系統(tǒng)平滑輸出控制策略。 首先,本文提出了基于熵權法的光伏輸出功率組合預測模型,該方法組合基于待預測日前一天功率的持續(xù)法預測模型、支持向量機預測模型和相似數(shù)據(jù)預測模型,采用熵權法確定三種模型的組合預測權重系數(shù),建立了基于熵權法的光伏輸出功率組合預測模型。Matlab仿真結果表明基于熵權法的光伏輸出功率組合預測模型提高了預測精度,對比三種單一預測模型,預測結果最大相對誤差和均方根誤差都有所減小,并且基于熵權法的光伏輸出功率組合預測模型能夠適應天氣類型變化,在不同的天氣類型下的預測效果都較好,適合工程應用。 其次,針對氣象條件相似天光伏輸出功率曲線具有很高的關聯(lián)度,本文提出了基于組合權重法選取相似日的光伏輸出功率預測方法�；诮M合權重法的相似日選取方法,首先選擇太陽輻照度為相似變量,采用組合權重相似日選取方法確定各歷史天與待預測天相似誤差,選出相似誤差最小的3個歷史天確定為待預測日的相似天。將相似天光伏輸出功率的平均值作為預測日光伏輸出功率預測值。該預測方法的關鍵是相似天選取時各基值點組合權重系數(shù)的恰當確定,本文先確定各基值點的主觀權重系數(shù)和客觀熵權,再采用最小鑒別信息原理融合上述兩種權重系數(shù),得到組合權重系數(shù)。Matlab仿真對比表明,基于組合權重法選取相似日的光伏輸出功率預測方法能夠選出相似程度很高的相似天,提高了光伏輸出功率的預測精度。 最后,本文提出基于光伏功率預測的風光儲系統(tǒng)平滑輸出控制策略。該方法以光伏輸出功率預測為基礎,利用下一時刻功率預測值,估算下一時刻電池荷電狀態(tài)的變化趨勢,從而調整儲能電池充放電量,該方法能夠實現(xiàn)系統(tǒng)輸出功率平滑控制并保持儲能電池系統(tǒng)SOC穩(wěn)定在正常范圍。采用Matlab仿真,驗證了該控制策略的有效性。
[Abstract]:Photovoltaic power prediction can effectively avoid the impact of intermittent and uncontrollable output power of grid-connected photovoltaic system, so it is of great significance to predict the power generation of photovoltaic system. In this paper, the current situation and prediction methods of photovoltaic power prediction are reviewed, aiming at the problem of photovoltaic power prediction accuracy. A combination prediction model of photovoltaic output power based on entropy weight method and a photovoltaic output power prediction model based on the method of combination weight similarity day selection are proposed, and a smooth output control strategy for wind-storage system based on PV power prediction results is proposed. First of all, this paper presents a photovoltaic output power combination prediction model based on entropy weight method, which is based on the continuous prediction model, support vector machine prediction model and similar data prediction model, which is based on the power prediction model one day before the day to be forecasted. The combined prediction weight coefficient of three models is determined by entropy weight method. The combined prediction model of photovoltaic output power based on entropy weight method is established. The simulation results of Matlab show that the combined prediction model of photovoltaic output power based on entropy weight method improves the prediction accuracy. Compared with the three single prediction models, the maximum relative error and root mean square error of the prediction results are all reduced, and the photovoltaic output power combination prediction model based on entropy weight method can adapt to the change of weather type. The prediction results of different weather types are good and suitable for engineering application. Secondly, in view of the high correlation degree of photovoltaic output power curve with similar weather conditions, this paper proposes a photovoltaic output power prediction method based on combination weight method to select similar days. Based on the combination weight method, the similar day selection method is used to select solar irradiance as a similar variable, and the similarity error between each historical day and the day to be predicted is determined by the combination weight similarity day selection method. Three historical days with the smallest similarity error are selected as the similar days to be predicted. The average value of photovoltaic output power of similar days is taken as the prediction value of photovoltaic output power. The key of this prediction method is to determine the combination weight coefficient of each base point when selecting the similar day. The subjective weight coefficient and objective entropy weight of each base point are determined first, and then the least discriminant information principle is used to fuse the above two weight coefficients. The simulation results of combined weight coefficient and Matlab show that the photovoltaic output power prediction method based on the combination weight method can select the similar days with high similarity degree and improve the precision of photovoltaic output power prediction. Finally, a smooth output control strategy based on PV power prediction is proposed. Based on the prediction of photovoltaic output power, the change trend of the charge state of the battery at the next moment is estimated by using the predicted value of the power at the next moment, and the charge and discharge quantity of the energy storage cell is adjusted. This method can realize the smooth control of the output power of the system and keep the SOC of the energy storage battery system stable in the normal range. The effectiveness of the control strategy is verified by Matlab simulation.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM61

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